WorldWideScience

Sample records for outlier detection applied

  1. Outlier detection using autoencoders

    CERN Document Server

    Lyudchik, Olga

    2016-01-01

    Outlier detection is a crucial part of any data analysis applications. The goal of outlier detection is to separate a core of regular observations from some polluting ones, called “outliers”. We propose an outlier detection method using deep autoencoder. In our research the invented method was applied to detect outlier points in the MNIST dataset of handwriting digits. The experimental results show that the proposed method has a potential to be used for anomaly detection.

  2. Selection of tests for outlier detection

    NARCIS (Netherlands)

    Bossers, H.C.M.; Hurink, Johann L.; Smit, Gerardus Johannes Maria

    Integrated circuits are tested thoroughly in order to meet the high demands on quality. As an additional step, outlier detection is used to detect potential unreliable chips such that quality can be improved further. However, it is often unclear to which tests outlier detection should be applied and

  3. Detecting isotopic ratio outliers

    International Nuclear Information System (INIS)

    Bayne, C.K.; Smith, D.H.

    1985-01-01

    An alternative method is proposed for improving isotopic ratio estimates. This method mathematically models pulse-count data and uses iterative reweighted Poisson regression to estimate model parameters to calculate the isotopic ratios. This computer-oriented approach provides theoretically better methods than conventional techniques to establish error limits and to identify outliers. 6 refs., 3 figs., 3 tabs

  4. Detecting isotopic ratio outliers

    Science.gov (United States)

    Bayne, C. K.; Smith, D. H.

    An alternative method is proposed for improving isotopic ratio estimates. This method mathematically models pulse-count data and uses iterative reweighted Poisson regression to estimate model parameters to calculate the isotopic ratios. This computer-oriented approach provides theoretically better methods than conventional techniques to establish error limits and to identify outliers.

  5. Detecting isotopic ratio outliers

    International Nuclear Information System (INIS)

    Bayne, C.K.; Smith, D.H.

    1986-01-01

    An alternative method is proposed for improving isotopic ratio estimates. This method mathematically models pulse-count data and uses iterative reweighted Poisson regression to estimate model parameters to calculate the isotopic ratios. This computer-oriented approach provides theoretically better methods than conventional techniques to establish error limits and to identify outliers

  6. A Modified Approach for Detection of Outliers

    Directory of Open Access Journals (Sweden)

    Iftikhar Hussain Adil

    2015-04-01

    Full Text Available Tukey’s boxplot is very popular tool for detection of outliers. It reveals the location, spread and skewness of the data. It works nicely for detection of outliers when the data are symmetric. When the data are skewed it covers boundary away from the whisker on the compressed side while declares erroneous outliers on the extended side of the distribution. Hubert and Vandervieren (2008 made adjustment in Tukey’s technique to overcome this problem. However another problem arises that is the adjusted boxplot constructs the interval of critical values which even exceeds from the extremes of the data. In this situation adjusted boxplot is unable to detect outliers. This paper gives solution of this problem and proposed approach detects outliers properly. The validity of the technique has been checked by constructing fences around the true 95% values of different distributions. Simulation technique has been applied by drawing different sample size from chi square, beta and lognormal distributions. Fences constructed by the modified technique are close to the true 95% than adjusted boxplot which proves its superiority on the existing technique.

  7. Stratification-Based Outlier Detection over the Deep Web

    OpenAIRE

    Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S.; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

    2016-01-01

    For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribu...

  8. Outlier Detection and Explanation for Domain Experts

    DEFF Research Database (Denmark)

    Micenková, Barbora

    In many data exploratory tasks, extraordinary and rarely occurring patterns called outliers are more interesting than the prevalent ones. For example, they could represent frauds in insurance, intrusions in network and system monitoring, or motion in video surveillance. Decades of research have...... to poor overall performance. Furthermore, in many applications some labeled examples of outliers are available but not sufficient enough in number as training data for standard supervised learning methods. As such, this valuable information is typically ignored. We introduce a new paradigm for outlier...... detection where supervised and unsupervised information are combined to improve the performance while reducing the sensitivity to parameters of individual outlier detection algorithms. We do this by learning a new representation using the outliers from outputs of unsupervised outlier detectors as input...

  9. The good, the bad and the outliers: automated detection of errors and outliers from groundwater hydrographs

    Science.gov (United States)

    Peterson, Tim J.; Western, Andrew W.; Cheng, Xiang

    2018-03-01

    Suspicious groundwater-level observations are common and can arise for many reasons ranging from an unforeseen biophysical process to bore failure and data management errors. Unforeseen observations may provide valuable insights that challenge existing expectations and can be deemed outliers, while monitoring and data handling failures can be deemed errors, and, if ignored, may compromise trend analysis and groundwater model calibration. Ideally, outliers and errors should be identified but to date this has been a subjective process that is not reproducible and is inefficient. This paper presents an approach to objectively and efficiently identify multiple types of errors and outliers. The approach requires only the observed groundwater hydrograph, requires no particular consideration of the hydrogeology, the drivers (e.g. pumping) or the monitoring frequency, and is freely available in the HydroSight toolbox. Herein, the algorithms and time-series model are detailed and applied to four observation bores with varying dynamics. The detection of outliers was most reliable when the observation data were acquired quarterly or more frequently. Outlier detection where the groundwater-level variance is nonstationary or the absolute trend increases rapidly was more challenging, with the former likely to result in an under-estimation of the number of outliers and the latter an overestimation in the number of outliers.

  10. Stratification-Based Outlier Detection over the Deep Web.

    Science.gov (United States)

    Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

    2016-01-01

    For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

  11. Detection of outliers in gas centrifuge experimental data

    International Nuclear Information System (INIS)

    Andrade, Monica C.V.; Nascimento, Claudio A.O.

    2005-01-01

    Isotope separation in a gas centrifuge is a very complex process. Development and optimization of a gas centrifuge requires experimentation. These data contain experimental errors, and like other experimental data, there may be some gross errors, also known as outliers. The detection of outliers in gas centrifuge experimental data may be quite complicated because there is not enough repetition for precise statistical determination and the physical equations may be applied only on the control of the mass flows. Moreover, the concentrations are poorly predicted by phenomenological models. This paper presents the application of a three-layer feed-forward neural network to the detection of outliers in a very extensive experiment for the analysis of the separation performance of a gas centrifuge. (author)

  12. Detection of outliers in a gas centrifuge experimental data

    Directory of Open Access Journals (Sweden)

    M. C. V. Andrade

    2005-09-01

    Full Text Available Isotope separation with a gas centrifuge is a very complex process. Development and optimization of a gas centrifuge requires experimentation. These data contain experimental errors, and like other experimental data, there may be some gross errors, also known as outliers. The detection of outliers in gas centrifuge experimental data is quite complicated because there is not enough repetition for precise statistical determination and the physical equations may be applied only to control of the mass flow. Moreover, the concentrations are poorly predicted by phenomenological models. This paper presents the application of a three-layer feed-forward neural network to the detection of outliers in analysis of performed on a very extensive experiment.

  13. INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

    Directory of Open Access Journals (Sweden)

    A. Bhushan

    2015-07-01

    Full Text Available In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

  14. Multivariate Functional Data Visualization and Outlier Detection

    KAUST Repository

    Dai, Wenlin; Genton, Marc G.

    2017-01-01

    This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate non-outlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data.

  15. Multivariate Functional Data Visualization and Outlier Detection

    KAUST Repository

    Dai, Wenlin

    2017-03-19

    This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate non-outlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data.

  16. Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots

    Directory of Open Access Journals (Sweden)

    Song Yang

    2006-01-01

    Full Text Available We developed a quality assurance (QA tool, namely microarray outlier filter (MOF, and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR dataset, which is used as a technical control in our standard hybridization procedure, 3 outlier arrays were identified out of 35 experiments. For a human blood dataset, 12 outlier arrays were identified from 185 experiments. In general, arrays from human blood samples displayed greater variation in their gene expression profiles than arrays from HUR samples. As a result, MOF identified two distinct patterns in the occurrence of outlier arrays. These results demonstrate that this methodology is a valuable QA practice to identify questionable microarray data prior to downstream analysis.

  17. Outlier Detection Techniques For Wireless Sensor Networks: A Survey

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2008-01-01

    In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are

  18. Statistical Outlier Detection for Jury Based Grading Systems

    DEFF Research Database (Denmark)

    Thompson, Mary Kathryn; Clemmensen, Line Katrine Harder; Rosas, Harvey

    2013-01-01

    This paper presents an algorithm that was developed to identify statistical outliers from the scores of grading jury members in a large project-based first year design course. The background and requirements for the outlier detection system are presented. The outlier detection algorithm...... and the follow-up procedures for score validation and appeals are described in detail. Finally, the impact of various elements of the outlier detection algorithm, their interactions, and the sensitivity of their numerical values are investigated. It is shown that the difference in the mean score produced...... by a grading jury before and after a suspected outlier is removed from the mean is the single most effective criterion for identifying potential outliers but that all of the criteria included in the algorithm have an effect on the outlier detection process....

  19. Outlier Detection in Structural Time Series Models

    DEFF Research Database (Denmark)

    Marczak, Martyna; Proietti, Tommaso

    investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality......Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general......–to–specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit–root autoregressions. By focusing on impulse– and step–indicator saturation, we...

  20. Adaptive distributed outlier detection for WSNs.

    Science.gov (United States)

    De Paola, Alessandra; Gaglio, Salvatore; Lo Re, Giuseppe; Milazzo, Fabrizio; Ortolani, Marco

    2015-05-01

    The paradigm of pervasive computing is gaining more and more attention nowadays, thanks to the possibility of obtaining precise and continuous monitoring. Ease of deployment and adaptivity are typically implemented by adopting autonomous and cooperative sensory devices; however, for such systems to be of any practical use, reliability and fault tolerance must be guaranteed, for instance by detecting corrupted readings amidst the huge amount of gathered sensory data. This paper proposes an adaptive distributed Bayesian approach for detecting outliers in data collected by a wireless sensor network; our algorithm aims at optimizing classification accuracy, time complexity and communication complexity, and also considering externally imposed constraints on such conflicting goals. The performed experimental evaluation showed that our approach is able to improve the considered metrics for latency and energy consumption, with limited impact on classification accuracy.

  1. Detection of Outliers in Regression Model for Medical Data

    Directory of Open Access Journals (Sweden)

    Stephen Raj S

    2017-07-01

    Full Text Available In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The detection of outliers and influential points is an important step of the regression analysis. Outlier detection methods have been used to detect and remove anomalous values from data. In this paper, we detect the presence of outliers in simple linear regression models for medical data set. Chatterjee and Hadi mentioned that the ordinary residuals are not appropriate for diagnostic purposes; a transformed version of them is preferable. First, we investigate the presence of outliers based on existing procedures of residuals and standardized residuals. Next, we have used the new approach of standardized scores for detecting outliers without the use of predicted values. The performance of the new approach was verified with the real-life data.

  2. An improved data clustering algorithm for outlier detection

    Directory of Open Access Journals (Sweden)

    Anant Agarwal

    2016-12-01

    Full Text Available Data mining is the extraction of hidden predictive information from large databases. This is a technology with potential to study and analyze useful information present in data. Data objects which do not usually fit into the general behavior of the data are termed as outliers. Outlier Detection in databases has numerous applications such as fraud detection, customized marketing, and the search for terrorism. By definition, outliers are rare occurrences and hence represent a small portion of the data. However, the use of Outlier Detection for various purposes is not an easy task. This research proposes a modified PAM for detecting outliers. The proposed technique has been implemented in JAVA. The results produced by the proposed technique are found better than existing technique in terms of outliers detected and time complexity.

  3. Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor

    OpenAIRE

    Liu Xin; Zhang Shaoliang; Zheng Pulin

    2015-01-01

    Spatial local outlier factor (SLOF) algorithm was adopted in this study for spatial outlier detection because of the limitations of the traditional static threshold detection. Based on the spatial characteristics of CO2 monitoring data obtained in the carbon capture and storage (CCS) project, the K-Nearest Neighbour (KNN) graph was constructed using the latitude and longitude information of the monitoring points to identify the spatial neighbourhood of the monitoring points. Then ...

  4. Good and Bad Neighborhood Approximations for Outlier Detection Ensembles

    DEFF Research Database (Denmark)

    Kirner, Evelyn; Schubert, Erich; Zimek, Arthur

    2017-01-01

    Outlier detection methods have used approximate neighborhoods in filter-refinement approaches. Outlier detection ensembles have used artificially obfuscated neighborhoods to achieve diverse ensemble members. Here we argue that outlier detection models could be based on approximate neighborhoods...... in the first place, thus gaining in both efficiency and effectiveness. It depends, however, on the type of approximation, as only some seem beneficial for the task of outlier detection, while no (large) benefit can be seen for others. In particular, we argue that space-filling curves are beneficial...

  5. Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor

    Directory of Open Access Journals (Sweden)

    Liu Xin

    2015-12-01

    Full Text Available Spatial local outlier factor (SLOF algorithm was adopted in this study for spatial outlier detection because of the limitations of the traditional static threshold detection. Based on the spatial characteristics of CO2 monitoring data obtained in the carbon capture and storage (CCS project, the K-Nearest Neighbour (KNN graph was constructed using the latitude and longitude information of the monitoring points to identify the spatial neighbourhood of the monitoring points. Then SLOF was adopted to calculate the outlier degrees of the monitoring points and the 3σ rule was employed to identify the spatial outlier. Finally, the selection of K value was analysed and the optimal one was selected. The results show that, compared with the static threshold method, the proposed algorithm has a higher detection precision. It can overcome the shortcomings of the static threshold method and improve the accuracy and diversity of local outlier detection, which provides a reliable reference for the safety assessment and warning of CCS monitoring.

  6. Adjusted functional boxplots for spatio-temporal data visualization and outlier detection

    KAUST Repository

    Sun, Ying

    2011-10-24

    This article proposes a simulation-based method to adjust functional boxplots for correlations when visualizing functional and spatio-temporal data, as well as detecting outliers. We start by investigating the relationship between the spatio-temporal dependence and the 1.5 times the 50% central region empirical outlier detection rule. Then, we propose to simulate observations without outliers on the basis of a robust estimator of the covariance function of the data. We select the constant factor in the functional boxplot to control the probability of correctly detecting no outliers. Finally, we apply the selected factor to the functional boxplot of the original data. As applications, the factor selection procedure and the adjusted functional boxplots are demonstrated on sea surface temperatures, spatio-temporal precipitation and general circulation model (GCM) data. The outlier detection performance is also compared before and after the factor adjustment. © 2011 John Wiley & Sons, Ltd.

  7. Using Person Fit Statistics to Detect Outliers in Survey Research

    Directory of Open Access Journals (Sweden)

    John M. Felt

    2017-05-01

    Full Text Available Context: When working with health-related questionnaires, outlier detection is important. However, traditional methods of outlier detection (e.g., boxplots can miss participants with “atypical” responses to the questions that otherwise have similar total (subscale scores. In addition to detecting outliers, it can be of clinical importance to determine the reason for the outlier status or “atypical” response.Objective: The aim of the current study was to illustrate how to derive person fit statistics for outlier detection through a statistical method examining person fit with a health-based questionnaire.Design and Participants: Patients treated for Cushing's syndrome (n = 394 were recruited from the Cushing's Support and Research Foundation's (CSRF listserv and Facebook page.Main Outcome Measure: Patients were directed to an online survey containing the CushingQoL (English version. A two-dimensional graded response model was estimated, and person fit statistics were generated using the Zh statistic.Results: Conventional outlier detections methods revealed no outliers reflecting extreme scores on the subscales of the CushingQoL. However, person fit statistics identified 18 patients with “atypical” response patterns, which would have been otherwise missed (Zh > |±2.00|.Conclusion: While the conventional methods of outlier detection indicated no outliers, person fit statistics identified several patients with “atypical” response patterns who otherwise appeared average. Person fit statistics allow researchers to delve further into the underlying problems experienced by these “atypical” patients treated for Cushing's syndrome. Annotated code is provided to aid other researchers in using this method.

  8. Algorithms for Speeding up Distance-Based Outlier Detection

    Data.gov (United States)

    National Aeronautics and Space Administration — The problem of distance-based outlier detection is difficult to solve efficiently in very large datasets because of potential quadratic time complexity. We address...

  9. OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL

    Directory of Open Access Journals (Sweden)

    JUN ZHAO

    Full Text Available The weighed total least square (WTLS estimate is very sensitive to the outliers in the partial EIV model. A new procedure for detecting outliers based on the data-snooping is presented in this paper. Firstly, a two-step iterated method of computing the WTLS estimates for the partial EIV model based on the standard LS theory is proposed. Secondly, the corresponding w-test statistics are constructed to detect outliers while the observations and coefficient matrix are contaminated with outliers, and a specific algorithm for detecting outliers is suggested. When the variance factor is unknown, it may be estimated by the least median squares (LMS method. At last, the simulated data and real data about two-dimensional affine transformation are analyzed. The numerical results show that the new test procedure is able to judge that the outliers locate in x component, y component or both components in coordinates while the observations and coefficient matrix are contaminated with outliers

  10. Distance Based Method for Outlier Detection of Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Haibin Zhang

    2016-01-01

    Full Text Available We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided as an outlier. Further, we formalize a sliding window based method to improve the outlier detection performance. Finally, to estimate the KDE by training sensor readings with errors, we introduce a Hidden Markov Model (HMM based method to estimate the most probable ground truth values which have the maximum probability to produce the training data. Simulation results show that the proposed method possesses a good detection accuracy with a low false alarm rate.

  11. Outlier Detection with Space Transformation and Spectral Analysis

    DEFF Research Database (Denmark)

    Dang, Xuan-Hong; Micenková, Barbora; Assent, Ira

    2013-01-01

    which rely on notions of distances or densities, this approach introduces a novel concept based on local quadratic entropy for evaluating the similarity of a data object with its neighbors. This information theoretic quantity is used to regularize the closeness amongst data instances and subsequently......Detecting a small number of outliers from a set of data observations is always challenging. In this paper, we present an approach that exploits space transformation and uses spectral analysis in the newly transformed space for outlier detection. Unlike most existing techniques in the literature...... benefits the process of mapping data into a usually lower dimensional space. Outliers are then identified by spectral analysis of the eigenspace spanned by the set of leading eigenvectors derived from the mapping procedure. The proposed technique is purely data-driven and imposes no assumptions regarding...

  12. Ensemble Learning Method for Outlier Detection and its Application to Astronomical Light Curves

    Science.gov (United States)

    Nun, Isadora; Protopapas, Pavlos; Sim, Brandon; Chen, Wesley

    2016-09-01

    Outlier detection is necessary for automated data analysis, with specific applications spanning almost every domain from financial markets to epidemiology to fraud detection. We introduce a novel mixture of the experts outlier detection model, which uses a dynamically trained, weighted network of five distinct outlier detection methods. After dimensionality reduction, individual outlier detection methods score each data point for “outlierness” in this new feature space. Our model then uses dynamically trained parameters to weigh the scores of each method, allowing for a finalized outlier score. We find that the mixture of experts model performs, on average, better than any single expert model in identifying both artificially and manually picked outliers. This mixture model is applied to a data set of astronomical light curves, after dimensionality reduction via time series feature extraction. Our model was tested using three fields from the MACHO catalog and generated a list of anomalous candidates. We confirm that the outliers detected using this method belong to rare classes, like Novae, He-burning, and red giant stars; other outlier light curves identified have no available information associated with them. To elucidate their nature, we created a website containing the light-curve data and information about these objects. Users can attempt to classify the light curves, give conjectures about their identities, and sign up for follow up messages about the progress made on identifying these objects. This user submitted data can be used further train of our mixture of experts model. Our code is publicly available to all who are interested.

  13. Comparative Study of Outlier Detection Algorithms via Fundamental Analysis Variables: An Application on Firms Listed in Borsa Istanbul

    Directory of Open Access Journals (Sweden)

    Senol Emir

    2016-04-01

    Full Text Available In a data set, an outlier refers to a data point that is considerably different from the others. Detecting outliers provides useful application-specific insights and leads to choosing right prediction models. Outlier detection (also known as anomaly detection or novelty detection has been studied in statistics and machine learning for a long time. It is an essential preprocessing step of data mining process. In this study, outlier detection step in the data mining process is applied for identifying the top 20 outlier firms. Three outlier detection algorithms are utilized using fundamental analysis variables of firms listed in Borsa Istanbul for the 2011-2014 period. The results of each algorithm are presented and compared. Findings show that 15 different firms are identified by three different outlier detection methods. KCHOL and SAHOL have the greatest number of appearances with 12 observations among these firms. By investigating the results, it is concluded that each of three algorithms makes different outlier firm lists due to differences in their approaches for outlier detection.

  14. Detection of additive outliers in seasonal time series

    DEFF Research Database (Denmark)

    Haldrup, Niels; Montañés, Antonio; Sansó, Andreu

    The detection and location of additive outliers in integrated variables has attracted much attention recently because such outliers tend to affect unit root inference among other things. Most of these procedures have been developed for non-seasonal processes. However, the presence of seasonality......) to deal with data sampled at a seasonal frequency and the size and power properties are discussed. We also show that the presence of periodic heteroscedasticity will inflate the size of the tests and hence will tend to identify an excessive number of outliers. A modified Perron-Rodriguez test which allows...... periodically varying variances is suggested and it is shown to have excellent properties in terms of both power and size...

  15. Music Outlier Detection Using Multiple Sequence Alignment and Independent Ensembles

    NARCIS (Netherlands)

    Bountouridis, D.; Koops, Hendrik Vincent; Wiering, F.; Veltkamp, R.C.

    2016-01-01

    The automated retrieval of related music documents, such as cover songs or folk melodies belonging to the same tune, has been an important task in the field of Music Information Retrieval (MIR). Yet outlier detection, the process of identifying those documents that deviate significantly from the

  16. A New Outlier Detection Method for Multidimensional Datasets

    KAUST Repository

    Abdel Messih, Mario A.

    2012-07-01

    This study develops a novel hybrid method for outlier detection (HMOD) that combines the idea of distance based and density based methods. The proposed method has two main advantages over most of the other outlier detection methods. The first advantage is that it works well on both dense and sparse datasets. The second advantage is that, unlike most other outlier detection methods that require careful parameter setting and prior knowledge of the data, HMOD is not very sensitive to small changes in parameter values within certain parameter ranges. The only required parameter to set is the number of nearest neighbors. In addition, we made a fully parallelized implementation of HMOD that made it very efficient in applications. Moreover, we proposed a new way of using the outlier detection for redundancy reduction in datasets where the confidence level that evaluates how accurate the less redundant dataset can be used to represent the original dataset can be specified by users. HMOD is evaluated on synthetic datasets (dense and mixed “dense and sparse”) and a bioinformatics problem of redundancy reduction of dataset of position weight matrices (PWMs) of transcription factor binding sites. In addition, in the process of assessing the performance of our redundancy reduction method, we developed a simple tool that can be used to evaluate the confidence level of reduced dataset representing the original dataset. The evaluation of the results shows that our method can be used in a wide range of problems.

  17. Application of median-equation approach for outlier detection in geodetic networks

    Directory of Open Access Journals (Sweden)

    Serif Hekimoglu

    Full Text Available In geodetic measurements some outliers may occur sometimes in data sets, depending on different reasons. There are two main approaches to detect outliers as Tests for outliers (Baarda's and Pope's Tests and robust methods (Danish method, Huber method etc.. These methods use the Least Squares Estimation (LSE. The outliers affect the LSE results, especially it smears the effects of the outliers on the good observations and sometimes wrong results may be obtained. To avoid these effects, a method that does not use LSE should be preferred. The median is a high breakdown point estimator and if it is applied for the outlier detection, reliable results can be obtained. In this study, a robust method which uses median with or as a treshould value on median residuals that are obtained from median equations is proposed. If the a priori variance of the observations is known, the reliability of the new approch is greater than the one in the case where the a priori variance is unknown.

  18. Shape based kinetic outlier detection in real-time PCR

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    D'Atri Mario

    2010-04-01

    Full Text Available Abstract Background Real-time PCR has recently become the technique of choice for absolute and relative nucleic acid quantification. The gold standard quantification method in real-time PCR assumes that the compared samples have similar PCR efficiency. However, many factors present in biological samples affect PCR kinetic, confounding quantification analysis. In this work we propose a new strategy to detect outlier samples, called SOD. Results Richards function was fitted on fluorescence readings to parameterize the amplification curves. There was not a significant correlation between calculated amplification parameters (plateau, slope and y-coordinate of the inflection point and the Log of input DNA demonstrating that this approach can be used to achieve a "fingerprint" for each amplification curve. To identify the outlier runs, the calculated parameters of each unknown sample were compared to those of the standard samples. When a significant underestimation of starting DNA molecules was found, due to the presence of biological inhibitors such as tannic acid, IgG or quercitin, SOD efficiently marked these amplification profiles as outliers. SOD was subsequently compared with KOD, the current approach based on PCR efficiency estimation. The data obtained showed that SOD was more sensitive than KOD, whereas SOD and KOD were equally specific. Conclusion Our results demonstrated, for the first time, that outlier detection can be based on amplification shape instead of PCR efficiency. SOD represents an improvement in real-time PCR analysis because it decreases the variance of data thus increasing the reliability of quantification.

  19. A NOTE ON THE CONVENTIONAL OUTLIER DETECTION TEST PROCEDURES

    Directory of Open Access Journals (Sweden)

    JIANFENG GUO

    Full Text Available Under the assumption of that the variance-covariance matrix is fully populated, Baarda's w-test is turn out to be completely different from the standardized least-squares residual. Unfortunately, this is not generally recognized. In the limiting case of only one degree of freedom, all the three types of test statistics, including Gaussian normal test, Student's t-test and Pope's Tau-test, will be invalid for identification of outliers: (1 all the squares of the Gaussian normal test statistic coincide with the goodness-of-fit (global test statistic, even for correlated observations. Hence, the failure of the global test implies that all the observations will be flagged as outliers, and thus the Gaussian normal test is inconclusive for localization of outliers; (2 the absolute values of the Tau-test statistic are all exactly equal to one, no matter whether the observations are contaminated. Therefore, the Tau-test cannot work for outlier detection in this situation; and (3 Student's t-test statistics are undefined.

  20. System and Method for Outlier Detection via Estimating Clusters

    Science.gov (United States)

    Iverson, David J. (Inventor)

    2016-01-01

    An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.

  1. Electricity Price Forecasting Based on AOSVR and Outlier Detection

    Institute of Scientific and Technical Information of China (English)

    Zhou Dianmin; Gao Lin; Gao Feng

    2005-01-01

    Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, this paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market.

  2. Supervised Outlier Detection in Large-Scale Mvs Point Clouds for 3d City Modeling Applications

    Science.gov (United States)

    Stucker, C.; Richard, A.; Wegner, J. D.; Schindler, K.

    2018-05-01

    We propose to use a discriminative classifier for outlier detection in large-scale point clouds of cities generated via multi-view stereo (MVS) from densely acquired images. What makes outlier removal hard are varying distributions of inliers and outliers across a scene. Heuristic outlier removal using a specific feature that encodes point distribution often delivers unsatisfying results. Although most outliers can be identified correctly (high recall), many inliers are erroneously removed (low precision), too. This aggravates object 3D reconstruction due to missing data. We thus propose to discriminatively learn class-specific distributions directly from the data to achieve high precision. We apply a standard Random Forest classifier that infers a binary label (inlier or outlier) for each 3D point in the raw, unfiltered point cloud and test two approaches for training. In the first, non-semantic approach, features are extracted without considering the semantic interpretation of the 3D points. The trained model approximates the average distribution of inliers and outliers across all semantic classes. Second, semantic interpretation is incorporated into the learning process, i.e. we train separate inlieroutlier classifiers per semantic class (building facades, roof, ground, vegetation, fields, and water). Performance of learned filtering is evaluated on several large SfM point clouds of cities. We find that results confirm our underlying assumption that discriminatively learning inlier-outlier distributions does improve precision over global heuristics by up to ≍ 12 percent points. Moreover, semantically informed filtering that models class-specific distributions further improves precision by up to ≍ 10 percent points, being able to remove very isolated building, roof, and water points while preserving inliers on building facades and vegetation.

  3. On the Evaluation of Outlier Detection: Measures, Datasets, and an Empirical Study Continued

    DEFF Research Database (Denmark)

    Campos, G. O.; Zimek, A.; Sander, J.

    2016-01-01

    The evaluation of unsupervised outlier detection algorithms is a constant challenge in data mining research. Little is known regarding the strengths and weaknesses of different standard outlier detection models, and the impact of parameter choices for these algorithms. The scarcity of appropriate...... are available online in the repository at: http://www.dbs.ifi.lmu.de/research/outlier-evaluation/...

  4. Development of a methodology for the detection of hospital financial outliers using information systems.

    Science.gov (United States)

    Okada, Sachiko; Nagase, Keisuke; Ito, Ayako; Ando, Fumihiko; Nakagawa, Yoshiaki; Okamoto, Kazuya; Kume, Naoto; Takemura, Tadamasa; Kuroda, Tomohiro; Yoshihara, Hiroyuki

    2014-01-01

    Comparison of financial indices helps to illustrate differences in operations and efficiency among similar hospitals. Outlier data tend to influence statistical indices, and so detection of outliers is desirable. Development of a methodology for financial outlier detection using information systems will help to reduce the time and effort required, eliminate the subjective elements in detection of outlier data, and improve the efficiency and quality of analysis. The purpose of this research was to develop such a methodology. Financial outliers were defined based on a case model. An outlier-detection method using the distances between cases in multi-dimensional space is proposed. Experiments using three diagnosis groups indicated successful detection of cases for which the profitability and income structure differed from other cases. Therefore, the method proposed here can be used to detect outliers. Copyright © 2013 John Wiley & Sons, Ltd.

  5. Time Series Outlier Detection Based on Sliding Window Prediction

    Directory of Open Access Journals (Sweden)

    Yufeng Yu

    2014-01-01

    Full Text Available In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI, which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

  6. Detection of outliers by neural network on the gas centrifuge experimental data of isotopic separation process

    International Nuclear Information System (INIS)

    Andrade, Monica de Carvalho Vasconcelos

    2004-01-01

    This work presents and discusses the neural network technique aiming at the detection of outliers on a set of gas centrifuge isotope separation experimental data. In order to evaluate the application of this new technique, the result obtained of the detection is compared to the result of the statistical analysis combined with the cluster analysis. This method for the detection of outliers presents a considerable potential in the field of data analysis and it is at the same time easier and faster to use and requests very less knowledge of the physics involved in the process. This work established a procedure for detecting experiments which are suspect to contain gross errors inside a data set where the usual techniques for identification of these errors cannot be applied or its use/demands an excessively long work. (author)

  7. Principal component analysis applied to Fourier transform infrared spectroscopy for the design of calibration sets for glycerol prediction models in wine and for the detection and classification of outlier samples.

    Science.gov (United States)

    Nieuwoudt, Helene H; Prior, Bernard A; Pretorius, Isak S; Manley, Marena; Bauer, Florian F

    2004-06-16

    Principal component analysis (PCA) was used to identify the main sources of variation in the Fourier transform infrared (FT-IR) spectra of 329 wines of various styles. The FT-IR spectra were gathered using a specialized WineScan instrument. The main sources of variation included the reducing sugar and alcohol content of the samples, as well as the stage of fermentation and the maturation period of the wines. The implications of the variation between the different wine styles for the design of calibration models with accurate predictive abilities were investigated using glycerol calibration in wine as a model system. PCA enabled the identification and interpretation of samples that were poorly predicted by the calibration models, as well as the detection of individual samples in the sample set that had atypical spectra (i.e., outlier samples). The Soft Independent Modeling of Class Analogy (SIMCA) approach was used to establish a model for the classification of the outlier samples. A glycerol calibration for wine was developed (reducing sugar content 8% v/v) with satisfactory predictive ability (SEP = 0.40 g/L). The RPD value (ratio of the standard deviation of the data to the standard error of prediction) was 5.6, indicating that the calibration is suitable for quantification purposes. A calibration for glycerol in special late harvest and noble late harvest wines (RS 31-147 g/L, alcohol > 11.6% v/v) with a prediction error SECV = 0.65 g/L, was also established. This study yielded an analytical strategy that combined the careful design of calibration sets with measures that facilitated the early detection and interpretation of poorly predicted samples and outlier samples in a sample set. The strategy provided a powerful means of quality control, which is necessary for the generation of accurate prediction data and therefore for the successful implementation of FT-IR in the routine analytical laboratory.

  8. Segmentation by Large Scale Hypothesis Testing - Segmentation as Outlier Detection

    DEFF Research Database (Denmark)

    Darkner, Sune; Dahl, Anders Lindbjerg; Larsen, Rasmus

    2010-01-01

    a microscope and we show how the method can handle transparent particles with significant glare point. The method generalizes to other problems. THis is illustrated by applying the method to camera calibration images and MRI of the midsagittal plane for gray and white matter separation and segmentation......We propose a novel and efficient way of performing local image segmentation. For many applications a threshold of pixel intensities is sufficient but determine the appropriate threshold value can be difficult. In cases with large global intensity variation the threshold value has to be adapted...... locally. We propose a method based on large scale hypothesis testing with a consistent method for selecting an appropriate threshold for the given data. By estimating the background distribution we characterize the segment of interest as a set of outliers with a certain probability based on the estimated...

  9. On damage detection in wind turbine gearboxes using outlier analysis

    Science.gov (United States)

    Antoniadou, Ifigeneia; Manson, Graeme; Dervilis, Nikolaos; Staszewski, Wieslaw J.; Worden, Keith

    2012-04-01

    The proportion of worldwide installed wind power in power systems increases over the years as a result of the steadily growing interest in renewable energy sources. Still, the advantages offered by the use of wind power are overshadowed by the high operational and maintenance costs, resulting in the low competitiveness of wind power in the energy market. In order to reduce the costs of corrective maintenance, the application of condition monitoring to gearboxes becomes highly important, since gearboxes are among the wind turbine components with the most frequent failure observations. While condition monitoring of gearboxes in general is common practice, with various methods having been developed over the last few decades, wind turbine gearbox condition monitoring faces a major challenge: the detection of faults under the time-varying load conditions prevailing in wind turbine systems. Classical time and frequency domain methods fail to detect faults under variable load conditions, due to the temporary effect that these faults have on vibration signals. This paper uses the statistical discipline of outlier analysis for the damage detection of gearbox tooth faults. A simplified two-degree-of-freedom gearbox model considering nonlinear backlash, time-periodic mesh stiffness and static transmission error, simulates the vibration signals to be analysed. Local stiffness reduction is used for the simulation of tooth faults and statistical processes determine the existence of intermittencies. The lowest level of fault detection, the threshold value, is considered and the Mahalanobis squared-distance is calculated for the novelty detection problem.

  10. On the Evaluation of Outlier Detection and One-Class Classification Methods

    DEFF Research Database (Denmark)

    Swersky, Lorne; Marques, Henrique O.; Sander, Jörg

    2016-01-01

    It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem. In this paper, we focus on the comparison of oneclass classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies ...

  11. An Unbiased Distance-based Outlier Detection Approach for High-dimensional Data

    DEFF Research Database (Denmark)

    Nguyen, Hoang Vu; Gopalkrishnan, Vivekanand; Assent, Ira

    2011-01-01

    than a global property. Different from existing approaches, it is not grid-based and dimensionality unbiased. Thus, its performance is impervious to grid resolution as well as the curse of dimensionality. In addition, our approach ranks the outliers, allowing users to select the number of desired...... outliers, thus mitigating the issue of high false alarm rate. Extensive empirical studies on real datasets show that our approach efficiently and effectively detects outliers, even in high-dimensional spaces....

  12. A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Xite Wang

    2017-01-01

    Full Text Available Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB. On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.

  13. Detection of Outliers in Panel Data of Intervention Effects Model Based on Variance of Remainder Disturbance

    Directory of Open Access Journals (Sweden)

    Yanfang Lyu

    2015-01-01

    Full Text Available The presence of outliers can result in seriously biased parameter estimates. In order to detect outliers in panel data models, this paper presents a modeling method to assess the intervention effects based on the variance of remainder disturbance using an arbitrary strictly positive twice continuously differentiable function. This paper also provides a Lagrange Multiplier (LM approach to detect and identify a general type of outlier. Furthermore, fixed effects models and random effects models are discussed to identify outliers and the corresponding LM test statistics are given. The LM test statistics for an individual-based model to detect outliers are given as a particular case. Finally, this paper performs an application using panel data and explains the advantages of the proposed method.

  14. Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks?

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2009-01-01

    Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at

  15. Methods of Detecting Outliers in A Regression Analysis Model. | Ogu ...

    African Journals Online (AJOL)

    A Boilers data with dependent variable Y (man-Hour) and four independent variables X1 (Boiler Capacity), X2 (Design Pressure), X3 (Boiler Type), X4 (Drum Type) were used. The analysis of the Boilers data reviewed an unexpected group of Outliers. The results from the findings showed that an observation can be outlying ...

  16. Outlier Detection in Urban Air Quality Sensor Networks

    NARCIS (Netherlands)

    van Zoest, V.M.; Stein, A.; Hoek, Gerard

    2018-01-01

    Low-cost urban air quality sensor networks are increasingly used to study the spatio-temporal variability in air pollutant concentrations. Recently installed low-cost urban sensors, however, are more prone to result in erroneous data than conventional monitors, e.g., leading to outliers. Commonly

  17. Adjusted functional boxplots for spatio-temporal data visualization and outlier detection

    KAUST Repository

    Sun, Ying; Genton, Marc G.

    2011-01-01

    This article proposes a simulation-based method to adjust functional boxplots for correlations when visualizing functional and spatio-temporal data, as well as detecting outliers. We start by investigating the relationship between the spatio

  18. An Improved Semisupervised Outlier Detection Algorithm Based on Adaptive Feature Weighted Clustering

    Directory of Open Access Journals (Sweden)

    Tingquan Deng

    2016-01-01

    Full Text Available There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal.

  19. Detecting outliers and learning complex structures with large spectroscopic surveys - a case study with APOGEE stars

    Science.gov (United States)

    Reis, Itamar; Poznanski, Dovi; Baron, Dalya; Zasowski, Gail; Shahaf, Sahar

    2018-05-01

    In this work, we apply and expand on a recently introduced outlier detection algorithm that is based on an unsupervised random forest. We use the algorithm to calculate a similarity measure for stellar spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). We show that the similarity measure traces non-trivial physical properties and contains information about complex structures in the data. We use it for visualization and clustering of the data set, and discuss its ability to find groups of highly similar objects, including spectroscopic twins. Using the similarity matrix to search the data set for objects allows us to find objects that are impossible to find using their best-fitting model parameters. This includes extreme objects for which the models fail, and rare objects that are outside the scope of the model. We use the similarity measure to detect outliers in the data set, and find a number of previously unknown Be-type stars, spectroscopic binaries, carbon rich stars, young stars, and a few that we cannot interpret. Our work further demonstrates the potential for scientific discovery when combining machine learning methods with modern survey data.

  20. [Outlier sample discriminating methods for building calibration model in melons quality detecting using NIR spectra].

    Science.gov (United States)

    Tian, Hai-Qing; Wang, Chun-Guang; Zhang, Hai-Jun; Yu, Zhi-Hong; Li, Jian-Kang

    2012-11-01

    Outlier samples strongly influence the precision of the calibration model in soluble solids content measurement of melons using NIR Spectra. According to the possible sources of outlier samples, three methods (predicted concentration residual test; Chauvenet test; leverage and studentized residual test) were used to discriminate these outliers respectively. Nine suspicious outliers were detected from calibration set which including 85 fruit samples. Considering the 9 suspicious outlier samples maybe contain some no-outlier samples, they were reclaimed to the model one by one to see whether they influence the model and prediction precision or not. In this way, 5 samples which were helpful to the model joined in calibration set again, and a new model was developed with the correlation coefficient (r) 0. 889 and root mean square errors for calibration (RMSEC) 0.6010 Brix. For 35 unknown samples, the root mean square errors prediction (RMSEP) was 0.854 degrees Brix. The performance of this model was more better than that developed with non outlier was eliminated from calibration set (r = 0.797, RMSEC= 0.849 degrees Brix, RMSEP = 1.19 degrees Brix), and more representative and stable with all 9 samples were eliminated from calibration set (r = 0.892, RMSEC = 0.605 degrees Brix, RMSEP = 0.862 degrees).

  1. Elimination of some unknown parameters and its effect on outlier detection

    Directory of Open Access Journals (Sweden)

    Serif Hekimoglu

    Full Text Available Outliers in observation set badly affect all the estimated unknown parameters and residuals, that is because outlier detection has a great importance for reliable estimation results. Tests for outliers (e.g. Baarda's and Pope's tests are frequently used to detect outliers in geodetic applications. In order to reduce the computational time, sometimes elimination of some unknown parameters, which are not of interest, is performed. In this case, although the estimated unknown parameters and residuals do not change, the cofactor matrix of the residuals and the redundancies of the observations change. In this study, the effects of the elimination of the unknown parameters on tests for outliers have been investigated. We have proved that the redundancies in initial functional model (IFM are smaller than the ones in reduced functional model (RFM where elimination is performed. To show this situation, a horizontal control network was simulated and then many experiences were performed. According to simulation results, tests for outlier in IFM are more reliable than the ones in RFM.

  2. IVS Combination Center at BKG - Robust Outlier Detection and Weighting Strategies

    Science.gov (United States)

    Bachmann, S.; Lösler, M.

    2012-12-01

    Outlier detection plays an important role within the IVS combination. Even if the original data is the same for all contributing Analysis Centers (AC), the analyzed data shows differences due to analysis software characteristics. The treatment of outliers is thus a fine line between keeping data heterogeneity and elimination of real outliers. Robust outlier detection based on the Least Median Square (LMS) is used within the IVS combination. This method allows reliable outlier detection with a small number of input parameters. A similar problem arises for the weighting of the individual solutions within the combination process. The variance component estimation (VCE) is used to control the weighting factor for each AC. The Operator-Software-Impact (OSI) method takes into account that the analyzed data is strongly influenced by the software and the responsible operator. It allows to make the VCE more sensitive to the diverse input data. This method has already been set up within GNSS data analysis as well as the analysis of troposphere data. The benefit of an OSI realization within the VLBI combination and its potential in weighting factor determination has not been investigated before.

  3. Outlier Detection in GNSS Pseudo-Range/Doppler Measurements for Robust Localization

    Directory of Open Access Journals (Sweden)

    Salim Zair

    2016-04-01

    Full Text Available In urban areas or space-constrained environments with obstacles, vehicle localization using Global Navigation Satellite System (GNSS data is hindered by Non-Line Of Sight (NLOS and multipath receptions. These phenomena induce faulty data that disrupt the precise localization of the GNSS receiver. In this study, we detect the outliers among the observations, Pseudo-Range (PR and/or Doppler measurements, and we evaluate how discarding them improves the localization. We specify a contrario modeling for GNSS raw data to derive an algorithm that partitions the dataset between inliers and outliers. Then, only the inlier data are considered in the localization process performed either through a classical Particle Filter (PF or a Rao-Blackwellization (RB approach. Both localization algorithms exclusively use GNSS data, but they differ by the way Doppler measurements are processed. An experiment has been performed with a GPS receiver aboard a vehicle. Results show that the proposed algorithms are able to detect the ‘outliers’ in the raw data while being robust to non-Gaussian noise and to intermittent satellite blockage. We compare the performance results achieved either estimating only PR outliers or estimating both PR and Doppler outliers. The best localization is achieved using the RB approach coupled with PR-Doppler outlier estimation.

  4. Outlier detection by robust Mahalanobis distance in geological data obtained by INAA to provenance studies

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Jose O. dos, E-mail: osmansantos@ig.com.br [Instituto Federal de Educacao, Ciencia e Tecnologia de Sergipe (IFS), Lagarto, SE (Brazil); Munita, Casimiro S., E-mail: camunita@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Soares, Emilio A.A., E-mail: easoares@ufan.edu.br [Universidade Federal do Amazonas (UFAM), Manaus, AM (Brazil). Dept. de Geociencias

    2013-07-01

    The detection of outlier in geochemical studies is one of the main difficulties in the interpretation of dataset because they can disturb the statistical method. The search for outliers in geochemical studies is usually based in the Mahalanobis distance (MD), since points in multivariate space that are a distance larger the some predetermined values from center of the data are considered outliers. However, the MD is very sensitive to the presence of discrepant samples. Many robust estimators for location and covariance have been introduced in the literature, such as Minimum Covariance Determinant (MCD) estimator. When MCD estimators are used to calculate the MD leads to the so-called Robust Mahalanobis Distance (RD). In this context, in this work RD was used to detect outliers in geological study of samples collected from confluence of Negro and Solimoes rivers. The purpose of this study was to study the contributions of the sediments deposited by the Solimoes and Negro rivers in the filling of the tectonic depressions at Parana do Ariau. For that 113 samples were analyzed by Instrumental Neutron Activation Analysis (INAA) in which were determined the concentration of As, Ba, Ce, Co, Cr, Cs, Eu, Fe, Hf, K, La, Lu, Na, Nd, Rb, Sb, Sc, Sm, U, Yb, Ta, Tb, Th and Zn. In the dataset was possible to construct the ellipse corresponding to robust Mahalanobis distance for each group of samples. The samples found outside of the tolerance ellipse were considered an outlier. The results showed that Robust Mahalanobis Distance was more appropriate for the identification of the outliers, once it is a more restrictive method. (author)

  5. Outlier detection by robust Mahalanobis distance in geological data obtained by INAA to provenance studies

    International Nuclear Information System (INIS)

    Santos, Jose O. dos; Munita, Casimiro S.; Soares, Emilio A.A.

    2013-01-01

    The detection of outlier in geochemical studies is one of the main difficulties in the interpretation of dataset because they can disturb the statistical method. The search for outliers in geochemical studies is usually based in the Mahalanobis distance (MD), since points in multivariate space that are a distance larger the some predetermined values from center of the data are considered outliers. However, the MD is very sensitive to the presence of discrepant samples. Many robust estimators for location and covariance have been introduced in the literature, such as Minimum Covariance Determinant (MCD) estimator. When MCD estimators are used to calculate the MD leads to the so-called Robust Mahalanobis Distance (RD). In this context, in this work RD was used to detect outliers in geological study of samples collected from confluence of Negro and Solimoes rivers. The purpose of this study was to study the contributions of the sediments deposited by the Solimoes and Negro rivers in the filling of the tectonic depressions at Parana do Ariau. For that 113 samples were analyzed by Instrumental Neutron Activation Analysis (INAA) in which were determined the concentration of As, Ba, Ce, Co, Cr, Cs, Eu, Fe, Hf, K, La, Lu, Na, Nd, Rb, Sb, Sc, Sm, U, Yb, Ta, Tb, Th and Zn. In the dataset was possible to construct the ellipse corresponding to robust Mahalanobis distance for each group of samples. The samples found outside of the tolerance ellipse were considered an outlier. The results showed that Robust Mahalanobis Distance was more appropriate for the identification of the outliers, once it is a more restrictive method. (author)

  6. Robust data reconciliation and outlier detection with swarm intelligence in a thermal reactor power calculation

    Energy Technology Data Exchange (ETDEWEB)

    Valdetaro, Eduardo Damianik, E-mail: valdtar@eletronuclear.gov.br [ELETRONUCLEAR - ELETROBRAS, Angra dos Reis, RJ (Brazil). Angra 2 Operating Dept.; Coordenacao dos Programas de Pos-Graduacao de Engenharia (PEN/COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Nuclear; Schirru, Roberto, E-mail: schirru@lmp.ufrj.br [Coordenacao dos Programas de Pos-Graduacao de Engenharia (PEN/COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Nuclear

    2011-07-01

    In Nuclear power plants, Data Reconciliation (DR) and Gross Errors Detection (GED) are techniques of increasing interest and are primarily used to keep mass and energy balance into account, which brings outcomes as a direct and indirect financial benefits. Data reconciliation is formulated by a constrained minimization problem, where the constraints correspond to energy and mass balance model. Statistical methods are used combined with the minimization of quadratic error form. Solving nonlinear optimization problem using conventional methods can be troublesome, because a multimodal function with differentiated solutions introduces some difficulties to search an optimal solution. Many techniques were developed to solve Data Reconciliation and Outlier Detection, some of them use, for example, Quadratic Programming, Lagrange Multipliers, Mixed-Integer Non Linear Programming and others use evolutionary algorithms like Genetic Algorithms (GA) and recently the use of the Particle Swarm Optimization (PSO) showed to be a potential tool as a global optimization algorithm when applied to data reconciliation. Robust Statistics is also increasing in interest and it is being used when measured data are contaminated by random errors and one can not assume the error is normally distributed, situation which reflects real problems situation. The aim of this work is to present a brief comparison between the classical data reconciliation technique and the robust data reconciliation and gross error detection with swarm intelligence procedure in calculating the thermal reactor power for a simplified heat circuit diagram of a steam turbine plant using real data obtained from Angra 2 Nuclear power plant. The main objective is to test the potential of the robust DR and GED method in a integrated framework using swarm intelligence and the three part redescending estimator of Hampel when applied to a real process condition. The results evaluate the potential use of the robust technique in

  7. Robust data reconciliation and outlier detection with swarm intelligence in a thermal reactor power calculation

    International Nuclear Information System (INIS)

    Valdetaro, Eduardo Damianik; Coordenacao dos Programas de Pos-Graduacao de Engenharia; Schirru, Roberto

    2011-01-01

    In Nuclear power plants, Data Reconciliation (DR) and Gross Errors Detection (GED) are techniques of increasing interest and are primarily used to keep mass and energy balance into account, which brings outcomes as a direct and indirect financial benefits. Data reconciliation is formulated by a constrained minimization problem, where the constraints correspond to energy and mass balance model. Statistical methods are used combined with the minimization of quadratic error form. Solving nonlinear optimization problem using conventional methods can be troublesome, because a multimodal function with differentiated solutions introduces some difficulties to search an optimal solution. Many techniques were developed to solve Data Reconciliation and Outlier Detection, some of them use, for example, Quadratic Programming, Lagrange Multipliers, Mixed-Integer Non Linear Programming and others use evolutionary algorithms like Genetic Algorithms (GA) and recently the use of the Particle Swarm Optimization (PSO) showed to be a potential tool as a global optimization algorithm when applied to data reconciliation. Robust Statistics is also increasing in interest and it is being used when measured data are contaminated by random errors and one can not assume the error is normally distributed, situation which reflects real problems situation. The aim of this work is to present a brief comparison between the classical data reconciliation technique and the robust data reconciliation and gross error detection with swarm intelligence procedure in calculating the thermal reactor power for a simplified heat circuit diagram of a steam turbine plant using real data obtained from Angra 2 Nuclear power plant. The main objective is to test the potential of the robust DR and GED method in a integrated framework using swarm intelligence and the three part redescending estimator of Hampel when applied to a real process condition. The results evaluate the potential use of the robust technique in

  8. Efficient estimation of dynamic density functions with an application to outlier detection

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali; Zhang, Xiangliang; Wang, Suojin

    2012-01-01

    In this paper, we propose a new method to estimate the dynamic density over data streams, named KDE-Track as it is based on a conventional and widely used Kernel Density Estimation (KDE) method. KDE-Track can efficiently estimate the density with linear complexity by using interpolation on a kernel model, which is incrementally updated upon the arrival of streaming data. Both theoretical analysis and experimental validation show that KDE-Track outperforms traditional KDE and a baseline method Cluster-Kernels on estimation accuracy of the complex density structures in data streams, computing time and memory usage. KDE-Track is also demonstrated on timely catching the dynamic density of synthetic and real-world data. In addition, KDE-Track is used to accurately detect outliers in sensor data and compared with two existing methods developed for detecting outliers and cleaning sensor data. © 2012 ACM.

  9. Calculation of climatic reference values and its use for automatic outlier detection in meteorological datasets

    Directory of Open Access Journals (Sweden)

    B. Téllez

    2008-04-01

    Full Text Available The climatic reference values for monthly and annual average air temperature and total precipitation in Catalonia – northeast of Spain – are calculated using a combination of statistical methods and geostatistical techniques of interpolation. In order to estimate the uncertainty of the method, the initial dataset is split into two parts that are, respectively, used for estimation and validation. The resulting maps are then used in the automatic outlier detection in meteorological datasets.

  10. Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series

    OpenAIRE

    Plazas-Nossa, Leonardo; Ávila Angulo, Miguel Antonio; Torres, Andrés

    2017-01-01

    Context:The UV-Vis absorbance collection using online optical captors for water quality detection may yield outliers and/or missing values. Therefore, pre-processing to correct these anomalies is required to improve the analysis of monitoring data. The aim of this study is to propose a method to detect outliers as well as to fill-in the gaps in time series. Method:Outliers are detected using Winsorising procedure and the application of the Discrete Fourier Transform (DFT) and the Inverse of F...

  11. Open-Source Radiation Exposure Extraction Engine (RE3) with Patient-Specific Outlier Detection.

    Science.gov (United States)

    Weisenthal, Samuel J; Folio, Les; Kovacs, William; Seff, Ari; Derderian, Vana; Summers, Ronald M; Yao, Jianhua

    2016-08-01

    We present an open-source, picture archiving and communication system (PACS)-integrated radiation exposure extraction engine (RE3) that provides study-, series-, and slice-specific data for automated monitoring of computed tomography (CT) radiation exposure. RE3 was built using open-source components and seamlessly integrates with the PACS. RE3 calculations of dose length product (DLP) from the Digital imaging and communications in medicine (DICOM) headers showed high agreement (R (2) = 0.99) with the vendor dose pages. For study-specific outlier detection, RE3 constructs robust, automatically updating multivariable regression models to predict DLP in the context of patient gender and age, scan length, water-equivalent diameter (D w), and scanned body volume (SBV). As proof of concept, the model was trained on 811 CT chest, abdomen + pelvis (CAP) exams and 29 outliers were detected. The continuous variables used in the outlier detection model were scan length (R (2)  = 0.45), D w (R (2) = 0.70), SBV (R (2) = 0.80), and age (R (2) = 0.01). The categorical variables were gender (male average 1182.7 ± 26.3 and female 1047.1 ± 26.9 mGy cm) and pediatric status (pediatric average 710.7 ± 73.6 mGy cm and adult 1134.5 ± 19.3 mGy cm).

  12. Estimating the number of components and detecting outliers using Angle Distribution of Loading Subspaces (ADLS) in PCA analysis.

    Science.gov (United States)

    Liu, Y J; Tran, T; Postma, G; Buydens, L M C; Jansen, J

    2018-08-22

    Principal Component Analysis (PCA) is widely used in analytical chemistry, to reduce the dimensionality of a multivariate data set in a few Principal Components (PCs) that summarize the predominant patterns in the data. An accurate estimate of the number of PCs is indispensable to provide meaningful interpretations and extract useful information. We show how existing estimates for the number of PCs may fall short for datasets with considerable coherence, noise or outlier presence. We present here how Angle Distribution of the Loading Subspaces (ADLS) can be used to estimate the number of PCs based on the variability of loading subspace across bootstrap resamples. Based on comprehensive comparisons with other well-known methods applied on simulated dataset, we show that ADLS (1) may quantify the stability of a PCA model with several numbers of PCs simultaneously; (2) better estimate the appropriate number of PCs when compared with the cross-validation and scree plot methods, specifically for coherent data, and (3) facilitate integrated outlier detection, which we introduce in this manuscript. We, in addition, demonstrate how the analysis of different types of real-life spectroscopic datasets may benefit from these advantages of ADLS. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  13. Rapid eye movement sleep behavior disorder as an outlier detection problem

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Sørensen, Gertrud Laura; Nikolic, M.

    2014-01-01

    OBJECTIVE: Idiopathic rapid eye movement (REM) sleep behavior disorder is a strong early marker of Parkinson's disease and is characterized by REM sleep without atonia and/or dream enactment. Because these measures are subject to individual interpretation, there is consequently need...... for quantitative methods to establish objective criteria. This study proposes a semiautomatic algorithm for the early detection of Parkinson's disease. This is achieved by distinguishing between normal REM sleep and REM sleep without atonia by considering muscle activity as an outlier detection problem. METHODS......: Sixteen healthy control subjects, 16 subjects with idiopathic REM sleep behavior disorder, and 16 subjects with periodic limb movement disorder were enrolled. Different combinations of five surface electromyographic channels, including the EOG, were tested. A muscle activity score was automatically...

  14. An optimized outlier detection algorithm for jury-based grading of engineering design projects

    DEFF Research Database (Denmark)

    Thompson, Mary Kathryn; Espensen, Christina; Clemmensen, Line Katrine Harder

    2016-01-01

    This work characterizes and optimizes an outlier detection algorithm to identify potentially invalid scores produced by jury members while grading engineering design projects. The paper describes the original algorithm and the associated adjudication process in detail. The impact of the various...... (the base rule and the three additional conditions) play a role in the algorithm's performance and should be included in the algorithm. Because there is significant interaction between the base rule and the additional conditions, many acceptable combinations that balance the FPR and FNR can be found......, but no true optimum seems to exist. The performance of the best optimizations and the original algorithm are similar. Therefore, it should be possible to choose new coefficient values for jury populations in other cultures and contexts logically and empirically without a full optimization as long...

  15. Raman fiber-optical method for colon cancer detection: Cross-validation and outlier identification approach

    Science.gov (United States)

    Petersen, D.; Naveed, P.; Ragheb, A.; Niedieker, D.; El-Mashtoly, S. F.; Brechmann, T.; Kötting, C.; Schmiegel, W. H.; Freier, E.; Pox, C.; Gerwert, K.

    2017-06-01

    Endoscopy plays a major role in early recognition of cancer which is not externally accessible and therewith in increasing the survival rate. Raman spectroscopic fiber-optical approaches can help to decrease the impact on the patient, increase objectivity in tissue characterization, reduce expenses and provide a significant time advantage in endoscopy. In gastroenterology an early recognition of malign and precursor lesions is relevant. Instantaneous and precise differentiation between adenomas as precursor lesions for cancer and hyperplastic polyps on the one hand and between high and low-risk alterations on the other hand is important. Raman fiber-optical measurements of colon biopsy samples taken during colonoscopy were carried out during a clinical study, and samples of adenocarcinoma (22), tubular adenomas (141), hyperplastic polyps (79) and normal tissue (101) from 151 patients were analyzed. This allows us to focus on the bioinformatic analysis and to set stage for Raman endoscopic measurements. Since spectral differences between normal and cancerous biopsy samples are small, special care has to be taken in data analysis. Using a leave-one-patient-out cross-validation scheme, three different outlier identification methods were investigated to decrease the influence of systematic errors, like a residual risk in misplacement of the sample and spectral dilution of marker bands (esp. cancerous tissue) and therewith optimize the experimental design. Furthermore other validations methods like leave-one-sample-out and leave-one-spectrum-out cross-validation schemes were compared with leave-one-patient-out cross-validation. High-risk lesions were differentiated from low-risk lesions with a sensitivity of 79%, specificity of 74% and an accuracy of 77%, cancer and normal tissue with a sensitivity of 79%, specificity of 83% and an accuracy of 81%. Additionally applied outlier identification enabled us to improve the recognition of neoplastic biopsy samples.

  16. Raman fiber-optical method for colon cancer detection: Cross-validation and outlier identification approach.

    Science.gov (United States)

    Petersen, D; Naveed, P; Ragheb, A; Niedieker, D; El-Mashtoly, S F; Brechmann, T; Kötting, C; Schmiegel, W H; Freier, E; Pox, C; Gerwert, K

    2017-06-15

    Endoscopy plays a major role in early recognition of cancer which is not externally accessible and therewith in increasing the survival rate. Raman spectroscopic fiber-optical approaches can help to decrease the impact on the patient, increase objectivity in tissue characterization, reduce expenses and provide a significant time advantage in endoscopy. In gastroenterology an early recognition of malign and precursor lesions is relevant. Instantaneous and precise differentiation between adenomas as precursor lesions for cancer and hyperplastic polyps on the one hand and between high and low-risk alterations on the other hand is important. Raman fiber-optical measurements of colon biopsy samples taken during colonoscopy were carried out during a clinical study, and samples of adenocarcinoma (22), tubular adenomas (141), hyperplastic polyps (79) and normal tissue (101) from 151 patients were analyzed. This allows us to focus on the bioinformatic analysis and to set stage for Raman endoscopic measurements. Since spectral differences between normal and cancerous biopsy samples are small, special care has to be taken in data analysis. Using a leave-one-patient-out cross-validation scheme, three different outlier identification methods were investigated to decrease the influence of systematic errors, like a residual risk in misplacement of the sample and spectral dilution of marker bands (esp. cancerous tissue) and therewith optimize the experimental design. Furthermore other validations methods like leave-one-sample-out and leave-one-spectrum-out cross-validation schemes were compared with leave-one-patient-out cross-validation. High-risk lesions were differentiated from low-risk lesions with a sensitivity of 79%, specificity of 74% and an accuracy of 77%, cancer and normal tissue with a sensitivity of 79%, specificity of 83% and an accuracy of 81%. Additionally applied outlier identification enabled us to improve the recognition of neoplastic biopsy samples. Copyright

  17. Modeling of activation data in the BrainMapTM database: Detection of outliers

    DEFF Research Database (Denmark)

    Nielsen, Finn Årup; Hansen, Lars Kai

    2002-01-01

    models is identification of novelty, i.e., low probability database events. We rank the novelty of the outliers and investigate the cause for 21 of the most novel, finding several outliers that are entry and transcription errors or infrequent or non-conforming terminology. We briefly discuss the use...

  18. Explaining outliers by subspace separability

    DEFF Research Database (Denmark)

    Micenková, Barbora; Ng, Raymond T.; Dang, Xuan-Hong

    2013-01-01

    Outliers are extraordinary objects in a data collection. Depending on the domain, they may represent errors, fraudulent activities or rare events that are subject of our interest. Existing approaches focus on detection of outliers or degrees of outlierness (ranking), but do not provide a possible...... with any existing outlier detection algorithm and it also includes a heuristic that gives a substantial speedup over the baseline strategy....

  19. Detecting outliers and/or leverage points: a robust two-stage procedure with bootstrap cut-off points

    Directory of Open Access Journals (Sweden)

    Ettore Marubini

    2014-01-01

    Full Text Available This paper presents a robust two-stage procedure for identification of outlying observations in regression analysis. The exploratory stage identifies leverage points and vertical outliers through a robust distance estimator based on Minimum Covariance Determinant (MCD. After deletion of these points, the confirmatory stage carries out an Ordinary Least Squares (OLS analysis on the remaining subset of data and investigates the effect of adding back in the previously deleted observations. Cut-off points pertinent to different diagnostics are generated by bootstrapping and the cases are definitely labelled as good-leverage, bad-leverage, vertical outliers and typical cases. The procedure is applied to four examples.

  20. A new approach for assessing the state of environment using isometric log-ratio transformation and outlier detection for computation of mean PCDD/F patterns in biota.

    Science.gov (United States)

    Lehmann, René

    2015-01-01

    To assess the state of the environment, various compartments are examined as part of monitoring programs. Within monitoring, a special focus is on chemical pollution. One of the most toxic substances ever synthesized is the well-known dioxin 2,3,7,8-TCDD (2,3,7,8-tetra-chlor-dibenzo-dioxin). Other PCDD/F (polychlorinated-dibenzo-dioxin and furan) can act toxic too. They are ubiquitary and persistent in various environmental compartments. Assessing the state of environment requires knowledge of typical local patterns of PCDD/F for as many compartments as possible. For various species of wild animals and plants (so called biota), I present the mean local congenere profiles of ubiquitary PCDD/F contamination reflecting typical patterns and levels of environmental burden for various years. Trends in time series of means can indicate success or failure of a measure of PCDD/F reduction. For short time series of mean patterns, it can be hard to detect trends. A new approach regarding proportions of outliers in the corresponding annual cross-sectional data sets in parallel can help detect decreasing or increasing environmental burden and support analysis of time series. Further, in this article, the true structure of PCDD/F data in biota is revealed, that is, the compositional data structure. It prevents direct application of statistical standard procedures to the data rendering results of statistical analysis meaningless. Results indicate that the compositional data structure of PCDD/F in biota is of great interest and should be taken into account in future studies. Isometric log-ratio (ilr) transformation is used, providing data statistical standard procedures that can be applied too. Focusing on the identification of typical PCDD/F patterns in biota, outliers are removed from annual data since they represent an extraordinary situation in the environment. Identification of outliers yields two advantages. First, typical (mean) profiles and levels of PCDD/F contamination

  1. Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series

    Directory of Open Access Journals (Sweden)

    Leonardo Plazas-Nossa

    2017-01-01

    Full Text Available Context: The UV-Vis absorbance collection using online optical captors for water quality detection may yield outliers and/or missing values. Therefore, data pre-processing is a necessary pre-requisite to monitoring data processing. Thus, the aim of this study is to propose a method that detects and removes outliers as well as fills gaps in time series. Method: Outliers are detected using Winsorising procedure and the application of the Discrete Fourier Transform (DFT and the Inverse of Fast Fourier Transform (IFFT to complete the time series. Together, these tools were used to analyse a case study comprising three sites in Colombia ((i Bogotá D.C. Salitre-WWTP (Waste Water Treatment Plant, influent; (ii Bogotá D.C. Gibraltar Pumping Station (GPS; and, (iii Itagüí, San Fernando-WWTP, influent (Medellín metropolitan area analysed via UV-Vis (Ultraviolet and Visible spectra. Results: Outlier detection with the proposed method obtained promising results when window parameter values are small and self-similar, despite that the three time series exhibited different sizes and behaviours. The DFT allowed to process different length gaps having missing values. To assess the validity of the proposed method, continuous subsets (a section of the absorbance time series without outlier or missing values were removed from the original time series obtaining an average 12% error rate in the three testing time series. Conclusions: The application of the DFT and the IFFT, using the 10% most important harmonics of useful values, can be useful for its later use in different applications, specifically for time series of water quality and quantity in urban sewer systems. One potential application would be the analysis of dry weather interesting to rain events, a feat achieved by detecting values that correspond to unusual behaviour in a time series. Additionally, the result hints at the potential of the method in correcting other hydrologic time series.

  2. Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

    Science.gov (United States)

    Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

    2016-03-01

    Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

  3. Learning Outlier Ensembles

    DEFF Research Database (Denmark)

    Micenková, Barbora; McWilliams, Brian; Assent, Ira

    into the existing unsupervised algorithms. In this paper, we show how to use powerful machine learning approaches to combine labeled examples together with arbitrary unsupervised outlier scoring algorithms. We aim to get the best out of the two worlds—supervised and unsupervised. Our approach is also a viable......Years of research in unsupervised outlier detection have produced numerous algorithms to score data according to their exceptionality. wever, the nature of outliers heavily depends on the application context and different algorithms are sensitive to outliers of different nature. This makes it very...... difficult to assess suitability of a particular algorithm without a priori knowledge. On the other hand, in many applications, some examples of outliers exist or can be obtain edin addition to the vast amount of unlabeled data. Unfortunately, this extra knowledge cannot be simply incorporated...

  4. An Efficient Method for Detection of Outliers in Tracer Curves Derived from Dynamic Contrast-Enhanced Imaging

    Directory of Open Access Journals (Sweden)

    Linning Ye

    2018-01-01

    Full Text Available Presence of outliers in tracer concentration-time curves derived from dynamic contrast-enhanced imaging can adversely affect the analysis of the tracer curves by model-fitting. A computationally efficient method for detecting outliers in tracer concentration-time curves is presented in this study. The proposed method is based on a piecewise linear model and implemented using a robust clustering algorithm. The method is noniterative and all the parameters are automatically estimated. To compare the proposed method with existing Gaussian model based and robust regression-based methods, simulation studies were performed by simulating tracer concentration-time curves using the generalized Tofts model and kinetic parameters derived from different tissue types. Results show that the proposed method and the robust regression-based method achieve better detection performance than the Gaussian model based method. Compared with the robust regression-based method, the proposed method can achieve similar detection performance with much faster computation speed.

  5. Analysis and detection of functional outliers in water quality parameters from different automated monitoring stations in the Nalón river basin (Northern Spain).

    Science.gov (United States)

    Piñeiro Di Blasi, J I; Martínez Torres, J; García Nieto, P J; Alonso Fernández, J R; Díaz Muñiz, C; Taboada, J

    2015-01-01

    The purposes and intent of the authorities in establishing water quality standards are to provide enhancement of water quality and prevention of pollution to protect the public health or welfare in accordance with the public interest for drinking water supplies, conservation of fish, wildlife and other beneficial aquatic life, and agricultural, industrial, recreational, and other reasonable and necessary uses as well as to maintain and improve the biological integrity of the waters. In this way, water quality controls involve a large number of variables and observations, often subject to some outliers. An outlier is an observation that is numerically distant from the rest of the data or that appears to deviate markedly from other members of the sample in which it occurs. An interesting analysis is to find those observations that produce measurements that are different from the pattern established in the sample. Therefore, identification of atypical observations is an important concern in water quality monitoring and a difficult task because of the multivariate nature of water quality data. Our study provides a new method for detecting outliers in water quality monitoring parameters, using turbidity, conductivity and ammonium ion as indicator variables. Until now, methods were based on considering the different parameters as a vector whose components were their concentration values. This innovative approach lies in considering water quality monitoring over time as continuous curves instead of discrete points, that is to say, the dataset of the problem are considered as a time-dependent function and not as a set of discrete values in different time instants. This new methodology, which is based on the concept of functional depth, was applied to the detection of outliers in water quality monitoring samples in the Nalón river basin with success. Results of this study were discussed here in terms of origin, causes, etc. Finally, the conclusions as well as advantages of

  6. A generalized Grubbs-Beck test statistic for detecting multiple potentially influential low outliers in flood series

    Science.gov (United States)

    Cohn, T.A.; England, J.F.; Berenbrock, C.E.; Mason, R.R.; Stedinger, J.R.; Lamontagne, J.R.

    2013-01-01

    he Grubbs-Beck test is recommended by the federal guidelines for detection of low outliers in flood flow frequency computation in the United States. This paper presents a generalization of the Grubbs-Beck test for normal data (similar to the Rosner (1983) test; see also Spencer and McCuen (1996)) that can provide a consistent standard for identifying multiple potentially influential low flows. In cases where low outliers have been identified, they can be represented as “less-than” values, and a frequency distribution can be developed using censored-data statistical techniques, such as the Expected Moments Algorithm. This approach can improve the fit of the right-hand tail of a frequency distribution and provide protection from lack-of-fit due to unimportant but potentially influential low flows (PILFs) in a flood series, thus making the flood frequency analysis procedure more robust.

  7. Iterative Outlier Removal: A Method for Identifying Outliers in Laboratory Recalibration Studies.

    Science.gov (United States)

    Parrinello, Christina M; Grams, Morgan E; Sang, Yingying; Couper, David; Wruck, Lisa M; Li, Danni; Eckfeldt, John H; Selvin, Elizabeth; Coresh, Josef

    2016-07-01

    Extreme values that arise for any reason, including those through nonlaboratory measurement procedure-related processes (inadequate mixing, evaporation, mislabeling), lead to outliers and inflate errors in recalibration studies. We present an approach termed iterative outlier removal (IOR) for identifying such outliers. We previously identified substantial laboratory drift in uric acid measurements in the Atherosclerosis Risk in Communities (ARIC) Study over time. Serum uric acid was originally measured in 1990-1992 on a Coulter DACOS instrument using an uricase-based measurement procedure. To recalibrate previous measured concentrations to a newer enzymatic colorimetric measurement procedure, uric acid was remeasured in 200 participants from stored plasma in 2011-2013 on a Beckman Olympus 480 autoanalyzer. To conduct IOR, we excluded data points >3 SDs from the mean difference. We continued this process using the resulting data until no outliers remained. IOR detected more outliers and yielded greater precision in simulation. The original mean difference (SD) in uric acid was 1.25 (0.62) mg/dL. After 4 iterations, 9 outliers were excluded, and the mean difference (SD) was 1.23 (0.45) mg/dL. Conducting only one round of outlier removal (standard approach) would have excluded 4 outliers [mean difference (SD) = 1.22 (0.51) mg/dL]. Applying the recalibration (derived from Deming regression) from each approach to the original measurements, the prevalence of hyperuricemia (>7 mg/dL) was 28.5% before IOR and 8.5% after IOR. IOR is a useful method for removal of extreme outliers irrelevant to recalibrating laboratory measurements, and identifies more extraneous outliers than the standard approach. © 2016 American Association for Clinical Chemistry.

  8. Outlier analysis

    CERN Document Server

    Aggarwal, Charu C

    2013-01-01

    With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions- the data can be of any type, structured or unstructured, and may be extremely large.Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and

  9. Examination of pulsed eddy current for inspection of second layer aircraft wing lap-joint structures using outlier detection methods

    Energy Technology Data Exchange (ETDEWEB)

    Butt, D.M., E-mail: Dennis.Butt@forces.gc.ca [Royal Military College of Canada, Dept. of Chemistry and Chemical Engineering, Kingston, Ontario (Canada); Underhill, P.R.; Krause, T.W., E-mail: Thomas.Krause@rmc.ca [Royal Military College of Canada, Dept. of Physics, Kingston, Ontario (Canada)

    2016-09-15

    Ageing aircraft are susceptible to fatigue cracks at bolt hole locations in multi-layer aluminum wing lap-joints due to cyclic loading conditions experienced during typical aircraft operation, Current inspection techniques require removal of fasteners to permit inspection of the second layer from within the bolt hole. Inspection from the top layer without fastener removal is desirable in order to minimize aircraft downtime while reducing the risk of collateral damage. The ability to detect second layer cracks without fastener removal has been demonstrated using a pulsed eddy current (PEC) technique. The technique utilizes a breakdown of the measured signal response into its principal components, each of which is multiplied by a representative factor known as a score. The reduced data set of scores, which represent the measured signal, are examined for outliers using cluster analysis methods in order to detect the presence of defects. However, the cluster analysis methodology is limited by the fact that a number of representative signals, obtained from fasteners where defects are not present, are required in order to perform classification of the data. Alternatively, blind outlier detection can be achieved without having to obtain representative defect-free signals, by using a modified smallest half-volume (MSHV) approach. Results obtained using this approach suggest that self-calibrating blind detection of cyclic fatigue cracks in second layer wing structures in the presence of ferrous fasteners is possible without prior knowledge of the sample under test and without the use of costly calibration standards. (author)

  10. Examination of pulsed eddy current for inspection of second layer aircraft wing lap-joint structures using outlier detection methods

    International Nuclear Information System (INIS)

    Butt, D.M.; Underhill, P.R.; Krause, T.W.

    2016-01-01

    Ageing aircraft are susceptible to fatigue cracks at bolt hole locations in multi-layer aluminum wing lap-joints due to cyclic loading conditions experienced during typical aircraft operation, Current inspection techniques require removal of fasteners to permit inspection of the second layer from within the bolt hole. Inspection from the top layer without fastener removal is desirable in order to minimize aircraft downtime while reducing the risk of collateral damage. The ability to detect second layer cracks without fastener removal has been demonstrated using a pulsed eddy current (PEC) technique. The technique utilizes a breakdown of the measured signal response into its principal components, each of which is multiplied by a representative factor known as a score. The reduced data set of scores, which represent the measured signal, are examined for outliers using cluster analysis methods in order to detect the presence of defects. However, the cluster analysis methodology is limited by the fact that a number of representative signals, obtained from fasteners where defects are not present, are required in order to perform classification of the data. Alternatively, blind outlier detection can be achieved without having to obtain representative defect-free signals, by using a modified smallest half-volume (MSHV) approach. Results obtained using this approach suggest that self-calibrating blind detection of cyclic fatigue cracks in second layer wing structures in the presence of ferrous fasteners is possible without prior knowledge of the sample under test and without the use of costly calibration standards. (author)

  11. Quartile and Outlier Detection on Heterogeneous Clusters Using Distributed Radix Sort

    International Nuclear Information System (INIS)

    Meredith, Jeremy S.; Vetter, Jeffrey S.

    2011-01-01

    In the past few years, performance improvements in CPUs and memory technologies have outpaced those of storage systems. When extrapolated to the exascale, this trend places strict limits on the amount of data that can be written to disk for full analysis, resulting in an increased reliance on characterizing in-memory data. Many of these characterizations are simple, but require sorted data. This paper explores an example of this type of characterization - the identification of quartiles and statistical outliers - and presents a performance analysis of a distributed heterogeneous radix sort as well as an assessment of current architectural bottlenecks.

  12. Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC-MS/MS

    Science.gov (United States)

    Bilbao, Aivett; Zhang, Ying; Varesio, Emmanuel; Luban, Jeremy; Strambio-De-Castillia, Caterina; Lisacek, Frédérique; Hopfgartner, Gérard

    2016-01-01

    Data-independent acquisition LC-MS/MS techniques complement supervised methods for peptide quantification. However, due to the wide precursor isolation windows, these techniques are prone to interference at the fragment ion level, which in turn is detrimental for accurate quantification. The “non-outlier fragment ion” (NOFI) ranking algorithm has been developed to assign low priority to fragment ions affected by interference. By using the optimal subset of high priority fragment ions these interfered fragment ions are effectively excluded from quantification. NOFI represents each fragment ion as a vector of four dimensions related to chromatographic and MS fragmentation attributes and applies multivariate outlier detection techniques. Benchmarking conducted on a well-defined quantitative dataset (i.e. the SWATH Gold Standard), indicates that NOFI on average is able to accurately quantify 11-25% more peptides than the commonly used Top-N library intensity ranking method. The sum of the area of the Top3-5 NOFIs produces similar coefficients of variation as compared to the library intensity method but with more accurate quantification results. On a biologically relevant human dendritic cell digest dataset, NOFI properly assigns low priority ranks to 85% of annotated interferences, resulting in sensitivity values between 0.92 and 0.80 against 0.76 for the Spectronaut interference detection algorithm. PMID:26412574

  13. Detection of outliers by neural network on the gas centrifuge experimental data of isotopic separation process; Aplicacao de redes neurais para deteccao de erros grosseiros em dados de processo de separacao de isotopos de uranio por ultracentrifugacao

    Energy Technology Data Exchange (ETDEWEB)

    Andrade, Monica de Carvalho Vasconcelos

    2004-07-01

    This work presents and discusses the neural network technique aiming at the detection of outliers on a set of gas centrifuge isotope separation experimental data. In order to evaluate the application of this new technique, the result obtained of the detection is compared to the result of the statistical analysis combined with the cluster analysis. This method for the detection of outliers presents a considerable potential in the field of data analysis and it is at the same time easier and faster to use and requests very less knowledge of the physics involved in the process. This work established a procedure for detecting experiments which are suspect to contain gross errors inside a data set where the usual techniques for identification of these errors cannot be applied or its use/demands an excessively long work. (author)

  14. Outlier Loci Detect Intraspecific Biodiversity amongst Spring and Autumn Spawning Herring across Local Scales.

    Directory of Open Access Journals (Sweden)

    Dorte Bekkevold

    Full Text Available Herring, Clupea harengus, is one of the ecologically and commercially most important species in European northern seas, where two distinct ecotypes have been described based on spawning time; spring and autumn. To date, it is unknown if these spring and autumn spawning herring constitute genetically distinct units. We assessed levels of genetic divergence between spring and autumn spawning herring in the Baltic Sea using two types of DNA markers, microsatellites and Single Nucleotide Polymorphisms, and compared the results with data for autumn spawning North Sea herring. Temporally replicated analyses reveal clear genetic differences between ecotypes and hence support reproductive isolation. Loci showing non-neutral behaviour, so-called outlier loci, show convergence between autumn spawning herring from demographically disjoint populations, potentially reflecting selective processes associated with autumn spawning ecotypes. The abundance and exploitation of the two ecotypes have varied strongly over space and time in the Baltic Sea, where autumn spawners have faced strong depression for decades. The results therefore have practical implications by highlighting the need for specific management of these co-occurring ecotypes to meet requirements for sustainable exploitation and ensure optimal livelihood for coastal communities.

  15. Portraying the Expression Landscapes of B-CellLymphoma-Intuitive Detection of Outlier Samples and of Molecular Subtypes

    Directory of Open Access Journals (Sweden)

    Lydia Hopp

    2013-12-01

    Full Text Available We present an analytic framework based on Self-Organizing Map (SOM machine learning to study large scale patient data sets. The potency of the approach is demonstrated in a case study using gene expression data of more than 200 mature aggressive B-cell lymphoma patients. The method portrays each sample with individual resolution, characterizes the subtypes, disentangles the expression patterns into distinct modules, extracts their functional context using enrichment techniques and enables investigation of the similarity relations between the samples. The method also allows to detect and to correct outliers caused by contaminations. Based on our analysis, we propose a refined classification of B-cell Lymphoma into four molecular subtypes which are characterized by differential functional and clinical characteristics.

  16. Detecting Outliers in Marathon Data by Means of the Andrews Plot

    Science.gov (United States)

    Stehlík, Milan; Wald, Helmut; Bielik, Viktor; Petrovič, Juraj

    2011-09-01

    For an optimal race performance, it is important, that the runner keeps steady pace during most of the time of the competition. First time runners or athletes without many competitions often experience an "blow out" after a few kilometers of the race. This could happen, because of strong emotional experiences or low control of running intensity. Competition pace of half marathon of the middle level recreational athletes is approximately 10 sec quicker than their training pace. If an athlete runs the first third of race (7 km) at a pace that is 20 sec quicker than is his capacity (trainability), he would experience an "blow out" in the last third of the race. This would be reflected by reducing the running intensity and inability to keep steady pace in the last kilometers of the race and in the final time as well. In sports science, there are many diagnostic methods ([3], [2], [6]) that are used for prediction of optimal race pace tempo and final time. Otherwise there is lacking practical evidence of diagnostics methods and its use in the field (competition, race). One of the conditions that needs to be carried out is that athletes have not only similar final times, but it is important that they keep constant pace as much as possible during whole race. For this reason it is very important to find outliers. Our experimental group consisted of 20 recreational trained athletes (mean age 32,6 years±8,9). Before the race the athletes were instructed to run on the basis of their subjective feeling and previous experience. The data (running pace of each kilometer, average and maximal heart rate of each kilometer) were collected by GPS-enabled personal trainer Forerunner 305.

  17. Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data

    NARCIS (Netherlands)

    Thornton, Dallas; van Capelleveen, Guido; Poel, Mannes; van Hillegersberg, Jos; Mueller, Roland

    Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative

  18. Damage Detection in an Operating Vestas V27 Wind Turbine Blade by use of Outlier Analysis

    DEFF Research Database (Denmark)

    Ulriksen, Martin Dalgaard; Tcherniak, Dmitri; Damkilde, Lars

    2015-01-01

    The present paper explores the application of a well-established vibration-based damage detection method to an operating Vestas V27 wind turbine blade. The blade is analyzed in a total of four states, namely, a healthy one plus three damaged ones in which trailing edge openings of increasing sizes...

  19. Using a Linear Regression Method to Detect Outliers in IRT Common Item Equating

    Science.gov (United States)

    He, Yong; Cui, Zhongmin; Fang, Yu; Chen, Hanwei

    2013-01-01

    Common test items play an important role in equating alternate test forms under the common item nonequivalent groups design. When the item response theory (IRT) method is applied in equating, inconsistent item parameter estimates among common items can lead to large bias in equated scores. It is prudent to evaluate inconsistency in parameter…

  20. A Near-linear Time Approximation Algorithm for Angle-based Outlier Detection in High-dimensional Data

    DEFF Research Database (Denmark)

    Pham, Ninh Dang; Pagh, Rasmus

    2012-01-01

    projection-based technique that is able to estimate the angle-based outlier factor for all data points in time near-linear in the size of the data. Also, our approach is suitable to be performed in parallel environment to achieve a parallel speedup. We introduce a theoretical analysis of the quality...... neighbor are deteriorated in high-dimensional data. Following up on the work of Kriegel et al. (KDD '08), we investigate the use of angle-based outlier factor in mining high-dimensional outliers. While their algorithm runs in cubic time (with a quadratic time heuristic), we propose a novel random......Outlier mining in d-dimensional point sets is a fundamental and well studied data mining task due to its variety of applications. Most such applications arise in high-dimensional domains. A bottleneck of existing approaches is that implicit or explicit assessments on concepts of distance or nearest...

  1. Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor

    OpenAIRE

    Laxhammar , Rikard; Falkman , Göran

    2012-01-01

    Part 4: First Conformal Prediction and Its Applications Workshop (COPA 2012); International audience; Automated detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms suffer from one or more of the following limitations: First, they are essentially designed for offline anomaly detection in databases. Second, they are insensitive to loca...

  2. Outlier Detection in Regression Using an Iterated One-Step Approximation to the Huber-Skip Estimator

    DEFF Research Database (Denmark)

    Johansen, Søren; Nielsen, Bent

    2013-01-01

    In regression we can delete outliers based upon a preliminary estimator and reestimate the parameters by least squares based upon the retained observations. We study the properties of an iteratively defined sequence of estimators based on this idea. We relate the sequence to the Huber-skip estima......In regression we can delete outliers based upon a preliminary estimator and reestimate the parameters by least squares based upon the retained observations. We study the properties of an iteratively defined sequence of estimators based on this idea. We relate the sequence to the Huber...... that the normalized estimation errors are tight and are close to a linear function of the kernel, thus providing a stochastic expansion of the estimators, which is the same as for the Huber-skip. This implies that the iterated estimator is a close approximation of the Huber-skip...

  3. The Space-Time Variation of Global Crop Yields, Detecting Simultaneous Outliers and Identifying the Teleconnections with Climatic Patterns

    Science.gov (United States)

    Najafi, E.; Devineni, N.; Pal, I.; Khanbilvardi, R.

    2017-12-01

    An understanding of the climate factors that influence the space-time variability of crop yields is important for food security purposes and can help us predict global food availability. In this study, we address how the crop yield trends of countries globally were related to each other during the last several decades and the main climatic variables that triggered high/low crop yields simultaneously across the world. Robust Principal Component Analysis (rPCA) is used to identify the primary modes of variation in wheat, maize, sorghum, rice, soybeans, and barley yields. Relations between these modes of variability and important climatic variables, especially anomalous sea surface temperature (SSTa), are examined from 1964 to 2010. rPCA is also used to identify simultaneous outliers in each year, i.e. systematic high/low crop yields across the globe. The results demonstrated spatiotemporal patterns of these crop yields and the climate-related events that caused them as well as the connection of outliers with weather extremes. We find that among climatic variables, SST has had the most impact on creating simultaneous crop yields variability and yield outliers in many countries. An understanding of this phenomenon can benefit global crop trade networks.

  4. Evaluating Outlier Identification Tests: Mahalanobis "D" Squared and Comrey "Dk."

    Science.gov (United States)

    Rasmussen, Jeffrey Lee

    1988-01-01

    A Monte Carlo simulation was used to compare the Mahalanobis "D" Squared and the Comrey "Dk" methods of detecting outliers in data sets. Under the conditions investigated, the "D" Squared technique was preferable as an outlier removal statistic. (SLD)

  5. Pendeteksian Outlier pada Regresi Nonlinier dengan Metode statistik Likelihood Displacement

    Directory of Open Access Journals (Sweden)

    Siti Tabi'atul Hasanah

    2012-11-01

    Full Text Available Outlier is an observation that much different (extreme from the other observational data, or data can be interpreted that do not follow the general pattern of the model. Sometimes outliers provide information that can not be provided by other data. That's why outliers should not just be eliminated. Outliers can also be an influential observation. There are many methods that can be used to detect of outliers. In previous studies done on outlier detection of linear regression. Next will be developed detection of outliers in nonlinear regression. Nonlinear regression here is devoted to multiplicative nonlinear regression. To detect is use of statistical method likelihood displacement. Statistical methods abbreviated likelihood displacement (LD is a method to detect outliers by removing the suspected outlier data. To estimate the parameters are used to the maximum likelihood method, so we get the estimate of the maximum. By using LD method is obtained i.e likelihood displacement is thought to contain outliers. Further accuracy of LD method in detecting the outliers are shown by comparing the MSE of LD with the MSE from the regression in general. Statistic test used is Λ. Initial hypothesis was rejected when proved so is an outlier.

  6. Sparsity-weighted outlier FLOODing (OFLOOD) method: Efficient rare event sampling method using sparsity of distribution.

    Science.gov (United States)

    Harada, Ryuhei; Nakamura, Tomotake; Shigeta, Yasuteru

    2016-03-30

    As an extension of the Outlier FLOODing (OFLOOD) method [Harada et al., J. Comput. Chem. 2015, 36, 763], the sparsity of the outliers defined by a hierarchical clustering algorithm, FlexDice, was considered to achieve an efficient conformational search as sparsity-weighted "OFLOOD." In OFLOOD, FlexDice detects areas of sparse distribution as outliers. The outliers are regarded as candidates that have high potential to promote conformational transitions and are employed as initial structures for conformational resampling by restarting molecular dynamics simulations. When detecting outliers, FlexDice defines a rank in the hierarchy for each outlier, which relates to sparsity in the distribution. In this study, we define a lower rank (first ranked), a medium rank (second ranked), and the highest rank (third ranked) outliers, respectively. For instance, the first-ranked outliers are located in a given conformational space away from the clusters (highly sparse distribution), whereas those with the third-ranked outliers are nearby the clusters (a moderately sparse distribution). To achieve the conformational search efficiently, resampling from the outliers with a given rank is performed. As demonstrations, this method was applied to several model systems: Alanine dipeptide, Met-enkephalin, Trp-cage, T4 lysozyme, and glutamine binding protein. In each demonstration, the present method successfully reproduced transitions among metastable states. In particular, the first-ranked OFLOOD highly accelerated the exploration of conformational space by expanding the edges. In contrast, the third-ranked OFLOOD reproduced local transitions among neighboring metastable states intensively. For quantitatively evaluations of sampled snapshots, free energy calculations were performed with a combination of umbrella samplings, providing rigorous landscapes of the biomolecules. © 2015 Wiley Periodicals, Inc.

  7. Hot spots, cluster detection and spatial outlier analysis of teen birth rates in the U.S., 2003–2012

    Science.gov (United States)

    Khan, Diba; Rossen, Lauren M.; Hamilton, Brady E.; He, Yulei; Wei, Rong; Dienes, Erin

    2017-01-01

    Teen birth rates have evidenced a significant decline in the United States over the past few decades. Most of the states in the US have mirrored this national decline, though some reports have illustrated substantial variation in the magnitude of these decreases across the U.S. Importantly, geographic variation at the county level has largely not been explored. We used National Vital Statistics Births data and Hierarchical Bayesian space-time interaction models to produce smoothed estimates of teen birth rates at the county level from 2003–2012. Results indicate that teen birth rates show evidence of clustering, where hot and cold spots occur, and identify spatial outliers. Findings from this analysis may help inform efforts targeting the prevention efforts by illustrating how geographic patterns of teen birth rates have changed over the past decade and where clusters of high or low teen birth rates are evident. PMID:28552189

  8. Hot spots, cluster detection and spatial outlier analysis of teen birth rates in the U.S., 2003-2012.

    Science.gov (United States)

    Khan, Diba; Rossen, Lauren M; Hamilton, Brady E; He, Yulei; Wei, Rong; Dienes, Erin

    2017-06-01

    Teen birth rates have evidenced a significant decline in the United States over the past few decades. Most of the states in the US have mirrored this national decline, though some reports have illustrated substantial variation in the magnitude of these decreases across the U.S. Importantly, geographic variation at the county level has largely not been explored. We used National Vital Statistics Births data and Hierarchical Bayesian space-time interaction models to produce smoothed estimates of teen birth rates at the county level from 2003-2012. Results indicate that teen birth rates show evidence of clustering, where hot and cold spots occur, and identify spatial outliers. Findings from this analysis may help inform efforts targeting the prevention efforts by illustrating how geographic patterns of teen birth rates have changed over the past decade and where clusters of high or low teen birth rates are evident. Published by Elsevier Ltd.

  9. A simple transformation independent method for outlier definition.

    Science.gov (United States)

    Johansen, Martin Berg; Christensen, Peter Astrup

    2018-04-10

    Definition and elimination of outliers is a key element for medical laboratories establishing or verifying reference intervals (RIs). Especially as inclusion of just a few outlying observations may seriously affect the determination of the reference limits. Many methods have been developed for definition of outliers. Several of these methods are developed for the normal distribution and often data require transformation before outlier elimination. We have developed a non-parametric transformation independent outlier definition. The new method relies on drawing reproducible histograms. This is done by using defined bin sizes above and below the median. The method is compared to the method recommended by CLSI/IFCC, which uses Box-Cox transformation (BCT) and Tukey's fences for outlier definition. The comparison is done on eight simulated distributions and an indirect clinical datasets. The comparison on simulated distributions shows that without outliers added the recommended method in general defines fewer outliers. However, when outliers are added on one side the proposed method often produces better results. With outliers on both sides the methods are equally good. Furthermore, it is found that the presence of outliers affects the BCT, and subsequently affects the determined limits of current recommended methods. This is especially seen in skewed distributions. The proposed outlier definition reproduced current RI limits on clinical data containing outliers. We find our simple transformation independent outlier detection method as good as or better than the currently recommended methods.

  10. Outlier Ranking via Subspace Analysis in Multiple Views of the Data

    DEFF Research Database (Denmark)

    Muller, Emmanuel; Assent, Ira; Iglesias, Patricia

    2012-01-01

    , a novel outlier ranking concept. Outrank exploits subspace analysis to determine the degree of outlierness. It considers different subsets of the attributes as individual outlier properties. It compares clustered regions in arbitrary subspaces and derives an outlierness score for each object. Its...... principled integration of multiple views into an outlierness measure uncovers outliers that are not detectable in the full attribute space. Our experimental evaluation demonstrates that Outrank successfully determines a high quality outlier ranking, and outperforms state-of-the-art outlierness measures....

  11. Exploring Outliers in Crowdsourced Ranking for QoE

    OpenAIRE

    Xu, Qianqian; Yan, Ming; Huang, Chendi; Xiong, Jiechao; Huang, Qingming; Yao, Yuan

    2017-01-01

    Outlier detection is a crucial part of robust evaluation for crowdsourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years. In this paper, we propose some simple and fast algorithms for outlier detection and robust QoE evaluation based on the nonconvex optimization principle. Several iterative procedures are designed with or without knowing the number of outliers in samples. Theoretical analysis is given to show that such procedures can reach stati...

  12. A comparative study of outlier detection for large-scale traffic data by one-class SVM and kernel density estimation

    Science.gov (United States)

    Ngan, Henry Y. T.; Yung, Nelson H. C.; Yeh, Anthony G. O.

    2015-02-01

    This paper aims at presenting a comparative study of outlier detection (OD) for large-scale traffic data. The traffic data nowadays are massive in scale and collected in every second throughout any modern city. In this research, the traffic flow dynamic is collected from one of the busiest 4-armed junction in Hong Kong in a 31-day sampling period (with 764,027 vehicles in total). The traffic flow dynamic is expressed in a high dimension spatial-temporal (ST) signal format (i.e. 80 cycles) which has a high degree of similarities among the same signal and across different signals in one direction. A total of 19 traffic directions are identified in this junction and lots of ST signals are collected in the 31-day period (i.e. 874 signals). In order to reduce its dimension, the ST signals are firstly undergone a principal component analysis (PCA) to represent as (x,y)-coordinates. Then, these PCA (x,y)-coordinates are assumed to be conformed as Gaussian distributed. With this assumption, the data points are further to be evaluated by (a) a correlation study with three variant coefficients, (b) one-class support vector machine (SVM) and (c) kernel density estimation (KDE). The correlation study could not give any explicit OD result while the one-class SVM and KDE provide average 59.61% and 95.20% DSRs, respectively.

  13. Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land

    International Nuclear Information System (INIS)

    Zhang Chaosheng; Tang Ya; Luo Lin; Xu Weilin

    2009-01-01

    Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box-Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with. - Outliers in urban geochemical databases can be detected to provide guidance for identification of potential contaminated land.

  14. Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Chaosheng, E-mail: chaosheng.zhang@nuigalway.i [School of Geography and Archaeology, National University of Ireland, Galway (Ireland); Tang Ya [Department of Environmental Sciences, Sichuan University, Chengdu, Sichuan 610065 (China); Luo Lin; Xu Weilin [State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, Sichuan 610065 (China)

    2009-11-15

    Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box-Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with. - Outliers in urban geochemical databases can be detected to provide guidance for identification of potential contaminated land.

  15. A Note on optimal estimation in the presence of outliers

    Directory of Open Access Journals (Sweden)

    John N. Haddad

    2017-06-01

    Full Text Available Haddad, J. 2017. A Note on optimal estimation in the presence of outliers. Lebanese Science Journal, 18(1: 136-141. The basic estimation problem of the mean and standard deviation of a random normal process in the presence of an outlying observation is considered. The value of the outlier is taken as a constraint imposed on the maximization problem of the log likelihood. It is shown that the optimal solution of the maximization problem exists and expressions for the estimates are given. Applications to estimation in the presence of outliers and outlier detection are discussed and illustrated through a simulation study and analysis of trade data

  16. Controller modification applied for active fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, Jakob; Poulsen, Niels Kjølstad

    2014-01-01

    This paper is focusing on active fault detection (AFD) for parametric faults in closed-loop systems. This auxiliary input applied for the fault detection will also disturb the external output and consequently reduce the performance of the controller. Therefore, only small auxiliary inputs are used...... with the result that the detection and isolation time can be long. In this paper it will be shown, that this problem can be handled by using a modification of the feedback controller. By applying the YJBK-parameterization (after Youla, Jabr, Bongiorno and Kucera) for the controller, it is possible to modify...... the frequency for the auxiliary input is selected. This gives that it is possible to apply an auxiliary input with a reduced amplitude. An example is included to show the results....

  17. Displaying an Outlier in Multivariate Data | Gordor | Journal of ...

    African Journals Online (AJOL)

    ... a multivariate data set is proposed. The technique involves the projection of the multidimensional data onto a single dimension called the outlier displaying component. When the observations are plotted on this component the outlier is appreciably revealed. Journal of Applied Science and Technology (JAST), Vol. 4, Nos.

  18. Adaptive prediction applied to seismic event detection

    International Nuclear Information System (INIS)

    Clark, G.A.; Rodgers, P.W.

    1981-01-01

    Adaptive prediction was applied to the problem of detecting small seismic events in microseismic background noise. The Widrow-Hoff LMS adaptive filter used in a prediction configuration is compared with two standard seismic filters as an onset indicator. Examples demonstrate the technique's usefulness with both synthetic and actual seismic data

  19. Adaptive prediction applied to seismic event detection

    Energy Technology Data Exchange (ETDEWEB)

    Clark, G.A.; Rodgers, P.W.

    1981-09-01

    Adaptive prediction was applied to the problem of detecting small seismic events in microseismic background noise. The Widrow-Hoff LMS adaptive filter used in a prediction configuration is compared with two standard seismic filters as an onset indicator. Examples demonstrate the technique's usefulness with both synthetic and actual seismic data.

  20. Slowing ash mortality: a potential strategy to slam emerald ash borer in outlier sites

    Science.gov (United States)

    Deborah G. McCullough; Nathan W. Siegert; John Bedford

    2009-01-01

    Several isolated outlier populations of emerald ash borer (Agrilus planipennis Fairmaire) were discovered in 2008 and additional outliers will likely be found as detection surveys and public outreach activities...

  1. Outlier-resilient complexity analysis of heartbeat dynamics

    Science.gov (United States)

    Lo, Men-Tzung; Chang, Yi-Chung; Lin, Chen; Young, Hsu-Wen Vincent; Lin, Yen-Hung; Ho, Yi-Lwun; Peng, Chung-Kang; Hu, Kun

    2015-03-01

    Complexity in physiological outputs is believed to be a hallmark of healthy physiological control. How to accurately quantify the degree of complexity in physiological signals with outliers remains a major barrier for translating this novel concept of nonlinear dynamic theory to clinical practice. Here we propose a new approach to estimate the complexity in a signal by analyzing the irregularity of the sign time series of its coarse-grained time series at different time scales. Using surrogate data, we show that the method can reliably assess the complexity in noisy data while being highly resilient to outliers. We further apply this method to the analysis of human heartbeat recordings. Without removing any outliers due to ectopic beats, the method is able to detect a degradation of cardiac control in patients with congestive heart failure and a more degradation in critically ill patients whose life continuation relies on extracorporeal membrane oxygenator (ECMO). Moreover, the derived complexity measures can predict the mortality of ECMO patients. These results indicate that the proposed method may serve as a promising tool for monitoring cardiac function of patients in clinical settings.

  2. Enhanced Isotopic Ratio Outlier Analysis (IROA Peak Detection and Identification with Ultra-High Resolution GC-Orbitrap/MS: Potential Application for Investigation of Model Organism Metabolomes

    Directory of Open Access Journals (Sweden)

    Yunping Qiu

    2018-01-01

    Full Text Available Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG. IROA uses two different 13C-enriched carbon sources (randomized 95% 12C and 95% 13C to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n, which aids in the identification of endogenous metabolites. Accurate m/z, n, and derivatization information are obtained from our GC/MS workflow for unknown metabolite identification, and aids in silico methodologies for identifying isomeric and non-annotated metabolites. We were able to mine more mass spectral information using the same Saccharomyces cerevisiae growth protocol (Qiu et al. Anal. Chem 2016 with the ultra-high resolution GC-Orbitrap/MS, using 10% ammonia in methane as the CI reagent gas. We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs using a GC-TOF/MS machine. For 55 selected metabolites identified from matched IROA CI and EI spectra, using the GC-Orbitrap/MS vs. GC-TOF/MS, the average mass deviation for GC-Orbitrap/MS was 1.48 ppm, however, the average mass deviation was 32.2 ppm for the GC-TOF/MS machine. In summary, the higher resolution and wider dynamic range of the GC-Orbitrap/MS enabled more accurate CFG, and the coupling of accurate mass GC/MS IROA methodology with in silico fragmentation has great

  3. Enhanced Isotopic Ratio Outlier Analysis (IROA) Peak Detection and Identification with Ultra-High Resolution GC-Orbitrap/MS: Potential Application for Investigation of Model Organism Metabolomes.

    Science.gov (United States)

    Qiu, Yunping; Moir, Robyn D; Willis, Ian M; Seethapathy, Suresh; Biniakewitz, Robert C; Kurland, Irwin J

    2018-01-18

    Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG). IROA uses two different 13 C-enriched carbon sources (randomized 95% 12 C and 95% 13 C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n), which aids in the identification of endogenous metabolites. Accurate m/z, n, and derivatization information are obtained from our GC/MS workflow for unknown metabolite identification, and aids in silico methodologies for identifying isomeric and non-annotated metabolites. We were able to mine more mass spectral information using the same Saccharomyces cerevisiae growth protocol (Qiu et al. Anal. Chem 2016) with the ultra-high resolution GC-Orbitrap/MS, using 10% ammonia in methane as the CI reagent gas. We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs using a GC-TOF/MS machine). For 55 selected metabolites identified from matched IROA CI and EI spectra, using the GC-Orbitrap/MS vs. GC-TOF/MS, the average mass deviation for GC-Orbitrap/MS was 1.48 ppm, however, the average mass deviation was 32.2 ppm for the GC-TOF/MS machine. In summary, the higher resolution and wider dynamic range of the GC-Orbitrap/MS enabled more accurate CFG, and the coupling of accurate mass GC/MS IROA methodology with in silico fragmentation has great potential in

  4. An MEF-Based Localization Algorithm against Outliers in Wireless Sensor Networks.

    Science.gov (United States)

    Wang, Dandan; Wan, Jiangwen; Wang, Meimei; Zhang, Qiang

    2016-07-07

    Precise localization has attracted considerable interest in Wireless Sensor Networks (WSNs) localization systems. Due to the internal or external disturbance, the existence of the outliers, including both the distance outliers and the anchor outliers, severely decreases the localization accuracy. In order to eliminate both kinds of outliers simultaneously, an outlier detection method is proposed based on the maximum entropy principle and fuzzy set theory. Since not all the outliers can be detected in the detection process, the Maximum Entropy Function (MEF) method is utilized to tolerate the errors and calculate the optimal estimated locations of unknown nodes. Simulation results demonstrate that the proposed localization method remains stable while the outliers vary. Moreover, the localization accuracy is highly improved by wisely rejecting outliers.

  5. Optical fiber-applied radiation detection system

    International Nuclear Information System (INIS)

    Nishiura, Ryuichi; Uranaka, Yasuo; Izumi, Nobuyuki

    2001-01-01

    A technique to measure radiation by using plastic scintillation fibers doped radiation fluorescent (scintillator) to plastic optical fiber for a radiation sensor, was developed. The technique contains some superiority such as high flexibility due to using fibers, relatively easy large area due to detecting portion of whole of fibers, and no electromagnetic noise effect due to optical radiation detection and signal transmission. Measurable to wide range of and continuous radiation distribution along optical fiber cable at a testing portion using scintillation fiber and flight time method, the optical fiber-applied radiation sensing system can effectively monitor space radiation dose or apparatus operation condition monitoring. And, a portable type scintillation optical fiber body surface pollution monitor can measure pollution concentration of radioactive materials attached onto body surface by arranging scintillation fiber processed to a plate with small size and flexibility around a man to be tested. Here were described on outline and fundamental properties of various application products using these plastic scintillation fiber. (G.K.)

  6. A statistical test for outlier identification in data envelopment analysis

    Directory of Open Access Journals (Sweden)

    Morteza Khodabin

    2010-09-01

    Full Text Available In the use of peer group data to assess individual, typical or best practice performance, the effective detection of outliers is critical for achieving useful results. In these ‘‘deterministic’’ frontier models, statistical theory is now mostly available. This paper deals with the statistical pared sample method and its capability of detecting outliers in data envelopment analysis. In the presented method, each observation is deleted from the sample once and the resulting linear program is solved, leading to a distribution of efficiency estimates. Based on the achieved distribution, a pared test is designed to identify the potential outlier(s. We illustrate the method through a real data set. The method could be used in a first step, as an exploratory data analysis, before using any frontier estimation.

  7. Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York

    Directory of Open Access Journals (Sweden)

    Goovaerts Pierre

    2004-07-01

    Full Text Available Abstract Background Complete Spatial Randomness (CSR is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA. Results We developed a typology of neutral models that progressively relaxes the assumptions of null hypotheses, allowing for the presence of spatial autocorrelation, non-uniform risk, and incorporation of spatially heterogeneous population sizes. Incorporation of spatial autocorrelation led to fewer significant ZIP codes than found in previous studies, confirming earlier claims that CSR can lead to over-identification of the number of significant spatial clusters or outliers. Accounting for population size through geostatistical filtering increased the size of clusters while removing most of the spatial outliers. Integration of regional background into the neutral models yielded substantially different spatial clusters and outliers, leading to the identification of ZIP codes where SMR values significantly depart from their regional background. Conclusion The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the background variation extant in real-world systems. In particular, this new

  8. Gear Fault Detection Effectiveness as Applied to Tooth Surface Pitting Fatigue Damage

    Science.gov (United States)

    Lewicki, David G.; Dempsey, Paula J.; Heath, Gregory F.; Shanthakumaran, Perumal

    2010-01-01

    A study was performed to evaluate fault detection effectiveness as applied to gear-tooth-pitting-fatigue damage. Vibration and oil-debris monitoring (ODM) data were gathered from 24 sets of spur pinion and face gears run during a previous endurance evaluation study. Three common condition indicators (RMS, FM4, and NA4 [Ed. 's note: See Appendix A-Definitions D were deduced from the time-averaged vibration data and used with the ODM to evaluate their performance for gear fault detection. The NA4 parameter showed to be a very good condition indicator for the detection of gear tooth surface pitting failures. The FM4 and RMS parameters perfomu:d average to below average in detection of gear tooth surface pitting failures. The ODM sensor was successful in detecting a significant 8lDOunt of debris from all the gear tooth pitting fatigue failures. Excluding outliers, the average cumulative mass at the end of a test was 40 mg.

  9. Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases

    Directory of Open Access Journals (Sweden)

    Xin Liu

    2018-01-01

    Full Text Available Mining outlier data guarantees access security and data scheduling of parallel databases and maintains high-performance operation of real-time databases. Traditional mining methods generate abundant interference data with reduced accuracy, efficiency, and stability, causing severe deficiencies. This paper proposes a new mining outlier data method, which is used to analyze real-time data features, obtain magnitude spectra models of outlier data, establish a decisional-tree information chain transmission model for outlier data in mobile Internet, obtain the information flow of internal outlier data in the information chain of a large real-time database, and cluster data. Upon local characteristic time scale parameters of information flow, the phase position features of the outlier data before filtering are obtained; the decision-tree outlier-classification feature-filtering algorithm is adopted to acquire signals for analysis and instant amplitude and to achieve the phase-frequency characteristics of outlier data. Wavelet transform threshold denoising is combined with signal denoising to analyze data offset, to correct formed detection filter model, and to realize outlier data mining. The simulation suggests that the method detects the characteristic outlier data feature response distribution, reduces response time, iteration frequency, and mining error rate, improves mining adaptation and coverage, and shows good mining outcomes.

  10. Optical fiber applied to radiation detection

    Energy Technology Data Exchange (ETDEWEB)

    Junior, Francisco A.B.; Costa, Antonella L.; Oliveira, Arno H. de; Vasconcelos, Danilo C., E-mail: fanbra@yahoo.com.br, E-mail: antonella@nuclear.ufmg.br, E-mail: heeren@nuclear.ufmg.br, E-mail: danilochagas@yahoo.com.br [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Escola de Engenharia. Departamento de Engenharia Nuclear

    2015-07-01

    In the last years, the production of optical fibers cables has make possible the development of a range of spectroscopic probes for in situ analysis performing beyond nondestructive tests, environmental monitoring, security investigation, application in radiotherapy for dose monitoring, verification and validation. In this work, a system using an optical fiber cable to light signal transmission from a NaI(Tl) radiation detector is presented. The innovative device takes advantage mainly of the optical fibers small signal attenuation and immunity to electromagnetic interference to application for radiation detection systems. The main aim was to simplify the detection system making it to reach areas where the conventional device cannot access due to its lack of mobility and external dimensions. Some tests with this innovative system are presented and the results stimulate the continuity of the researches. (author)

  11. A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series

    Directory of Open Access Journals (Sweden)

    Francisco Javier Duque-Pintor

    2016-09-01

    Full Text Available The occurrence of outliers in real-world phenomena is quite usual. If these anomalous data are not properly treated, unreliable models can be generated. Many approaches in the literature are focused on a posteriori detection of outliers. However, a new methodology to a priori predict the occurrence of such data is proposed here. Thus, the main goal of this work is to predict the occurrence of outliers in time series, by using, for the first time, imbalanced classification techniques. In this sense, the problem of forecasting outlying data has been transformed into a binary classification problem, in which the positive class represents the occurrence of outliers. Given that the number of outliers is much lower than the number of common values, the resultant classification problem is imbalanced. To create training and test sets, robust statistical methods have been used to detect outliers in both sets. Once the outliers have been detected, the instances of the dataset are labeled accordingly. Namely, if any of the samples composing the next instance are detected as an outlier, the label is set to one. As a study case, the methodology has been tested on electricity demand time series in the Spanish electricity market, in which most of the outliers were properly forecast.

  12. A Note on the Vogelsang Test for Additive Outliers

    DEFF Research Database (Denmark)

    Haldrup, Niels; Sansó, Andreu

    The role of additive outliers in integrated time series has attractedsome attention recently and research shows that outlier detection shouldbe an integral part of unit root testing procedures. Recently, Vogelsang(1999) suggested an iterative procedure for the detection of multiple additiveoutliers...... in integrated time series. However, the procedure appearsto suffr from serious size distortions towards the finding of too manyoutliers as has been shown by Perron and Rodriguez (2003). In this notewe prove the inconsistency of the test in each step of the iterative procedureand hence alternative routes need...

  13. Outlier identification in urban soils and its implications for identification of potential contaminated land

    Science.gov (United States)

    Zhang, Chaosheng

    2010-05-01

    Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box-Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with.

  14. Comparison of the effectiveness of ISJ and SSR markers and detection of outlier loci in conservation genetics of Pulsatilla patens populations.

    Science.gov (United States)

    Bilska, Katarzyna; Szczecińska, Monika

    2016-01-01

    populations of P. patens for ISJ markers, but not for SSR markers. The results of the present study suggest that ISJ markers can complement the analyses based on SSRs. However, neutral and adaptive markers should not be alternatively applied. Neutral microsatellite markers cannot depict the full range of genetic variation in a population because they do not enable to analyze functional variation. Although ISJ markers are less polymorphic, they can contribute to the reliability of analyses based on SSRs.

  15. Baseline Estimation and Outlier Identification for Halocarbons

    Science.gov (United States)

    Wang, D.; Schuck, T.; Engel, A.; Gallman, F.

    2017-12-01

    The aim of this paper is to build a baseline model for halocarbons and to statistically identify the outliers under specific conditions. In this paper, time series of regional CFC-11 and Chloromethane measurements was discussed, which taken over the last 4 years at two locations, including a monitoring station at northwest of Frankfurt am Main (Germany) and Mace Head station (Ireland). In addition to analyzing time series of CFC-11 and Chloromethane, more importantly, a statistical approach of outlier identification is also introduced in this paper in order to make a better estimation of baseline. A second-order polynomial plus harmonics are fitted to CFC-11 and chloromethane mixing ratios data. Measurements with large distance to the fitting curve are regard as outliers and flagged. Under specific requirement, the routine is iteratively adopted without the flagged measurements until no additional outliers are found. Both model fitting and the proposed outlier identification method are realized with the help of a programming language, Python. During the period, CFC-11 shows a gradual downward trend. And there is a slightly upward trend in the mixing ratios of Chloromethane. The concentration of chloromethane also has a strong seasonal variation, mostly due to the seasonal cycle of OH. The usage of this statistical method has a considerable effect on the results. This method efficiently identifies a series of outliers according to the standard deviation requirements. After removing the outliers, the fitting curves and trend estimates are more reliable.

  16. Factor-based forecasting in the presence of outliers

    DEFF Research Database (Denmark)

    Kristensen, Johannes Tang

    2014-01-01

    Macroeconomic forecasting using factor models estimated by principal components has become a popular research topic with many both theoretical and applied contributions in the literature. In this paper we attempt to address an often neglected issue in these models: The problem of outliers...... in the data. Most papers take an ad-hoc approach to this problem and simply screen datasets prior to estimation and remove anomalous observations. We investigate whether forecasting performance can be improved by using the original unscreened dataset and replacing principal components with a robust...... apply the estimator in a simulated real-time forecasting exercise to test its merits. We use a newly compiled dataset of US macroeconomic series spanning the period 1971:2–2012:10. Our findings suggest that the chosen treatment of outliers does affect forecasting performance and that in many cases...

  17. SU-F-T-97: Outlier Identification in Radiation Therapy Knowledge Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sheng, Y [Duke University, Durham, NC (United States); Ge, Y [University of North Carolina at Charlotte, Charlotte, NC (United States); Yuan, L; Yin, F; Wu, Q [Duke University Medical Center, Durham, NC (United States); Li, T [Thomas Jefferson University, Philadelphia, PA (United States)

    2016-06-15

    Purpose: To investigate the impact of outliers on knowledge modeling in radiation therapy, and develop a systematic workflow for identifying and analyzing geometric and dosimetric outliers using pelvic cases. Methods: Four groups (G1-G4) of pelvic plans were included: G1 (37 prostate cases), G2 (37 prostate plus lymph node cases), and G3 (37 prostate bed cases) are all clinical IMRT cases. G4 are 10 plans outside G1 re-planned with dynamic-arc to simulate dosimetric outliers. The workflow involves 2 steps: 1. identify geometric outliers, assess impact and clean up; 2. identify dosimetric outliers, assess impact and clean up.1. A baseline model was trained with all G1 cases. G2/G3 cases were then individually added to the baseline model as geometric outliers. The impact on the model was assessed by comparing leverage statistic of inliers (G1) and outliers (G2/G3). Receiver-operating-characteristics (ROC) analysis was performed to determine optimal threshold. 2. A separate baseline model was trained with 32 G1 cases. Each G4 case (dosimetric outliers) was then progressively added to perturb this model. DVH predictions were performed using these perturbed models for remaining 5 G1 cases. Normal tissue complication probability (NTCP) calculated from predicted DVH were used to evaluate dosimetric outliers’ impact. Results: The leverage of inliers and outliers was significantly different. The Area-Under-Curve (AUC) for differentiating G2 from G1 was 0.94 (threshold: 0.22) for bladder; and 0.80 (threshold: 0.10) for rectum. For differentiating G3 from G1, the AUC (threshold) was 0.68 (0.09) for bladder, 0.76 (0.08) for rectum. Significant increase in NTCP started from models with 4 dosimetric outliers for bladder (p<0.05), and with only 1 dosimetric outlier for rectum (p<0.05). Conclusion: We established a systematic workflow for identifying and analyzing geometric and dosimetric outliers, and investigated statistical metrics for detecting. Results validated the

  18. PEMODELAN ARIMA DAN DETEKSI OUTLIER DATA CURAH HUJAN SEBAGAI EVALUASI SISTEM RADIO GELOMBANG MILIMETER

    Directory of Open Access Journals (Sweden)

    Achmad Mauludiyanto

    2009-01-01

    Full Text Available The purpose of this paper is to provide the results of Arima modeling and outlier detection in the rainfall data in Surabaya. This paper explained about the steps in the formation of rainfall models, especially Box-Jenkins procedure for Arima modeling and outlier detection. Early stages of modeling stasioneritas Arima is the identification of data, both in mean and variance. Stasioneritas evaluation data in the variance can be done with Box-Cox transformation. Meanwhile, in the mean stasioneritas can be done with the plot data and forms of ACF. Identification of ACF and PACF of the stationary data is used to determine the order of allegations Arima model. The next stage is to estimate the parameters and diagnostic checks to see the suitability model. Process diagnostics check conducted to evaluate whether the residual model is eligible berdistribusi white noise and normal. Ljung-Box Test is a test that can be used to validate the white noise condition, while the Kolmogorov-Smirnov Test is an evaluation test for normal distribution. Residual normality test results showed that the residual model of Arima not white noise, and indicates the existence of outlier in the data. Thus, the next step taken is outlier detection to eliminate outlier effects and increase the accuracy of predictions of the model Arima. Arima modeling implementation and outlier detection is done by using MINITAB package and MATLAB. The research shows that the modeling Arima and outlier detection can reduce the prediction error as measured by the criteria Mean Square Error (MSE. Quantitatively, the decline in the value of MSE by incorporating outlier detection is 23.7%, with an average decline 6.5%.

  19. ZODET: software for the identification, analysis and visualisation of outlier genes in microarray expression data.

    Directory of Open Access Journals (Sweden)

    Daniel L Roden

    Full Text Available Complex human diseases can show significant heterogeneity between patients with the same phenotypic disorder. An outlier detection strategy was developed to identify variants at the level of gene transcription that are of potential biological and phenotypic importance. Here we describe a graphical software package (z-score outlier detection (ZODET that enables identification and visualisation of gross abnormalities in gene expression (outliers in individuals, using whole genome microarray data. Mean and standard deviation of expression in a healthy control cohort is used to detect both over and under-expressed probes in individual test subjects. We compared the potential of ZODET to detect outlier genes in gene expression datasets with a previously described statistical method, gene tissue index (GTI, using a simulated expression dataset and a publicly available monocyte-derived macrophage microarray dataset. Taken together, these results support ZODET as a novel approach to identify outlier genes of potential pathogenic relevance in complex human diseases. The algorithm is implemented using R packages and Java.The software is freely available from http://www.ucl.ac.uk/medicine/molecular-medicine/publications/microarray-outlier-analysis.

  20. Improving Electronic Sensor Reliability by Robust Outlier Screening

    Directory of Open Access Journals (Sweden)

    Federico Cuesta

    2013-10-01

    Full Text Available Electronic sensors are widely used in different application areas, and in some of them, such as automotive or medical equipment, they must perform with an extremely low defect rate. Increasing reliability is paramount. Outlier detection algorithms are a key component in screening latent defects and decreasing the number of customer quality incidents (CQIs. This paper focuses on new spatial algorithms (Good Die in a Bad Cluster with Statistical Bins (GDBC SB and Bad Bin in a Bad Cluster (BBBC and an advanced outlier screening method, called Robust Dynamic Part Averaging Testing (RDPAT, as well as two practical improvements, which significantly enhance existing algorithms. Those methods have been used in production in Freescale® Semiconductor probe factories around the world for several years. Moreover, a study was conducted with production data of 289,080 dice with 26 CQIs to determine and compare the efficiency and effectiveness of all these algorithms in identifying CQIs.

  1. New approach for the identification of implausible values and outliers in longitudinal childhood anthropometric data.

    Science.gov (United States)

    Shi, Joy; Korsiak, Jill; Roth, Daniel E

    2018-03-01

    We aimed to demonstrate the use of jackknife residuals to take advantage of the longitudinal nature of available growth data in assessing potential biologically implausible values and outliers. Artificial errors were induced in 5% of length, weight, and head circumference measurements, measured on 1211 participants from the Maternal Vitamin D for Infant Growth (MDIG) trial from birth to 24 months of age. Each child's sex- and age-standardized z-score or raw measurements were regressed as a function of age in child-specific models. Each error responsible for a biologically implausible decrease between a consecutive pair of measurements was identified based on the higher of the two absolute values of jackknife residuals in each pair. In further analyses, outliers were identified as those values beyond fixed cutoffs of the jackknife residuals (e.g., greater than +5 or less than -5 in primary analyses). Kappa, sensitivity, and specificity were calculated over 1000 simulations to assess the ability of the jackknife residual method to detect induced errors and to compare these methods with the use of conditional growth percentiles and conventional cross-sectional methods. Among the induced errors that resulted in a biologically implausible decrease in measurement between two consecutive values, the jackknife residual method identified the correct value in 84.3%-91.5% of these instances when applied to the sex- and age-standardized z-scores, with kappa values ranging from 0.685 to 0.795. Sensitivity and specificity of the jackknife method were higher than those of the conditional growth percentile method, but specificity was lower than for conventional cross-sectional methods. Using jackknife residuals provides a simple method to identify biologically implausible values and outliers in longitudinal child growth data sets in which each child contributes at least 4 serial measurements. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

  2. Universal ligation-detection-reaction microarray applied for compost microbes

    Directory of Open Access Journals (Sweden)

    Romantschuk Martin

    2008-12-01

    Full Text Available Abstract Background Composting is one of the methods utilised in recycling organic communal waste. The composting process is dependent on aerobic microbial activity and proceeds through a succession of different phases each dominated by certain microorganisms. In this study, a ligation-detection-reaction (LDR based microarray method was adapted for species-level detection of compost microbes characteristic of each stage of the composting process. LDR utilises the specificity of the ligase enzyme to covalently join two adjacently hybridised probes. A zip-oligo is attached to the 3'-end of one probe and fluorescent label to the 5'-end of the other probe. Upon ligation, the probes are combined in the same molecule and can be detected in a specific location on a universal microarray with complementary zip-oligos enabling equivalent hybridisation conditions for all probes. The method was applied to samples from Nordic composting facilities after testing and optimisation with fungal pure cultures and environmental clones. Results Probes targeted for fungi were able to detect 0.1 fmol of target ribosomal PCR product in an artificial reaction mixture containing 100 ng competing fungal ribosomal internal transcribed spacer (ITS area or herring sperm DNA. The detection level was therefore approximately 0.04% of total DNA. Clone libraries were constructed from eight compost samples. The LDR microarray results were in concordance with the clone library sequencing results. In addition a control probe was used to monitor the per-spot hybridisation efficiency on the array. Conclusion This study demonstrates that the LDR microarray method is capable of sensitive and accurate species-level detection from a complex microbial community. The method can detect key species from compost samples, making it a basis for a tool for compost process monitoring in industrial facilities.

  3. Swarm, genetic and evolutionary programming algorithms applied to multiuser detection

    Directory of Open Access Journals (Sweden)

    Paul Jean Etienne Jeszensky

    2005-02-01

    Full Text Available In this paper, the particles swarm optimization technique, recently published in the literature, and applied to Direct Sequence/Code Division Multiple Access systems (DS/CDMA with multiuser detection (MuD is analyzed, evaluated and compared. The Swarm algorithm efficiency when applied to the DS-CDMA multiuser detection (Swarm-MuD is compared through the tradeoff performance versus computational complexity, being the complexity expressed in terms of the number of necessary operations in order to reach the performance obtained through the optimum detector or the Maximum Likelihood detector (ML. The comparison is accomplished among the genetic algorithm, evolutionary programming with cloning and Swarm algorithm under the same simulation basis. Additionally, it is proposed an heuristics-MuD complexity analysis through the number of computational operations. Finally, an analysis is carried out for the input parameters of the Swarm algorithm in the attempt to find the optimum parameters (or almost-optimum for the algorithm applied to the MuD problem.

  4. The outlier sample effects on multivariate statistical data processing geochemical stream sediment survey (Moghangegh region, North West of Iran)

    International Nuclear Information System (INIS)

    Ghanbari, Y.; Habibnia, A.; Memar, A.

    2009-01-01

    In geochemical stream sediment surveys in Moghangegh Region in north west of Iran, sheet 1:50,000, 152 samples were collected and after the analyze and processing of data, it revealed that Yb, Sc, Ni, Li, Eu, Cd, Co, as contents in one sample is far higher than other samples. After detecting this sample as an outlier sample, the effect of this sample on multivariate statistical data processing for destructive effects of outlier sample in geochemical exploration was investigated. Pearson and Spear man correlation coefficient methods and cluster analysis were used for multivariate studies and the scatter plot of some elements together the regression profiles are given in case of 152 and 151 samples and the results are compared. After investigation of multivariate statistical data processing results, it was realized that results of existence of outlier samples may appear as the following relations between elements: - true relation between two elements, which have no outlier frequency in the outlier sample. - false relation between two elements which one of them has outlier frequency in the outlier sample. - complete false relation between two elements which both have outlier frequency in the outlier sample

  5. Analyzing contentious relationships and outlier genes in phylogenomics.

    Science.gov (United States)

    Walker, Joseph F; Brown, Joseph W; Smith, Stephen A

    2018-06-08

    Recent studies have demonstrated that conflict is common among gene trees in phylogenomic studies, and that less than one percent of genes may ultimately drive species tree inference in supermatrix analyses. Here, we examined two datasets where supermatrix and coalescent-based species trees conflict. We identified two highly influential "outlier" genes in each dataset. When removed from each dataset, the inferred supermatrix trees matched the topologies obtained from coalescent analyses. We also demonstrate that, while the outlier genes in the vertebrate dataset have been shown in a previous study to be the result of errors in orthology detection, the outlier genes from a plant dataset did not exhibit any obvious systematic error and therefore may be the result of some biological process yet to be determined. While topological comparisons among a small set of alternate topologies can be helpful in discovering outlier genes, they can be limited in several ways, such as assuming all genes share the same topology. Coalescent species tree methods relax this assumption but do not explicitly facilitate the examination of specific edges. Coalescent methods often also assume that conflict is the result of incomplete lineage sorting (ILS). Here we explored a framework that allows for quickly examining alternative edges and support for large phylogenomic datasets that does not assume a single topology for all genes. For both datasets, these analyses provided detailed results confirming the support for coalescent-based topologies. This framework suggests that we can improve our understanding of the underlying signal in phylogenomic datasets by asking more targeted edge-based questions.

  6. Applied network security monitoring collection, detection, and analysis

    CERN Document Server

    Sanders, Chris

    2013-01-01

    Applied Network Security Monitoring is the essential guide to becoming an NSM analyst from the ground up. This book takes a fundamental approach to NSM, complete with dozens of real-world examples that teach you the key concepts of NSM. Network security monitoring is based on the principle that prevention eventually fails. In the current threat landscape, no matter how much you try, motivated attackers will eventually find their way into your network. At that point, it is your ability to detect and respond to that intrusion that can be the difference between a small incident and a major di

  7. Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

    Science.gov (United States)

    Saleh Ahmar, Ansari; Guritno, Suryo; Abdurakhman; Rahman, Abdul; Awi; Alimuddin; Minggi, Ilham; Arif Tiro, M.; Kasim Aidid, M.; Annas, Suwardi; Utami Sutiksno, Dian; Ahmar, Dewi S.; Ahmar, Kurniawan H.; Abqary Ahmar, A.; Zaki, Ahmad; Abdullah, Dahlan; Rahim, Robbi; Nurdiyanto, Heri; Hidayat, Rahmat; Napitupulu, Darmawan; Simarmata, Janner; Kurniasih, Nuning; Andretti Abdillah, Leon; Pranolo, Andri; Haviluddin; Albra, Wahyudin; Arifin, A. Nurani M.

    2018-01-01

    The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. In the simulation data is obtained that the data contained AO initial models are ARIMA (2,0,0) with MSE = 36,780, after the detection and correction of data obtained by the iteration of the model ARIMA (2,0,0) with the coefficients obtained from the regression Zt = 0,106+0,204Z t-1+0,401Z t-2-329X 1(t)+115X 2(t)+35,9X 3(t) and MSE = 19,365. This shows that there is an improvement of forecasting error rate data.

  8. Fuzzy Treatment of Candidate Outliers in Measurements

    Directory of Open Access Journals (Sweden)

    Giampaolo E. D'Errico

    2012-01-01

    Full Text Available Robustness against the possible occurrence of outlying observations is critical to the performance of a measurement process. Open questions relevant to statistical testing for candidate outliers are reviewed. A novel fuzzy logic approach is developed and exemplified in a metrology context. A simulation procedure is presented and discussed by comparing fuzzy versus probabilistic models.

  9. MODVOLC2: A Hybrid Time Series Analysis for Detecting Thermal Anomalies Applied to Thermal Infrared Satellite Data

    Science.gov (United States)

    Koeppen, W. C.; Wright, R.; Pilger, E.

    2009-12-01

    We developed and tested a new, automated algorithm, MODVOLC2, which analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes, fires, and gas flares. MODVOLC2 combines two previously developed algorithms, a simple point operation algorithm (MODVOLC) and a more complex time series analysis (Robust AVHRR Techniques, or RAT) to overcome the limitations of using each approach alone. MODVOLC2 has four main steps: (1) it uses the original MODVOLC algorithm to process the satellite data on a pixel-by-pixel basis and remove thermal outliers, (2) it uses the remaining data to calculate reference and variability images for each calendar month, (3) it compares the original satellite data and any newly acquired data to the reference images normalized by their variability, and it detects pixels that fall outside the envelope of normal thermal behavior, (4) it adds any pixels detected by MODVOLC to those detected in the time series analysis. Using test sites at Anatahan and Kilauea volcanoes, we show that MODVOLC2 was able to detect ~15% more thermal anomalies than using MODVOLC alone, with very few, if any, known false detections. Using gas flares from the Cantarell oil field in the Gulf of Mexico, we show that MODVOLC2 provided results that were unattainable using a time series-only approach. Some thermal anomalies (e.g., Cantarell oil field flares) are so persistent that an additional, semi-automated 12-µm correction must be applied in order to correctly estimate both the number of anomalies and the total excess radiance being emitted by them. Although all available data should be included to make the best possible reference and variability images necessary for the MODVOLC2, we estimate that at least 80 images per calendar month are required to generate relatively good statistics from which to run MODVOLC2, a condition now globally met by a decade of MODIS observations. We also found

  10. GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets.

    Directory of Open Access Journals (Sweden)

    John Patrick Mpindi

    Full Text Available BACKGROUND: Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes', a hallmark of potential oncogenes. METHODOLOGY: A new statistical method (the gene tissue index, GTI was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29 of these genes, and 17 of these 19 genes (90% showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target. CONCLUSIONS/SIGNIFICANCE: Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is

  11. Poland’s Trade with East Asia: An Outlier Approach

    Directory of Open Access Journals (Sweden)

    Tseng Shoiw-Mei

    2015-12-01

    Full Text Available Poland achieved an excellent reputation for economic transformation during the recent global recession. The European debt crisis, however, quickly forced the reorientation of Poland’s trade outside of the European Union (EU, especially toward the dynamic region of East Asia. This study analyzes time series data from 1999 to 2013 to detect outliers in order to determine the bilateral trade paths between Poland and each East Asian country during the events of Poland’s accession to the EU in 2004, the global financial crisis from 2008 to 2009, and the European debt crisis from 2010 to 2013. From the Polish standpoint, the results showed significantly clustering outliers in the above periods and in the general trade paths from dependence through distancing and improvement to the chance of approaching East Asian partners. This study also shows that not only China but also several other countries present an excellent opportunity for boosting bilateral trade, especially with regard to Poland’s exports.

  12. Latent Clustering Models for Outlier Identification in Telecom Data

    Directory of Open Access Journals (Sweden)

    Ye Ouyang

    2016-01-01

    Full Text Available Collected telecom data traffic has boomed in recent years, due to the development of 4G mobile devices and other similar high-speed machines. The ability to quickly identify unexpected traffic data in this stream is critical for mobile carriers, as it can be caused by either fraudulent intrusion or technical problems. Clustering models can help to identify issues by showing patterns in network data, which can quickly catch anomalies and highlight previously unseen outliers. In this article, we develop and compare clustering models for telecom data, focusing on those that include time-stamp information management. Two main models are introduced, solved in detail, and analyzed: Gaussian Probabilistic Latent Semantic Analysis (GPLSA and time-dependent Gaussian Mixture Models (time-GMM. These models are then compared with other different clustering models, such as Gaussian model and GMM (which do not contain time-stamp information. We perform computation on both sample and telecom traffic data to show that the efficiency and robustness of GPLSA make it the superior method to detect outliers and provide results automatically with low tuning parameters or expertise requirement.

  13. A computational study on outliers in world music.

    Science.gov (United States)

    Panteli, Maria; Benetos, Emmanouil; Dixon, Simon

    2017-01-01

    The comparative analysis of world music cultures has been the focus of several ethnomusicological studies in the last century. With the advances of Music Information Retrieval and the increased accessibility of sound archives, large-scale analysis of world music with computational tools is today feasible. We investigate music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world. In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as 'outliers'. We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation. Our findings suggest that Botswana is the country with the most distinct recordings in the corpus and China is the country with the most distinct recordings when considering spatial correlation. Our analysis includes a comparison of musical attributes and styles that contribute to the 'uniqueness' of the music of each country.

  14. Assessment of average of normals (AON) procedure for outlier-free datasets including qualitative values below limit of detection (LoD): an application within tumor markers such as CA 15-3, CA 125, and CA 19-9.

    Science.gov (United States)

    Usta, Murat; Aral, Hale; Mete Çilingirtürk, Ahmet; Kural, Alev; Topaç, Ibrahim; Semerci, Tuna; Hicri Köseoğlu, Mehmet

    2016-11-01

    Average of normals (AON) is a quality control procedure that is sensitive only to systematic errors that can occur in an analytical process in which patient test results are used. The aim of this study was to develop an alternative model in order to apply the AON quality control procedure to datasets that include qualitative values below limit of detection (LoD). The reported patient test results for tumor markers, such as CA 15-3, CA 125, and CA 19-9, analyzed by two instruments, were retrieved from the information system over a period of 5 months, using the calibrator and control materials with the same lot numbers. The median as a measure of central tendency and the median absolute deviation (MAD) as a measure of dispersion were used for the complementary model of AON quality control procedure. The u bias values, which were determined for the bias component of the measurement uncertainty, were partially linked to the percentages of the daily median values of the test results that fall within the control limits. The results for these tumor markers, in which lower limits of reference intervals are not medically important for clinical diagnosis and management, showed that the AON quality control procedure, using the MAD around the median, can be applied for datasets including qualitative values below LoD.

  15. Statistical methods for damage detection applied to civil structures

    DEFF Research Database (Denmark)

    Gres, Szymon; Ulriksen, Martin Dalgaard; Döhler, Michael

    2017-01-01

    Damage detection consists of monitoring the deviations of a current system from its reference state, characterized by some nominal property repeatable for every healthy state. Preferably, the damage detection is performed directly on vibration data, hereby avoiding modal identification of the str...

  16. Robust Regression Procedures for Predictor Variable Outliers.

    Science.gov (United States)

    1982-03-01

    space of probability dis- tributions. Then the influence function of the estimator is defined to be the derivative of the functional evaluated at the...measure of the impact of an outlier x0 on the estimator . . . . . .0 10 T(F) is the " influence function " which is defined to be T(F) - lirT(F")-T(F...positive and negative directions. An em- pirical influence function can be defined in a similar fashion simply by replacing F with F in eqn. (3.4).n

  17. Optimum outlier model for potential improvement of environmental cleaning and disinfection.

    Science.gov (United States)

    Rupp, Mark E; Huerta, Tomas; Cavalieri, R J; Lyden, Elizabeth; Van Schooneveld, Trevor; Carling, Philip; Smith, Philip W

    2014-06-01

    The effectiveness and efficiency of 17 housekeepers in terminal cleaning 292 hospital rooms was evaluated through adenosine triphosphate detection. A subgroup of housekeepers was identified who were significantly more effective and efficient than their coworkers. These optimum outliers may be used in performance improvement to optimize environmental cleaning.

  18. Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images

    Directory of Open Access Journals (Sweden)

    Akara Sopharak

    2013-07-01

    Full Text Available Diabetic retinopathy is the commonest cause of blindness in working age people. It is characterised and graded by the development of retinal microaneurysms, haemorrhages and exudates. The damage caused by diabetic retinopathy can be prevented if it is treated in its early stages. Therefore, automated early detection can limit the severity of the disease, improve the follow-up management of diabetic patients and assist ophthalmologists in investigating and treating the disease more efficiently. This review focuses on microaneurysm detection as the earliest clinically localised characteristic of diabetic retinopathy, a frequently observed complication in both Type 1 and Type 2 diabetes. Algorithms used for microaneurysm detection from retinal images are reviewed. A number of features used to extract microaneurysm are summarised. Furthermore, a comparative analysis of reported methods used to automatically detect microaneurysms is presented and discussed. The performance of methods and their complexity are also discussed.

  19. A Positive Deviance Approach to Early Childhood Obesity: Cross-Sectional Characterization of Positive Outliers

    OpenAIRE

    Foster, Byron Alexander; Farragher, Jill; Parker, Paige; Hale, Daniel E.

    2015-01-01

    Objective: Positive deviance methodology has been applied in the developing world to address childhood malnutrition and has potential for application to childhood obesity in the United States. We hypothesized that among children at high-risk for obesity, evaluating normal weight children will enable identification of positive outlier behaviors and practices.

  20. A tandem regression-outlier analysis of a ligand cellular system for key structural modifications around ligand binding.

    Science.gov (United States)

    Lin, Ying-Ting

    2013-04-30

    A tandem technique of hard equipment is often used for the chemical analysis of a single cell to first isolate and then detect the wanted identities. The first part is the separation of wanted chemicals from the bulk of a cell; the second part is the actual detection of the important identities. To identify the key structural modifications around ligand binding, the present study aims to develop a counterpart of tandem technique for cheminformatics. A statistical regression and its outliers act as a computational technique for separation. A PPARγ (peroxisome proliferator-activated receptor gamma) agonist cellular system was subjected to such an investigation. Results show that this tandem regression-outlier analysis, or the prioritization of the context equations tagged with features of the outliers, is an effective regression technique of cheminformatics to detect key structural modifications, as well as their tendency of impact to ligand binding. The key structural modifications around ligand binding are effectively extracted or characterized out of cellular reactions. This is because molecular binding is the paramount factor in such ligand cellular system and key structural modifications around ligand binding are expected to create outliers. Therefore, such outliers can be captured by this tandem regression-outlier analysis.

  1. Long-range alpha detection applied to soil surface monitoring

    International Nuclear Information System (INIS)

    Caress, R.W.; Allander, K.S.; Bounds, J.A.; Catlett, M.M.; MacArthur, D.W.; Rutherford, D.A.

    1992-01-01

    The long-range alpha detection (LRAD) technique depends on the detection of ion pairs generated by alpha particles losing energy in air rather than on detection of the alpha particles themselves. Typical alpha particles generated by uranium will travel less than 3 cm in air. In contrast, the ions have been successfully detected many inches or feet away from the contamination. Since LRAD detection systems are sensitive to all ions simultaneously, large LRAD soil surface monitors (SSMS) can be used to collect all of the ions from a large sample. The LRAD SSMs are designed around the fan-less LRAD detector. In this case a five-sided box with an open bottom is placed on the soil surface. Ions generated by alpha decays on the soil surface are collected on a charged copper plate within the box. These ions create a small current from the plate to ground which is monitored with a sensitive electrometer. The current measured is proportional to the number of ions in the box, which is, in turn, proportional to the amount of alpha contamination on the surface of the soil. This report includes the design and construction of a 1-m by 1-m SSM as well as the results of a study at Fernald, OH, as part of the Uranium in Soils Integrated Demonstration

  2. Identification of unusual events in multichannel bridge monitoring data using wavelet transform and outlier analysis

    Science.gov (United States)

    Omenzetter, Piotr; Brownjohn, James M. W.; Moyo, Pilate

    2003-08-01

    Continuously operating instrumented structural health monitoring (SHM) systems are becoming a practical alternative to replace visual inspection for assessment of condition and soundness of civil infrastructure. However, converting large amount of data from an SHM system into usable information is a great challenge to which special signal processing techniques must be applied. This study is devoted to identification of abrupt, anomalous and potentially onerous events in the time histories of static, hourly sampled strains recorded by a multi-sensor SHM system installed in a major bridge structure in Singapore and operating continuously for a long time. Such events may result, among other causes, from sudden settlement of foundation, ground movement, excessive traffic load or failure of post-tensioning cables. A method of outlier detection in multivariate data has been applied to the problem of finding and localizing sudden events in the strain data. For sharp discrimination of abrupt strain changes from slowly varying ones wavelet transform has been used. The proposed method has been successfully tested using known events recorded during construction of the bridge, and later effectively used for detection of anomalous post-construction events.

  3. Simulation of Neutron Backscattering applied to organic material detection

    International Nuclear Information System (INIS)

    Forero, N. C.; Cruz, A. H.; Cristancho, F.

    2007-01-01

    The Neutron Backscattering technique is tested when performing the task of localizing hydrogenated explosives hidden in soil. Detector system, landmine, soil and neutron source are simulated with Geant4 in order to obtain the number of neutrons detected when several parameters like mine composition, relative position mine-source and soil moisture are varied

  4. Construction of composite indices in presence of outliers

    OpenAIRE

    Mishra, SK

    2008-01-01

    Effects of outliers on mean, standard deviation and Pearson’s correlation coefficient are well known. The Principal Components analysis uses Pearson’s product moment correlation coefficients to construct composite indices from indicator variables and hence may be very sensitive to effects of outliers in data. Median, mean deviation and Bradley’s coefficient of absolute correlation are less susceptible to effects of outliers. This paper proposes a method to obtain composite indices by maximiza...

  5. Infrared light sensor applied to early detection of tooth decay

    Science.gov (United States)

    Benjumea, Eberto; Espitia, José; Díaz, Leonardo; Torres, Cesar

    2017-08-01

    The approach dentistry to dental care is gradually shifting to a model focused on early detection and oral-disease prevention; one of the most important methods of prevention of tooth decay is opportune diagnosis of decay and reconstruction. The present study aimed to introduce a procedure for early diagnosis of tooth decay and to compare result of experiment of this method with other common treatments. In this setup, a laser emitting infrared light is injected in core of one bifurcated fiber-optic and conduced to tooth surface and with the same bifurcated fiber the radiation reflected for the same tooth is collected and them conduced to surface of sensor that measures thermal and light frequencies to detect early signs of decay below a tooth surface, where demineralization is difficult to spot with x-ray technology. This device will can be used to diagnose tooth decay without any chemicals and rays such as high power lasers or X-rays.

  6. Applying Parametric Fault Detection to a Mechanical System

    DEFF Research Database (Denmark)

    Felício, P.; Stoustrup, Jakob; Niemann, H.

    2002-01-01

    A way of doing parametric fault detection is described. It is based on the representation of parameter changes as linear fractional transformations (lfts). We describe a model with parametric uncertainty. Then a stabilizing controller is chosen and its robustness properties are studied via mu. Th....... The parameter changes (faults) are estimated based on estimates of the fictitious signals that enter the delta block in the lft. These signal estimators are designed by H-infinity techniques. The chosen example is an inverted pendulum....

  7. Identification of Outlier Loci Responding to Anthropogenic and Natural Selection Pressure in Stream Insects Based on a Self-Organizing Map

    Directory of Open Access Journals (Sweden)

    Bin Li

    2016-05-01

    Full Text Available Water quality maintenance should be considered from an ecological perspective since water is a substrate ingredient in the biogeochemical cycle and is closely linked with ecosystem functioning and services. Addressing the status of live organisms in aquatic ecosystems is a critical issue for appropriate prediction and water quality management. Recently, genetic changes in biological organisms have garnered more attention due to their in-depth expression of environmental stress on aquatic ecosystems in an integrative manner. We demonstrate that genetic diversity would adaptively respond to environmental constraints in this study. We applied a self-organizing map (SOM to characterize complex Amplified Fragment Length Polymorphisms (AFLP of aquatic insects in six streams in Japan with natural and anthropogenic variability. After SOM training, the loci compositions of aquatic insects effectively responded to environmental selection pressure. To measure how important the role of loci compositions was in the population division, we altered the AFLP data by flipping the existence of given loci individual by individual. Subsequently we recognized the cluster change of the individuals with altered data using the trained SOM. Based on SOM recognition of these altered data, we determined the outlier loci (over 90th percentile that showed drastic changes in their belonging clusters (D. Subsequently environmental responsiveness (Ek’ was also calculated to address relationships with outliers in different species. Outlier loci were sensitive to slightly polluted conditions including Chl-a, NH4-N, NOX-N, PO4-P, and SS, and the food material, epilithon. Natural environmental factors such as altitude and sediment additionally showed relationships with outliers in somewhat lower levels. Poly-loci like responsiveness was detected in adapting to environmental constraints. SOM training followed by recognition shed light on developing algorithms de novo to

  8. [Advances of NIR spectroscopy technology applied in seed quality detection].

    Science.gov (United States)

    Zhu, Li-wei; Ma, Wen-guang; Hu, Jin; Zheng, Yun-ye; Tian, Yi-xin; Guan, Ya-jing; Hu, Wei-min

    2015-02-01

    Near infrared spectroscopy (NIRS) technology developed fast in recent years, due to its rapid speed, less pollution, high-efficiency and other advantages. It has been widely used in many fields such as food, chemical industry, pharmacy, agriculture and so on. The seed is the most basic and important agricultural capital goods, and seed quality is important for agricultural production. Most methods presently used for seed quality detecting were destructive, slow and needed pretreatment, therefore, developing one kind of method that is simple and rapid has great significance for seed quality testing. This article reviewed the application and trends of NIRS technology in testing of seed constituents, vigor, disease and insect pests etc. For moisture, starch, protein, fatty acid and carotene content, the model identification rates were high as their relative contents were high; for trace organic, the identification rates were low as their relative content were low. The heat-damaged seeds with low vigor were discriminated by NIRS, the seeds stored for different time could also been identified. The discrimination of frost-damaged seeds was impossible. The NIRS could be used to identify health and infected disease seeds, and did the classification for the health degree; it could identify parts of the fungal pathogens. The NIRS could identify worm-eaten and health seeds, and further distinguished the insect species, however the identification effects for small larval and low injury level of insect pests was not good enough. Finally, in present paper existing problems and development trends for NIRS in seed quality detection was discussed, especially the single seed detecting technology which was characteristic of the seed industry, the standardization of its spectral acquisition accessories will greatly improve its applicability.

  9. Artificial Intelligence Methods Applied to Parameter Detection of Atrial Fibrillation

    Science.gov (United States)

    Arotaritei, D.; Rotariu, C.

    2015-09-01

    In this paper we present a novel method to develop an atrial fibrillation (AF) based on statistical descriptors and hybrid neuro-fuzzy and crisp system. The inference of system produce rules of type if-then-else that care extracted to construct a binary decision system: normal of atrial fibrillation. We use TPR (Turning Point Ratio), SE (Shannon Entropy) and RMSSD (Root Mean Square of Successive Differences) along with a new descriptor, Teager- Kaiser energy, in order to improve the accuracy of detection. The descriptors are calculated over a sliding window that produce very large number of vectors (massive dataset) used by classifier. The length of window is a crisp descriptor meanwhile the rest of descriptors are interval-valued type. The parameters of hybrid system are adapted using Genetic Algorithm (GA) algorithm with fitness single objective target: highest values for sensibility and sensitivity. The rules are extracted and they are part of the decision system. The proposed method was tested using the Physionet MIT-BIH Atrial Fibrillation Database and the experimental results revealed a good accuracy of AF detection in terms of sensitivity and specificity (above 90%).

  10. Agglomerative concentric hypersphere clustering applied to structural damage detection

    Science.gov (United States)

    Silva, Moisés; Santos, Adam; Santos, Reginaldo; Figueiredo, Eloi; Sales, Claudomiro; Costa, João C. W. A.

    2017-08-01

    The present paper proposes a novel cluster-based method, named as agglomerative concentric hypersphere (ACH), to detect structural damage in engineering structures. Continuous structural monitoring systems often require unsupervised approaches to automatically infer the health condition of a structure. However, when a structure is under linear and nonlinear effects caused by environmental and operational variability, data normalization procedures are also required to overcome these effects. The proposed approach aims, through a straightforward clustering procedure, to discover automatically the optimal number of clusters, representing the main state conditions of a structural system. Three initialization procedures are introduced to evaluate the impact of deterministic and stochastic initializations on the performance of this approach. The ACH is compared to state-of-the-art approaches, based on Gaussian mixture models and Mahalanobis squared distance, on standard data sets from a post-tensioned bridge located in Switzerland: the Z-24 Bridge. The proposed approach demonstrates more efficiency in modeling the normal condition of the structure and its corresponding main clusters. Furthermore, it reveals a better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, demonstrating a promising applicability in real-world structural health monitoring scenarios.

  11. The masking breakdown point of multivariate outlier identification rules

    OpenAIRE

    Becker, Claudia; Gather, Ursula

    1997-01-01

    In this paper, we consider one-step outlier identifiation rules for multivariate data, generalizing the concept of so-called alpha outlier identifiers, as presented in Davies and Gather (1993) for the case of univariate samples. We investigate, how the finite-sample breakdown points of estimators used in these identification rules influence the masking behaviour of the rules.

  12. What 'outliers' tell us about missed opportunities for tuberculosis control: a cross-sectional study of patients in Mumbai, India

    Directory of Open Access Journals (Sweden)

    Porter John DH

    2010-05-01

    Full Text Available Abstract Background India's Revised National Tuberculosis Control Programme (RNTCP is deemed highly successful in terms of detection and cure rates. However, some patients experience delays in accessing diagnosis and treatment. Patients falling between the 96th and 100th percentiles for these access indicators are often ignored as atypical 'outliers' when assessing programme performance. They may, however, provide clues to understanding why some patients never reach the programme. This paper examines the underlying vulnerabilities of patients with extreme values for delays in accessing the RNTCP in Mumbai city, India. Methods We conducted a cross-sectional study with 266 new sputum positive patients registered with the RNTCP in Mumbai. Patients were classified as 'outliers' if patient, provider and system delays were beyond the 95th percentile for the respective variable. Case profiles of 'outliers' for patient, provider and system delays were examined and compared with the rest of the sample to identify key factors responsible for delays. Results Forty-two patients were 'outliers' on one or more of the delay variables. All 'outliers' had a significantly lower per capita income than the remaining sample. The lack of economic resources was compounded by social, structural and environmental vulnerabilities. Longer patient delays were related to patients' perception of symptoms as non-serious. Provider delays were incurred as a result of private providers' failure to respond to tuberculosis in a timely manner. Diagnostic and treatment delays were minimal, however, analysis of the 'outliers' revealed the importance of social support in enabling access to the programme. Conclusion A proxy for those who fail to reach the programme, these case profiles highlight unique vulnerabilities that need innovative approaches by the RNTCP. The focus on 'outliers' provides a less resource- and time-intensive alternative to community-based studies for

  13. A computational study on outliers in world music

    Science.gov (United States)

    Benetos, Emmanouil; Dixon, Simon

    2017-01-01

    The comparative analysis of world music cultures has been the focus of several ethnomusicological studies in the last century. With the advances of Music Information Retrieval and the increased accessibility of sound archives, large-scale analysis of world music with computational tools is today feasible. We investigate music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world. In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as ‘outliers’. We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation. Our findings suggest that Botswana is the country with the most distinct recordings in the corpus and China is the country with the most distinct recordings when considering spatial correlation. Our analysis includes a comparison of musical attributes and styles that contribute to the ‘uniqueness’ of the music of each country. PMID:29253027

  14. Outlier removal, sum scores, and the inflation of the Type I error rate in independent samples t tests: the power of alternatives and recommendations.

    Science.gov (United States)

    Bakker, Marjan; Wicherts, Jelte M

    2014-09-01

    In psychology, outliers are often excluded before running an independent samples t test, and data are often nonnormal because of the use of sum scores based on tests and questionnaires. This article concerns the handling of outliers in the context of independent samples t tests applied to nonnormal sum scores. After reviewing common practice, we present results of simulations of artificial and actual psychological data, which show that the removal of outliers based on commonly used Z value thresholds severely increases the Type I error rate. We found Type I error rates of above 20% after removing outliers with a threshold value of Z = 2 in a short and difficult test. Inflations of Type I error rates are particularly severe when researchers are given the freedom to alter threshold values of Z after having seen the effects thereof on outcomes. We recommend the use of nonparametric Mann-Whitney-Wilcoxon tests or robust Yuen-Welch tests without removing outliers. These alternatives to independent samples t tests are found to have nominal Type I error rates with a minimal loss of power when no outliers are present in the data and to have nominal Type I error rates and good power when outliers are present. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  15. Outlier detection algorithms for least squares time series regression

    DEFF Research Database (Denmark)

    Johansen, Søren; Nielsen, Bent

    We review recent asymptotic results on some robust methods for multiple regression. The regressors include stationary and non-stationary time series as well as polynomial terms. The methods include the Huber-skip M-estimator, 1-step Huber-skip M-estimators, in particular the Impulse Indicator Sat...

  16. Outlier detection in UV/Vis spectrophotometric data

    NARCIS (Netherlands)

    Lepot, M.J.; Aubin, Jean Baptiste; Clemens, F.H.L.R.; Mašić, Alma

    2017-01-01

    UV/Vis spectrophotometers have been used to monitor water quality since the early 2000s. Calibration of these devices requires sampling campaigns to elaborate relations between recorded spectra and measured concentrations. In order to build robust calibration data sets, several spectra must be

  17. Methods of Detecting Outliers in A Regression Analysis Model ...

    African Journals Online (AJOL)

    PROF. O. E. OSUAGWU

    2013-06-01

    Jun 1, 2013 ... especially true in observational studies .... Simple linear regression and multiple ... The simple linear ..... Grubbs,F.E (1950): Sample Criteria for Testing Outlying observations: Annals of ... In experimental design, the Relative.

  18. Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers

    Directory of Open Access Journals (Sweden)

    Hachey Mark

    2009-10-01

    Full Text Available Abstract Background The ability to evaluate geographic heterogeneity of cancer incidence and mortality is important in cancer surveillance. Many statistical methods for evaluating global clustering and local cluster patterns are developed and have been examined by many simulation studies. However, the performance of these methods on two extreme cases (global clustering evaluation and local anomaly (outlier detection has not been thoroughly investigated. Methods We compare methods for global clustering evaluation including Tango's Index, Moran's I, and Oden's I*pop; and cluster detection methods such as local Moran's I and SaTScan elliptic version on simulated count data that mimic global clustering patterns and outliers for cancer cases in the continental United States. We examine the power and precision of the selected methods in the purely spatial analysis. We illustrate Tango's MEET and SaTScan elliptic version on a 1987-2004 HIV and a 1950-1969 lung cancer mortality data in the United States. Results For simulated data with outlier patterns, Tango's MEET, Moran's I and I*pop had powers less than 0.2, and SaTScan had powers around 0.97. For simulated data with global clustering patterns, Tango's MEET and I*pop (with 50% of total population as the maximum search window had powers close to 1. SaTScan had powers around 0.7-0.8 and Moran's I has powers around 0.2-0.3. In the real data example, Tango's MEET indicated the existence of global clustering patterns in both the HIV and lung cancer mortality data. SaTScan found a large cluster for HIV mortality rates, which is consistent with the finding from Tango's MEET. SaTScan also found clusters and outliers in the lung cancer mortality data. Conclusion SaTScan elliptic version is more efficient for outlier detection compared with the other methods evaluated in this article. Tango's MEET and Oden's I*pop perform best in global clustering scenarios among the selected methods. The use of SaTScan for

  19. Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data

    Directory of Open Access Journals (Sweden)

    Keita Mori

    2013-01-01

    Full Text Available Molecular heterogeneity of cancer, partially caused by various chromosomal aberrations or gene mutations, can yield substantial heterogeneity in gene expression profile in cancer samples. To detect cancer-related genes which are active only in a subset of cancer samples or cancer outliers, several methods have been proposed in the context of multiple testing. Such cancer outlier analyses will generally suffer from a serious lack of power, compared with the standard multiple testing setting where common activation of genes across all cancer samples is supposed. In this paper, we consider information sharing across genes and cancer samples, via a parametric normal mixture modeling of gene expression levels of cancer samples across genes after a standardization using the reference, normal sample data. A gene-based statistic for gene selection is developed on the basis of a posterior probability of cancer outlier for each cancer sample. Some efficiency improvement by using our method was demonstrated, even under settings with misspecified, heavy-tailed t-distributions. An application to a real dataset from hematologic malignancies is provided.

  20. The obligation of physicians to medical outliers: a Kantian and Hegelian synthesis

    Directory of Open Access Journals (Sweden)

    Marco Alan P

    2004-06-01

    Full Text Available Abstract Background Patients who present to medical practices without health insurance or with serious co-morbidities can become fiscal disasters to those who care for them. Their consumption of scarce resources has caused consternation among providers and institutions, especially as it concerns the amount and type of care they should receive. In fact, some providers may try to avoid caring for them altogether, or at least try to limit their institutional or practice exposure to them. Discussion We present a philosophical discourse, with emphasis on the writings of Immanuel Kant and G.F.W. Hegel, as to why physicians have the moral imperative to give such "outliers" considerate and thoughtful care. Outliers are defined and the ideals of morality, responsibility, good will, duty, and principle are applied to the care of patients whose financial means are meager and to those whose care is physiologically futile. Actions of moral worth, unconditional good will, and doing what is right are examined. Summary Outliers are a legitimate economic concern to individual practitioners and institutions, however this should not lead to an evasion of care. These patients should be identified early in their course of care, but such identification should be preceded by a well-planned recognition of this burden and appropriate staffing and funding should be secured. A thoughtful team approach by medical practices and their institutions, involving both clinicians and non-clinicians, should be pursued.

  1. The obligation of physicians to medical outliers: a Kantian and Hegelian synthesis.

    Science.gov (United States)

    Papadimos, Thomas J; Marco, Alan P

    2004-06-03

    Patients who present to medical practices without health insurance or with serious co-morbidities can become fiscal disasters to those who care for them. Their consumption of scarce resources has caused consternation among providers and institutions, especially as it concerns the amount and type of care they should receive. In fact, some providers may try to avoid caring for them altogether, or at least try to limit their institutional or practice exposure to them. We present a philosophical discourse, with emphasis on the writings of Immanuel Kant and G.F.W. Hegel, as to why physicians have the moral imperative to give such "outliers" considerate and thoughtful care. Outliers are defined and the ideals of morality, responsibility, good will, duty, and principle are applied to the care of patients whose financial means are meager and to those whose care is physiologically futile. Actions of moral worth, unconditional good will, and doing what is right are examined. Outliers are a legitimate economic concern to individual practitioners and institutions, however this should not lead to an evasion of care. These patients should be identified early in their course of care, but such identification should be preceded by a well-planned recognition of this burden and appropriate staffing and funding should be secured. A thoughtful team approach by medical practices and their institutions, involving both clinicians and non-clinicians, should be pursued.

  2. Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis.

    Science.gov (United States)

    Westerholt, Rene; Steiger, Enrico; Resch, Bernd; Zipf, Alexander

    2016-01-01

    Twitter and related social media feeds have become valuable data sources to many fields of research. Numerous researchers have thereby used social media posts for spatial analysis, since many of them contain explicit geographic locations. However, despite its widespread use within applied research, a thorough understanding of the underlying spatial characteristics of these data is still lacking. In this paper, we investigate how topological outliers influence the outcomes of spatial analyses of social media data. These outliers appear when different users contribute heterogeneous information about different phenomena simultaneously from similar locations. As a consequence, various messages representing different spatial phenomena are captured closely to each other, and are at risk to be falsely related in a spatial analysis. Our results reveal indications for corresponding spurious effects when analyzing Twitter data. Further, we show how the outliers distort the range of outcomes of spatial analysis methods. This has significant influence on the power of spatial inferential techniques, and, more generally, on the validity and interpretability of spatial analysis results. We further investigate how the issues caused by topological outliers are composed in detail. We unveil that multiple disturbing effects are acting simultaneously and that these are related to the geographic scales of the involved overlapping patterns. Our results show that at some scale configurations, the disturbances added through overlap are more severe than at others. Further, their behavior turns into a volatile and almost chaotic fluctuation when the scales of the involved patterns become too different. Overall, our results highlight the critical importance of thoroughly considering the specific characteristics of social media data when analyzing them spatially.

  3. On the identification of Dragon Kings among extreme-valued outliers

    Science.gov (United States)

    Riva, M.; Neuman, S. P.; Guadagnini, A.

    2013-07-01

    Extreme values of earth, environmental, ecological, physical, biological, financial and other variables often form outliers to heavy tails of empirical frequency distributions. Quite commonly such tails are approximated by stretched exponential, log-normal or power functions. Recently there has been an interest in distinguishing between extreme-valued outliers that belong to the parent population of most data in a sample and those that do not. The first type, called Gray Swans by Nassim Nicholas Taleb (often confused in the literature with Taleb's totally unknowable Black Swans), is drawn from a known distribution of the tails which can thus be extrapolated beyond the range of sampled values. However, the magnitudes and/or space-time locations of unsampled Gray Swans cannot be foretold. The second type of extreme-valued outliers, termed Dragon Kings by Didier Sornette, may in his view be sometimes predicted based on how other data in the sample behave. This intriguing prospect has recently motivated some authors to propose statistical tests capable of identifying Dragon Kings in a given random sample. Here we apply three such tests to log air permeability data measured on the faces of a Berea sandstone block and to synthetic data generated in a manner statistically consistent with these measurements. We interpret the measurements to be, and generate synthetic data that are, samples from α-stable sub-Gaussian random fields subordinated to truncated fractional Gaussian noise (tfGn). All these data have frequency distributions characterized by power-law tails with extreme-valued outliers about the tail edges.

  4. On the identification of Dragon Kings among extreme-valued outliers

    Directory of Open Access Journals (Sweden)

    M. Riva

    2013-07-01

    Full Text Available Extreme values of earth, environmental, ecological, physical, biological, financial and other variables often form outliers to heavy tails of empirical frequency distributions. Quite commonly such tails are approximated by stretched exponential, log-normal or power functions. Recently there has been an interest in distinguishing between extreme-valued outliers that belong to the parent population of most data in a sample and those that do not. The first type, called Gray Swans by Nassim Nicholas Taleb (often confused in the literature with Taleb's totally unknowable Black Swans, is drawn from a known distribution of the tails which can thus be extrapolated beyond the range of sampled values. However, the magnitudes and/or space–time locations of unsampled Gray Swans cannot be foretold. The second type of extreme-valued outliers, termed Dragon Kings by Didier Sornette, may in his view be sometimes predicted based on how other data in the sample behave. This intriguing prospect has recently motivated some authors to propose statistical tests capable of identifying Dragon Kings in a given random sample. Here we apply three such tests to log air permeability data measured on the faces of a Berea sandstone block and to synthetic data generated in a manner statistically consistent with these measurements. We interpret the measurements to be, and generate synthetic data that are, samples from α-stable sub-Gaussian random fields subordinated to truncated fractional Gaussian noise (tfGn. All these data have frequency distributions characterized by power-law tails with extreme-valued outliers about the tail edges.

  5. Performance of computer-aided detection applied to full-field digital mammography in detection of breast cancers

    International Nuclear Information System (INIS)

    Sadaf, Arifa; Crystal, Pavel; Scaranelo, Anabel; Helbich, Thomas

    2011-01-01

    Objective: The aim of this retrospective study was to evaluate performance of computer-aided detection (CAD) with full-field digital mammography (FFDM) in detection of breast cancers. Materials and Methods: CAD was retrospectively applied to standard mammographic views of 127 cases with biopsy proven breast cancers detected with FFDM (Senographe 2000, GE Medical Systems). CAD sensitivity was assessed in total group of 127 cases and for subgroups based on breast density, mammographic lesion type, mammographic lesion size, histopathology and mode of presentation. Results: Overall CAD sensitivity was 91% (115 of 127 cases). There were no statistical differences (p > 0.1) in CAD detection of cancers in dense breasts 90% (53/59) versus non-dense breasts 91% (62/68). There was statistical difference (p 20 mm 97% (22/23). Conclusion: CAD applied to FFDM showed 100% sensitivity in identifying cancers manifesting as microcalcifications only and high sensitivity 86% (71/83) for other mammographic appearances of cancer. Sensitivity is influenced by lesion size. CAD in FFDM is an adjunct helping radiologist in early detection of breast cancers.

  6. Cross-visit tumor sub-segmentation and registration with outlier rejection for dynamic contrast-enhanced MRI time series data.

    Science.gov (United States)

    Buonaccorsi, G A; Rose, C J; O'Connor, J P B; Roberts, C; Watson, Y; Jackson, A; Jayson, G C; Parker, G J M

    2010-01-01

    Clinical trials of anti-angiogenic and vascular-disrupting agents often use biomarkers derived from DCE-MRI, typically reporting whole-tumor summary statistics and so overlooking spatial parameter variations caused by tissue heterogeneity. We present a data-driven segmentation method comprising tracer-kinetic model-driven registration for motion correction, conversion from MR signal intensity to contrast agent concentration for cross-visit normalization, iterative principal components analysis for imputation of missing data and dimensionality reduction, and statistical outlier detection using the minimum covariance determinant to obtain a robust Mahalanobis distance. After applying these techniques we cluster in the principal components space using k-means. We present results from a clinical trial of a VEGF inhibitor, using time-series data selected because of problems due to motion and outlier time series. We obtained spatially-contiguous clusters that map to regions with distinct microvascular characteristics. This methodology has the potential to uncover localized effects in trials using DCE-MRI-based biomarkers.

  7. Identificación de outliers en muestras multivariantes

    OpenAIRE

    Pérez Díez de los Ríos, José Luis

    1987-01-01

    En esta memoria se analiza la problemática de las observaciones Outliers en nuestras Multivariantes describiéndose las distintas técnicas que existen en la actualidad para la identificación de Outliers en nuestras multidimensionales y poniéndose de manifiesto que la mayoría de ellas son generalizaciones de ideas desarrolladas para el caso univariante o técnicas basadas en representaciones graficas. Se aborda a continuación el denominado efecto de enmascaramiento que se puede presentar cuando...

  8. A new approach for structural health monitoring by applying anomaly detection on strain sensor data

    Science.gov (United States)

    Trichias, Konstantinos; Pijpers, Richard; Meeuwissen, Erik

    2014-03-01

    Structural Health Monitoring (SHM) systems help to monitor critical infrastructures (bridges, tunnels, etc.) remotely and provide up-to-date information about their physical condition. In addition, it helps to predict the structure's life and required maintenance in a cost-efficient way. Typically, inspection data gives insight in the structural health. The global structural behavior, and predominantly the structural loading, is generally measured with vibration and strain sensors. Acoustic emission sensors are more and more used for measuring global crack activity near critical locations. In this paper, we present a procedure for local structural health monitoring by applying Anomaly Detection (AD) on strain sensor data for sensors that are applied in expected crack path. Sensor data is analyzed by automatic anomaly detection in order to find crack activity at an early stage. This approach targets the monitoring of critical structural locations, such as welds, near which strain sensors can be applied during construction and/or locations with limited inspection possibilities during structural operation. We investigate several anomaly detection techniques to detect changes in statistical properties, indicating structural degradation. The most effective one is a novel polynomial fitting technique, which tracks slow changes in sensor data. Our approach has been tested on a representative test structure (bridge deck) in a lab environment, under constant and variable amplitude fatigue loading. In both cases, the evolving cracks at the monitored locations were successfully detected, autonomously, by our AD monitoring tool.

  9. Exploiting the information content of hydrological ''outliers'' for goodness-of-fit testing

    Directory of Open Access Journals (Sweden)

    F. Laio

    2010-10-01

    Full Text Available Validation of probabilistic models based on goodness-of-fit tests is an essential step for the frequency analysis of extreme events. The outcome of standard testing techniques, however, is mainly determined by the behavior of the hypothetical model, FX(x, in the central part of the distribution, while the behavior in the tails of the distribution, which is indeed very relevant in hydrological applications, is relatively unimportant for the results of the tests. The maximum-value test, originally proposed as a technique for outlier detection, is a suitable, but seldom applied, technique that addresses this problem. The test is specifically targeted to verify if the maximum (or minimum values in the sample are consistent with the hypothesis that the distribution FX(x is the real parent distribution. The application of this test is hindered by the fact that the critical values for the test should be numerically obtained when the parameters of FX(x are estimated on the same sample used for verification, which is the standard situation in hydrological applications. We propose here a simple, analytically explicit, technique to suitably account for this effect, based on the application of censored L-moments estimators of the parameters. We demonstrate, with an application that uses artificially generated samples, the superiority of this modified maximum-value test with respect to the standard version of the test. We also show that the test has comparable or larger power with respect to other goodness-of-fit tests (e.g., chi-squared test, Anderson-Darling test, Fung and Paul test, in particular when dealing with small samples (sample size lower than 20–25 and when the parent distribution is similar to the distribution being tested.

  10. Outlier robustness for wind turbine extrapolated extreme loads

    DEFF Research Database (Denmark)

    Natarajan, Anand; Verelst, David Robert

    2012-01-01

    . Stochastic identification of numerical artifacts in simulated loads is demonstrated using the method of principal component analysis. The extrapolation methodology is made robust to outliers through a weighted loads approach, whereby the eigenvalues of the correlation matrix obtained using the loads with its...

  11. Outliers, Cheese, and Rhizomes: Variations on a Theme of Limitation

    Science.gov (United States)

    Stone, Lynda

    2011-01-01

    All research has limitations, for example, from paradigm, concept, theory, tradition, and discipline. In this article Lynda Stone describes three exemplars that are variations on limitation and are "extraordinary" in that they change what constitutes future research in each domain. Malcolm Gladwell's present day study of outliers makes a…

  12. Reduction of ZTD outliers through improved GNSS data processing and screening strategies

    Science.gov (United States)

    Stepniak, Katarzyna; Bock, Olivier; Wielgosz, Pawel

    2018-03-01

    Though Global Navigation Satellite System (GNSS) data processing has been significantly improved over the years, it is still commonly observed that zenith tropospheric delay (ZTD) estimates contain many outliers which are detrimental to meteorological and climatological applications. In this paper, we show that ZTD outliers in double-difference processing are mostly caused by sub-daily data gaps at reference stations, which cause disconnections of clusters of stations from the reference network and common mode biases due to the strong correlation between stations in short baselines. They can reach a few centimetres in ZTD and usually coincide with a jump in formal errors. The magnitude and sign of these biases are impossible to predict because they depend on different errors in the observations and on the geometry of the baselines. We elaborate and test a new baseline strategy which solves this problem and significantly reduces the number of outliers compared to the standard strategy commonly used for positioning (e.g. determination of national reference frame) in which the pre-defined network is composed of a skeleton of reference stations to which secondary stations are connected in a star-like structure. The new strategy is also shown to perform better than the widely used strategy maximizing the number of observations available in many GNSS programs. The reason is that observations are maximized before processing, whereas the final number of used observations can be dramatically lower because of data rejection (screening) during the processing. The study relies on the analysis of 1 year of GPS (Global Positioning System) data from a regional network of 136 GNSS stations processed using Bernese GNSS Software v.5.2. A post-processing screening procedure is also proposed to detect and remove a few outliers which may still remain due to short data gaps. It is based on a combination of range checks and outlier checks of ZTD and formal errors. The accuracy of the

  13. The high cost of low-acuity ICU outliers.

    Science.gov (United States)

    Dahl, Deborah; Wojtal, Greg G; Breslow, Michael J; Holl, Randy; Huguez, Debra; Stone, David; Korpi, Gloria

    2012-01-01

    Direct variable costs were determined on each hospital day for all patients with an intensive care unit (ICU) stay in four Phoenix-area hospital ICUs. Average daily direct variable cost in the four ICUs ranged from $1,436 to $1,759 and represented 69.4 percent and 45.7 percent of total hospital stay cost for medical and surgical patients, respectively. Daily ICU cost and length of stay (LOS) were higher in patients with higher ICU admission acuity of illness as measured by the APACHE risk prediction methodology; 16.2 percent of patients had an ICU stay in excess of six days, and these LOS outliers accounted for 56.7 percent of total ICU cost. While higher-acuity patients were more likely to be ICU LOS outliers, 11.1 percent of low-risk patients were outliers. The low-risk group included 69.4 percent of the ICU population and accounted for 47 percent of all LOS outliers. Low-risk LOS outliers accounted for 25.3 percent of ICU cost and incurred fivefold higher hospital stay costs and mortality rates. These data suggest that severity of illness is an important determinant of daily resource consumption and LOS, regardless of whether the patient arrives in the ICU with high acuity or develops complications that increase acuity. The finding that a substantial number of long-stay patients come into the ICU with low acuity and deteriorate after ICU admission is not widely recognized and represents an important opportunity to improve patient outcomes and lower costs. ICUs should consider adding low-risk LOS data to their quality and financial performance reports.

  14. Performances of the New Real Time Tsunami Detection Algorithm applied to tide gauges data

    Science.gov (United States)

    Chierici, F.; Embriaco, D.; Morucci, S.

    2017-12-01

    Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection (TDA) based on the real-time tide removal and real-time band-pass filtering of seabed pressure time series acquired by Bottom Pressure Recorders. The TDA algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability with respect to the most widely used tsunami detection algorithm, while containing the computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. In this work we present the performance of the TDA algorithm applied to tide gauge data. We have adapted the new tsunami detection algorithm and the Monte Carlo test methodology to tide gauges. Sea level data acquired by coastal tide gauges in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event generated by Tohoku earthquake on March 11th 2011, using data recorded by several tide gauges scattered all over the Pacific area.

  15. Nonlinear Optimization-Based Device-Free Localization with Outlier Link Rejection

    Directory of Open Access Journals (Sweden)

    Wendong Xiao

    2015-04-01

    Full Text Available Device-free localization (DFL is an emerging wireless technique for estimating the location of target that does not have any attached electronic device. It has found extensive use in Smart City applications such as healthcare at home and hospitals, location-based services at smart spaces, city emergency response and infrastructure security. In DFL, wireless devices are used as sensors that can sense the target by transmitting and receiving wireless signals collaboratively. Many DFL systems are implemented based on received signal strength (RSS measurements and the location of the target is estimated by detecting the changes of the RSS measurements of the wireless links. Due to the uncertainty of the wireless channel, certain links may be seriously polluted and result in erroneous detection. In this paper, we propose a novel nonlinear optimization approach with outlier link rejection (NOOLR for RSS-based DFL. It consists of three key strategies, including: (1 affected link identification by differential RSS detection; (2 outlier link rejection via geometrical positional relationship among links; (3 target location estimation by formulating and solving a nonlinear optimization problem. Experimental results demonstrate that NOOLR is robust to the fluctuation of the wireless signals with superior localization accuracy compared with the existing Radio Tomographic Imaging (RTI approach.

  16. 42 CFR 484.240 - Methodology used for the calculation of the outlier payment.

    Science.gov (United States)

    2010-10-01

    ... for each case-mix group. (b) The outlier threshold for each case-mix group is the episode payment... the same for all case-mix groups. (c) The outlier payment is a proportion of the amount of estimated...

  17. Setup Instructions for the Applied Anomaly Detection Tool (AADT) Web Server

    Science.gov (United States)

    2016-09-01

    tool has been developed for many platforms: Android , iOS, and Windows. The Windows version has been developed as a web server that allows the...Microsoft Windows. 15. SUBJECT TERMS Applied Anomaly Detection Tool, AADT, Windows, server, web service, installation 16. SECURITY CLASSIFICATION OF: 17...instructional information about identifying them as groups and individually. The software has been developed for several different platforms: Android

  18. GMDH and neural networks applied in monitoring and fault detection in sensors in nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Bueno, Elaine Inacio [Instituto Federal de Educacao, Ciencia e Tecnologia, Guarulhos, SP (Brazil); Pereira, Iraci Martinez; Silva, Antonio Teixeira e, E-mail: martinez@ipen.b, E-mail: teixeira@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2011-07-01

    In this work a new monitoring and fault detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and artificial neural networks (ANNs) which was applied in the IEA-R1 research reactor at IPEN. The monitoring and fault detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second to the process information using ANNs. The preprocess information was divided in two parts. In the first part, the GMDH algorithm was used to generate a better database estimate, called matrix z, which was used to train the ANNs. In the second part the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one theoretical model and for models using different sets of reactor variables. After an exhausting study dedicated to the sensors monitoring, the fault detection in sensors was developed by simulating faults in the sensors database using values of +5%, +10%, +15% and +20% in these sensors database. The good results obtained through the present methodology shows the viability of using GMDH algorithm in the study of the best input variables to the ANNs, thus making possible the use of these methods in the implementation of a new monitoring and fault detection methodology applied in sensors. (author)

  19. GMDH and neural networks applied in monitoring and fault detection in sensors in nuclear power plants

    International Nuclear Information System (INIS)

    Bueno, Elaine Inacio; Pereira, Iraci Martinez; Silva, Antonio Teixeira e

    2011-01-01

    In this work a new monitoring and fault detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and artificial neural networks (ANNs) which was applied in the IEA-R1 research reactor at IPEN. The monitoring and fault detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second to the process information using ANNs. The preprocess information was divided in two parts. In the first part, the GMDH algorithm was used to generate a better database estimate, called matrix z, which was used to train the ANNs. In the second part the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one theoretical model and for models using different sets of reactor variables. After an exhausting study dedicated to the sensors monitoring, the fault detection in sensors was developed by simulating faults in the sensors database using values of +5%, +10%, +15% and +20% in these sensors database. The good results obtained through the present methodology shows the viability of using GMDH algorithm in the study of the best input variables to the ANNs, thus making possible the use of these methods in the implementation of a new monitoring and fault detection methodology applied in sensors. (author)

  20. Outliers and Extremes: Dragon-Kings or Dragon-Fools?

    Science.gov (United States)

    Schertzer, D. J.; Tchiguirinskaia, I.; Lovejoy, S.

    2012-12-01

    Geophysics seems full of monsters like Victor Hugo's Court of Miracles and monstrous extremes have been statistically considered as outliers with respect to more normal events. However, a characteristic magnitude separating abnormal events from normal ones would be at odd with the generic scaling behaviour of nonlinear systems, contrary to "fat tailed" probability distributions and self-organized criticality. More precisely, it can be shown [1] how the apparent monsters could be mere manifestations of a singular measure mishandled as a regular measure. Monstrous fluctuations are the rule, not outliers and they are more frequent than usually thought up to the point that (theoretical) statistical moments can easily be infinite. The empirical estimates of the latter are erratic and diverge with sample size. The corresponding physics is that intense small scale events cannot be smoothed out by upscaling. However, based on a few examples, it has also been argued [2] that one should consider "genuine" outliers of fat tailed distributions so monstrous that they can be called "dragon-kings". We critically analyse these arguments, e.g. finite sample size and statistical estimates of the largest events, multifractal phase transition vs. more classical phase transition. We emphasize the fact that dragon-kings are not needed in order that the largest events become predictable. This is rather reminiscent of the Feast of Fools picturesquely described by Victor Hugo. [1] D. Schertzer, I. Tchiguirinskaia, S. Lovejoy et P. Hubert (2010): No monsters, no miracles: in nonlinear sciences hydrology is not an outlier! Hydrological Sciences Journal, 55 (6) 965 - 979. [2] D. Sornette (2009): Dragon-Kings, Black Swans and the Prediction of Crises. International Journal of Terraspace Science and Engineering 1(3), 1-17.

  1. Efficient alpha particle detection by CR-39 applying 50 Hz-HV electrochemical etching method

    International Nuclear Information System (INIS)

    Sohrabi, M.; Soltani, Z.

    2016-01-01

    Alpha particles can be detected by CR-39 by applying either chemical etching (CE), electrochemical etching (ECE), or combined pre-etching and ECE usually through a multi-step HF-HV ECE process at temperatures much higher than room temperature. By applying pre-etching, characteristics responses of fast-neutron-induced recoil tracks in CR-39 by HF-HV ECE versus KOH normality (N) have shown two high-sensitivity peaks around 5–6 and 15–16 N and a large-diameter peak with a minimum sensitivity around 10–11 N at 25°C. On the other hand, 50 Hz-HV ECE method recently advanced in our laboratory detects alpha particles with high efficiency and broad registration energy range with small ECE tracks in polycarbonate (PC) detectors. By taking advantage of the CR-39 sensitivity to alpha particles, efficacy of 50 Hz-HV ECE method and CR-39 exotic responses under different KOH normalities, detection characteristics of 0.8 MeV alpha particle tracks were studied in 500 μm CR-39 for different fluences, ECE duration and KOH normality. Alpha registration efficiency increased as ECE duration increased to 90 ± 2% after 6–8 h beyond which plateaus are reached. Alpha track density versus fluence is linear up to 10 6  tracks cm −2 . The efficiency and mean track diameter versus alpha fluence up to 10 6  alphas cm −2 decrease as the fluence increases. Background track density and minimum detection limit are linear functions of ECE duration and increase as normality increases. The CR-39 processed for the first time in this study by 50 Hz-HV ECE method proved to provide a simple, efficient and practical alpha detection method at room temperature. - Highlights: • Alpha particles of 0.8 MeV were detected in CR-39 by 50 Hz-HV ECE method. • Efficiency/track diameter was studied vs fluence and time for 3 KOH normality. • Background track density and minimum detection limit vs duration were studied. • A new simple, efficient and low-cost alpha detection method

  2. Stoicism, the physician, and care of medical outliers

    Directory of Open Access Journals (Sweden)

    Papadimos Thomas J

    2004-12-01

    Full Text Available Abstract Background Medical outliers present a medical, psychological, social, and economic challenge to the physicians who care for them. The determinism of Stoic thought is explored as an intellectual basis for the pursuit of a correct mental attitude that will provide aid and comfort to physicians who care for medical outliers, thus fostering continued physician engagement in their care. Discussion The Stoic topics of good, the preferable, the morally indifferent, living consistently, and appropriate actions are reviewed. Furthermore, Zeno's cardinal virtues of Justice, Temperance, Bravery, and Wisdom are addressed, as are the Stoic passions of fear, lust, mental pain, and mental pleasure. These concepts must be understood by physicians if they are to comprehend and accept the Stoic view as it relates to having the proper attitude when caring for those with long-term and/or costly illnesses. Summary Practicing physicians, especially those that are hospital based, and most assuredly those practicing critical care medicine, will be emotionally challenged by the medical outlier. A Stoic approach to such a social and psychological burden may be of benefit.

  3. Unsupervised Condition Change Detection In Large Diesel Engines

    DEFF Research Database (Denmark)

    Pontoppidan, Niels Henrik; Larsen, Jan

    2003-01-01

    This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can be investig...... be investigated further. The method is successfully applied to unsupervised condition change detection in large diesel engines from acoustical emission sensor signal and compared to more classical techniques based on principal component analysis and Gaussian mixture models.......This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can...

  4. Fault Detection Based on Tracking Differentiator Applied on the Suspension System of Maglev Train

    Directory of Open Access Journals (Sweden)

    Hehong Zhang

    2015-01-01

    Full Text Available A fault detection method based on the optimized tracking differentiator is introduced. It is applied on the acceleration sensor of the suspension system of maglev train. It detects the fault of the acceleration sensor by comparing the acceleration integral signal with the speed signal obtained by the optimized tracking differentiator. This paper optimizes the control variable when the states locate within or beyond the two-step reachable region to improve the performance of the approximate linear discrete tracking differentiator. Fault-tolerant control has been conducted by feedback based on the speed signal acquired from the optimized tracking differentiator when the acceleration sensor fails. The simulation and experiment results show the practical usefulness of the presented method.

  5. Interest of the technical detection of the sentinel node applied to uterine cancers: about three cases

    International Nuclear Information System (INIS)

    Ech charraq, I.; Ben Rais, N.; Ech charra, I.; Albertini, A.F.

    2009-01-01

    Introduction The sentinel node technique (S.N.) was proposed in cervical cancers in order to optimise the diagnosis of metastases and the lymphatic micrometastases in the early stages while avoiding useless wide clearings out. The identification of this node is done by injection of a dye and/or a radioactive colloid and its ablation for pathological examination. Patients and methods We report the case of three patients followed for a uterine cancer having benefited from a lymphoscintigraphy before surgery. During the surgical procedure, the detection of the sentinel node was carried out after cervical injection of blue dye and using a gamma detection probe. Results The lymphoscintigraphy was positive for two cases with a positive detection for the three cases during the operation. The pathological study revealed a node metastasis for one case. The technical of the sentinel node applied to uterine cancers appears realizable essentially for uterine cancers of early stage (I). However the risk of false negative can be observed in advanced cancer (III), as it is the case of our patient having a negative lymphoscintigraphy. Conclusion The nuclear medicine is important in the detection of the sentinel node of various cancers, uterine cancer included, thus allowing an appropriate cardiologic management. (authors)

  6. Applying long short-term memory recurrent neural networks to intrusion detection

    Directory of Open Access Journals (Sweden)

    Ralf C. Staudemeyer

    2015-07-01

    Full Text Available We claim that modelling network traffic as a time series with a supervised learning approach, using known genuine and malicious behaviour, improves intrusion detection. To substantiate this, we trained long short-term memory (LSTM recurrent neural networks with the training data provided by the DARPA / KDD Cup ’99 challenge. To identify suitable LSTM-RNN network parameters and structure we experimented with various network topologies. We found networks with four memory blocks containing two cells each offer a good compromise between computational cost and detection performance. We applied forget gates and shortcut connections respectively. A learning rate of 0.1 and up to 1,000 epochs showed good results. We tested the performance on all features and on extracted minimal feature sets respectively. We evaluated different feature sets for the detection of all attacks within one network and also to train networks specialised on individual attack classes. Our results show that the LSTM classifier provides superior performance in comparison to results previously published results of strong static classifiers. With 93.82% accuracy and 22.13 cost, LSTM outperforms the winning entries of the KDD Cup ’99 challenge by far. This is due to the fact that LSTM learns to look back in time and correlate consecutive connection records. For the first time ever, we have demonstrated the usefulness of LSTM networks to intrusion detection.

  7. Deteksi Outlier Transaksi Menggunakan Visualisasi-Olap Pada Data Warehouse Perguruan Tinggi Swasta

    Directory of Open Access Journals (Sweden)

    Gusti Ngurah Mega Nata

    2016-07-01

    Full Text Available Mendeteksi outlier pada data warehouse merupakan hal penting. Data pada data warehouse sudah diagregasi dan memiliki model multidimensional. Agregasi pada data warehouse dilakukan karena data warehouse digunakan untuk menganalisis data secara cepat pada top level manajemen. Sedangkan, model data multidimensional digunakan untuk melihat data dari berbagai dimensi objek bisnis. Jadi, Mendeteksi outlier pada data warehouse membutuhkan teknik yang dapat melihat outlier pada data yang sudah diagregasi dan dapat melihat dari berbagai dimensi objek bisnis. Mendeteksi outlier pada data warehouse akan menjadi tantangan baru.        Di lain hal, Visualisasi On-line Analytic process (OLAP merupakan tugas penting dalam menyajikan informasi trend (report pada data warehouse dalam bentuk visualisasi data. Pada penelitian ini, visualisasi OLAP digunakan untuk deteksi outlier transaksi. Maka, dalam penelitian ini melakukan analisis untuk mendeteksi outlier menggunakan visualisasi-OLAP. Operasi OLAP yang digunakan yaitu operasi drill-down. Jenis visualisasi yang akan digunakan yaitu visualisasi satu dimensi, dua dimensi dan multi dimensi menggunakan tool weave desktop. Pembangunan data warehouse dilakukan secara button-up. Studi kasus dilakukan pada perguruan tinggi swasta. Kasus yang diselesaikan yaitu mendeteksi outlier transaki pembayaran mahasiswa pada setiap semester. Deteksi outlier pada visualisasi data menggunakan satu tabel dimensional lebih mudah dianalisis dari pada deteksi outlier pada visualisasi data menggunakan dua atau multi tabel dimensional. Dengan kata lain semakin banyak tabel dimensi yang terlibat semakin sulit analisis deteksi outlier yang dilakukan. Kata kunci — Deteksi Outlier,  Visualisasi OLAP, Data warehouse

  8. Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2017-06-01

    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

  9. Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2017-06-01

    Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

  10. A method for separating seismo-ionospheric TEC outliers from heliogeomagnetic disturbances by using nu-SVR

    Energy Technology Data Exchange (ETDEWEB)

    Pattisahusiwa, Asis [Bandung Institute of Technology (Indonesia); Liong, The Houw; Purqon, Acep [Earth physics and complex systems research group, Bandung Institute of Technology (Indonesia)

    2015-09-30

    Seismo-Ionospheric is a study of ionosphere disturbances associated with seismic activities. In many previous researches, heliogeomagnetic or strong earthquake activities can caused the disturbances in the ionosphere. However, it is difficult to separate these disturbances based on related sources. In this research, we proposed a method to separate these disturbances/outliers by using nu-SVR with the world-wide GPS data. TEC data related to the 26th December 2004 Sumatra and the 11th March 2011 Honshu earthquakes had been analyzed. After analyzed TEC data in several location around the earthquake epicenter and compared with geomagnetic data, the method shows a good result in the average to detect the source of these outliers. This method is promising to use in the future research.

  11. Risetime discrimination applied to pressurized Xe gas proportional counter for hard x-ray detection

    International Nuclear Information System (INIS)

    Fujii, Masami; Doi, Kosei

    1978-01-01

    A high pressure Xe proportional counter has been developed for hard X-ray observation. This counter has better energy-resolving power than a NaI scintillation counter, and the realization of large area is relatively easy. This counter is constructed with a cylindrical aluminum tube, and this tube can be used at 40 atmospheric pressure. The detection efficiency curves were obtained in relation to gas pressure. It is necessary to reduce impurities in the Xe gas to increase the energy-resolving power of the counter. The increase of gas pressure made the resolving power worse. The characteristics of the counter were stable for at least a few months. The wave form discrimination was applied to reduce the background signals such as pulses caused by charged particles and gamma-ray. This method has been used for normal pressure counter, and in the present study, it was applied for the high pressure counter. It was found that the discrimination method was able to be applied to this case. (Kato, T.)

  12. In vivo Raman spectroscopy detects increased epidermal antioxidative potential with topically applied carotenoids

    International Nuclear Information System (INIS)

    Lademann, J; Richter, H; Patzelt, A; Darvin, M; Sterry, W; Fluhr, J W; Caspers, P J; Van der Pol, A; Zastrow, L

    2009-01-01

    In the present study, the distribution of the carotenoids as a marker for the complete antioxidative potential in human skin was investigated before and after the topical application of carotenoids by in vivo Raman spectroscopy with an excitation wavelength of 785 nm. The carotenoid profile was assessed after a short term topical application in 4 healthy volunteers. In the untreated skin, the highest concentration of natural carotenoids was detected in different layers of the stratum corneum (SC) close to the skin surface. After topical application of carotenoids, an increase in the antioxidative potential in the skin could be observed. Topically applied carotenoids penetrate deep into the epidermis down to approximately 24 μm. This study supports the hypothesis that antioxidative substances are secreted via eccrine sweat glands and/or sebaceous glands to the skin surface. Subsequently they penetrate into the different layers of the SC

  13. ROBUST: an interactive FORTRAN-77 package for exploratory data analysis using parametric, ROBUST and nonparametric location and scale estimates, data transformations, normality tests, and outlier assessment

    Science.gov (United States)

    Rock, N. M. S.

    ROBUST calculates 53 statistics, plus significance levels for 6 hypothesis tests, on each of up to 52 variables. These together allow the following properties of the data distribution for each variable to be examined in detail: (1) Location. Three means (arithmetic, geometric, harmonic) are calculated, together with the midrange and 19 high-performance robust L-, M-, and W-estimates of location (combined, adaptive, trimmed estimates, etc.) (2) Scale. The standard deviation is calculated along with the H-spread/2 (≈ semi-interquartile range), the mean and median absolute deviations from both mean and median, and a biweight scale estimator. The 23 location and 6 scale estimators programmed cover all possible degrees of robustness. (3) Normality: Distributions are tested against the null hypothesis that they are normal, using the 3rd (√ h1) and 4th ( b 2) moments, Geary's ratio (mean deviation/standard deviation), Filliben's probability plot correlation coefficient, and a more robust test based on the biweight scale estimator. These statistics collectively are sensitive to most usual departures from normality. (4) Presence of outliers. The maximum and minimum values are assessed individually or jointly using Grubbs' maximum Studentized residuals, Harvey's and Dixon's criteria, and the Studentized range. For a single input variable, outliers can be either winsorized or eliminated and all estimates recalculated iteratively as desired. The following data-transformations also can be applied: linear, log 10, generalized Box Cox power (including log, reciprocal, and square root), exponentiation, and standardization. For more than one variable, all results are tabulated in a single run of ROBUST. Further options are incorporated to assess ratios (of two variables) as well as discrete variables, and be concerned with missing data. Cumulative S-plots (for assessing normality graphically) also can be generated. The mutual consistency or inconsistency of all these measures

  14. How Can Synchrotron Radiation Techniques Be Applied for Detecting Microstructures in Amorphous Alloys?

    Directory of Open Access Journals (Sweden)

    Gu-Qing Guo

    2015-11-01

    Full Text Available In this work, how synchrotron radiation techniques can be applied for detecting the microstructure in metallic glass (MG is studied. The unit cells are the basic structural units in crystals, though it has been suggested that the co-existence of various clusters may be the universal structural feature in MG. Therefore, it is a challenge to detect microstructures of MG even at the short-range scale by directly using synchrotron radiation techniques, such as X-ray diffraction and X-ray absorption methods. Here, a feasible scheme is developed where some state-of-the-art synchrotron radiation-based experiments can be combined with simulations to investigate the microstructure in MG. By studying a typical MG composition (Zr70Pd30, it is found that various clusters do co-exist in its microstructure, and icosahedral-like clusters are the popular structural units. This is the structural origin where there is precipitation of an icosahedral quasicrystalline phase prior to phase transformation from glass to crystal when heating Zr70Pd30 MG.

  15. Fourier Transform Infrared Radiation Spectroscopy Applied for Wood Rot Decay and Mould Fungi Growth Detection

    Directory of Open Access Journals (Sweden)

    Bjørn Petter Jelle

    2012-01-01

    Full Text Available Material characterization may be carried out by the attenuated total reflectance (ATR Fourier transform infrared (FTIR radiation spectroscopical technique, which represents a powerful experimental tool. The ATR technique may be applied on both solid state materials, liquids, and gases with none or only minor sample preparations, also including materials which are nontransparent to IR radiation. This facilitation is made possible by pressing the sample directly onto various crystals, for example, diamond, with high refractive indices, in a special reflectance setup. Thus ATR saves time and enables the study of materials in a pristine condition, that is, the comprehensive sample preparation by pressing thin KBr pellets in traditional FTIR transmittance spectroscopy is hence avoided. Materials and their ageing processes, both ageing by natural and accelerated climate exposure, decomposition and formation of chemical bonds and products, may be studied in an ATR-FTIR analysis. In this work, the ATR-FTIR technique is utilized to detect wood rot decay and mould fungi growth on various building material substrates. An experimental challenge and aim is to be able to detect the wood rot decay and mould fungi growth at early stages when it is barely visible to the naked eye. Another goal is to be able to distinguish between various species of fungi and wood rot.

  16. Cost-effectiveness analysis in melanoma detection: A transition model applied to dermoscopy.

    Science.gov (United States)

    Tromme, Isabelle; Legrand, Catherine; Devleesschauwer, Brecht; Leiter, Ulrike; Suciu, Stefan; Eggermont, Alexander; Sacré, Laurine; Baurain, Jean-François; Thomas, Luc; Beutels, Philippe; Speybroeck, Niko

    2016-11-01

    The main aim of this study is to demonstrate how our melanoma disease model (MDM) can be used for cost-effectiveness analyses (CEAs) in the melanoma detection field. In particular, we used the data of two cohorts of Belgian melanoma patients to investigate the cost-effectiveness of dermoscopy. A MDM, previously constructed to calculate the melanoma burden, was slightly modified to be suitable for CEAs. Two cohorts of patients entered into the model to calculate morbidity, mortality and costs. These cohorts were constituted by melanoma patients diagnosed by dermatologists adequately, or not adequately, trained in dermoscopy. Effectiveness and costs were calculated for each cohort and compared. Effectiveness was expressed in quality-adjusted life years (QALYs), a composite measure depending on melanoma-related morbidity and mortality. Costs included costs of treatment and follow-up as well as costs of detection in non-melanoma patients and costs of excision and pathology of benign lesions excised to rule out melanoma. The result of our analysis concluded that melanoma diagnosis by dermatologists adequately trained in dermoscopy resulted in both a gain of QALYs (less morbidity and/or mortality) and a reduction in costs. This study demonstrates how our MDM can be used in CEAs in the melanoma detection field. The model and the methodology suggested in this paper were applied to two cohorts of Belgian melanoma patients. Their analysis concluded that adequate dermoscopy training is cost-effective. The results should be confirmed by a large-scale randomised study. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. A Pareto scale-inflated outlier model and its Bayesian analysis

    OpenAIRE

    Scollnik, David P. M.

    2016-01-01

    This paper develops a Pareto scale-inflated outlier model. This model is intended for use when data from some standard Pareto distribution of interest is suspected to have been contaminated with a relatively small number of outliers from a Pareto distribution with the same shape parameter but with an inflated scale parameter. The Bayesian analysis of this Pareto scale-inflated outlier model is considered and its implementation using the Gibbs sampler is discussed. The paper contains three wor...

  18. Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic.

    Science.gov (United States)

    Gu, Jinghua; Xuan, Jianhua; Riggins, Rebecca B; Chen, Li; Wang, Yue; Clarke, Robert

    2012-08-01

    Identification of transcriptional regulatory networks (TRNs) is of significant importance in computational biology for cancer research, providing a critical building block to unravel disease pathways. However, existing methods for TRN identification suffer from the inclusion of excessive 'noise' in microarray data and false-positives in binding data, especially when applied to human tumor-derived cell line studies. More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context. In this article, we propose to establish a link between the quality of one target gene to represent its regulator and the uncertainty of its expression to represent other target genes. Specifically, an outlier sum statistic was used to measure the aggregated evidence for regulation events between target genes and their corresponding transcription factors. A Gibbs sampling method was then developed to estimate the marginal distribution of the outlier sum statistic, hence, to uncover underlying regulatory relationships. To evaluate the effectiveness of our proposed method, we compared its performance with that of an existing sampling-based method using both simulation data and yeast cell cycle data. The experimental results show that our method consistently outperforms the competing method in different settings of signal-to-noise ratio and network topology, indicating its robustness for biological applications. Finally, we applied our method to breast cancer cell line data and demonstrated its ability to extract biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. The Gibbs sampler MATLAB package is freely available at http://www.cbil.ece.vt.edu/software.htm. xuan@vt.edu Supplementary data are available at Bioinformatics online.

  19. Applying the GNSS Volcanic Ash Plume Detection Technique to Consumer Navigation Receivers

    Science.gov (United States)

    Rainville, N.; Palo, S.; Larson, K. M.

    2017-12-01

    Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS) rely on predictably structured and constant power RF signals to fulfill their primary use for navigation and timing. When the received strength of GNSS signals deviates from the expected baseline, it is typically due to a change in the local environment. This can occur when signal reflections from the ground are modified by changes in snow or soil moisture content, as well as by attenuation of the signal from volcanic ash. This effect allows GNSS signals to be used as a source for passive remote sensing. Larson et al. (2017) have developed a detection technique for volcanic ash plumes based on the attenuation seen at existing geodetic GNSS sites. Since these existing networks are relatively sparse, this technique has been extended to use lower cost consumer GNSS receiver chips to enable higher density measurements of volcanic ash. These low-cost receiver chips have been integrated into a fully stand-alone sensor, with independent power, communications, and logging capabilities as part of a Volcanic Ash Plume Receiver (VAPR) network. A mesh network of these sensors transmits data to a local base-station which then streams the data real-time to a web accessible server. Initial testing of this sensor network has uncovered that a different detection approach is necessary when using consumer GNSS receivers and antennas. The techniques to filter and process the lower quality data from consumer receivers will be discussed and will be applied to initial results from a functioning VAPR network installation.

  20. Active and passive infrared thermography applied to the detection and characterization of hidden defects in structure

    Science.gov (United States)

    Dumoulin, Jean

    2013-04-01

    direct thermal modelling or inverse thermal modelling will be presented and discussed. Conclusion and perspectives will be proposed in link with structure monitoring or cultural heritage applications. References [1] Maldague, X.P.V. "Theory and practice of infrared technology for non-destructive testing", John Wiley & sons Inc., 2001. [2] Dumoulin J. and Averty R., « Development of an infrared system coupled with a weather station for real time atmospheric corrections using GPU computing: Application to bridge monitoring", QIRT 2012, Naples, Italy, June 2012. [3] J. Dumoulin, L. Ibos, C. Ibarra-Castanedo, A Mazioud, M. Marchetti, X. Maldague and A. Bendada, « Active infrared thermography applied to defect detection and characterization on asphalt pavement samples: comparison between experiments and numerical simulations », Journal of Modern Optics, Special Issue on Advanced Infrared Technology and Applications, Volume 57, Issue 18, October 2010 , pages 1759 - 1769, doi:10.1080/09500340.2010.522738 [4] F. Taillade, M. Quiertant, K. Benzarti, J. Dumoulin, Ch. Aubagnac, Chapter 9: "Nondestructive Evaluation of FRP Strengthening Systems Bonded on Concrete Structures using Pulsed Stimulated Infrared Thermography ", pp 193-208, Book title "Infrared Thermography", Editeur Raghu V. Prakash, ISBN 978-953-51-0242-7, Intech, open access at the following address http://www.intechopen.com/books/editor/infrared-thermography, march 2012. [5] Cooley J.W., Tukey J.W., "An algorithm for the machine calculation of complex Fourier series", Mathematics of Computation, vol. 19, n° 90, 1965, p. 297-301. [6] Rajic N., "Principal component thermography for flaw contrast enhancement and flaw depth characterization in composite structures", Composite Structures, vol 58, pp 521-528, 2002. [7] Marinetti S., Grinzato E., Bison P. G., Bozzi E., Chimenti M., Pieri G. and Salvetti O. "Statistical analysis of IR thermographic sequences by PCA," Infrared Physics & Technology vol 46 pp 85-91, 2004.

  1. Outlier identification procedures for contingency tables using maximum likelihood and $L_1$ estimates

    NARCIS (Netherlands)

    Kuhnt, S.

    2004-01-01

    Observed cell counts in contingency tables are perceived as outliers if they have low probability under an anticipated loglinear Poisson model. New procedures for the identification of such outliers are derived using the classical maximum likelihood estimator and an estimator based on the L1 norm.

  2. 42 CFR 412.84 - Payment for extraordinarily high-cost cases (cost outliers).

    Science.gov (United States)

    2010-10-01

    ... obtains accurate data with which to calculate either an operating or capital cost-to-charge ratio (or both... outlier payments will be based on operating and capital cost-to-charge ratios calculated based on a ratio... outliers). 412.84 Section 412.84 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF...

  3. Applying the J-optimal channelized quadratic observer to SPECT myocardial perfusion defect detection

    Science.gov (United States)

    Kupinski, Meredith K.; Clarkson, Eric; Ghaly, Michael; Frey, Eric C.

    2016-03-01

    To evaluate performance on a perfusion defect detection task from 540 image pairs of myocardial perfusion SPECT image data we apply the J-optimal channelized quadratic observer (J-CQO). We compare AUC values of the linear Hotelling observer and J-CQO when the defect location is fixed and when it occurs in one of two locations. As expected, when the location is fixed a single channels maximizes AUC; location variability requires multiple channels to maximize the AUC. The AUC is estimated from both the projection data and reconstructed images. J-CQO is quadratic since it uses the first- and second- order statistics of the image data from both classes. The linear data reduction by the channels is described by an L x M channel matrix and in prior work we introduced an iterative gradient-based method for calculating the channel matrix. The dimensionality reduction from M measurements to L channels yields better estimates of these sample statistics from smaller sample sizes, and since the channelized covariance matrix is L x L instead of M x M, the matrix inverse is easier to compute. The novelty of our approach is the use of Jeffrey's divergence (J) as the figure of merit (FOM) for optimizing the channel matrix. We previously showed that the J-optimal channels are also the optimum channels for the AUC and the Bhattacharyya distance when the channel outputs are Gaussian distributed with equal means. This work evaluates the use of J as a surrogate FOM (SFOM) for AUC when these statistical conditions are not satisfied.

  4. An approach to the analysis of SDSS spectroscopic outliers based on self-organizing maps. Designing the outlier analysis software package for the next Gaia survey

    Science.gov (United States)

    Fustes, D.; Manteiga, M.; Dafonte, C.; Arcay, B.; Ulla, A.; Smith, K.; Borrachero, R.; Sordo, R.

    2013-11-01

    Aims: A new method applied to the segmentation and further analysis of the outliers resulting from the classification of astronomical objects in large databases is discussed. The method is being used in the framework of the Gaia satellite Data Processing and Analysis Consortium (DPAC) activities to prepare automated software tools that will be used to derive basic astrophysical information that is to be included in final Gaia archive. Methods: Our algorithm has been tested by means of simulated Gaia spectrophotometry, which is based on SDSS observations and theoretical spectral libraries covering a wide sample of astronomical objects. Self-organizing maps networks are used to organize the information in clusters of objects, as homogeneously as possible according to their spectral energy distributions, and to project them onto a 2D grid where the data structure can be visualized. Results: We demonstrate the usefulness of the method by analyzing the spectra that were rejected by the SDSS spectroscopic classification pipeline and thus classified as "UNKNOWN". First, our method can help distinguish between astrophysical objects and instrumental artifacts. Additionally, the application of our algorithm to SDSS objects of unknown nature has allowed us to identify classes of objects with similar astrophysical natures. In addition, the method allows for the potential discovery of hundreds of new objects, such as white dwarfs and quasars. Therefore, the proposed method is shown to be very promising for data exploration and knowledge discovery in very large astronomical databases, such as the archive from the upcoming Gaia mission.

  5. A Geometrical-Statistical Approach to Outlier Removal for TDOA Measurements

    Science.gov (United States)

    Compagnoni, Marco; Pini, Alessia; Canclini, Antonio; Bestagini, Paolo; Antonacci, Fabio; Tubaro, Stefano; Sarti, Augusto

    2017-08-01

    The curse of outlier measurements in estimation problems is a well known issue in a variety of fields. Therefore, outlier removal procedures, which enables the identification of spurious measurements within a set, have been developed for many different scenarios and applications. In this paper, we propose a statistically motivated outlier removal algorithm for time differences of arrival (TDOAs), or equivalently range differences (RD), acquired at sensor arrays. The method exploits the TDOA-space formalism and works by only knowing relative sensor positions. As the proposed method is completely independent from the application for which measurements are used, it can be reliably used to identify outliers within a set of TDOA/RD measurements in different fields (e.g. acoustic source localization, sensor synchronization, radar, remote sensing, etc.). The proposed outlier removal algorithm is validated by means of synthetic simulations and real experiments.

  6. The variance of length of stay and the optimal DRG outlier payments.

    Science.gov (United States)

    Felder, Stefan

    2009-09-01

    Prospective payment schemes in health care often include supply-side insurance for cost outliers. In hospital reimbursement, prospective payments for patient discharges, based on their classification into diagnosis related group (DRGs), are complemented by outlier payments for long stay patients. The outlier scheme fixes the length of stay (LOS) threshold, constraining the profit risk of the hospitals. In most DRG systems, this threshold increases with the standard deviation of the LOS distribution. The present paper addresses the adequacy of this DRG outlier threshold rule for risk-averse hospitals with preferences depending on the expected value and the variance of profits. It first shows that the optimal threshold solves the hospital's tradeoff between higher profit risk and lower premium loading payments. It then demonstrates for normally distributed truncated LOS that the optimal outlier threshold indeed decreases with an increase in the standard deviation.

  7. The Plasma Focus Technology Applied to the Detection of Hydrogenated Substances

    International Nuclear Information System (INIS)

    Ramos, R.; Moreno, C.; Gonzalez, J.; Clausse, A

    2003-01-01

    The feasibility study of an industrial application of thermonuclear pulsors is presented.An experiment was conducted to detect hydrogenated substances using PF technology.The detection system is composed by two neutron detectors operated simultaneously on every shot.The first detector is used to register the PF neutron yield in each shot; whereas the other one was designed to detect neutrons scattered by the blanket.We obtained the detector sensitivity charts as a function of the position in space and frontal area of the substance to be detected

  8. A comparison of damage detection methods applied to civil engineering structures

    DEFF Research Database (Denmark)

    Gres, Szymon; Andersen, Palle; Johansen, Rasmus Johan

    2018-01-01

    Facilitating detection of early-stage damage is crucial for in-time repairs and cost-optimized maintenance plans of civil engineering structures. Preferably, the damage detection is performed by use of output vibration data, hereby avoiding modal identification of the structure. Most of the work...

  9. A comparison of damage detection methods applied to civil engineering structures

    DEFF Research Database (Denmark)

    Gres, Szymon; Andersen, Palle; Johansen, Rasmus Johan

    2017-01-01

    Facilitating detection of early-stage damage is crucial for in-time repairs and cost-optimized maintenance plans of civil engineering structures. Preferably, the damage detection is performed by use of output vibration data, hereby avoiding modal identification of the structure. Most of the work...

  10. Advancing early detection of autism spectrum disorder by applying an integrated two-stage screening approach

    NARCIS (Netherlands)

    Oosterling, Iris J.; Wensing, Michel; Swinkels, Sophie H.; van der Gaag, Rutger Jan; Visser, Janne C.; Woudenberg, Tim; Minderaa, Ruud; Steenhuis, Mark-Peter; Buitelaar, Jan K.

    Background: Few field trials exist on the impact of implementing guidelines for the early detection of autism spectrum disorders (ASD). The aims of the present study were to develop and evaluate a clinically relevant integrated early detection programme based on the two-stage screening approach of

  11. The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

    Science.gov (United States)

    Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

    2014-09-01

    In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

  12. Treatment of Outliers via Interpolation Method with Neural Network Forecast Performances

    Science.gov (United States)

    Wahir, N. A.; Nor, M. E.; Rusiman, M. S.; Gopal, K.

    2018-04-01

    Outliers often lurk in many datasets, especially in real data. Such anomalous data can negatively affect statistical analyses, primarily normality, variance, and estimation aspects. Hence, handling the occurrences of outliers require special attention. Therefore, it is important to determine the suitable ways in treating outliers so as to ensure that the quality of the analyzed data is indeed high. As such, this paper discusses an alternative method to treat outliers via linear interpolation method. In fact, assuming outlier as a missing value in the dataset allows the application of the interpolation method to interpolate the outliers thus, enabling the comparison of data series using forecast accuracy before and after outlier treatment. With that, the monthly time series of Malaysian tourist arrivals from January 1998 until December 2015 had been used to interpolate the new series. The results indicated that the linear interpolation method, which was comprised of improved time series data, displayed better results, when compared to the original time series data in forecasting from both Box-Jenkins and neural network approaches.

  13. Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness

    NARCIS (Netherlands)

    Kamphuis, C.; Frank, E.; Burke, J.; Verkerk, G.A.; Jago, J.

    2013-01-01

    The hypothesis was that sensors currently available on farm that monitor behavioral and physiological characteristics have potential for the detection of lameness in dairy cows. This was tested by applying additive logistic regression to variables derived from sensor data. Data were collected

  14. Moving standard deviation and moving sum of outliers as quality tools for monitoring analytical precision.

    Science.gov (United States)

    Liu, Jiakai; Tan, Chin Hon; Badrick, Tony; Loh, Tze Ping

    2018-02-01

    An increase in analytical imprecision (expressed as CV a ) can introduce additional variability (i.e. noise) to the patient results, which poses a challenge to the optimal management of patients. Relatively little work has been done to address the need for continuous monitoring of analytical imprecision. Through numerical simulations, we describe the use of moving standard deviation (movSD) and a recently described moving sum of outlier (movSO) patient results as means for detecting increased analytical imprecision, and compare their performances against internal quality control (QC) and the average of normal (AoN) approaches. The power of detecting an increase in CV a is suboptimal under routine internal QC procedures. The AoN technique almost always had the highest average number of patient results affected before error detection (ANPed), indicating that it had generally the worst capability for detecting an increased CV a . On the other hand, the movSD and movSO approaches were able to detect an increased CV a at significantly lower ANPed, particularly for measurands that displayed a relatively small ratio of biological variation to CV a. CONCLUSION: The movSD and movSO approaches are effective in detecting an increase in CV a for high-risk measurands with small biological variation. Their performance is relatively poor when the biological variation is large. However, the clinical risks of an increase in analytical imprecision is attenuated for these measurands as an increased analytical imprecision will only add marginally to the total variation and less likely to impact on the clinical care. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

  15. Development of a faulty reactivity detection system applying a digital H∞ estimator

    International Nuclear Information System (INIS)

    Suzuki, Katsuo; Suzudo, Tomoaki; Nabeshima, Kunihiko

    2004-01-01

    This paper concerns an application of digital optimal H ∞ estimator to the detection of faulty reactivity in real-time. The detection system, fundamentally based on the reactivity balance method, is composed of three modules, i.e. the net reactivity estimator, the feedback reactivity estimator and the reactivity balance circuit. H ∞ optimal filters are used for these two reactivity estimators, and the nonlinear neutronics are taken into consideration especially for the design of the net reactivity estimator. A series of performance test of the detection system are conducted by using numerical simulations of reactor dynamics with the insertion of a faulty reactivity for an experimental fast breeder reactor JOYO. The system detects the typical artificial reactivity insertions during a few seconds with no stationary offset and the accuracy of 0.1 cent, and is satisfactory for its practical use. (author)

  16. A measurement-based fault detection approach applied to monitor robots swarm

    KAUST Repository

    Khaldi, Belkacem; Harrou, Fouzi; Sun, Ying; Cherif, Foudil

    2017-01-01

    present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart

  17. Image processing techniques applied to the detection of optic disk: a comparison

    Science.gov (United States)

    Kumari, Vijaya V.; Narayanan, Suriya N.

    2010-02-01

    In retinal image analysis, the detection of optic disk is of paramount importance. It facilitates the tracking of various anatomical features and also in the extraction of exudates, drusens etc., present in the retina of human eye. The health of retina crumbles with age in some people during the presence of exudates causing Diabetic Retinopathy. The existence of exudates increases the risk for age related macular Degeneration (AMRD) and it is the leading cause for blindness in people above the age of 50.A prompt diagnosis when the disease is at the early stage can help to prevent irreversible damages to the diabetic eye. Screening to detect diabetic retinopathy helps to prevent the visual loss. The optic disk detection is the rudimentary requirement for the screening. In this paper few methods for optic disk detection were compared which uses both the properties of optic disk and model based approaches. They are uniquely used to give accurate results in the retinal images.

  18. Comparison of algorithms for blood stain detection applied to forensic hyperspectral imagery

    Science.gov (United States)

    Yang, Jie; Messinger, David W.; Mathew, Jobin J.; Dube, Roger R.

    2016-05-01

    Blood stains are among the most important types of evidence for forensic investigation. They contain valuable DNA information, and the pattern of the stains can suggest specifics about the nature of the violence that transpired at the scene. Early detection of blood stains is particularly important since the blood reacts physically and chemically with air and materials over time. Accurate identification of blood remnants, including regions that might have been intentionally cleaned, is an important aspect of forensic investigation. Hyperspectral imaging might be a potential method to detect blood stains because it is non-contact and provides substantial spectral information that can be used to identify regions in a scene with trace amounts of blood. The potential complexity of scenes in which such vast violence occurs can be high when the range of scene material types and conditions containing blood stains at a crime scene are considered. Some stains are hard to detect by the unaided eye, especially if a conscious effort to clean the scene has occurred (we refer to these as "latent" blood stains). In this paper we present the initial results of a study of the use of hyperspectral imaging algorithms for blood detection in complex scenes. We describe a hyperspectral imaging system which generates images covering 400 nm - 700 nm visible range with a spectral resolution of 10 nm. Three image sets of 31 wavelength bands were generated using this camera for a simulated indoor crime scene in which blood stains were placed on a T-shirt and walls. To detect blood stains in the scene, Principal Component Analysis (PCA), Subspace Reed Xiaoli Detection (SRXD), and Topological Anomaly Detection (TAD) algorithms were used. Comparison of the three hyperspectral image analysis techniques shows that TAD is most suitable for detecting blood stains and discovering latent blood stains.

  19. GIS applied to location of fires detection towers in domain area of tropical forest.

    Science.gov (United States)

    Eugenio, Fernando Coelho; Rosa Dos Santos, Alexandre; Fiedler, Nilton Cesar; Ribeiro, Guido Assunção; da Silva, Aderbal Gomes; Juvanhol, Ronie Silva; Schettino, Vitor Roberto; Marcatti, Gustavo Eduardo; Domingues, Getúlio Fonseca; Alves Dos Santos, Gleissy Mary Amaral Dino; Pezzopane, José Eduardo Macedo; Pedra, Beatriz Duguy; Banhos, Aureo; Martins, Lima Deleon

    2016-08-15

    In most countries, the loss of biodiversity caused by the fires is worrying. In this sense, the fires detection towers are crucial for rapid identification of fire outbreaks and can also be used in environmental inspection, biodiversity monitoring, telecommunications mechanisms, telemetry and others. Currently the methodologies for allocating fire detection towers over large areas are numerous, complex and non-standardized by government supervisory agencies. Therefore, this study proposes and evaluates different methodologies to best location of points to install fire detection towers considering the topography, risk areas, conservation units and heat spots. Were used Geographic Information Systems (GIS) techniques and unaligned stratified systematic sampling for implementing and evaluating 9 methods for allocating fire detection towers. Among the methods evaluated, the C3 method was chosen, represented by 140 fire detection towers, with coverage of: a) 67% of the study area, b) 73.97% of the areas with high risk, c) 70.41% of the areas with very high risk, d) 70.42% of the conservation units and e) 84.95% of the heat spots in 2014. The proposed methodology can be adapted to areas of other countries. Copyright © 2016 Elsevier B.V. All rights reserved.

  20. Long-range alpha detection applied to soil contamination and waste monitoring

    International Nuclear Information System (INIS)

    MacArthur, D.W.; Allander, K.S.; Bounds, J.A.; Close, D.A.; McAtee, J.L.

    1992-01-01

    Alpha contamination monitoring has been traditionally limited by the short range of alpha particles in air and through detector windows. The long-range alpha detector (LRAD) described in this paper circumvents that limitation by detecting alpha-produced ions, rather than alpha particles directly. Since the LRAD is sensitive to all ions, it can monitor all contamination present on a large surface at one time. Because air is the ''detector gas,'' the LRAD can detect contamination on any surface to which air can penetrate. We present data showing the sensitivity of LRAD detectors, as well as documenting their ability to detect alpha sources in previously unmonitorable locations, and verifying the ion lifetime. Specific designs and results for soil contamination and waste monitors are also included

  1. A measurement-based fault detection approach applied to monitor robots swarm

    KAUST Repository

    Khaldi, Belkacem

    2017-07-10

    Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.

  2. Computer-aided detection system applied to full-field digital mammograms

    International Nuclear Information System (INIS)

    Vega Bolivar, Alfonso; Sanchez Gomez, Sonia; Merino, Paula; Alonso-Bartolome, Pilar; Ortega Garcia, Estrella; Munoz Cacho, Pedro; Hoffmeister, Jeffrey W.

    2010-01-01

    Background: Although mammography remains the mainstay for breast cancer screening, it is an imperfect examination with a sensitivity of 75-92% for breast cancer. Computer-aided detection (CAD) has been developed to improve mammographic detection of breast cancer. Purpose: To retrospectively estimate CAD sensitivity and false-positive rate with full-field digital mammograms (FFDMs). Material and Methods: CAD was used to evaluate 151 cases of ductal carcinoma in situ (DCIS) (n=48) and invasive breast cancer (n=103) detected with FFDM. Retrospectively, CAD sensitivity was estimated based on breast density, mammographic presentation, histopathology type, and lesion size. CAD false-positive rate was estimated with screening FFDMs from 200 women. Results: CAD detected 93% (141/151) of cancer cases: 97% (28/29) in fatty breasts, 94% (81/86) in breasts containing scattered fibroglandular densities, 90% (28/31) in heterogeneously dense breasts, and 80% (4/5) in extremely dense breasts. CAD detected 98% (54/55) of cancers manifesting as calcifications, 89% (74/83) as masses, and 100% (13/13) as mixed masses and calcifications. CAD detected 92% (73/79) of invasive ductal carcinomas, 89% (8/9) of invasive lobular carcinomas, 93% (14/15) of other invasive carcinomas, and 96% (46/48) of DCIS. CAD sensitivity for cancers 1-10 mm was 87% (47/54); 11-20 mm, 99% (70/71); 21-30 mm, 86% (12/14); and larger than 30 mm, 100% (12/12). The CAD false-positive rate was 2.5 marks per case. Conclusion: CAD with FFDM showed a high sensitivity in identifying cancers manifesting as calcifications or masses. CAD sensitivity was maintained in small lesions (1-20 mm) and invasive lobular carcinomas, which have lower mammographic sensitivity

  3. Computer-aided detection system applied to full-field digital mammograms

    Energy Technology Data Exchange (ETDEWEB)

    Vega Bolivar, Alfonso; Sanchez Gomez, Sonia; Merino, Paula; Alonso-Bartolome, Pilar; Ortega Garcia, Estrella (Dept. of Radiology, Univ. Marques of Valdecilla Hospital, Santander (Spain)), e-mail: avegab@telefonica.net; Munoz Cacho, Pedro (Dept. of Statistics, Univ. Marques of Valdecilla Hospital, Santander (Spain)); Hoffmeister, Jeffrey W. (iCAD, Inc., Nashua, NH (United States))

    2010-12-15

    Background: Although mammography remains the mainstay for breast cancer screening, it is an imperfect examination with a sensitivity of 75-92% for breast cancer. Computer-aided detection (CAD) has been developed to improve mammographic detection of breast cancer. Purpose: To retrospectively estimate CAD sensitivity and false-positive rate with full-field digital mammograms (FFDMs). Material and Methods: CAD was used to evaluate 151 cases of ductal carcinoma in situ (DCIS) (n=48) and invasive breast cancer (n=103) detected with FFDM. Retrospectively, CAD sensitivity was estimated based on breast density, mammographic presentation, histopathology type, and lesion size. CAD false-positive rate was estimated with screening FFDMs from 200 women. Results: CAD detected 93% (141/151) of cancer cases: 97% (28/29) in fatty breasts, 94% (81/86) in breasts containing scattered fibroglandular densities, 90% (28/31) in heterogeneously dense breasts, and 80% (4/5) in extremely dense breasts. CAD detected 98% (54/55) of cancers manifesting as calcifications, 89% (74/83) as masses, and 100% (13/13) as mixed masses and calcifications. CAD detected 92% (73/79) of invasive ductal carcinomas, 89% (8/9) of invasive lobular carcinomas, 93% (14/15) of other invasive carcinomas, and 96% (46/48) of DCIS. CAD sensitivity for cancers 1-10 mm was 87% (47/54); 11-20 mm, 99% (70/71); 21-30 mm, 86% (12/14); and larger than 30 mm, 100% (12/12). The CAD false-positive rate was 2.5 marks per case. Conclusion: CAD with FFDM showed a high sensitivity in identifying cancers manifesting as calcifications or masses. CAD sensitivity was maintained in small lesions (1-20 mm) and invasive lobular carcinomas, which have lower mammographic sensitivity

  4. Perseveration effects in detection tasks with correlated decision intervals. [applied to pilot collision avoidance

    Science.gov (United States)

    Gai, E. G.; Curry, R. E.

    1978-01-01

    An investigation of the behavior of the human decisionmaker is described for a task related to the problem of a pilot using a traffic situation display to avoid collisions. This sequential signal detection task is characterized by highly correlated signals with time varying strength. Experimental results are presented and the behavior of the observers is analyzed using the theory of Markov processes and classical signal detection theory. Mathematical models are developed which describe the main result of the experiment: that correlation in sequential signals induced perseveration in the observer response and a strong tendency to repeat their previous decision, even when they were wrong.

  5. Improvement of statistical methods for detecting anomalies in climate and environmental monitoring systems

    Science.gov (United States)

    Yakunin, A. G.; Hussein, H. M.

    2018-01-01

    The article shows how the known statistical methods, which are widely used in solving financial problems and a number of other fields of science and technology, can be effectively applied after minor modification for solving such problems in climate and environment monitoring systems, as the detection of anomalies in the form of abrupt changes in signal levels, the occurrence of positive and negative outliers and the violation of the cycle form in periodic processes.

  6. Validation of the Applied Biosystems RapidFinder Shiga Toxin-Producing E. coli (STEC) Detection Workflow.

    Science.gov (United States)

    Cloke, Jonathan; Matheny, Sharon; Swimley, Michelle; Tebbs, Robert; Burrell, Angelia; Flannery, Jonathan; Bastin, Benjamin; Bird, Patrick; Benzinger, M Joseph; Crowley, Erin; Agin, James; Goins, David; Salfinger, Yvonne; Brodsky, Michael; Fernandez, Maria Cristina

    2016-11-01

    The Applied Biosystems™ RapidFinder™ STEC Detection Workflow (Thermo Fisher Scientific) is a complete protocol for the rapid qualitative detection of Escherichia coli (E. coli) O157:H7 and the "Big 6" non-O157 Shiga-like toxin-producing E. coli (STEC) serotypes (defined as serogroups: O26, O45, O103, O111, O121, and O145). The RapidFinder STEC Detection Workflow makes use of either the automated preparation of PCR-ready DNA using the Applied Biosystems PrepSEQ™ Nucleic Acid Extraction Kit in conjunction with the Applied Biosystems MagMAX™ Express 96-well magnetic particle processor or the Applied Biosystems PrepSEQ Rapid Spin kit for manual preparation of PCR-ready DNA. Two separate assays comprise the RapidFinder STEC Detection Workflow, the Applied Biosystems RapidFinder STEC Screening Assay and the Applied Biosystems RapidFinder STEC Confirmation Assay. The RapidFinder STEC Screening Assay includes primers and probes to detect the presence of stx1 (Shiga toxin 1), stx2 (Shiga toxin 2), eae (intimin), and E. coli O157 gene targets. The RapidFinder STEC Confirmation Assay includes primers and probes for the "Big 6" non-O157 STEC and E. coli O157:H7. The use of these two assays in tandem allows a user to detect accurately the presence of the "Big 6" STECs and E. coli O157:H7. The performance of the RapidFinder STEC Detection Workflow was evaluated in a method comparison study, in inclusivity and exclusivity studies, and in a robustness evaluation. The assays were compared to the U.S. Department of Agriculture (USDA), Food Safety and Inspection Service (FSIS) Microbiology Laboratory Guidebook (MLG) 5.09: Detection, Isolation and Identification of Escherichia coli O157:H7 from Meat Products and Carcass and Environmental Sponges for raw ground beef (73% lean) and USDA/FSIS-MLG 5B.05: Detection, Isolation and Identification of Escherichia coli non-O157:H7 from Meat Products and Carcass and Environmental Sponges for raw beef trim. No statistically significant

  7. Technology applied in the operation and detection of antipersonnel mines: state of the art

    Directory of Open Access Journals (Sweden)

    Javier Andrés Ledezma-Ríos

    2017-06-01

    Full Text Available The main objective of this investigation is to know the different technologies implemented for the detection of antipersonnel mines, documented by different bibliographic means of the latest updates used for the detection of buried objects, the factors that affect the loss of energy of the waves as transmitters of information between them, the characteristics of the soil, the amplitude of the emitted signal, the frequency and the conditions of the terrain. This paper informs about the computational means, of their work with the different algorithms to model correct information of what is happening with the phenomenon of detection. Thus, through this research, the scientific community is informed on the parameters of magnetic susceptibility, the percentage of water and porosity of the environment where the emitted waves react, the difficulty of the stability of the signal to be captured to detect antipersonnel mines, in a geographical context. Currently, PVC tubes, cans, syringes and hand-held devices are being used for their production, and the waves will behave differently against these materials.

  8. E Pluribus Analysis: Applying a Superforecasting Methodology to the Detection of Homegrown Violence

    Science.gov (United States)

    2018-03-01

    act of violence is not supported with other predicate crimes such as money laundering , arms trafficking, possession of banned substances, or other...apparatuses, do not rely on elaborate support networks or detectable money trails, and often select targets that are difficult to anticipate and defend. In

  9. Quantum Dots Applied to Methodology on Detection of Pesticide and Veterinary Drug Residues.

    Science.gov (United States)

    Zhou, Jia-Wei; Zou, Xue-Mei; Song, Shang-Hong; Chen, Guan-Hua

    2018-02-14

    The pesticide and veterinary drug residues brought by large-scale agricultural production have become one of the issues in the fields of food safety and environmental ecological security. It is necessary to develop the rapid, sensitive, qualitative and quantitative methodology for the detection of pesticide and veterinary drug residues. As one of the achievements of nanoscience, quantum dots (QDs) have been widely used in the detection of pesticide and veterinary drug residues. In these methodology studies, the used QD-signal styles include fluorescence, chemiluminescence, electrochemical luminescence, photoelectrochemistry, etc. QDs can also be assembled into sensors with different materials, such as QD-enzyme, QD-antibody, QD-aptamer, and QD-molecularly imprinted polymer sensors, etc. Plenty of study achievements in the field of detection of pesticide and veterinary drug residues have been obtained from the different combinations among these signals and sensors. They are summarized in this paper to provide a reference for the QD application in the detection of pesticide and veterinary drug residues.

  10. Applied Behavior Analysis Is Ideal for the Development of a Land Mine Detection Technology Using Animals

    Science.gov (United States)

    Jones, B. M.

    2011-01-01

    The detection and subsequent removal of land mines and unexploded ordnance (UXO) from many developing countries are slow, expensive, and dangerous tasks, but have the potential to improve the well-being of millions of people. Consequently, those involved with humanitarian mine and UXO clearance are actively searching for new and more efficient…

  11. A Comparison of Vibration and Oil Debris Gear Damage Detection Methods Applied to Pitting Damage

    Science.gov (United States)

    Dempsey, Paula J.

    2000-01-01

    Helicopter Health Usage Monitoring Systems (HUMS) must provide reliable, real-time performance monitoring of helicopter operating parameters to prevent damage of flight critical components. Helicopter transmission diagnostics are an important part of a helicopter HUMS. In order to improve the reliability of transmission diagnostics, many researchers propose combining two technologies, vibration and oil monitoring, using data fusion and intelligent systems. Some benefits of combining multiple sensors to make decisions include improved detection capabilities and increased probability the event is detected. However, if the sensors are inaccurate, or the features extracted from the sensors are poor predictors of transmission health, integration of these sensors will decrease the accuracy of damage prediction. For this reason, one must verify the individual integrity of vibration and oil analysis methods prior to integrating the two technologies. This research focuses on comparing the capability of two vibration algorithms, FM4 and NA4, and a commercially available on-line oil debris monitor to detect pitting damage on spur gears in the NASA Glenn Research Center Spur Gear Fatigue Test Rig. Results from this research indicate that the rate of change of debris mass measured by the oil debris monitor is comparable to the vibration algorithms in detecting gear pitting damage.

  12. USBeSafe: Applying One Class SVM for Effective USB Event Anomaly Detection

    Science.gov (United States)

    2016-04-25

    2012. [5] Phil Muncaster. Indian navy computers stormed by malware-ridden USBs. 2012. [6] Ponemon. 2011 Second Annual Cost of Cyber Crime Study...Zhang, and Shanshan Sun. “A mixed unsu- pervised clustering-based intrusion detection model”. In: Genetic and Evolutionary Computing, 2009. WGEC’09

  13. Applying IPFIX Protocol for Detection of Distributed Denial of Service Attacks against Cloud Infrastructure

    Directory of Open Access Journals (Sweden)

    M. R. Mukhtarov

    2011-12-01

    Full Text Available The way of monitoring deviations in network traffic behavior inside “Cloud Infrastructure” using IPFIX protocol is suggested in the paper. The proposed algorithm is applied for registration of “Distributed Denial of Service” attacks against “Cloud Infrastructure”.

  14. New enhanced sensitivity infrared laser spectroscopy techniques applied to reactive plasmas and trace gas detection

    NARCIS (Netherlands)

    Welzel, S.

    2009-01-01

    Infrared laser absorption spectroscopy (IRLAS) employing both tuneable diode and quantum cascade lasers (TDLs, QCLs) has been applied with both high sensitivity and high time resolution to plasma diagnostics and trace gas measurements. TDLAS combined with a conventional White type multiple pass cell

  15. Dynamic Water Surface Detection Algorithm Applied on PROBA-V Multispectral Data

    Directory of Open Access Journals (Sweden)

    Luc Bertels

    2016-12-01

    Full Text Available Water body detection worldwide using spaceborne remote sensing is a challenging task. A global scale multi-temporal and multi-spectral image analysis method for water body detection was developed. The PROBA-V microsatellite has been fully operational since December 2013 and delivers daily near-global synthesis with a spatial resolution of 1 km and 333 m. The Red, Near-InfRared (NIR and Short Wave InfRared (SWIR bands of the atmospherically corrected 10-day synthesis images are first Hue, Saturation and Value (HSV color transformed and subsequently used in a decision tree classification for water body detection. To minimize commission errors four additional data layers are used: the Normalized Difference Vegetation Index (NDVI, Water Body Potential Mask (WBPM, Permanent Glacier Mask (PGM and Volcanic Soil Mask (VSM. Threshold values on the hue and value bands, expressed by a parabolic function, are used to detect the water bodies. Beside the water bodies layer, a quality layer, based on the water bodies occurrences, is available in the output product. The performance of the Water Bodies Detection Algorithm (WBDA was assessed using Landsat 8 scenes over 15 regions selected worldwide. A mean Commission Error (CE of 1.5% was obtained while a mean Omission Error (OE of 15.4% was obtained for minimum Water Surface Ratio (WSR = 0.5 and drops to 9.8% for minimum WSR = 0.6. Here, WSR is defined as the fraction of the PROBA-V pixel covered by water as derived from high spatial resolution images, e.g., Landsat 8. Both the CE = 1.5% and OE = 9.8% (WSR = 0.6 fall within the user requirements of 15%. The WBDA is fully operational in the Copernicus Global Land Service and products are freely available.

  16. TrigDB for improving the reliability of the epicenter locations by considering the neighborhood station's trigger and cutting out of outliers in operation of Earthquake Early Warning System.

    Science.gov (United States)

    Chi, H. C.; Park, J. H.; Lim, I. S.; Seong, Y. J.

    2016-12-01

    TrigDB is initially developed for the discrimination of teleseismic-origin false alarm in the case with unreasonably associated triggers producing mis-located epicenters. We have applied TrigDB to the current EEWS(Earthquake Early Warning System) from 2014. During the early stage of testing EEWS from 2011, we adapted ElarmS from US Berkeley BSL to Korean seismic network and applied more than 5 years. We found out that the real-time testing results of EEWS in Korea showed that all events inside of seismic network with bigger than magnitude 3.0 were well detected. However, two events located at sea area gave false location results with magnitude over 4.0 due to the long period and relatively high amplitude signals related to the teleseismic waves or regional deep sources. These teleseismic-relevant false events were caused by logical co-relation during association procedure and the corresponding geometric distribution of associated stations is crescent-shaped. Seismic stations are not deployed uniformly, so the expected bias ratio varies with evaluated epicentral location. This ratio is calculated in advance and stored into database, called as TrigDB, for the discrimination of teleseismic-origin false alarm. We upgraded this method, so called `TrigDB back filling', updating location with supplementary association of stations comparing triggered times between sandwiched stations which was not associated previously based on predefined criteria such as travel-time. And we have tested a module to reject outlier trigger times by setting a criteria comparing statistical values(Sigma) to the triggered times. The criteria of cutting off the outlier is slightly slow to work until the number of stations more than 8, however, the result of location is very much improved.

  17. An application of robust ridge regression model in the presence of outliers to real data problem

    Science.gov (United States)

    Shariff, N. S. Md.; Ferdaos, N. A.

    2017-09-01

    Multicollinearity and outliers are often leads to inconsistent and unreliable parameter estimates in regression analysis. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is believed are affected by the presence of outlier. The combination of GM-estimation and ridge parameter that is robust towards both problems is on interest in this study. As such, both techniques are employed to investigate the relationship between stock market price and macroeconomic variables in Malaysia due to curiosity of involving the multicollinearity and outlier problem in the data set. There are four macroeconomic factors selected for this study which are Consumer Price Index (CPI), Gross Domestic Product (GDP), Base Lending Rate (BLR) and Money Supply (M1). The results demonstrate that the proposed procedure is able to produce reliable results towards the presence of multicollinearity and outliers in the real data.

  18. Probabilistic Neural Networks for Chemical Sensor Array Pattern Recognition: Comparison Studies, Improvements and Automated Outlier Rejection

    National Research Council Canada - National Science Library

    Shaffer, Ronald E

    1998-01-01

    For application to chemical sensor arrays, the ideal pattern recognition is accurate, fast, simple to train, robust to outliers, has low memory requirements, and has the ability to produce a measure...

  19. The influence of outliers on a model for the estimation of ...

    African Journals Online (AJOL)

    Veekunde

    problems that violate these assumptions is the problem of outliers. .... A normal probability plot of the ordered residuals on the normal order statistics, which are the ... observations from the normal distribution with zero mean and unit variance.

  20. Adaptive Outlier-tolerant Exponential Smoothing Prediction Algorithms with Applications to Predict the Temperature in Spacecraft

    OpenAIRE

    Hu Shaolin; Zhang Wei; Li Ye; Fan Shunxi

    2011-01-01

    The exponential smoothing prediction algorithm is widely used in spaceflight control and in process monitoring as well as in economical prediction. There are two key conundrums which are open: one is about the selective rule of the parameter in the exponential smoothing prediction, and the other is how to improve the bad influence of outliers on prediction. In this paper a new practical outlier-tolerant algorithm is built to select adaptively proper parameter, and the exponential smoothing pr...

  1. Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis

    Directory of Open Access Journals (Sweden)

    Zhou Qing

    2017-01-01

    Full Text Available Sensorless Drive Diagnosis can be used to assess the process data without the need for additional cost-intensive sensor technology, and you can understand the synchronous motor and connecting parts of the damaged state. Considering the number of features involved in the process data, it is necessary to perform feature selection and reduce the data dimension in the process of fault detection. In this paper, the MOEA / D algorithm based on multi-objective optimization is used to obtain the weight vector of all the features in the original data set. It is more suitable to classify or make decisions based on these features. In order to ensure the fastness and convenience sensorless drive diagnosis, in this paper, the classic Bayesian network learning algorithm-K2 algorithm is used to study the network structure of each feature in sensorless drive, which makes the fault detection and elimination process more targeted.

  2. Applying Hotspot Detection Methods in Forestry: A Case Study of Chestnut Oak Regeneration

    International Nuclear Information System (INIS)

    Fei, S.

    2010-01-01

    Hotspot detection has been widely adopted in health sciences for disease surveillance, but rarely in natural resource disciplines. In this paper, two spatial scan statistics (SaT Scan and Cluster Seer) and a non spatial classification and regression trees method were evaluated as techniques for identifying chestnut oak (Quercus Montana) regeneration hotspots among 50 mixed-oak stands in the central Appalachian region of the eastern United States. Hotspots defined by the three methods had a moderate level of conformity and revealed similar chestnut oak regeneration site affinity. Chestnut oak regeneration hotspots were positively associated with the abundance of chestnut oak trees in the over story and a moderate cover of heather species (Vaccinium and Gaylussacia spp.) but were negatively associated with the abundance of hay scented fern (Dennstaedtia punctilobula) and mountain laurel (Kalmia latiforia). In general, hotspot detection is a viable tool for assisting natural resource managers with identifying areas possessing significantly high or low tree regeneration.

  3. Applying Hotspot Detection Methods in Forestry: A Case Study of Chestnut Oak Regeneration

    Directory of Open Access Journals (Sweden)

    Songlin Fei

    2010-01-01

    Full Text Available Hotspot detection has been widely adopted in health sciences for disease surveillance, but rarely in natural resource disciplines. In this paper, two spatial scan statistics (SaTScan and ClusterSeer and a nonspatial classification and regression trees method were evaluated as techniques for identifying chestnut oak (Quercus Montana regeneration hotspots among 50 mixed-oak stands in the central Appalachian region of the eastern United States. Hotspots defined by the three methods had a moderate level of conformity and revealed similar chestnut oak regeneration site affinity. Chestnut oak regeneration hotspots were positively associated with the abundance of chestnut oak trees in the overstory and a moderate cover of heather species (Vaccinium and Gaylussacia spp. but were negatively associated with the abundance of hayscented fern (Dennstaedtia punctilobula and mountain laurel (Kalmia latiforia. In general, hotspot detection is a viable tool for assisting natural resource managers with identifying areas possessing significantly high or low tree regeneration.

  4. Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts.

    Science.gov (United States)

    Moghimi, Fatemeh Hoda; Cheung, Michael; Wickramasinghe, Nilmini

    2013-01-01

    Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of real time intelligent risk detection decision support systems using predictive analytic techniques such as data mining. To illustrate the power and potential of data science technologies in healthcare decision making scenarios, the use of an intelligent risk detection (IRD) model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes.

  5. A photoacoustic technique applied to detection of ethylene emissions in edible coated passion fruit

    International Nuclear Information System (INIS)

    Alves, G V L; Santos, W C dos; Vargas, H; Silva, M G da; Waldman, W R; Oliveira, J G

    2010-01-01

    Photoacoustic spectroscopy was applied to study the physiological behavior of passion fruit when coated with edible films. The results have shown a reduction of the ethylene emission rate. Weight loss monitoring has not shown any significant differences between the coated and uncoated passion fruit. On the other hand, slower color changes of coated samples suggest a slowdown of the ripening process in coated passion fruit.

  6. Fourier Transform Infrared Radiation Spectroscopy Applied for Wood Rot Decay and Mould Fungi Growth Detection

    OpenAIRE

    Jelle, Bjørn Petter; Hovde, Per Jostein

    2012-01-01

    Material characterization may be carried out by the attenuated total reflectance (ATR) Fourier transform infrared (FTIR) radiation spectroscopical technique, which represents a powerful experimental tool. The ATR technique may be applied on both solid state materials, liquids, and gases with none or only minor sample preparations, also including materials which are nontransparent to IR radiation. This facilitation is made possible by pressing the sample directly onto various crystals, for exa...

  7. Cluster detection methods applied to the Upper Cape Cod cancer data

    Directory of Open Access Journals (Sweden)

    Ozonoff David

    2005-09-01

    Full Text Available Abstract Background A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. Methods We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. Results The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. Conclusion The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.

  8. Robust volcano plot: identification of differential metabolites in the presence of outliers.

    Science.gov (United States)

    Kumar, Nishith; Hoque, Md Aminul; Sugimoto, Masahiro

    2018-04-11

    The identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers. We propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets. Two numerical experiments are used to evaluate the performance of the proposed technique against nine existing techniques, including the t-test and the Kruskal-Wallis test. Artificially generated data with outliers reveal that the proposed method results in a lower misclassification error rate and a greater area under the receiver operating characteristic curve compared with existing methods. An experimentally measured breast cancer dataset to which outliers were artificially added reveals that our proposed method produces only two non-overlapping differential metabolites whereas the other nine methods produced between seven and 57 non-overlapping differential metabolites. Our data analyses show that the performance of the proposed differential metabolite identification technique is better than that of existing methods. Thus, the proposed method can contribute to analysis of metabolomics data with outliers. The R package and user manual of the proposed method are available at https://github.com/nishithkumarpaul/Rvolcano .

  9. Group method of data handling and neral networks applied in monitoring and fault detection in sensors in nuclear power plants

    International Nuclear Information System (INIS)

    Bueno, Elaine Inacio

    2011-01-01

    The increasing demand in the complexity, efficiency and reliability in modern industrial systems stimulated studies on control theory applied to the development of Monitoring and Fault Detection system. In this work a new Monitoring and Fault Detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and Artificial Neural Networks (ANNs) which was applied to the IEA-R1 research reactor at IPEN. The Monitoring and Fault Detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second part to the process information using ANNs. The GMDH algorithm was used in two different ways: firstly, the GMDH algorithm was used to generate a better database estimated, called matrix z , which was used to train the ANNs. After that, the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one Theoretical Model and four Models using different sets of reactor variables. After an exhausting study dedicated to the sensors Monitoring, the Fault Detection in sensors was developed by simulating faults in the sensors database using values of 5%, 10%, 15% and 20% in these sensors database. The results obtained using GMDH algorithm in the choice of the best input variables to the ANNs were better than that using only ANNs, thus making possible the use of these methods in the implementation of a new Monitoring and Fault Detection methodology applied in sensors. (author)

  10. Plasma-treated polyethylene film: A smart material applied for Salmonella Typhimurium detection

    Energy Technology Data Exchange (ETDEWEB)

    Peng-Ubol, Triranat [Department of Chemistry, Faculty of Science, Mahidol University, Rama 6 Rd, Phayathai, Bangkok 10400 (Thailand); Phinyocheep, Pranee, E-mail: scppo@mahidol.ac.th [Department of Chemistry, Faculty of Science, Mahidol University, Rama 6 Rd, Phayathai, Bangkok 10400 (Thailand); Daniel, Philippe [Laboratoire de Physique de l' Etat Condense (LPEC-UMR CNRS 6087), Universite du Maine, Avenue Olivier Messiaen, 72085, Le Mans Cedex 9 (France); Panbangred, Watanalai [Department of Biotechnology and Mahidol University-Osaka University Collaborative Research Center for Bioscience and Biotechnology (MU-OU: CRC), Faculty of Science, Mahidol University, Rama 6 Rd, Phayathai, Bangkok 10400 (Thailand); Pilard, Jean-Francois [Unite de Chimie Organique Moleculaire et Macromoleculaire (UCO2M-UMR CNRS 6011), Universite du Maine, Avenue Olivier Messiaen, 72085 Le Mans Cedex 9 (France); Thouand, Gerald; Durand-Thouand, Marie-Jose [Genie des Procedes Environnement et Agroalimentaire (GEPEA UMR CNRS 6144), Departement Genie Biologique, IUT de la Roche/Yon, Universite de Nantes, 18 Bd G. Defferre, 85035 La Roche sur Yon (France)

    2012-12-01

    Salmonella is a major cause of foodborne illness worldwide and is not allowed to be present in any food in all countries. The purpose of this study is to develop a simple alternative method for the detection of Salmonella based on functionalized polyethylene (PE) surfaces. Salmonella Typhimurium was used as a model bacterium. PE film was treated using dielectric plasma in order to alter the wettability of the PE surface and consequently introduce functionality on the surface. The PE film characterized by ATR-FTIR spectroscopy revealed the presence of C=O stretching of ketones, aldehydes and carboxylic acids. The antibodies against O or H antigens of Salmonella and S. Typhimurium were then respectively immobilized on the PE surface after activation of the carboxylic group using NHS/EDC followed by protein A. The evidences from ATR-FTIR, scanning electron microscopy and optical microscopy showed the presence of S. Typhimurium attached to the plasma treated PE surfaces via the two types of anti-Salmonella antibody. The plasma treated PE film developed is simple and allows efficient association of bacterial cells on the treated surfaces without the necessity of time-consuming centrifugation and washing steps for isolation of the cells. This material is considered to be a smart material applicable for S. Typhimurium detection. Highlights: Black-Right-Pointing-Pointer We developed a functionalized polyethylene film for bacterial detection. Black-Right-Pointing-Pointer We modified the surface of polyethylene film by plasma treatment. Black-Right-Pointing-Pointer ATR-FTIR spectroscopy was used to analyze the functionality on the PE surface. Black-Right-Pointing-Pointer We introduced Salmonella Typhimurium on the modified PE film. Black-Right-Pointing-Pointer SEM revealed the presence of S. Typhimurium on the plasma treated PE film.

  11. Plasma-treated polyethylene film: A smart material applied for Salmonella Typhimurium detection

    International Nuclear Information System (INIS)

    Peng-Ubol, Triranat; Phinyocheep, Pranee; Daniel, Philippe; Panbangred, Watanalai; Pilard, Jean-François; Thouand, Gerald; Durand-Thouand, Marie-José

    2012-01-01

    Salmonella is a major cause of foodborne illness worldwide and is not allowed to be present in any food in all countries. The purpose of this study is to develop a simple alternative method for the detection of Salmonella based on functionalized polyethylene (PE) surfaces. Salmonella Typhimurium was used as a model bacterium. PE film was treated using dielectric plasma in order to alter the wettability of the PE surface and consequently introduce functionality on the surface. The PE film characterized by ATR-FTIR spectroscopy revealed the presence of C=O stretching of ketones, aldehydes and carboxylic acids. The antibodies against O or H antigens of Salmonella and S. Typhimurium were then respectively immobilized on the PE surface after activation of the carboxylic group using NHS/EDC followed by protein A. The evidences from ATR-FTIR, scanning electron microscopy and optical microscopy showed the presence of S. Typhimurium attached to the plasma treated PE surfaces via the two types of anti-Salmonella antibody. The plasma treated PE film developed is simple and allows efficient association of bacterial cells on the treated surfaces without the necessity of time-consuming centrifugation and washing steps for isolation of the cells. This material is considered to be a smart material applicable for S. Typhimurium detection. Highlights: ► We developed a functionalized polyethylene film for bacterial detection. ► We modified the surface of polyethylene film by plasma treatment. ► ATR-FTIR spectroscopy was used to analyze the functionality on the PE surface. ► We introduced Salmonella Typhimurium on the modified PE film. ► SEM revealed the presence of S. Typhimurium on the plasma treated PE film.

  12. Joint Diagonalization Applied to the Detection and Discrimination of Unexploded Ordnance

    Science.gov (United States)

    2012-08-01

    center (Das et al., 1990; Barrow and Nelson, 2001; Bell et al., 2001; Pasion and Oldenburg, 2001; Zhang et al., 2003; Smith and Mor- rison, 2004; Tarokh et...2011.01.007. Moyes, R., R. Lloyd, and R. McGrath, 2002, Explosive remnants of war: Unexploded ordnance and post-conflict communities: Landmine Action. Pasion ...detection and discrimination: Proceedings of SPIE, 7664, 766408, doi: 10.1117/12/850654. Shubitidze, F., J. P. Fernández, I. Shamatava, L. R. Pasion , B. E

  13. A pulse stacking method of particle counting applied to position sensitive detection

    International Nuclear Information System (INIS)

    Basilier, E.

    1976-03-01

    A position sensitive particle counting system is described. A cyclic readout imaging device serves as an intermediate information buffer. Pulses are allowed to stack in the imager at very high counting rates. Imager noise is completely discriminated to provide very wide dynamic range. The system has been applied to a detector using cascaded microchannel plates. Pulse height spread produced by the plates causes some loss of information. The loss is comparable to the input loss of the plates. The improvement in maximum counting rate is several hundred times over previous systems that do not permit pulse stacking. (Auth.)

  14. A positive deviance approach to early childhood obesity: cross-sectional characterization of positive outliers.

    Science.gov (United States)

    Foster, Byron Alexander; Farragher, Jill; Parker, Paige; Hale, Daniel E

    2015-06-01

    Positive deviance methodology has been applied in the developing world to address childhood malnutrition and has potential for application to childhood obesity in the United States. We hypothesized that among children at high-risk for obesity, evaluating normal weight children will enable identification of positive outlier behaviors and practices. In a community at high-risk for obesity, a cross-sectional mixed-methods analysis was done of normal weight, overweight, and obese children, classified by BMI percentile. Parents were interviewed using a semistructured format in regard to their children's general health, feeding and activity practices, and perceptions of weight. Interviews were conducted in 40 homes in the lower Rio Grande Valley in Texas with a largely Hispanic (87.5%) population. Demographics, including income, education, and food assistance use, did not vary between groups. Nearly all (93.8%) parents of normal weight children perceived their child to be lower than the median weight. Group differences were observed for reported juice and yogurt consumption. Differences in both emotional feeding behaviors and parents' internalization of reasons for healthy habits were identified as different between groups. We found subtle variations in reported feeding and activity practices by weight status among healthy children in a population at high risk for obesity. The behaviors and attitudes described were consistent with previous literature; however, the local strategies associated with a healthy weight are novel, potentially providing a basis for a specific intervention in this population.

  15. Color-based scale-invariant feature detection applied in robot vision

    Science.gov (United States)

    Gao, Jian; Huang, Xinhan; Peng, Gang; Wang, Min; Li, Xinde

    2007-11-01

    The scale-invariant feature detecting methods always require a lot of computation yet sometimes still fail to meet the real-time demands in robot vision fields. To solve the problem, a quick method for detecting interest points is presented. To decrease the computation time, the detector selects as interest points those whose scale normalized Laplacian values are the local extrema in the nonholonomic pyramid scale space. The descriptor is built with several subregions, whose width is proportional to the scale factor, and the coordinates of the descriptor are rotated in relation to the interest point orientation just like the SIFT descriptor. The eigenvector is computed in the original color image and the mean values of the normalized color g and b in each subregion are chosen to be the factors of the eigenvector. Compared with the SIFT descriptor, this descriptor's dimension has been reduced evidently, which can simplify the point matching process. The performance of the method is analyzed in theory in this paper and the experimental results have certified its validity too.

  16. A spatial approach of magnitude-squared coherence applied to selective attention detection.

    Science.gov (United States)

    Bonato Felix, Leonardo; de Souza Ranaudo, Fernando; D'affonseca Netto, Aluizio; Ferreira Leite Miranda de Sá, Antonio Mauricio

    2014-05-30

    Auditory selective attention is the human ability of actively focusing in a certain sound stimulus while avoiding all other ones. This ability can be used, for example, in behavioral studies and brain-machine interface. In this work we developed an objective method - called Spatial Coherence - to detect the side where a subject is focusing attention to. This method takes into consideration the Magnitude Squared Coherence and the topographic distribution of responses among electroencephalogram electrodes. The individuals were stimulated with amplitude-modulated tones binaurally and were oriented to focus attention to only one of the stimuli. The results indicate a contralateral modulation of ASSR in the attention condition and are in agreement with prior studies. Furthermore, the best combination of electrodes led to a hit rate of 82% for 5.03 commands per minute. Using a similar paradigm, in a recent work, a maximum hit rate of 84.33% was achieved, but with a greater a classification time (20s, i.e. 3 commands per minute). It seems that Spatial Coherence is a useful technique for detecting focus of auditory selective attention. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Detecting anomalous nuclear materials accounting transactions: Applying machine learning to plutonium processing facilities

    International Nuclear Information System (INIS)

    Vaccaro, H.S.

    1989-01-01

    Nuclear materials accountancy is the only safeguards measure that provides direct evidence of the status of nuclear materials. Of the six categories that gives rise to inventory differences, the technical capability is now in place to implement the technical innovations necessary to reduce the human error categories. There are really three main approaches to detecting anomalies in materials control and accountability (MC ampersand A) data: (1) Statistical: numeric methods such as the Page's Test, CUSUM, CUMUF, SITMUF, etc., can detect anomalies in metric (numeric) data. (2) Expert systems: Human expert's rules can be encoded into software systems such as ART, KEE, or Prolog. (3) Machine learning: Training data, such as historical MC ampersand A records, can be fed to a classifier program or neutral net or other machine learning algorithm. The Wisdom ampersand Sense (W ampersand S) software is a combination of approaches 2 and 3. The W ampersand S program includes full features for adding administrative rules and expert judgment rules to the rule base. if desired, the software can enforce consistency among all rules in the rule base

  18. Image processing applied to automatic detection of defects during ultrasonic examination

    International Nuclear Information System (INIS)

    Moysan, J.

    1992-10-01

    This work is a study about image processing applied to ultrasonic BSCAN images which are obtained in the field of non destructive testing of weld. The goal is to define what image processing techniques can bring to ameliorate the exploitation of the data collected and, more precisely, what image processing can do to extract the meaningful echoes which enable to characterize and to size the defects. The report presents non destructive testing by ultrasounds in the nuclear field and it indicates specificities of the propagation of ultrasonic waves in austenitic weld. It gives a state of the art of the data processing applied to ultrasonic images in nondestructive evaluation. A new image analysis is then developed. It is based on a powerful tool, the co-occurrence matrix. This matrix enables to represent, in a whole representation, relations between amplitudes of couples of pixels. From the matrix analysis, a new complete and automatic method has been set down in order to define a threshold which separates echoes from noise. An automatic interpretation of the ultrasonic echoes is then possible. Complete validation has been done with standard pieces

  19. Functionalized gold nanoparticle supported sensory mechanisms applied in detection of chemical and biological threat agents: A review

    International Nuclear Information System (INIS)

    Upadhyayula, Venkata K.K.

    2012-01-01

    Highlights: ► Smart sensors are needed for detection of chemical and biological threat agents. ► Smart sensors detect analytes with rapid speed, high sensitivity and selectivity. ► Functionalized gold nanoparticles (GNPs) can potentially smart sense threat agents. ► Functionalized GNPs support multiple analytical methods for sensing threat agents. ► Threat agents of all types can be detected using functionalized GNPs. - Abstract: There is a great necessity for development of novel sensory concepts supportive of smart sensing capabilities in defense and homeland security applications for detection of chemical and biological threat agents. A smart sensor is a detection device that can exhibit important features such as speed, sensitivity, selectivity, portability, and more importantly, simplicity in identifying a target analyte. Emerging nanomaterial based sensors, particularly those developed by utilizing functionalized gold nanoparticles (GNPs) as a sensing component potentially offer many desirable features needed for threat agent detection. The sensitiveness of physical properties expressed by GNPs, e.g. color, surface plasmon resonance, electrical conductivity and binding affinity are significantly enhanced when they are subjected to functionalization with an appropriate metal, organic or biomolecular functional groups. This sensitive nature of functionalized GNPs can be potentially exploited in the design of threat agent detection devices with smart sensing capabilities. In the presence of a target analyte (i.e., a chemical or biological threat agent) a change proportional to concentration of the analyte is observed, which can be measured either by colorimetric, fluorimetric, electrochemical or spectroscopic means. This article provides a review of how functionally modified gold colloids are applied in the detection of a broad range of threat agents, including radioactive substances, explosive compounds, chemical warfare agents, biotoxins, and

  20. Functionalized gold nanoparticle supported sensory mechanisms applied in detection of chemical and biological threat agents: A review

    Energy Technology Data Exchange (ETDEWEB)

    Upadhyayula, Venkata K.K., E-mail: Upadhyayula.Venkata@epa.gov [Oak Ridge Institute of Science and Education (ORISE), MC-100-44, PO Box 117, Oak Ridge, TN 37831 (United States)

    2012-02-17

    Highlights: Black-Right-Pointing-Pointer Smart sensors are needed for detection of chemical and biological threat agents. Black-Right-Pointing-Pointer Smart sensors detect analytes with rapid speed, high sensitivity and selectivity. Black-Right-Pointing-Pointer Functionalized gold nanoparticles (GNPs) can potentially smart sense threat agents. Black-Right-Pointing-Pointer Functionalized GNPs support multiple analytical methods for sensing threat agents. Black-Right-Pointing-Pointer Threat agents of all types can be detected using functionalized GNPs. - Abstract: There is a great necessity for development of novel sensory concepts supportive of smart sensing capabilities in defense and homeland security applications for detection of chemical and biological threat agents. A smart sensor is a detection device that can exhibit important features such as speed, sensitivity, selectivity, portability, and more importantly, simplicity in identifying a target analyte. Emerging nanomaterial based sensors, particularly those developed by utilizing functionalized gold nanoparticles (GNPs) as a sensing component potentially offer many desirable features needed for threat agent detection. The sensitiveness of physical properties expressed by GNPs, e.g. color, surface plasmon resonance, electrical conductivity and binding affinity are significantly enhanced when they are subjected to functionalization with an appropriate metal, organic or biomolecular functional groups. This sensitive nature of functionalized GNPs can be potentially exploited in the design of threat agent detection devices with smart sensing capabilities. In the presence of a target analyte (i.e., a chemical or biological threat agent) a change proportional to concentration of the analyte is observed, which can be measured either by colorimetric, fluorimetric, electrochemical or spectroscopic means. This article provides a review of how functionally modified gold colloids are applied in the detection of a broad

  1. Determinants of long-term growth : New results applying robust estimation and extreme bounds analysis

    NARCIS (Netherlands)

    Sturm, J.-E.; de Haan, J.

    2005-01-01

    Two important problems exist in cross-country growth studies: outliers and model uncertainty. Employing Sala-i-Martin's (1997a,b) data set, we first use robust estimation and analyze to what extent outliers influence OLS regressions. We then use both OLS and robust estimation techniques in applying

  2. Applying signal-detection theory to the study of observer accuracy and bias in behavioral assessment.

    Science.gov (United States)

    Lerman, Dorothea C; Tetreault, Allison; Hovanetz, Alyson; Bellaci, Emily; Miller, Jonathan; Karp, Hilary; Mahmood, Angela; Strobel, Maggie; Mullen, Shelley; Keyl, Alice; Toupard, Alexis

    2010-01-01

    We evaluated the feasibility and utility of a laboratory model for examining observer accuracy within the framework of signal-detection theory (SDT). Sixty-one individuals collected data on aggression while viewing videotaped segments of simulated teacher-child interactions. The purpose of Experiment 1 was to determine if brief feedback and contingencies for scoring accurately would bias responding reliably. Experiment 2 focused on one variable (specificity of the operational definition) that we hypothesized might decrease the likelihood of bias. The effects of social consequences and information about expected behavior change were examined in Experiment 3. Results indicated that feedback and contingencies reliably biased responding and that the clarity of the definition only moderately affected this outcome.

  3. Early detection of poor adherers to statins: applying individualized surveillance to pay for performance.

    Directory of Open Access Journals (Sweden)

    Andrew J Zimolzak

    Full Text Available Medication nonadherence costs $300 billion annually in the US. Medicare Advantage plans have a financial incentive to increase medication adherence among members because the Centers for Medicare and Medicaid Services (CMS now awards substantive bonus payments to such plans, based in part on population adherence to chronic medications. We sought to build an individualized surveillance model that detects early which beneficiaries will fall below the CMS adherence threshold.This was a retrospective study of over 210,000 beneficiaries initiating statins, in a database of private insurance claims, from 2008-2011. A logistic regression model was constructed to use statin adherence from initiation to day 90 to predict beneficiaries who would not meet the CMS measure of proportion of days covered 0.8 or above, from day 91 to 365. The model controlled for 15 additional characteristics. In a sensitivity analysis, we varied the number of days of adherence data used for prediction.Lower adherence in the first 90 days was the strongest predictor of one-year nonadherence, with an odds ratio of 25.0 (95% confidence interval 23.7-26.5 for poor adherence at one year. The model had an area under the receiver operating characteristic curve of 0.80. Sensitivity analysis revealed that predictions of comparable accuracy could be made only 40 days after statin initiation. When members with 30-day supplies for their first statin fill had predictions made at 40 days, and members with 90-day supplies for their first fill had predictions made at 100 days, poor adherence could be predicted with 86% positive predictive value.To preserve their Medicare Star ratings, plan managers should identify or develop effective programs to improve adherence. An individualized surveillance approach can be used to target members who would most benefit, recognizing the tradeoff between improved model performance over time and the advantage of earlier detection.

  4. Outlier Removal and the Relation with Reporting Errors and Quality of Psychological Research

    Science.gov (United States)

    Bakker, Marjan; Wicherts, Jelte M.

    2014-01-01

    Background The removal of outliers to acquire a significant result is a questionable research practice that appears to be commonly used in psychology. In this study, we investigated whether the removal of outliers in psychology papers is related to weaker evidence (against the null hypothesis of no effect), a higher prevalence of reporting errors, and smaller sample sizes in these papers compared to papers in the same journals that did not report the exclusion of outliers from the analyses. Methods and Findings We retrieved a total of 2667 statistical results of null hypothesis significance tests from 153 articles in main psychology journals, and compared results from articles in which outliers were removed (N = 92) with results from articles that reported no exclusion of outliers (N = 61). We preregistered our hypotheses and methods and analyzed the data at the level of articles. Results show no significant difference between the two types of articles in median p value, sample sizes, or prevalence of all reporting errors, large reporting errors, and reporting errors that concerned the statistical significance. However, we did find a discrepancy between the reported degrees of freedom of t tests and the reported sample size in 41% of articles that did not report removal of any data values. This suggests common failure to report data exclusions (or missingness) in psychological articles. Conclusions We failed to find that the removal of outliers from the analysis in psychological articles was related to weaker evidence (against the null hypothesis of no effect), sample size, or the prevalence of errors. However, our control sample might be contaminated due to nondisclosure of excluded values in articles that did not report exclusion of outliers. Results therefore highlight the importance of more transparent reporting of statistical analyses. PMID:25072606

  5. 'Intelligent' triggering methodology for improved detectability of wavelength modulation diode laser absorption spectrometry applied to window-equipped graphite furnaces

    International Nuclear Information System (INIS)

    Gustafsson, Joergen; Axner, Ove

    2003-01-01

    The wavelength modulation-diode laser absorption spectrometry (WM-DLAS) technique experiences a limited detectability when window-equipped sample compartments are used because of multiple reflections between components in the optical system (so-called etalon effects). The problem is particularly severe when the technique is used with a window-equipped graphite furnace (GF) as atomizer since the heating of the furnace induces drifts of the thickness of the windows and thereby also of the background signals. This paper presents a new detection methodology for WM-DLAS applied to a window-equipped GF in which the influence of the background signals from the windows is significantly reduced. The new technique, which is based upon a finding that the WM-DLAS background signals from a window-equipped GF are reproducible over a considerable period of time, consists of a novel 'intelligent' triggering procedure in which the GF is triggered at a user-chosen 'position' in the reproducible drift-cycle of the WM-DLAS background signal. The new methodology makes also use of 'higher-than-normal' detection harmonics, i.e. 4f or 6f, since these previously have shown to have a higher signal-to-background ratio than 2f-detection when the background signals originates from thin etalons. The results show that this new combined background-drift-reducing methodology improves the limit of detection of the WM-DLAS technique used with a window-equipped GF by several orders of magnitude as compared to ordinary 2f-detection, resulting in a limit of detection for a window-equipped GF that is similar to that of an open GF

  6. Perceptual thresholds for detecting modifications applied to the acoustical properties of a violin.

    Science.gov (United States)

    Fritz, Claudia; Cross, Ian; Moore, Brian C J; Woodhouse, Jim

    2007-12-01

    This study is the first step in the psychoacoustic exploration of perceptual differences between the sounds of different violins. A method was used which enabled the same performance to be replayed on different "virtual violins," so that the relationships between acoustical characteristics of violins and perceived qualities could be explored. Recordings of real performances were made using a bridge-mounted force transducer, giving an accurate representation of the signal from the violin string. These were then played through filters corresponding to the admittance curves of different violins. Initially, limits of listener performance in detecting changes in acoustical characteristics were characterized. These consisted of shifts in frequency or increases in amplitude of single modes or frequency bands that have been proposed previously to be significant in the perception of violin sound quality. Thresholds were significantly lower for musically trained than for nontrained subjects but were not significantly affected by the violin used as a baseline. Thresholds for the musicians typically ranged from 3 to 6 dB for amplitude changes and 1.5%-20% for frequency changes. Interpretation of the results using excitation patterns showed that thresholds for the best subjects were quite well predicted by a multichannel model based on optimal processing.

  7. Applying Fourier Transform Mid Infrared Spectroscopy to Detect the Adulteration of Salmo salar with Oncorhynchus mykiss

    Science.gov (United States)

    Moreira, Maria João

    2018-01-01

    The aim of this study was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy coupled with chemometric methods to detect fish adulteration. Muscles of Atlantic salmon (Salmo salar) (SS) and Salmon trout (Onconrhynchus mykiss) (OM) muscles were mixed in different percentages and transformed into mini-burgers. These were stored at 3 °C, then examined at 0, 72, 160, and 240 h for deteriorative microorganisms. Mini-burgers was submitted to Soxhlet extraction, following which lipid extracts were analyzed by FTIR. The principal component analysis (PCA) described the studied adulteration using four principal components with an explained variance of 95.60%. PCA showed that the absorbance in the spectral region from 721, 1097, 1370, 1464, 1655, 2805, to 2935, 3009 cm−1 may be attributed to biochemical fingerprints related to differences between SS and OM. The partial least squares regression (PLS-R) predicted the presence/absence of adulteration in fish samples of an external set with high accuracy. The proposed methods have the advantage of allowing quick measurements, despite the storage time of the adulterated fish. FTIR combined with chemometrics showed that a methodology to identify the adulteration of SS with OM can be established, even when stored for different periods of time. PMID:29621135

  8. A color spectrographic phonocardiography (CSP applied to the detection and characterization of heart murmurs: preliminary results

    Directory of Open Access Journals (Sweden)

    Hassani Kamran

    2011-05-01

    Full Text Available Abstract Background Although cardiac auscultation remains important to detect abnormal sounds and murmurs indicative of cardiac pathology, the application of electronic methods remains seldom used in everyday clinical practice. In this report we provide preliminary data showing how the phonocardiogram can be analyzed using color spectrographic techniques and discuss how such information may be of future value for noninvasive cardiac monitoring. Methods We digitally recorded the phonocardiogram using a high-speed USB interface and the program Gold Wave http://www.goldwave.com in 55 infants and adults with cardiac structural disease as well as from normal individuals and individuals with innocent murmurs. Color spectrographic analysis of the signal was performed using Spectrogram (Version 16 as a well as custom MATLAB code. Results Our preliminary data is presented as a series of seven cases. Conclusions We expect the application of spectrographic techniques to phonocardiography to grow substantially as ongoing research demonstrates its utility in various clinical settings. Our evaluation of a simple, low-cost phonocardiographic recording and analysis system to assist in determining the characteristic features of heart murmurs shows promise in helping distinguish innocent systolic murmurs from pathological murmurs in children and is expected to useful in other clinical settings as well.

  9. Solvent-accessible surface area: How well can be applied to hot-spot detection?

    Science.gov (United States)

    Martins, João M; Ramos, Rui M; Pimenta, António C; Moreira, Irina S

    2014-03-01

    A detailed comprehension of protein-based interfaces is essential for the rational drug development. One of the key features of these interfaces is their solvent accessible surface area profile. With that in mind, we tested a group of 12 SASA-based features for their ability to correlate and differentiate hot- and null-spots. These were tested in three different data sets, explicit water MD, implicit water MD, and static PDB structure. We found no discernible improvement with the use of more comprehensive data sets obtained from molecular dynamics. The features tested were shown to be capable of discerning between hot- and null-spots, while presenting low correlations. Residue standardization such as rel SASAi or rel/res SASAi , improved the features as a tool to predict ΔΔGbinding values. A new method using support machine learning algorithms was developed: SBHD (Sasa-Based Hot-spot Detection). This method presents a precision, recall, and F1 score of 0.72, 0.81, and 0.76 for the training set and 0.91, 0.73, and 0.81 for an independent test set. Copyright © 2013 Wiley Periodicals, Inc.

  10. Time integration and statistical regulation applied to mobile objects detection in a sequence of images

    International Nuclear Information System (INIS)

    Letang, Jean-Michel

    1993-01-01

    This PhD thesis deals with the detection of moving objects in monocular image sequences. The first section presents the inherent problems of motion analysis in real applications. We propose a method robust to perturbations frequently encountered during acquisition of outdoor scenes. It appears three main directions for investigations, all of them pointing out the importance of the temporal axis, which is a specific dimension for motion analysis. In the first part, the image sequence is considered as a set of temporal signals. The temporal multi-scale decomposition enables the characterization of various dynamical behaviors of the objects being in the scene at a given instant. A second module integrates motion information. This elementary trajectography of moving objects provides a temporal prediction map, giving a confidence level of motion presence. Interactions between both sets of data are expressed within a statistical regularization. Markov random field models supply a formal framework to convey a priori knowledge of the primitives to be evaluated. A calibration method with qualitative boxes is presented to estimate model parameters. Our approach requires only simple computations and leads to a rather fast algorithm, that we evaluate in the last section over various typical sequences. (author) [fr

  11. Corrected Integral Shape Averaging Applied to Obstructive Sleep Apnea Detection from the Electrocardiogram

    Directory of Open Access Journals (Sweden)

    C. O'Brien

    2007-01-01

    Full Text Available We present a technique called corrected integral shape averaging (CISA for quantifying shape and shape differences in a set of signals. CISA can be used to account for signal differences which are purely due to affine time warping (jitter and dilation/compression, and hence provide access to intrinsic shape fluctuations. CISA can also be used to define a distance between shapes which has useful mathematical properties; a mean shape signal for a set of signals can be defined, which minimizes the sum of squared shape distances of the set from the mean. The CISA procedure also allows joint estimation of the affine time parameters. Numerical simulations are presented to support the algorithm for obtaining the CISA mean and parameters. Since CISA provides a well-defined shape distance, it can be used in shape clustering applications based on distance measures such as k-means. We present an application in which CISA shape clustering is applied to P-waves extracted from the electrocardiogram of subjects suffering from sleep apnea. The resulting shape clustering distinguishes ECG segments recorded during apnea from those recorded during normal breathing with a sensitivity of 81% and specificity of 84%.

  12. Radioautography and fluorography applied to the detection of radioactive compounds separated by electrophoresis and chromatography

    International Nuclear Information System (INIS)

    Simonnet, Gerard; Combe, Jose

    1976-01-01

    Radioautography permits the location of radioactive compounds on a wide variety of supporting media after electrophoresis or chromatography: paper, a thin layer of silica gel or polyacrylamide gel. Latent images are obtained by applying an appropriate photographic film against the gel or paper or plaque in question and leaving them for a sufficient time. The latent image is then rendered visible by standard photographic development, which results in black spots on the film corresponding to radioactive regions on the support. The use of a particular radioactive tracer implies the use of electrophoresis and chromatography in order to control the radiochemical purity of the product, and thus the validity of the results obtained. Radiolysis products, arising from chemical degradation of the product provoked by the radiation emitted, are impurities which assume a greater importance with increasing specific radioactivities. In the case of 3 H-thymidine of specific activity greater than 5 or 10 mCi/mmole, for example, the incidence of radiolysis is such that after two months of storage the product is totally inutilisable

  13. Applying post classification change detection technique to monitor an Egyptian coastal zone (Abu Qir Bay

    Directory of Open Access Journals (Sweden)

    Mamdouh M. El-Hattab

    2016-06-01

    Full Text Available Land cover changes considered as one of the important global phenomena exerting perhaps one of the most significant effects on the environment than any other factor. It is, therefore, vital that accurate data on land cover changes are made available to facilitate the understanding of the link between land cover changes and environmental changes to allow planners to make effective decisions. In this paper, the post classification approach was used to detect and assess land cover changes of one of the important coastal zones in Egypt, Abu Qir Bay zone, based on the comparative analysis of independently produced classification images of the same area at different dates. In addition to satellite images, socioeconomic data were used with the aid of land use model EGSLR to indicate relation between land cover and land use changes. Results indicated that changes in different land covers reflected the changes in occupation status in specific zones. For example, in the south of Idku Lake zone, it was observed that the occupation of settlers changed from being unskilled workers to fishermen based on the expansion of the area of fish farms. Change rates increased dramatically in the period from 2004 to 2013 as remarkable negative changes were found especially in fruits and palm trees (i.e. loss of about 66 km2 of land having fruits and palm trees due to industrialization in the coastal area. Also, a rapid urbanization was monitored along the coastline of Abu Qir Bay zone due to the political conditions in Egypt (25th of January Revolution within this period and which resulted to the temporary absence of monitoring systems to regulate urbanization.

  14. Applying UV cameras for SO2 detection to distant or optically thick volcanic plumes

    Science.gov (United States)

    Kern, Christoph; Werner, Cynthia; Elias, Tamar; Sutton, A. Jeff; Lübcke, Peter

    2013-01-01

    Ultraviolet (UV) camera systems represent an exciting new technology for measuring two dimensional sulfur dioxide (SO2) distributions in volcanic plumes. The high frame rate of the cameras allows the retrieval of SO2 emission rates at time scales of 1 Hz or higher, thus allowing the investigation of high-frequency signals and making integrated and comparative studies with other high-data-rate volcano monitoring techniques possible. One drawback of the technique, however, is the limited spectral information recorded by the imaging systems. Here, a framework for simulating the sensitivity of UV cameras to various SO2 distributions is introduced. Both the wavelength-dependent transmittance of the optical imaging system and the radiative transfer in the atmosphere are modeled. The framework is then applied to study the behavior of different optical setups and used to simulate the response of these instruments to volcanic plumes containing varying SO2 and aerosol abundances located at various distances from the sensor. Results show that UV radiative transfer in and around distant and/or optically thick plumes typically leads to a lower sensitivity to SO2 than expected when assuming a standard Beer–Lambert absorption model. Furthermore, camera response is often non-linear in SO2 and dependent on distance to the plume and plume aerosol optical thickness and single scatter albedo. The model results are compared with camera measurements made at Kilauea Volcano (Hawaii) and a method for integrating moderate resolution differential optical absorption spectroscopy data with UV imagery to retrieve improved SO2 column densities is discussed.

  15. Advanced nondestructive techniques applied for the detection of discontinuities in aluminum foams

    Science.gov (United States)

    Katchadjian, Pablo; García, Alejandro; Brizuela, Jose; Camacho, Jorge; Chiné, Bruno; Mussi, Valerio; Britto, Ivan

    2018-04-01

    Metal foams are finding an increasing range of applications by their lightweight structure and physical, chemical and mechanical properties. Foams can be used to fill closed moulds for manufacturing structural foam parts of complex shape [1]; foam filled structures are expected to provide good mechanical properties and energy absorption capabilities. The complexity of the foaming process and the number of parameters to simultaneously control, demand a preliminary and hugely wide experimental activity to manufacture foamed components with a good quality. That is why there are many efforts to improve the structure of foams, in order to obtain a product with good properties. The problem is that even for seemingly identical foaming conditions, the effective foaming can vary significantly from one foaming trial to another. The variation of the foams often is related by structural imperfections, joining region (foam-foam or foam-wall mold) or difficulties in achieving a complete filling of the mould. That is, in a closed mold, the result of the mold filling and its structure or defects are not known a priori and can eventually vary significantly. These defects can cause a drastic deterioration of the mechanical properties [2] and lead to a low performance in its application. This work proposes the use of advanced nondestructive techniques for evaluating the foam distribution after filling the mold to improve the manufacturing process. To achieved this purpose ultrasonic technique (UT) and cone beam computed tomography (CT) were applied on plate and structures of different thicknesses filled with foam of different porosity. UT was carried out on transmission mode with low frequency air-coupled transducers [3], in focused and unfocused configurations.

  16. Pathway-based outlier method reveals heterogeneous genomic structure of autism in blood transcriptome.

    Science.gov (United States)

    Campbell, Malcolm G; Kohane, Isaac S; Kong, Sek Won

    2013-09-24

    Decades of research strongly suggest that the genetic etiology of autism spectrum disorders (ASDs) is heterogeneous. However, most published studies focus on group differences between cases and controls. In contrast, we hypothesized that the heterogeneity of the disorder could be characterized by identifying pathways for which individuals are outliers rather than pathways representative of shared group differences of the ASD diagnosis. Two previously published blood gene expression data sets--the Translational Genetics Research Institute (TGen) dataset (70 cases and 60 unrelated controls) and the Simons Simplex Consortium (Simons) dataset (221 probands and 191 unaffected family members)--were analyzed. All individuals of each dataset were projected to biological pathways, and each sample's Mahalanobis distance from a pooled centroid was calculated to compare the number of case and control outliers for each pathway. Analysis of a set of blood gene expression profiles from 70 ASD and 60 unrelated controls revealed three pathways whose outliers were significantly overrepresented in the ASD cases: neuron development including axonogenesis and neurite development (29% of ASD, 3% of control), nitric oxide signaling (29%, 3%), and skeletal development (27%, 3%). Overall, 50% of cases and 8% of controls were outliers in one of these three pathways, which could not be identified using group comparison or gene-level outlier methods. In an independently collected data set consisting of 221 ASD and 191 unaffected family members, outliers in the neurogenesis pathway were heavily biased towards cases (20.8% of ASD, 12.0% of control). Interestingly, neurogenesis outliers were more common among unaffected family members (Simons) than unrelated controls (TGen), but the statistical significance of this effect was marginal (Chi squared P < 0.09). Unlike group difference approaches, our analysis identified the samples within the case and control groups that manifested each expression

  17. A collaborative computing framework of cloud network and WBSN applied to fall detection and 3-D motion reconstruction.

    Science.gov (United States)

    Lai, Chin-Feng; Chen, Min; Pan, Jeng-Shyang; Youn, Chan-Hyun; Chao, Han-Chieh

    2014-03-01

    As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

  18. Global Disease Detection-Achievements in Applied Public Health Research, Capacity Building, and Public Health Diplomacy, 2001-2016.

    Science.gov (United States)

    Rao, Carol Y; Goryoka, Grace W; Henao, Olga L; Clarke, Kevin R; Salyer, Stephanie J; Montgomery, Joel M

    2017-11-01

    The Centers for Disease Control and Prevention has established 10 Global Disease Detection (GDD) Program regional centers around the world that serve as centers of excellence for public health research on emerging and reemerging infectious diseases. The core activities of the GDD Program focus on applied public health research, surveillance, laboratory, public health informatics, and technical capacity building. During 2015-2016, program staff conducted 205 discrete projects on a range of topics, including acute respiratory illnesses, health systems strengthening, infectious diseases at the human-animal interface, and emerging infectious diseases. Projects incorporated multiple core activities, with technical capacity building being most prevalent. Collaborating with host countries to implement such projects promotes public health diplomacy. The GDD Program continues to work with countries to strengthen core capacities so that emerging diseases can be detected and stopped faster and closer to the source, thereby enhancing global health security.

  19. A study of outliers in statistical distributions of mechanical properties of structural steels

    International Nuclear Information System (INIS)

    Oefverbeck, P.; Oestberg, G.

    1977-01-01

    The safety against failure of pressure vessels can be assessed by statistical methods, so-called probabilistic fracture mechanics. The data base for such estimations is admittedly rather meagre, making it necessary to assume certain conventional statistical distributions. Since the failure rates arrived at are low, for nuclear vessels of the order of 10 - to 10 - per year, the extremes of the variables involved, among other things the mechanical properties of the steel used, are of particular interest. A question sometimes raised is whether outliers, or values exceeding the extremes in the assumed distributions, might occur. In order to explore this possibility a study has been made of strength values of three qualities of structural steels, available in samples of up to about 12,000. Statistical evaluation of these samples with respect to outliers, using standard methods for this purpose, revealed the presence of such outliers in most cases, with a frequency of occurrence of, typically, a few values per thousand, estimated by the methods described. Obviously, statistical analysis alone cannot be expected to shed any light on the causes of outliers. Thus, the interpretation of these results with respect to their implication for the probabilistic estimation of the integrety of pressure vessels must await further studies of a similar nature in which the test specimens corresponding to outliers can be recovered and examined metallographically. For the moment the results should be regarded only as a factor to be considered in discussions of the safety of pressure vessels. (author)

  20. Identification of Outliers in Grace Data for Indo-Gangetic Plain Using Various Methods (Z-Score, Modified Z-score and Adjusted Boxplot) and Its Removal

    Science.gov (United States)

    Srivastava, S.

    2015-12-01

    Gravity Recovery and Climate Experiment (GRACE) data are widely used for the hydrological studies for large scale basins (≥100,000 sq km). GRACE data (Stokes Coefficients or Equivalent Water Height) used for hydrological studies are not direct observations but result from high level processing of raw data from the GRACE mission. Different partner agencies like CSR, GFZ and JPL implement their own methodology and their processing methods are independent from each other. The primary source of errors in GRACE data are due to measurement and modeling errors and the processing strategy of these agencies. Because of different processing methods, the final data from all the partner agencies are inconsistent with each other at some epoch. GRACE data provide spatio-temporal variations in Earth's gravity which is mainly attributed to the seasonal fluctuations in water level on Earth surfaces and subsurface. During the quantification of error/uncertainties, several high positive and negative peaks were observed which do not correspond to any hydrological processes but may emanate from a combination of primary error sources, or some other geophysical processes (e.g. Earthquakes, landslide, etc.) resulting in redistribution of earth's mass. Such peaks can be considered as outliers for hydrological studies. In this work, an algorithm has been designed to extract outliers from the GRACE data for Indo-Gangetic plain, which considers the seasonal variations and the trend in data. Different outlier detection methods have been used such as Z-score, modified Z-score and adjusted boxplot. For verification, assimilated hydrological (GLDAS) and hydro-meteorological data are used as the reference. The results have shown that the consistency amongst all data sets improved significantly after the removal of outliers.

  1. Screening mammography-detected cancers: the sensitivity of the computer-aided detection system as applied to full-field digital mammography

    International Nuclear Information System (INIS)

    Yang, Sang Kyu; Cho, Nariya; Ko, Eun Sook; Kim, Do Yeon; Moon, Woo Kyung

    2006-01-01

    We wanted to evaluate the sensitivity of the computer-aided detection (CAD) system for performing full-field digital mammography (FFDM) on the breast cancers that were originally detected by screening mammography. The CAD system (Image Checker v3.1, R2 Technology, Los Altos, Calif.) together with a full-field digital mammography system (Senographe 2000D, GE Medical Systems, Buc, France) was prospectively applied to the mammograms of 70 mammographically detected breast cancer patients (age range, 37-69; median age, 51 years) who had negative findings on their clinical examinations. The sensitivity of the CAD system, according to histopathologic findings and radiologic primary features (i.e, mass, microcalcifications or mass with microcalcifications) and also the false-positive marking rate were then determined. The CAD system correctly depicted 67 of 70 breast cancer lesions (97.5%). The CAD system marked 29 of 30 breast cancers that presented with microcalcifications only (sensitivity 96.7%) and all 18 breast cancers the presented with mass together with microcalcifications (sensitivity 100%). Twenty of the 22 lesions that appeared as a mass only were marked correctly by the CAD system (sensitivity 90.9%). The CAD system correctly depicted all 22 lesions of ductal carcinoma in situ (sensitivity: 100%), all 13 lesions of invasive ductal carcinoma with ductal carcinoma in situ (sensitivity: 100%) and the 1 lesion of invasive lobular carcinoma (sensitivity: 100%). Thirty one of the 34 lesions of invasive ductal carcinoma were marked correctly by the CAD system (sensitivity: 91.8%). The rate of false-positive marks was 0.21 mass marks per image and 0.16 microcalcification marks per image. The overall rate of false-positive marks was 0.37 per image. The CAD system using FFDM is useful for the detection of asymptomatic breast cancers, and it has a high overall tumor detection rate. The false negative cases were found in relatively small invasive ductal carcinoma

  2. Screening mammography-detected cancers: the sensitivity of the computer-aided detection system as applied to full-field digital mammography

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Sang Kyu; Cho, Nariya; Ko, Eun Sook; Kim, Do Yeon; Moon, Woo Kyung [College of Medicine Seoul National University and The Insititute of Radiation Medicine, Seoul National University Research Center, Seoul (Korea, Republic of)

    2006-04-15

    We wanted to evaluate the sensitivity of the computer-aided detection (CAD) system for performing full-field digital mammography (FFDM) on the breast cancers that were originally detected by screening mammography. The CAD system (Image Checker v3.1, R2 Technology, Los Altos, Calif.) together with a full-field digital mammography system (Senographe 2000D, GE Medical Systems, Buc, France) was prospectively applied to the mammograms of 70 mammographically detected breast cancer patients (age range, 37-69; median age, 51 years) who had negative findings on their clinical examinations. The sensitivity of the CAD system, according to histopathologic findings and radiologic primary features (i.e, mass, microcalcifications or mass with microcalcifications) and also the false-positive marking rate were then determined. The CAD system correctly depicted 67 of 70 breast cancer lesions (97.5%). The CAD system marked 29 of 30 breast cancers that presented with microcalcifications only (sensitivity 96.7%) and all 18 breast cancers the presented with mass together with microcalcifications (sensitivity 100%). Twenty of the 22 lesions that appeared as a mass only were marked correctly by the CAD system (sensitivity 90.9%). The CAD system correctly depicted all 22 lesions of ductal carcinoma in situ (sensitivity: 100%), all 13 lesions of invasive ductal carcinoma with ductal carcinoma in situ (sensitivity: 100%) and the 1 lesion of invasive lobular carcinoma (sensitivity: 100%). Thirty one of the 34 lesions of invasive ductal carcinoma were marked correctly by the CAD system (sensitivity: 91.8%). The rate of false-positive marks was 0.21 mass marks per image and 0.16 microcalcification marks per image. The overall rate of false-positive marks was 0.37 per image. The CAD system using FFDM is useful for the detection of asymptomatic breast cancers, and it has a high overall tumor detection rate. The false negative cases were found in relatively small invasive ductal carcinoma.

  3. A pilot study of dentists' assessment of caries detection and staging systems applied to early caries: PEARL Network findings.

    Science.gov (United States)

    Thompson, Van P; Schenkel, Andrew B; Penugonda, Bapanaiah; Wolff, Mark S; Zeller, Gregory G; Wu, Hongyu; Vena, Don; Grill, Ashley C; Curro, Frederick A

    2016-01-01

    The International Caries Detection and Assessment System (ICDAS II) and the Caries Classification System (CCS) are caries stage description systems proposed for adoption into clinical practice. This pilot study investigated clinicians' training in and use of these systems for detection of early caries and recommendations for individual tooth treatment. Patient participants (N = 8) with a range of noncavitated lesions (CCS ranks 2 and 4 and ICDAS II ranks 2-4) identified by a team of calibrated examiners were recruited from the New York University College of Dentistry clinic. Eighteen dentists-8 from the Practitioners Engaged in Applied Research and Learning (PEARL) Network and 10 recruited from the Academy of General Dentistry-were randomly assigned to 1 of 3 groups: 5 dentists used only visual-tactile (VT) examination, 7 were trained in the ICDAS II, and 6 were trained in the CCS. Lesion stage for each tooth was determined by the ICDAS II and CCS groups, and recommended treatment was decided by all groups. Teeth were assessed both with and without radiographs. Caries was detected in 92.7% (95% CI, 88%-96%) of the teeth by dentists with CCS training, 88.8% (95% CI, 84%-92%) of the teeth by those with ICDAS II training, and 62.3% (95% CI, 55%-69%) of teeth by the VT group. Web-based training was acceptable to all dentists in the CCS group (6 of 6) but fewer of the dentists in the ICDAS II group (5 of 7). The modified CCS translated clinically to more accurate caries detection, particularly compared to detection by untrained dentists (VT group). Moreover, the CCS was more accepted than was the ICDAS II, but dentists in both groups were open to the application of these systems. Agreement on caries staging requires additional training prior to a larger validation study.

  4. Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk

    Science.gov (United States)

    Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Patel, Bhavika; Heidari, Morteza; Liu, Hong; Zheng, Bin

    2018-05-01

    This study aims to investigate the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to help predict short-term breast cancer risk. An image dataset including four view mammograms acquired from 1044 women was retrospectively assembled. All mammograms were originally interpreted as negative by radiologists. In the next subsequent mammography screening, 402 women were diagnosed with breast cancer and 642 remained negative. An existing CAD scheme was applied ‘as is’ to process each image. From CAD-generated results, four detection features including the total number of (1) initial detection seeds and (2) the final detected false-positive regions, (3) average and (4) sum of detection scores, were computed from each image. Then, by combining the features computed from two bilateral images of left and right breasts from either craniocaudal or mediolateral oblique view, two logistic regression models were trained and tested using a leave-one-case-out cross-validation method to predict the likelihood of each testing case being positive in the next subsequent screening. The new prediction model yielded the maximum prediction accuracy with an area under a ROC curve of AUC  =  0.65  ±  0.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of (2.95, 6.83). The results also showed an increasing trend in the adjusted odds ratio and risk prediction scores (p  breast cancer risk.

  5. A robust ridge regression approach in the presence of both multicollinearity and outliers in the data

    Science.gov (United States)

    Shariff, Nurul Sima Mohamad; Ferdaos, Nur Aqilah

    2017-08-01

    Multicollinearity often leads to inconsistent and unreliable parameter estimates in regression analysis. This situation will be more severe in the presence of outliers it will cause fatter tails in the error distributions than the normal distributions. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is expected to be affected by the presence of outliers due to some assumptions imposed in the modeling procedure. Thus, the robust version of existing ridge method with some modification in the inverse matrix and the estimated response value is introduced. The performance of the proposed method is discussed and comparisons are made with several existing estimators namely, Ordinary Least Squares (OLS), ridge regression and robust ridge regression based on GM-estimates. The finding of this study is able to produce reliable parameter estimates in the presence of both multicollinearity and outliers in the data.

  6. Preliminary studies on DNA retardation by MutS applied to the detection of point mutations in clinical samples

    International Nuclear Information System (INIS)

    Stanislawska-Sachadyn, Anna; Paszko, Zygmunt; Kluska, Anna; Skasko, Elzibieta; Sromek, Maria; Balabas, Aneta; Janiec-Jankowska, Aneta; Wisniewska, Alicja; Kur, Jozef; Sachadyn, Pawel

    2005-01-01

    MutS ability to bind DNA mismatches was applied to the detection of point mutations in PCR products. MutS recognized mismatches from single up to five nucleotides and retarded the electrophoretic migration of mismatched DNA. The electrophoretic detection of insertions/deletions above three nucleotides is also possible without MutS, thanks to the DNA mobility shift caused by the presence of large insertion/deletion loops in the heteroduplex DNA. Thus, the method enables the search for a broad range of mutations: from single up to several nucleotides. The mobility shift assays were carried out in polyacrylamide gels stained with SYBR-Gold. One assay required 50-200 ng of PCR product and 1-3 μg of Thermus thermophilus his 6 -MutS protein. The advantages of this approach are: the small amounts of DNA required for the examination, simple and fast staining, no demand for PCR product purification, no labelling and radioisotopes required. The method was tested in the detection of cancer predisposing mutations in RET, hMSH2, hMLH1, BRCA1, BRCA2 and NBS1 genes. The approach appears to be promising in screening for unknown point mutations

  7. Leak detection in the primary reactor coolant piping of nuclear power plant by applying beam-microphone technology

    International Nuclear Information System (INIS)

    Kasai, Yoshimitsu; Shimanskiy, Sergey; Naoi, Yosuke; Kanazawa, Junichi

    2004-01-01

    A microphone leak detection method was applied to the inlet piping of the ATR-prototype reactor, Fugen. Statistical analysis results showed that the cross-correlation method provided the effective results for detection of a small leakage. However, such a technique has limited application due to significant distortion of the signals on the reactor site. As one of the alternative methods, the beam-microphone provides necessary spatial selectivity and its performance is less affected by signal distortion. A prototype of the beam-microphone was developed and then tested at the O-arai Engineering Center of the Japan Nuclear Cycle Development Institute (JNC). On-site testing of the beam-microphone was carried out in the inlet piping room of an RBMK reactor of the Leningrad Nuclear Power Plant (LNPP) in Russia. A leak sound imitator was used to simulate the leakage sound under the leakage flow condition of 1-3 gpm (0.23-0.7 m 3 /h). Analysis showed that signal distortion does not seriously affect the performance of this method, and that sound reflection may result in the appearance of ghost sound sources. The test results showed that the influences of sound reflection and background noise were smaller at the high frequencies where the leakage location could be estimated with an angular accuracy of 5deg which is the range of localization accuracy required for the leak detection system. (author)

  8. Fetal cardiac cine imaging using highly accelerated dynamic MRI with retrospective motion correction and outlier rejection.

    Science.gov (United States)

    van Amerom, Joshua F P; Lloyd, David F A; Price, Anthony N; Kuklisova Murgasova, Maria; Aljabar, Paul; Malik, Shaihan J; Lohezic, Maelene; Rutherford, Mary A; Pushparajah, Kuberan; Razavi, Reza; Hajnal, Joseph V

    2018-01-01

    Development of a MRI acquisition and reconstruction strategy to depict fetal cardiac anatomy in the presence of maternal and fetal motion. The proposed strategy involves i) acquisition and reconstruction of highly accelerated dynamic MRI, followed by image-based ii) cardiac synchronization, iii) motion correction, iv) outlier rejection, and finally v) cardiac cine reconstruction. Postprocessing entirely was automated, aside from a user-defined region of interest delineating the fetal heart. The method was evaluated in 30 mid- to late gestational age singleton pregnancies scanned without maternal breath-hold. The combination of complementary acquisition/reconstruction and correction/rejection steps in the pipeline served to improve the quality of the reconstructed 2D cine images, resulting in increased visibility of small, dynamic anatomical features. Artifact-free cine images successfully were produced in 36 of 39 acquired data sets; prolonged general fetal movements precluded processing of the remaining three data sets. The proposed method shows promise as a motion-tolerant framework to enable further detail in MRI studies of the fetal heart and great vessels. Processing data in image-space allowed for spatial and temporal operations to be applied to the fetal heart in isolation, separate from extraneous changes elsewhere in the field of view. Magn Reson Med 79:327-338, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

  9. Transmission and signal loss in mask designs for a dual neutron and gamma imager applied to mobile standoff detection

    International Nuclear Information System (INIS)

    Ayaz-Maierhafer, Birsen; Hayward, Jason P.; Ziock, Klaus P.; Blackston, Matthew A.; Fabris, Lorenzo

    2013-01-01

    In order to design a next-generation, dual neutron and gamma imager for mobile standoff detection which uses coded aperture imaging as its primary detection modality, the following design parameters have been investigated for gamma and neutron radiation incident upon a hybrid, coded mask: (1) transmission through mask elements for various mask materials and thicknesses; and (2) signal attenuation in the mask versus angle of incidence. Each of these parameters directly affects detection significance, as quantified by the signal-to-noise ratio. The hybrid mask consists of two or three layers: organic material for fast neutron attenuation and scattering, Cd for slow neutron absorption (if applied), and one of three of the following photon or photon and slow neutron attenuating materials—Linotype alloy, CLYC, or CZT. In the MCNP model, a line source of gamma rays (100–2500 keV), fast neutrons (1000–10,000 keV) or thermal neutrons was positioned above the hybrid mask. The radiation penetrating the mask was simply tallied at the surface of an ideal detector, which was located below the surface of the last mask layer. The transmission was calculated as the ratio of the particles transmitted through the fixed aperture to the particles passing through the closed mask. In order to determine the performance of the mask considering relative motion between the source and detector, simulations were used to calculate the signal attenuation for incident radiation angles of 0–50°. The results showed that a hybrid mask can be designed to sufficiently reduce both transmission through the mask and signal loss at large angles of incidence, considering both gamma ray and fast neutron radiations. With properly selected material thicknesses, the signal loss of a hybrid mask, which is necessarily thicker than the mask required for either single mode imaging, is not a setback to the system's detection significance

  10. Quality of Care at Hospitals Identified as Outliers in Publicly Reported Mortality Statistics for Percutaneous Coronary Intervention.

    Science.gov (United States)

    Waldo, Stephen W; McCabe, James M; Kennedy, Kevin F; Zigler, Corwin M; Pinto, Duane S; Yeh, Robert W

    2017-05-16

    Public reporting of percutaneous coronary intervention (PCI) outcomes may create disincentives for physicians to provide care for critically ill patients, particularly at institutions with worse clinical outcomes. We thus sought to evaluate the procedural management and in-hospital outcomes of patients treated for acute myocardial infarction before and after a hospital had been publicly identified as a negative outlier. Using state reports, we identified hospitals that were recognized as negative PCI outliers in 2 states (Massachusetts and New York) from 2002 to 2012. State hospitalization files were used to identify all patients with an acute myocardial infarction within these states. Procedural management and in-hospital outcomes were compared among patients treated at outlier hospitals before and after public report of outlier status. Patients at nonoutlier institutions were used to control for temporal trends. Among 86 hospitals, 31 were reported as outliers for excess mortality. Outlier facilities were larger, treating more patients with acute myocardial infarction and performing more PCIs than nonoutlier hospitals ( P fashion (interaction P =0.50) after public report of outlier status. The likelihood of in-hospital mortality decreased at outlier institutions (RR, 0.83; 95% CI, 0.81-0.85) after public report, and to a lesser degree at nonoutlier institutions (RR, 0.90; 95% CI, 0.87-0.92; interaction P <0.001). Among patients that underwent PCI, in-hospital mortality decreased at outlier institutions after public recognition of outlier status in comparison with prior (RR, 0.72; 9% CI, 0.66-0.79), a decline that exceeded the reduction at nonoutlier institutions (RR, 0.87; 95% CI, 0.80-0.96; interaction P <0.001). Large hospitals with higher clinical volume are more likely to be designated as negative outliers. The rates of percutaneous revascularization increased similarly at outlier and nonoutlier institutions after report of outlier status. After outlier

  11. Online platform for applying space–time scan statistics for prospectively detecting emerging hot spots of dengue fever

    Directory of Open Access Journals (Sweden)

    Chien-Chou Chen

    2016-11-01

    Full Text Available Abstract Background Cases of dengue fever have increased in areas of Southeast Asia in recent years. Taiwan hit a record-high 42,856 cases in 2015, with the majority in southern Tainan and Kaohsiung Cities. Leveraging spatial statistics and geo-visualization techniques, we aim to design an online analytical tool for local public health workers to prospectively identify ongoing hot spots of dengue fever weekly at the village level. Methods A total of 57,516 confirmed cases of dengue fever in 2014 and 2015 were obtained from the Taiwan Centers for Disease Control (TCDC. Incorporating demographic information as covariates with cumulative cases (365 days in a discrete Poisson model, we iteratively applied space–time scan statistics by SaTScan software to detect the currently active cluster of dengue fever (reported as relative risk in each village of Tainan and Kaohsiung every week. A village with a relative risk >1 and p value <0.05 was identified as a dengue-epidemic area. Assuming an ongoing transmission might continuously spread for two consecutive weeks, we estimated the sensitivity and specificity for detecting outbreaks by comparing the scan-based classification (dengue-epidemic vs. dengue-free village with the true cumulative case numbers from the TCDC’s surveillance statistics. Results Among the 1648 villages in Tainan and Kaohsiung, the overall sensitivity for detecting outbreaks increases as case numbers grow in a total of 92 weekly simulations. The specificity for detecting outbreaks behaves inversely, compared to the sensitivity. On average, the mean sensitivity and specificity of 2-week hot spot detection were 0.615 and 0.891 respectively (p value <0.001 for the covariate adjustment model, as the maximum spatial and temporal windows were specified as 50% of the total population at risk and 28 days. Dengue-epidemic villages were visualized and explored in an interactive map. Conclusions We designed an online analytical tool for

  12. Applying quantitative metabolomics based on chemical isotope labeling LC-MS for detecting potential milk adulterant in human milk.

    Science.gov (United States)

    Mung, Dorothea; Li, Liang

    2018-02-25

    There is an increasing demand for donor human milk to feed infants for various reasons including that a mother may be unable to provide sufficient amounts of milk for their child or the milk is considered unsafe for the baby. Selling and buying human milk via the Internet has gained popularity. However, there is a risk of human milk sold containing other adulterants such as animal or plant milk. Analytical tools for rapid detection of adulterants in human milk are needed. We report a quantitative metabolomics method for detecting potential milk adulterants (soy, almond, cow, goat and infant formula milk) in human milk. It is based on the use of a high-performance chemical isotope labeling (CIL) LC-MS platform to profile the metabolome of an unknown milk sample, followed by multivariate or univariate comparison of the resultant metabolomic profile with that of human milk to determine the differences. Using dansylation LC-MS to profile the amine/phenol submetabolome, we could detect an average of 4129 ± 297 (n = 9) soy metabolites, 3080 ± 470 (n = 9) almond metabolites, 4256 ± 136 (n = 18) cow metabolites, 4318 ± 198 (n = 9) goat metabolites, 4444 ± 563 (n = 9) infant formula metabolites, and 4020 ± 375 (n = 30) human metabolites. This high level of coverage allowed us to readily differentiate the six different types of samples. From the analysis of binary mixtures of human milk containing 5, 10, 25, 50 and 75% other type of milk, we demonstrated that this method could be used to detect the presence of as low as 5% adulterant in human milk. We envisage that this method could be applied to detect contaminant or adulterant in other types of food or drinks. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. The effects of additive outliers on tests for unit roots and cointegration

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); N. Haldrup (Niels)

    1994-01-01

    textabstractThe properties of the univariate Dickey-Fuller test and the Johansen test for the cointegrating rank when there exist additive outlying observations in the time series are examined. The analysis provides analytical as well as numerical evidence that additive outliers may produce spurious

  14. Outlier treatment for improving parameter estimation of group contribution based models for upper flammability limit

    DEFF Research Database (Denmark)

    Frutiger, Jerome; Abildskov, Jens; Sin, Gürkan

    2015-01-01

    Flammability data is needed to assess the risk of fire and explosions. This study presents a new group contribution (GC) model to predict the upper flammability limit UFL oforganic chemicals. Furthermore, it provides a systematic method for outlier treatment inorder to improve the parameter...

  15. Identifying multiple outliers in linear regression: robust fit and clustering approach

    International Nuclear Information System (INIS)

    Robiah Adnan; Mohd Nor Mohamad; Halim Setan

    2001-01-01

    This research provides a clustering based approach for determining potential candidates for outliers. This is modification of the method proposed by Serbert et. al (1988). It is based on using the single linkage clustering algorithm to group the standardized predicted and residual values of data set fit by least trimmed of squares (LTS). (Author)

  16. Automated chromatographic system with polarimetric detection laser applied in the control of fermentation processes and seaweed extracts characterization

    International Nuclear Information System (INIS)

    Fajer, V.; Naranjo, S.; Mora, W.; Patinno, R.; Coba, E.; Michelena, G.

    2012-01-01

    There are presented applications and innovations of chromatographic and polarimetric systems in which develop methodologies for measuring the input molasses and the resulting product of a fermentation process of alcohol from a rich honey and evaluation of the fermentation process honey servery in obtaining a drink native to the Yucatan region. Composition was assessed optically active substances in seaweed, of interest to the pharmaceutical industry. The findings provide measurements alternative raw materials and products of the sugar industry, beekeeping and pharmaceutical liquid chromatography with automated polarimetric detection reduces measurement times up to 15 min, making it comparable to the times of high chromatography resolution, significantly reducing operating costs. By chromatography system with polarimetric detection (SCDP) is new columns have included standard size designed by the authors, which allow process samples with volumes up to 1 ml and reduce measurement time to 15 min, decreasing to 5 times the volume sample and halving the time of measurement. Was evaluated determining the concentration of substances using the peaks of the chromatograms obtained for the different columns and calculate the uncertainty of measurements. The results relating to the improvement of a data acquisition program (ADQUIPOL v.2.0) and new programs for the preparation of chromatograms (CROMAPOL CROMAPOL V.1.0 and V.1.2) provide important benefits, which allow a considerable saving of time the processing of the results and can be applied in other chromatography systems with the appropriate adjustments. (Author)

  17. High performance liquid chromatography-charged aerosol detection applying an inverse gradient for quantification of rhamnolipid biosurfactants.

    Science.gov (United States)

    Behrens, Beate; Baune, Matthias; Jungkeit, Janek; Tiso, Till; Blank, Lars M; Hayen, Heiko

    2016-07-15

    A method using high performance liquid chromatography coupled to charged-aerosol detection (HPLC-CAD) was developed for the quantification of rhamnolipid biosurfactants. Qualitative sample composition was determined by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). The relative quantification of different derivatives of rhamnolipids including di-rhamnolipids, mono-rhamnolipids, and their precursors 3-(3-hydroxyalkanoyloxy)alkanoic acids (HAAs) differed for two compared LC-MS instruments and revealed instrument dependent responses. Our here reported HPLC-CAD method provides uniform response. An inverse gradient was applied for the absolute quantification of rhamnolipid congeners to account for the detector's dependency on the solvent composition. The CAD produces a uniform response not only for the analytes but also for structurally different (nonvolatile) compounds. It was demonstrated that n-dodecyl-β-d-maltoside or deoxycholic acid can be used as alternative standards. The method of HPLC-ultra violet (UV) detection after a derivatization of rhamnolipids and HAAs to their corresponding phenacyl esters confirmed the obtained results but required additional, laborious sample preparation steps. Sensitivity determined as limit of detection and limit of quantification for four mono-rhamnolipids was in the range of 0.3-1.0 and 1.2-2.0μg/mL, respectively, for HPLC-CAD and 0.4 and 1.5μg/mL, respectively, for HPLC-UV. Linearity for HPLC-CAD was at least 0.996 (R(2)) in the calibrated range of about 1-200μg/mL. Hence, the here presented HPLC-CAD method allows absolute quantification of rhamnolipids and derivatives. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Pooled Calibrations and Retainment of Outliers Improved Chemical Analysis

    DEFF Research Database (Denmark)

    Andersen, Jens; Sattar Hassan Alfaloje, Haedar

    2013-01-01

    . The results indicate t hat the procedures outlined in the Eurachem/CITAC Guide are of tremendous value to analytical sciences because they direct researcher’s attention towards the concept of consensus values rather than tow ards true values. Introduction of certified reference materials (CRM’s) in metrology...... has provided much new information on working habits in professiona l laboratories and CRM’s may be applied to establish the true level of uncertainty for a given type of ana lytical method. Finally, it is proposed to devise a new procedure of method val idation that facilitates QA in general, thus...

  19. Pooled calibrations and retainment of outliers improve chemical analysis

    DEFF Research Database (Denmark)

    Andersen, Jens; Alfaloje, Haedar S.H.

    2012-01-01

    indicate that the procedures outlined in the Eurachem/CITAC Guide are of tremendous value to analytical sciences because they direct researcher's attention towards the concept of consensus values rather than towards true values. Introduction of certified reference materials (CRM’s) in metrology has...... provided much new information on working habits in professional laboratories and CRM’s may be applied to establish the true level of uncertainty for a given type of analytical method. Finally, it is proposed to devise a new procedure of method validation that facilitates QA in general, thus saving many...

  20. Outlier identification in colorectal surgery should separate elective and nonelective service components.

    Science.gov (United States)

    Byrne, Ben E; Mamidanna, Ravikrishna; Vincent, Charles A; Faiz, Omar D

    2014-09-01

    The identification of health care institutions with outlying outcomes is of great importance for reporting health care results and for quality improvement. Historically, elective surgical outcomes have received greater attention than nonelective results, although some studies have examined both. Differences in outlier identification between these patient groups have not been adequately explored. The aim of this study was to compare the identification of institutional outliers for mortality after elective and nonelective colorectal resection in England. This was a cohort study using routine administrative data. Ninety-day mortality was determined by using statutory records of death. Adjusted Trust-level mortality rates were calculated by using multiple logistic regression. High and low mortality outliers were identified and compared across funnel plots for elective and nonelective surgery. All English National Health Service Trusts providing colorectal surgery to an unrestricted patient population were studied. Adults admitted for colorectal surgery between April 2006 and March 2012 were included. Segmental colonic or rectal resection was performed. The primary outcome measured was 90-day mortality. Included were 195,118 patients, treated at 147 Trusts. Ninety-day mortality rates after elective and nonelective surgery were 4% and 18%. No unit with high outlying mortality for elective surgery was a high outlier for nonelective mortality and vice versa. Trust level, observed-to-expected mortality for elective and nonelective surgery, was moderately correlated (Spearman ρ = 0.50, pinstitutional mortality outlier after elective and nonelective colorectal surgery was not closely related. Therefore, mortality rates should be reported for both patient cohorts separately. This would provide a broad picture of the state of colorectal services and help direct research and quality improvement activities.

  1. SQL injection detection system

    OpenAIRE

    Vargonas, Vytautas

    2017-01-01

    SQL injection detection system Programmers do not always ensure security of developed systems. That is why it is important to look for solutions outside being reliant on developers. In this work SQL injection detection system is proposed. The system analyzes HTTP request parameters and detects intrusions. It is based on unsupervised machine learning. Trained by regular request data system detects outlier user parameters. Since training is not reliant on previous knowledge of SQL injections, t...

  2. Shell-vial culture and real-time PCR applied to Rickettsia typhi and Rickettsia felis detection.

    Science.gov (United States)

    Segura, Ferran; Pons, Immaculada; Pla, Júlia; Nogueras, María-Mercedes

    2015-11-01

    Murine typhus is a zoonosis transmitted by fleas, whose etiological agent is Rickettsia typhi. Rickettsia felis infection can produces similar symptoms. Both are intracellular microorganisms. Therefore, their diagnosis is difficult and their infections can be misdiagnosed. Early diagnosis prevents severity and inappropriate treatment regimens. Serology can't be applied during the early stages of infection because it requires seroconversion. Shell-vial (SV) culture assay is a powerful tool to detect Rickettsia. The aim of the study was to optimize SV using a real-time PCR as monitoring method. Moreover, the study analyzes which antibiotics are useful to isolate these microorganisms from fleas avoiding contamination by other bacteria. For the first purpose, SVs were inoculated with each microorganism. They were incubated at different temperatures and monitored by real-time PCR and classical methods (Gimenez staining and indirect immunofluorescence assay). R. typhi grew at all temperatures. R. felis grew at 28 and 32 °C. Real-time PCR was more sensitive than classical methods and it detected microorganisms much earlier. Besides, the assay sensitivity was improved by increasing the number of SV. For the second purpose, microorganisms and fleas were incubated and monitored in different concentrations of antibiotics. Gentamicin, sufamethoxazole, trimethoprim were useful for R. typhi isolation. Gentamicin, streptomycin, penicillin, and amphotericin B were useful for R. felis isolation. Finally, the optimized conditions were used to isolate R. felis from fleas collected at a veterinary clinic. R. felis was isolated at 28 and 32 °C. However, successful establishment of cultures were not possible probably due to sub-optimal conditions of samples.

  3. Applying a nonlinear, pitch-catch, ultrasonic technique for the detection of kissing bonds in friction stir welds.

    Science.gov (United States)

    Delrue, Steven; Tabatabaeipour, Morteza; Hettler, Jan; Van Den Abeele, Koen

    2016-05-01

    Friction stir welding (FSW) is a promising technology for the joining of aluminum alloys and other metallic admixtures that are hard to weld by conventional fusion welding. Although FSW generally provides better fatigue properties than traditional fusion welding methods, fatigue properties are still significantly lower than for the base material. Apart from voids, kissing bonds for instance, in the form of closed cracks propagating along the interface of the stirred and heat affected zone, are inherent features of the weld and can be considered as one of the main causes of a reduced fatigue life of FSW in comparison to the base material. The main problem with kissing bond defects in FSW, is that they currently are very difficult to detect using existing NDT methods. Besides, in most cases, the defects are not directly accessible from the exposed surface. Therefore, new techniques capable of detecting small kissing bond flaws need to be introduced. In the present paper, a novel and practical approach is introduced based on a nonlinear, single-sided, ultrasonic technique. The proposed inspection technique uses two single element transducers, with the first transducer transmitting an ultrasonic signal that focuses the ultrasonic waves at the bottom side of the sample where cracks are most likely to occur. The large amount of energy at the focus activates the kissing bond, resulting in the generation of nonlinear features in the wave propagation. These nonlinear features are then captured by the second transducer operating in pitch-catch mode, and are analyzed, using pulse inversion, to reveal the presence of a defect. The performance of the proposed nonlinear, pitch-catch technique, is first illustrated using a numerical study of an aluminum sample containing simple, vertically oriented, incipient cracks. Later, the proposed technique is also applied experimentally on a real-life friction stir welded butt joint containing a kissing bond flaw. Copyright © 2016

  4. Outlier detection in healthcare fraud: A case study in the Medicaid dental domain

    NARCIS (Netherlands)

    van Capelleveen, Guido Cornelis; Poel, Mannes; Mueller, Roland; Thornton, Dallas; van Hillegersberg, Jos

    Health care insurance fraud is a pressing problem, causing substantial and increasing costs in medical insurance programs. Due to large amounts of claims submitted, estimated at 5 billion per day, review of individual claims or providers is a difficult task. This encourages the employment of

  5. Outlier loci detect intraspecific biodiversity amongst spring and autumn spawning herring across local scales

    DEFF Research Database (Denmark)

    Bekkevold, Dorte; Gross, Riho; Arula, Timo

    2016-01-01

    Herring, Clupea harengus, is one of the ecologically and commercially most important species in European northern seas, where two distinct ecotypes have been described based on spawning time; spring and autumn. To date, it is unknown if these spring and autumn spawning herring constitute genetica...... of these co-occurring ecotypes to meet requirements for sustainable exploitation and ensure optimal livelihood for coastal communities....

  6. Using Unsupervised Machine Learning for Outlier Detection in Data to Improve Wind Power Production Prediction

    OpenAIRE

    Åkerberg, Ludvig

    2017-01-01

    The expansion of wind power for electrical energy production has increased in recent years and shows no signs of slowing down. This unpredictable source of energy has contributed to destabilization of the electrical grid causing the energy market prices to vary significantly on a daily basis. For energy producers and consumers to make good investments, methods have been developed to make predictions of wind power production. These methods are often based on machine learning were historical we...

  7. AnyOut : Anytime Outlier Detection Approach for High-dimensional Data

    DEFF Research Database (Denmark)

    Assent, Ira; Kranen, Philipp; Baldauf, Corinna

    2012-01-01

    With the increase of sensor and monitoring applications, data mining on streaming data is receiving increasing research attention. As data is continuously generated, mining algorithms need to be able to analyze the data in a one-pass fashion. In many applications the rate at which the data objects...

  8. Outlier Detection for Sensor Systems (ODSS): A MATLAB Macro for Evaluating Microphone Sensor Data Quality.

    Science.gov (United States)

    Vasta, Robert; Crandell, Ian; Millican, Anthony; House, Leanna; Smith, Eric

    2017-10-13

    Microphone sensor systems provide information that may be used for a variety of applications. Such systems generate large amounts of data. One concern is with microphone failure and unusual values that may be generated as part of the information collection process. This paper describes methods and a MATLAB graphical interface that provides rapid evaluation of microphone performance and identifies irregularities. The approach and interface are described. An application to a microphone array used in a wind tunnel is used to illustrate the methodology.

  9. Comparative study of methods on outlying data detection in experimental results

    International Nuclear Information System (INIS)

    Oliveira, P.M.S.; Munita, C.S.; Hazenfratz, R.

    2009-01-01

    The interpretation of experimental results through multivariate statistical methods might reveal the outliers existence, which is rarely taken into account by the analysts. However, their presence can influence the results interpretation, generating false conclusions. This paper shows the importance of the outliers determination for one data base of 89 samples of ceramic fragments, analyzed by neutron activation analysis. The results were submitted to five procedures to detect outliers: Mahalanobis distance, cluster analysis, principal component analysis, factor analysis, and standardized residual. The results showed that although cluster analysis is one of the procedures most used to identify outliers, it can fail by not showing the samples that are easily identified as outliers by other methods. In general, the statistical procedures for the identification of the outliers are little known by the analysts. (author)

  10. Técnica de aprendizado semissupervisionado para detecção de outliers

    OpenAIRE

    Fabio Willian Zamoner

    2014-01-01

    Detecção de outliers desempenha um importante papel para descoberta de conhecimento em grandes bases de dados. O estudo é motivado por inúmeras aplicações reais como fraudes de cartões de crédito, detecção de falhas em componentes industriais, intrusão em redes de computadores, aprovação de empréstimos e monitoramento de condições médicas. Um outlier é definido como uma observação que desvia das outras observações em relação a uma medida e exerce considerável influência na análise de dados...

  11. Anomaly Detection using the "Isolation Forest" algorithm

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    Anomaly detection can provide clues about an outlying minority class in your data: hackers in a set of network events, fraudsters in a set of credit card transactions, or exotic particles in a set of high-energy collisions. In this talk, we analyze a real dataset of breast tissue biopsies, with malignant results forming the minority class. The "Isolation Forest" algorithm finds anomalies by deliberately “overfitting” models that memorize each data point. Since outliers have more empty space around them, they take fewer steps to memorize. Intuitively, a house in the country can be identified simply as “that house out by the farm”, while a house in the city needs a longer description like “that house in Brooklyn, near Prospect Park, on Union Street, between the firehouse and the library, not far from the French restaurant”. We first use anomaly detection to find outliers in the biopsy data, then apply traditional predictive modeling to discover rules that separate anomalies from normal data...

  12. The source of prehistoric obsidian artefacts from the Polynesian outlier of Taumako in the Solomon Islands

    Energy Technology Data Exchange (ETDEWEB)

    Leach, Foss [Otago Univ., Dunedin (New Zealand). Dept. of Anthropology

    1985-01-01

    Six obsidian artefacts from the Polynesian outlier of Taumako in the Solomon Islands dating to between 500 and 1000 B.C. were analysed for trace elements by the PIXE-PIGME method. Four are shown to derive from Vanuatu, but the remaining two artefacts do not match any of the known 66 sources in the Pacific region. Continuing difficulties with the methodology of Pacific obsidian sourcing are discussed. 14 refs; 2 tables.

  13. Outlier Loci and Selection Signatures of Simple Sequence Repeats (SSRs) in Flax (Linum usitatissimum L.).

    Science.gov (United States)

    Soto-Cerda, Braulio J; Cloutier, Sylvie

    2013-01-01

    Genomic microsatellites (gSSRs) and expressed sequence tag-derived SSRs (EST-SSRs) have gained wide application for elucidating genetic diversity and population structure in plants. Both marker systems are assumed to be selectively neutral when making demographic inferences, but this assumption is rarely tested. In this study, three neutrality tests were assessed for identifying outlier loci among 150 SSRs (85 gSSRs and 65 EST-SSRs) that likely influence estimates of population structure in three differentiated flax sub-populations ( F ST  = 0.19). Moreover, the utility of gSSRs, EST-SSRs, and the combined sets of SSRs was also evaluated in assessing genetic diversity and population structure in flax. Six outlier loci were identified by at least two neutrality tests showing footprints of balancing selection. After removing the outlier loci, the STRUCTURE analysis and the dendrogram topology of EST-SSRs improved. Conversely, gSSRs and combined SSRs results did not change significantly, possibly as a consequence of the higher number of neutral loci assessed. Taken together, the genetic structure analyses established the superiority of gSSRs to determine the genetic relationships among flax accessions, although the combined SSRs produced the best results. Genetic diversity parameters did not differ statistically ( P  > 0.05) between gSSRs and EST-SSRs, an observation partially explained by the similar number of repeat motifs. Our study provides new insights into the ability of gSSRs and EST-SSRs to measure genetic diversity and structure in flax and confirms the importance of testing for the occurrence of outlier loci to properly assess natural and breeding populations, particularly in studies considering only few loci.

  14. The Outlier Sectors: Areas of Non-Free Trade in the North American Free Trade Agreement

    OpenAIRE

    Eric T. Miller

    2002-01-01

    Since its entry into force, the North American Free Trade Agreement (NAFTA) has been enormously influential as a model for trade liberalization. While trade in goods among Canada, the United States and Mexico has been liberalized to a significant degree, this most famous of agreements nonetheless contains areas of recalcitrant protectionism. The first part of this paper identifies these "outlier sectors" and classifies them by primary source advocating protectionism, i.e., producer interests ...

  15. Robust Wavelet Estimation to Eliminate Simultaneously the Effects of Boundary Problems, Outliers, and Correlated Noise

    Directory of Open Access Journals (Sweden)

    Alsaidi M. Altaher

    2012-01-01

    Full Text Available Classical wavelet thresholding methods suffer from boundary problems caused by the application of the wavelet transformations to a finite signal. As a result, large bias at the edges and artificial wiggles occur when the classical boundary assumptions are not satisfied. Although polynomial wavelet regression and local polynomial wavelet regression effectively reduce the risk of this problem, the estimates from these two methods can be easily affected by the presence of correlated noise and outliers, giving inaccurate estimates. This paper introduces two robust methods in which the effects of boundary problems, outliers, and correlated noise are simultaneously taken into account. The proposed methods combine thresholding estimator with either a local polynomial model or a polynomial model using the generalized least squares method instead of the ordinary one. A primary step that involves removing the outlying observations through a statistical function is considered as well. The practical performance of the proposed methods has been evaluated through simulation experiments and real data examples. The results are strong evidence that the proposed method is extremely effective in terms of correcting the boundary bias and eliminating the effects of outliers and correlated noise.

  16. Portable hyperspectral device as a valuable tool for the detection of protective agents applied on hystorical buildings

    Science.gov (United States)

    Vettori, S.; Pecchioni, E.; Camaiti, M.; Garfagnoli, F.; Benvenuti, M.; Costagliola, P.; Moretti, S.

    2012-04-01

    In the recent past, a wide range of protective products (in most cases, synthetic polymers) have been applied to the surfaces of ancient buildings/artefacts to preserve them from alteration [1]. The lack of a detailed mapping of the permanence and efficacy of these treatments, in particular when applied on large surfaces such as building facades, may be particularly noxious when new restoration treatments are needed and the best choice of restoration protocols has to be taken. The presence of protective compounds on stone surfaces may be detected in laboratory by relatively simple diagnostic tests, which, however, normally require invasive (or micro-invasive) sampling methodologies and are time-consuming, thus limiting their use only to a restricted number of samples and sampling sites. On the contrary, hyperspectral sensors are rapid, non-invasive and non-destructive tools capable of analyzing different materials on the basis of their different patterns of absorption at specific wavelengths, and so particularly suitable for the field of cultural heritage [2,3]. In addition, they can be successfully used to discriminate between inorganic (i.e. rocks and minerals) and organic compounds, as well as to acquire, in short times, many spectra and compositional maps at relatively low costs. In this study we analyzed a number of stone samples (Carrara Marble and biogenic calcarenites - "Lecce Stone" and "Maastricht Stone"-) after treatment of their surfaces with synthetic polymers (synthetic wax, acrylic, perfluorinated and silicon based polymers) of common use in conservation-restoration practice. The hyperspectral device used for this purpose was ASD FieldSpec FR Pro spectroradiometer, a portable, high-resolution instrument designed to acquire Visible and Near-Infrared (VNIR: 350-1000 nm) and Short-Wave Infrared (SWIR: 1000-2500 nm) punctual reflectance spectra with a rapid data collection time (about 0.1 s for each spectrum). The reflectance spectra so far obtained in

  17. Anomalous human behavior detection: An Adaptive approach

    NARCIS (Netherlands)

    Leeuwen, C. van; Halma, A.; Schutte, K.

    2013-01-01

    Detection of anomalies (outliers or abnormal instances) is an important element in a range of applications such as fault, fraud, suspicious behavior detection and knowledge discovery. In this article we propose a new method for anomaly detection and performed tested its ability to detect anomalous

  18. An unsupervised learning algorithm for fatigue crack detection in waveguides

    International Nuclear Information System (INIS)

    Rizzo, Piervincenzo; Cammarata, Marcello; Kent Harries; Dutta, Debaditya; Sohn, Hoon

    2009-01-01

    Ultrasonic guided waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges, and high sensitivity to small flaws. This paper describes an SHM method based on UGWs and outlier analysis devoted to the detection and quantification of fatigue cracks in structural waveguides. The method combines the advantages of UGWs with the outcomes of the discrete wavelet transform (DWT) to extract defect-sensitive features aimed at performing a multivariate diagnosis of damage. In particular, the DWT is exploited to generate a set of relevant wavelet coefficients to construct a uni-dimensional or multi-dimensional damage index vector. The vector is fed to an outlier analysis to detect anomalous structural states. The general framework presented in this paper is applied to the detection of fatigue cracks in a steel beam. The probing hardware consists of a National Instruments PXI platform that controls the generation and detection of the ultrasonic signals by means of piezoelectric transducers made of lead zirconate titanate. The effectiveness of the proposed approach to diagnose the presence of defects as small as a few per cent of the waveguide cross-sectional area is demonstrated

  19. Prospective casemix-based funding, analysis and financial impact of cost outliers in all-patient refined diagnosis related groups in three Belgian general hospitals.

    Science.gov (United States)

    Pirson, Magali; Martins, Dimitri; Jackson, Terri; Dramaix, Michèle; Leclercq, Pol

    2006-03-01

    This study examined the impact of cost outliers in term of hospital resources consumption, the financial impact of the outliers under the Belgium casemix-based system, and the validity of two "proxies" for costs: length of stay and charges. The cost of all hospital stays at three Belgian general hospitals were calculated for the year 2001. High resource use outliers were selected according to the following rule: 75th percentile +1.5 xinter-quartile range. The frequency of cost outliers varied from 7% to 8% across hospitals. Explanatory factors were: major or extreme severity of illness, longer length of stay, and intensive care unit stay. Cost outliers account for 22-30% of hospital costs. One-third of length-of-stay outliers are not cost outliers, and nearly one-quarter of charges outliers are not cost outliers. The current funding system in Belgium does not penalize hospitals having a high percentage of outliers. The billing generated by these patients largely compensates for costs generated. Length of stay and charges are not a good approximation to select cost outliers.

  20. Identification of outliers and positive deviants for healthcare improvement: looking for high performers in hypoglycemia safety in patients with diabetes

    Directory of Open Access Journals (Sweden)

    Brigid Wilson

    2017-11-01

    Full Text Available Abstract Background The study objectives were to determine: (1 how statistical outliers exhibiting low rates of diabetes overtreatment performed on a reciprocal measure – rates of diabetes undertreatment; and (2 the impact of different criteria on high performing outlier status. Methods The design was serial cross-sectional, using yearly Veterans Health Administration (VHA administrative data (2009–2013. Our primary outcome measure was facility rate of HbA1c overtreatment of diabetes in patients at risk for hypoglycemia. Outlier status was assessed by using two approaches: calculating a facility outlier value within year, comparator group, and A1c threshold while incorporating at risk population sizes; and examining standardized model residuals across year and A1c threshold. Facilities with outlier values in the lowest decile for all years of data using more than one threshold and comparator or with time-averaged model residuals in the lowest decile for all A1c thresholds were considered high performing outliers. Results Using outlier values, three of the 27 high performers from 2009 were also identified in 2010–2013 and considered outliers. There was only modest overlap between facilities identified as top performers based on three thresholds: A1c  9% than VA average in the population of patients at high risk for hypoglycemia. Conclusions Statistical identification of positive deviants for diabetes overtreatment was dependent upon the specific measures and approaches used. Moreover, because two facilities may arrive at the same results via very different pathways, it is important to consider that a “best” practice may actually reflect a separate “worst” practice.

  1. Impact of outlier status on critical care patient outcomes: Does boarding medical intensive care unit patients make a difference?

    Science.gov (United States)

    Ahmad, Danish; Moeller, Katherine; Chowdhury, Jared; Patel, Vishal; Yoo, Erika J

    2018-04-01

    To evaluate the impact of outlier status, or the practice of boarding ICU patients in distant critical care units, on clinical and utilization outcomes. Retrospective observational study of all consecutive admissions to the MICU service between April 1, 2014-January 3, 2016, at an urban university hospital. Of 1931 patients, 117 were outliers (6.1%) for the entire duration of their ICU stay. In adjusted analyses, there was no association between outlier status and hospital (OR 1.21, 95% CI 0.72-2.05, p=0.47) or ICU mortality (OR 1.20, 95% CI 0.64-2.25, p=0.57). Outliers had shorter hospital and ICU lengths of stay (LOS) in addition to fewer ventilator days. Crossover patients who had variable outlier exposure also had no increase in hospital (OR 1.61; 95% CI 0.80-3.23; p=0.18) or ICU mortality (OR 1.05; 95% CI 0.43-2.54; p=0.92) after risk-adjustment. Boarding of MICU patients in distant units during times of bed nonavailability does not negatively influence patient mortality or LOS. Increased hospital and ventilator utilization observed among non-outliers in the home unit may be attributable, at least in part, to differences in patient characteristics. Prospective investigation into the practice of ICU boarding will provide further confirmation of its safety. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Analysis of the moderate resolution imaging spectroradiometer contextual algorithm for small fire detection, Journal of Applied Remote Sensing Vol.3

    Science.gov (United States)

    W. Wang; J.J. Qu; X. Hao; Y. Liu

    2009-01-01

    In the southeastern United States, most wildland fires are of low intensity. A substantial number of these fires cannot be detected by the MODIS contextual algorithm. To improve the accuracy of fire detection for this region, the remote-sensed characteristics of these fires have to be...

  3. Protein Detection Using the Multiplexed Proximity Extension Assay (PEA) from Plasma and Vaginal Fluid Applied to the Indicating FTA Elute Micro Card™

    Science.gov (United States)

    Berggrund, Malin; Ekman, Daniel; Gustavsson, Inger; Sundfeldt, Karin; Olovsson, Matts; Enroth, Stefan; Gyllensten, Ulf

    2016-01-01

    The indicating FTA elute micro card™ has been developed to collect and stabilize the nucleic acid in biological samples and is widely used in human and veterinary medicine and other disciplines. This card is not recommended for protein analyses, since surface treatment may denature proteins. We studied the ability to analyse proteins in human plasma and vaginal fluid as applied to the indicating FTA elute micro card™ using the sensitive proximity extension assay (PEA). Among 92 proteins in the Proseek Multiplex Oncology Iv2 panel, 87 were above the limit of detection (LOD) in liquid plasma and 56 among 92 above LOD in plasma applied to FTA cards. Washing and protein elution protocols were compared to identify an optimal method. Liquid-based cytology samples showed a lower number of proteins above LOD than FTA cards with vaginal fluid samples applied. Our results demonstrate that samples applied to the indicating FTA elute micro card™ are amendable to protein analyses, given that a sensitive protein detection assay is used. The results imply that biological samples applied to FTA cards can be used for DNA, RNA and protein detection. PMID:28936257

  4. Protein Detection Using the Multiplexed Proximity Extension Assay (PEA from Plasma and Vaginal Fluid Applied to the Indicating FTA Elute Micro Card™

    Directory of Open Access Journals (Sweden)

    Malin Berggrund

    2016-01-01

    Full Text Available The indicating FTA elute micro card™ has been developed to collect and stabilize the nucleic acid in biological samples and is widely used in human and veterinary medicine and other disciplines. This card is not recommended for protein analyses, since surface treatment may denature proteins. We studied the ability to analyse proteins in human plasma and vaginal fluid as applied to the indicating FTA elute micro card™ using the sensitive proximity extension assay (PEA. Among 92 proteins in the Proseek Multiplex Oncology Iv2 panel, 87 were above the limit of detection (LOD in liquid plasma and 56 among 92 above LOD in plasma applied to FTA cards. Washing and protein elution protocols were compared to identify an optimal method. Liquid-based cytology samples showed a lower number of proteins above LOD than FTA cards with vaginal fluid samples applied. Our results demonstrate that samples applied to the indicating FTA elute micro card™ are amendable to protein analyses, given that a sensitive protein detection assay is used. The results imply that biological samples applied to FTA cards can be used for DNA, RNA and protein detection.

  5. Protein Detection Using the Multiplexed Proximity Extension Assay (PEA) from Plasma and Vaginal Fluid Applied to the Indicating FTA Elute Micro Card™.

    Science.gov (United States)

    Berggrund, Malin; Ekman, Daniel; Gustavsson, Inger; Sundfeldt, Karin; Olovsson, Matts; Enroth, Stefan; Gyllensten, Ulf

    2016-01-01

    The indicating FTA elute micro card™ has been developed to collect and stabilize the nucleic acid in biological samples and is widely used in human and veterinary medicine and other disciplines. This card is not recommended for protein analyses, since surface treatment may denature proteins. We studied the ability to analyse proteins in human plasma and vaginal fluid as applied to the indicating FTA elute micro card™ using the sensitive proximity extension assay (PEA). Among 92 proteins in the Proseek Multiplex Oncology Iv2 panel, 87 were above the limit of detection (LOD) in liquid plasma and 56 among 92 above LOD in plasma applied to FTA cards. Washing and protein elution protocols were compared to identify an optimal method. Liquid-based cytology samples showed a lower number of proteins above LOD than FTA cards with vaginal fluid samples applied. Our results demonstrate that samples applied to the indicating FTA elute micro card™ are amendable to protein analyses, given that a sensitive protein detection assay is used. The results imply that biological samples applied to FTA cards can be used for DNA, RNA and protein detection.

  6. Applied superconductivity

    CERN Document Server

    Newhouse, Vernon L

    1975-01-01

    Applied Superconductivity, Volume II, is part of a two-volume series on applied superconductivity. The first volume dealt with electronic applications and radiation detection, and contains a chapter on liquid helium refrigeration. The present volume discusses magnets, electromechanical applications, accelerators, and microwave and rf devices. The book opens with a chapter on high-field superconducting magnets, covering applications and magnet design. Subsequent chapters discuss superconductive machinery such as superconductive bearings and motors; rf superconducting devices; and future prospec

  7. Patterns of Care for Biologic-Dosing Outliers and Nonoutliers in Biologic-Naive Patients with Rheumatoid Arthritis.

    Science.gov (United States)

    Delate, Thomas; Meyer, Roxanne; Jenkins, Daniel

    2017-08-01

    Although most biologic medications for patients with rheumatoid arthritis (RA) have recommended fixed dosing, actual biologic dosing may vary among real-world patients, since some patients can receive higher (high-dose outliers) or lower (low-dose outliers) doses than what is recommended in medication package inserts. To describe the patterns of care for biologic-dosing outliers and nonoutliers in biologic-naive patients with RA. This was a retrospective, longitudinal cohort study of patients with RA who were not pregnant and were aged ≥ 18 and 110% of the approved dose in the package insert at any time during the study period. Baseline patient profiles, treatment exposures, and outcomes were collected during the 180 days before and up to 2 years after biologic initiation and compared across index biologic outlier groups. Patients were followed for at least 1 year, with a subanalysis of those patients who remained as members for 2 years. This study included 434 RA patients with 1 year of follow-up and 372 RA patients with 2 years of follow-up. Overall, the vast majority of patients were female (≈75%) and had similar baseline characteristics. Approximately 10% of patients were outliers in both follow-up cohorts. ETN patients were least likely to become outliers, and ADA patients were most likely to become outliers. Of all outliers during the 1-year follow-up, patients were more likely to be a high-dose outlier (55%) than a low-dose outlier (45%). Median 1- and 2-year adjusted total biologic costs (based on wholesale acquisition costs) were higher for ADA and ETA nonoutliers than for IFX nonoutliers. Biologic persistence was highest for IFX patients. Charlson Comorbidity Index score, ETN and IFX index biologic, and treatment with a nonbiologic disease-modifying antirheumatic drug (DMARD) before biologic initiation were associated with becoming high- or low-dose outliers (c-statistic = 0.79). Approximately 1 in 10 study patients with RA was identified as a

  8. Outlier SNP markers reveal fine-scale genetic structuring across European hake populations (Merluccius merluccius)

    DEFF Research Database (Denmark)

    Milano, I.; Babbucci, M.; Cariani, A.

    2014-01-01

    fishery. Analysis of 850 individuals from 19 locations across the entire distribution range showed evidence for several outlier loci, with significantly higher resolving power. While 299 putatively neutral SNPs confirmed the genetic break between basins (FCT = 0.016) and weak differentiation within basins...... even when neutral markers provide genetic homogeneity across populations. Here, 381 SNPs located in transcribed regions were used to assess largeand fine-scale population structure in the European hake (Merluccius merluccius), a widely distributed demersal species of high priority for the European...

  9. Simulation of space-borne tsunami detection using GNSS-Reflectometry applied to tsunamis in the Indian Ocean

    Directory of Open Access Journals (Sweden)

    R. Stosius

    2010-06-01

    Full Text Available Within the German-Indonesian Tsunami Early Warning System project GITEWS (Rudloff et al., 2009, a feasibility study on a future tsunami detection system from space has been carried out. The Global Navigation Satellite System Reflectometry (GNSS-R is an innovative way of using reflected GNSS signals for remote sensing, e.g. sea surface altimetry. In contrast to conventional satellite radar altimetry, multiple height measurements within a wide field of view can be made simultaneously. With a dedicated Low Earth Orbit (LEO constellation of satellites equipped with GNSS-R, densely spaced sea surface height measurements could be established to detect tsunamis. This simulation study compares the Walker and the meshed comb constellation with respect to their global reflection point distribution. The detection performance of various LEO constellation scenarios with GPS, GLONASS and Galileo as signal sources is investigated. The study concentrates on the detection performance for six historic tsunami events in the Indian Ocean generated by earthquakes of different magnitudes, as well as on different constellation types and orbit parameters. The GNSS-R carrier phase is compared with the PARIS or code altimetry approach. The study shows that Walker constellations have a much better reflection point distribution compared to the meshed comb constellation. Considering simulation assumptions and assuming technical feasibility it can be demonstrated that strong tsunamis with magnitudes (M ≥8.5 can be detected with certainty from any orbit altitude within 15–25 min by a 48/8 or 81/9 Walker constellation if tsunami waves of 20 cm or higher can be detected by space-borne GNSS-R. The carrier phase approach outperforms the PARIS altimetry approach especially at low orbit altitudes and for a low number of LEO satellites.

  10. Universal Linear Fit Identification: A Method Independent of Data, Outliers and Noise Distribution Model and Free of Missing or Removed Data Imputation.

    Science.gov (United States)

    Adikaram, K K L B; Hussein, M A; Effenberger, M; Becker, T

    2015-01-01

    Data processing requires a robust linear fit identification method. In this paper, we introduce a non-parametric robust linear fit identification method for time series. The method uses an indicator 2/n to identify linear fit, where n is number of terms in a series. The ratio Rmax of amax - amin and Sn - amin*n and that of Rmin of amax - amin and amax*n - Sn are always equal to 2/n, where amax is the maximum element, amin is the minimum element and Sn is the sum of all elements. If any series expected to follow y = c consists of data that do not agree with y = c form, Rmax > 2/n and Rmin > 2/n imply that the maximum and minimum elements, respectively, do not agree with linear fit. We define threshold values for outliers and noise detection as 2/n * (1 + k1) and 2/n * (1 + k2), respectively, where k1 > k2 and 0 ≤ k1 ≤ n/2 - 1. Given this relation and transformation technique, which transforms data into the form y = c, we show that removing all data that do not agree with linear fit is possible. Furthermore, the method is independent of the number of data points, missing data, removed data points and nature of distribution (Gaussian or non-Gaussian) of outliers, noise and clean data. These are major advantages over the existing linear fit methods. Since having a perfect linear relation between two variables in the real world is impossible, we used artificial data sets with extreme conditions to verify the method. The method detects the correct linear fit when the percentage of data agreeing with linear fit is less than 50%, and the deviation of data that do not agree with linear fit is very small, of the order of ±10-4%. The method results in incorrect detections only when numerical accuracy is insufficient in the calculation process.

  11. Universal Linear Fit Identification: A Method Independent of Data, Outliers and Noise Distribution Model and Free of Missing or Removed Data Imputation.

    Directory of Open Access Journals (Sweden)

    K K L B Adikaram

    Full Text Available Data processing requires a robust linear fit identification method. In this paper, we introduce a non-parametric robust linear fit identification method for time series. The method uses an indicator 2/n to identify linear fit, where n is number of terms in a series. The ratio Rmax of amax - amin and Sn - amin*n and that of Rmin of amax - amin and amax*n - Sn are always equal to 2/n, where amax is the maximum element, amin is the minimum element and Sn is the sum of all elements. If any series expected to follow y = c consists of data that do not agree with y = c form, Rmax > 2/n and Rmin > 2/n imply that the maximum and minimum elements, respectively, do not agree with linear fit. We define threshold values for outliers and noise detection as 2/n * (1 + k1 and 2/n * (1 + k2, respectively, where k1 > k2 and 0 ≤ k1 ≤ n/2 - 1. Given this relation and transformation technique, which transforms data into the form y = c, we show that removing all data that do not agree with linear fit is possible. Furthermore, the method is independent of the number of data points, missing data, removed data points and nature of distribution (Gaussian or non-Gaussian of outliers, noise and clean data. These are major advantages over the existing linear fit methods. Since having a perfect linear relation between two variables in the real world is impossible, we used artificial data sets with extreme conditions to verify the method. The method detects the correct linear fit when the percentage of data agreeing with linear fit is less than 50%, and the deviation of data that do not agree with linear fit is very small, of the order of ±10-4%. The method results in incorrect detections only when numerical accuracy is insufficient in the calculation process.

  12. On the bi-dimensional variational decomposition applied to nonstationary vibration signals for rolling bearing crack detection in coal cutters

    International Nuclear Information System (INIS)

    Jiang, Yu; Li, Zhixiong; Zhang, Chao; Peng, Z; Hu, Chao

    2016-01-01

    This work aims to detect rolling bearing cracks using a variational approach. An original method that appropriately incorporates bi-dimensional variational mode decomposition (BVMD) into discriminant diffusion maps (DDM) is proposed to analyze the nonstationary vibration signals recorded from the cracked rolling bearings in coal cutters. The advantage of this variational decomposition based diffusion map (VDDM) method in comparison to the current DDM is that the intrinsic vibration mode of the crack can be filtered into a limited bandwidth in the frequency domain with an estimated central frequency, thus discarding the interference signal components in the vibration signals and significantly improving the crack detection performance. In addition, the VDDM is able to simultaneously process two-channel sensor signals to reduce information leakage. Experimental validation using rolling bearing crack vibration signals demonstrates that the VDDM separated the raw signals into four intrinsic modes, including one roller vibration mode, one roller cage vibration mode, one inner race vibration mode, and one outer race vibration mode. Hence, reliable fault features were extracted from the outer race vibration mode, and satisfactory crack identification performance was achieved. The comparison between the proposed VDDM and existing approaches indicated that the VDDM method was more efficient and reliable for crack detection in coal cutter rolling bearings. As an effective catalyst for rolling bearing crack detection, this newly proposed method is useful for practical applications. (paper)

  13. Applying Information Retrieval Techniques to Detect Duplicates and to Rank References in the Preliminary Phases of Systematic Literature Reviews

    Directory of Open Access Journals (Sweden)

    Ramon Abilio

    2015-08-01

    Full Text Available Systematic Literature Review (SLR is a means to synthesize relevant and high quality studies related to a specific topic or research questions. In the Primary Selection stage of an SLR, the selection of studies is usually performed manually by reading title, abstract and keywords of each study. In the last years, the number of published scientific studies has grown increasing the effort to perform this sort of reviews. In this paper, we proposed strategies to detect non-papers and duplicated references in results exported by search engines, and strategies to rank the references in decreasing order of importance for an SLR, regarding the terms in the search string. These strategies are based on Information Retrieval techniques. We implemented the strategies and carried out an experimental evaluation of their applicability using two real datasets. As results, the strategy to detect non-papers presented 100% of precision and 50% of recall; the strategy to detect duplicates detected more duplicates than the manual inspection; and one of the strategies to rank relevant references presented 50% of precision and 80% of recall. Therefore, the results show that the proposed strategies can minimize the effort in the Primary Selection stage of an SLR.

  14. Continuous fraction collection of gas chromatographic separations with parallel mass spectrometric detection applied to cell-based bioactivity analysis

    NARCIS (Netherlands)

    Jonker, Willem; Zwart, Nick; Stockl, Jan B.; de Koning, Sjaak; Schaap, Jaap; Lamoree, Marja H.; Somsen, Govert W.; Hamers, Timo; Kool, Jeroen

    2017-01-01

    We describe the development and evaluation of a GC-MS fractionation platform that combines high-resolution fraction collection of full chromatograms with parallel MS detection. A y-split at the column divides the effluent towards the MS detector and towards an inverted y-piece where vaporized trap

  15. New comprehensive denaturing-gradient-gel-electrophoresis assay for KRAS mutation detection applied to paraffin-embedded tumours

    NARCIS (Netherlands)

    Hayes, VM; Westra, JL; Verlind, E; Bleeker, W; Plukker, JT; Hofstra, RMW; Buys, CHCM

    2000-01-01

    A comprehensive mutation detection assay is presented for the entire coding region and all splice site junctions of the KRAS oncogene. The assay is based on denaturing gradient gel electrophoresis and applicable to archival paraffin-embedded tumour material. All KRAS amplicons are analysed within

  16. Tailor-made Surgical Guide Reduces Incidence of Outliers of Cup Placement.

    Science.gov (United States)

    Hananouchi, Takehito; Saito, Masanobu; Koyama, Tsuyoshi; Sugano, Nobuhiko; Yoshikawa, Hideki

    2010-04-01

    Malalignment of the cup in total hip arthroplasty (THA) increases the risks of postoperative complications such as neck cup impingement, dislocation, and wear. We asked whether a tailor-made surgical guide based on CT images would reduce the incidence of outliers beyond 10 degrees from preoperatively planned alignment of the cup compared with those without the surgical guide. We prospectively followed 38 patients (38 hips, Group 1) having primary THA with the conventional technique and 31 patients (31 hips, Group 2) using the surgical guide. We designed the guide for Group 2 based on CT images and fixed it to the acetabular edge with a Kirschner wire to indicate the planned cup direction. Postoperative CT images showed the guide reduced the number of outliers compared with the conventional method (Group 1, 23.7%; Group 2, 0%). The surgical guide provided more reliable cup insertion compared with conventional techniques. Level II, therapeutic study. See the Guidelines for Authors for a complete description of levels of evidence.

  17. The Super‑efficiency Model and its Use for Ranking and Identification of Outliers

    Directory of Open Access Journals (Sweden)

    Kristína Kočišová

    2017-01-01

    Full Text Available This paper employs non‑radial and non‑oriented super‑efficiency SBM model under the assumption of a variable return to scale to analyse performance of twenty‑two Czech and Slovak domestic commercial banks in 2015. The banks were ranked according to asset‑oriented and profit‑oriented intermediation approach. We pooled the cross‑country data and used them to define a common best‑practice efficiency frontier. This allowed us to focus on determining relative differences in efficiency across banks. The average efficiency was evaluated separately on the “national” and “international” level. Based on the results of analysis can be seen that in Slovak banking sector the level of super‑efficiency was lower compared to Czech banks. Also, the number of super‑efficient banks was lower in a case of Slovakia under both approaches. The boxplot analysis was used to determine the outliers in the dataset. The results suggest that the exclusion of outliers led to the better statistical characteristic of estimated efficiency.

  18. Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data

    Science.gov (United States)

    Yin, Shen; Wang, Guang; Yang, Xu

    2014-07-01

    In practical industrial applications, the key performance indicator (KPI)-related prediction and diagnosis are quite important for the product quality and economic benefits. To meet these requirements, many advanced prediction and monitoring approaches have been developed which can be classified into model-based or data-driven techniques. Among these approaches, partial least squares (PLS) is one of the most popular data-driven methods due to its simplicity and easy implementation in large-scale industrial process. As PLS is totally based on the measured process data, the characteristics of the process data are critical for the success of PLS. Outliers and missing values are two common characteristics of the measured data which can severely affect the effectiveness of PLS. To ensure the applicability of PLS in practical industrial applications, this paper introduces a robust version of PLS to deal with outliers and missing values, simultaneously. The effectiveness of the proposed method is finally demonstrated by the application results of the KPI-related prediction and diagnosis on an industrial benchmark of Tennessee Eastman process.

  19. Effect of the wire geometry and an externally applied magnetic field on the detection efficiency of superconducting nanowire single-photon detectors

    Energy Technology Data Exchange (ETDEWEB)

    Lusche, Robert; Semenov, Alexey; Huebers, Heinz-Willhelm [DLR, Institut fuer Planetenforschung, Berlin (Germany); Ilin, Konstantin; Siegel, Michael [Karlsruher Institut fuer Technologie (Germany); Korneeva, Yuliya; Trifonov, Andrey; Korneev, Alexander; Goltsman, Gregory [Moscow State Pedagogical University (Russian Federation)

    2013-07-01

    The interest in single-photon detectors in the near-infrared wavelength regime for applications, e.g. in quantum cryptography has immensely increased in the last years. Superconducting nanowire single-photon detectors (SNSPD) already show quite reasonable detection efficiencies in the NIR which can even be further improved. Novel theoretical approaches including vortex-assisted photon counting state that the detection efficiency in the long wavelength region can be enhanced by the detector geometry and an applied magnetic field. We present spectral measurements in the wavelength range from 350-2500 nm of the detection efficiency of meander-type TaN and NbN SNSPD with varying nanowire line width from 80 to 250 nm. Due to the used experimental setup we can accurately normalize the measured spectra and are able to extract the intrinsic detection efficiency (IDE) of our detectors. The results clearly indicate an improvement of the IDE depending on the wire width according to the theoretic models. Furthermore we experimentally found that the smallest detectable photon-flux can be increased by applying a small magnetic field to the detectors.

  20. Evaluation of Manual Ultrasonic Examinations Applied to Detect Flaws in Primary System Dissimilar Metal Welds at North Anna Power Station

    International Nuclear Information System (INIS)

    Anderson, Michael T.; Diaz, Aaron A.; Doctor, Steven R.

    2012-01-01

    During a recent inservice inspection (ISI) of a dissimilar metal weld (DMW) in an inlet (hot leg) steam generator nozzle at North Anna Power Station Unit 1, several axially oriented flaws went undetected by the licensee's manual ultrasonic testing (UT) technique. The flaws were subsequently detected as a result of outside diameter (OD) surface machining in preparation for a full structural weld overlay. The machining operation uncovered the existence of two through-wall flaws, based on the observance of primary water leaking from the DMW. Further ultrasonic tests were then performed, and a total of five axially oriented flaws, classified as primary water stress corrosion cracking (PWSCC), were detected in varied locations around the weld circumference.

  1. A new methodology for strategic planning using technological maps and detection of emerging research fronts applied to radiopharmacy

    International Nuclear Information System (INIS)

    Didio, Robert Joseph

    2011-01-01

    This research aims the development of a new methodology to support the strategic planning, using the process of elaboration of technological maps (TRM - Technological Roadmaps), associated with application of the detection process of emerging fronts of research in databases of scientific publications and patents. The innovation introduced in this research is the customization of the process of TRM to the radiopharmacy and, specifically, its association to the technique of detection of emerging fronts of research, in order to prove results and to establish a new and very useful methodology to the strategic planning of this area of businesses. The business unit DIRF - Diretoria de Radiofarmacia - of IPEN CNEN/SP was used as base of the study and implementation of this methodology presented in this work. (author)

  2. Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

    Science.gov (United States)

    Gadsden, S. Andrew; Kirubarajan, T.

    2017-05-01

    Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

  3. Secondary ion mass spectrometry and environment. SIMS as applied to the detection of stable and radioactive isotopes in marine organisms

    International Nuclear Information System (INIS)

    Chassard-Bouchaud, C.; Escaig, F.; Hallegot, P.

    1984-01-01

    Several marine species of economical interest, Crustacea (crabs and prawns) and Molluscs (common mussels and oysters) were collected from coastal waters of France: English Channel, Atlantic Ocean and Mediterranean Sea and of Japan. Microanalyses which were performed at the tissue and cell levels, using Secondary Ion Mass Spectrometry, revealed many contaminants; stable isotopes as well as radioactive actinids such as uranium were detected. Uptake, storage and excretion target organs were identified [fr

  4. Mii School: New 3D Technologies Applied in Education to Detect Drug Abuses and Bullying in Adolescents

    Science.gov (United States)

    Carmona, José Alberto; Espínola, Moisés; Cangas, Adolfo J.; Iribarne, Luis

    Mii School is a 3D school simulator developed with Blender and used by psychology researchers for the detection of drugs abuses, bullying and mental disorders in adolescents. The school simulator created is an interactive video game where the players, in this case the students, have to choose, along 17 scenes simulated, the options that better define their personalities. In this paper we present a technical characteristics description and the first results obtained in a real school.

  5. A voting-based statistical cylinder detection framework applied to fallen tree mapping in terrestrial laser scanning point clouds

    Science.gov (United States)

    Polewski, Przemyslaw; Yao, Wei; Heurich, Marco; Krzystek, Peter; Stilla, Uwe

    2017-07-01

    This paper introduces a statistical framework for detecting cylindrical shapes in dense point clouds. We target the application of mapping fallen trees in datasets obtained through terrestrial laser scanning. This is a challenging task due to the presence of ground vegetation, standing trees, DTM artifacts, as well as the fragmentation of dead trees into non-collinear segments. Our method shares the concept of voting in parameter space with the generalized Hough transform, however two of its significant drawbacks are improved upon. First, the need to generate samples on the shape's surface is eliminated. Instead, pairs of nearby input points lying on the surface cast a vote for the cylinder's parameters based on the intrinsic geometric properties of cylindrical shapes. Second, no discretization of the parameter space is required: the voting is carried out in continuous space by means of constructing a kernel density estimator and obtaining its local maxima, using automatic, data-driven kernel bandwidth selection. Furthermore, we show how the detected cylindrical primitives can be efficiently merged to obtain object-level (entire tree) semantic information using graph-cut segmentation and a tailored dynamic algorithm for eliminating cylinder redundancy. Experiments were performed on 3 plots from the Bavarian Forest National Park, with ground truth obtained through visual inspection of the point clouds. It was found that relative to sample consensus (SAC) cylinder fitting, the proposed voting framework can improve the detection completeness by up to 10 percentage points while maintaining the correctness rate.

  6. A framework for data compression and damage detection in structural health monitoring applied on a laboratory three-story structure

    Directory of Open Access Journals (Sweden)

    Manoel Afonso Pereira de Lima

    2016-09-01

    Full Text Available Structural Health Monitoring (SHM is an important technique used to preserve many types of structures in the short and long run, using sensor networks to continuously gather the desired data. However, this causes a strong impact in the data size to be stored and processed. A common solution to this is using compression algorithms, where the level of data compression should be adequate enough to allow the correct damage identification. In this work, we use the data sets from a laboratory three-story structure to evaluate the performance of common compression algorithms which, then, are combined with damage detection algorithms used in SHM. We also analyze how the use of Independent Component Analysis, a common technique to reduce noise in raw data, can assist the detection performance. The results showed that Piecewise Linear Histogram combined with Nonlinear PCA have the best trade-off between compression and detection for small error thresholds while Adaptive PCA with Principal Component Analysis perform better with higher values.

  7. Fast label-free detection of Legionella spp. in biofilms by applying immunomagnetic beads and Raman spectroscopy.

    Science.gov (United States)

    Kusić, Dragana; Rösch, Petra; Popp, Jürgen

    2016-03-01

    Legionellae colonize biofilms, can form a biofilm by itself and multiply intracellularly within the protozoa commonly found in water distribution systems. Approximately half of the known species are pathogenic and have been connected to severe multisystem Legionnaires' disease. The detection methods for Legionella spp. in water samples are still based on cultivation, which is time consuming due to the slow growth of this bacterium. Here, we developed a cultivation-independent, label-free and fast detection method for legionellae in a biofilm matrix based on the Raman spectroscopic analysis of isolated single cells via immunomagnetic separation (IMS). A database comprising the Raman spectra of single bacterial cells captured and separated from the biofilms formed by each species was used to build the identification method based on a support vector machine (SVM) discriminative classifier. The complete method allows the detection of Legionella spp. in 100 min. Cross-reactivity of Legionella spp. specific immunomagnetic beads to the other studied genera was tested, where only small cell amounts of Pseudomonas aeruginosa, Klebsiella pneumoniae and Escherichia coli compared to the initial number of cells were isolated by the immunobeads. Nevertheless, the Raman spectra collected from isolated non-targeted bacteria were well-discriminated from the Raman spectra collected from isolated Legionella cells, whereby the Raman spectra of the independent dataset of Legionella strains were assigned with an accuracy of 98.6%. In addition, Raman spectroscopy was also used to differentiate between isolated Legionella species. Copyright © 2016 Elsevier GmbH. All rights reserved.

  8. Principal components in the discrimination of outliers: A study in simulation sample data corrected by Pearson's and Yates´s chi-square distance

    Directory of Open Access Journals (Sweden)

    Manoel Vitor de Souza Veloso

    2016-04-01

    Full Text Available Current study employs Monte Carlo simulation in the building of a significance test to indicate the principal components that best discriminate against outliers. Different sample sizes were generated by multivariate normal distribution with different numbers of variables and correlation structures. Corrections by chi-square distance of Pearson´s and Yates's were provided for each sample size. Pearson´s correlation test showed the best performance. By increasing the number of variables, significance probabilities in favor of hypothesis H0 were reduced. So that the proposed method could be illustrated, a multivariate time series was applied with regard to sales volume rates in the state of Minas Gerais, obtained in different market segments.

  9. More efficient integrated safeguards by applying a reasonable detection probability for maintaining low presence probability of undetected nuclear proliferating activities

    International Nuclear Information System (INIS)

    Otsuka, Naoto

    2013-01-01

    Highlights: • A theoretical foundation is presented for more efficient Integrated Safeguards (IS). • Probability of undetected nuclear proliferation activities should be maintained low. • For nations under IS, the probability to start proliferation activities is very low. • The fact can decrease the detection probability of IS by dozens of percentage points. • The cost of IS per nation can be cut down by reducing inspection frequencies etc. - Abstract: A theoretical foundation is presented for implementing more efficiently the present International Atomic Energy Agency (IAEA) integrated safeguards (ISs) on the basis of fuzzy evaluation of the probability that the evaluated nation will continue peaceful activities. It is shown that by determining the presence probability of undetected nuclear proliferating activities, nations under IS can be maintained at acceptably low proliferation risk levels even if the detection probability of current IS is decreased by dozens of percentage from the present value. This makes it possible to reduce inspection frequency and the number of collected samples, allowing the IAEA to cut costs per nation. This will contribute to further promotion and application of IS to more nations by the IAEA, and more efficient utilization of IAEA resources from the viewpoint of whole IS framework

  10. A High-Precision Time-Frequency Entropy Based on Synchrosqueezing Generalized S-Transform Applied in Reservoir Detection

    Directory of Open Access Journals (Sweden)

    Hui Chen

    2018-06-01

    Full Text Available According to the fact that high frequency will be abnormally attenuated when seismic signals travel across reservoirs, a new method, which is named high-precision time-frequency entropy based on synchrosqueezing generalized S-transform, is proposed for hydrocarbon reservoir detection in this paper. First, the proposed method obtains the time-frequency spectra by synchrosqueezing generalized S-transform (SSGST, which are concentrated around the real instantaneous frequency of the signals. Then, considering the characteristics and effects of noises, we give a frequency constraint condition to calculate the entropy based on time-frequency spectra. The synthetic example verifies that the entropy will be abnormally high when seismic signals have an abnormal attenuation. Besides, comparing with the GST time-frequency entropy and the original SSGST time-frequency entropy in field data, the results of the proposed method show higher precision. Moreover, the proposed method can not only accurately detect and locate hydrocarbon reservoirs, but also effectively suppress the impact of random noises.

  11. Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome

    International Nuclear Information System (INIS)

    Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; Moore, Kathleen; Liu, Hong; Zheng, Bin

    2016-01-01

    To investigate the feasibility of automated segmentation of visceral and subcutaneous fat areas from computed tomography (CT) images of ovarian cancer patients and applying the computed adiposity-related image features to predict chemotherapy outcome. A computerized image processing scheme was developed to segment visceral and subcutaneous fat areas, and compute adiposity-related image features. Then, logistic regression models were applied to analyze association between the scheme-generated assessment scores and progression-free survival (PFS) of patients using a leave-one-case-out cross-validation method and a dataset involving 32 patients. The correlation coefficients between automated and radiologist’s manual segmentation of visceral and subcutaneous fat areas were 0.76 and 0.89, respectively. The scheme-generated prediction scores using adiposity-related radiographic image features significantly associated with patients’ PFS (p < 0.01). Using a computerized scheme enables to more efficiently and robustly segment visceral and subcutaneous fat areas. The computed adiposity-related image features also have potential to improve accuracy in predicting chemotherapy outcome

  12. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

    Science.gov (United States)

    Hussain, Lal

    2018-06-01

    Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

  13. A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography

    International Nuclear Information System (INIS)

    Timp, Sheila; Karssemeijer, Nico

    2004-01-01

    Mass segmentation plays a crucial role in computer-aided diagnosis (CAD) systems for classification of suspicious regions as normal, benign, or malignant. In this article we present a robust and automated segmentation technique--based on dynamic programming--to segment mass lesions from surrounding tissue. In addition, we propose an efficient algorithm to guarantee resulting contours to be closed. The segmentation method based on dynamic programming was quantitatively compared with two other automated segmentation methods (region growing and the discrete contour model) on a dataset of 1210 masses. For each mass an overlap criterion was calculated to determine the similarity with manual segmentation. The mean overlap percentage for dynamic programming was 0.69, for the other two methods 0.60 and 0.59, respectively. The difference in overlap percentage was statistically significant. To study the influence of the segmentation method on the performance of a CAD system two additional experiments were carried out. The first experiment studied the detection performance of the CAD system for the different segmentation methods. Free-response receiver operating characteristics analysis showed that the detection performance was nearly identical for the three segmentation methods. In the second experiment the ability of the classifier to discriminate between malignant and benign lesions was studied. For region based evaluation the area A z under the receiver operating characteristics curve was 0.74 for dynamic programming, 0.72 for the discrete contour model, and 0.67 for region growing. The difference in A z values obtained by the dynamic programming method and region growing was statistically significant. The differences between other methods were not significant

  14. Detection of delamination defects in plate type fuel elements applying an automated C-Scan ultrasonic system

    International Nuclear Information System (INIS)

    Katchadjian, P.; Desimone, C.; Ziobrowski, C.; Garcia, A.

    2002-01-01

    For the inspection of plate type fuel elements to be used in Research Nuclear Reactors it was applied an immersion pulse-echo ultrasonic technique. For that reason an automated movement system was implemented according to the axes X, Y and Z that allows to automate the test and to show the results obtained in format of C-Scan, facilitating the immediate identification of possible defects and making repetitive the inspection. In this work problems found during the laboratory tests and factors that difficult the inspection are commented. Also the results of C-Scans over UMo fuel elements with pattern defects are shown. Finally, the main characteristics of the transducer with the one the better results were obtained are detailed. (author)

  15. Non-contact detection of myocardium's mechanical activity by ultrawideband RF-radar and interpretation applying electrocardiography.

    Science.gov (United States)

    Thiel, F; Kreiseler, D; Seifert, F

    2009-11-01

    Electromagnetic waves can propagate through the body and are reflected at interfaces between materials with different dielectric properties. Therefore the reason for using ultrawideband (UWB) radar for probing the human body in the frequency range from 100 MHz up to 10 GHz is obvious and suggests an ability to monitor the motion of organs within the human body as well as obtaining images of internal structures. The specific advantages of UWB sensors are high temporal and spatial resolutions, penetration into object, low integral power, and compatibility with established narrowband systems. The sensitivity to ultralow power signals makes them suitable for human medical applications including mobile and continuous noncontact supervision of vital functions. Since no ionizing radiation is used, and due to the ultralow specific absorption rate applied, UWB techniques permit noninvasive sensing with no potential risks. This research aims at the synergetic use of UWB sounding combined with magnetic resonance imaging (MRI) to gain complementary information for improved functional diagnosis and imaging, especially to accelerate and enhance cardiac MRI by applying UWB radar as a noncontact navigator of myocardial contraction. To this end a sound understanding of how myocardial's mechanic is rendered by reflected and postprocessed UWB radar signals must be achieved. Therefore, we have executed the simultaneous acquisition and evaluation of radar signals with signals from a high-resolution electrocardiogram. The noncontact UWB illumination was done from several radiographic standard positions to monitor selected superficial myocardial areas during the cyclic physiological myocardial deformation in three different respiratory states. From our findings we could conclude that UWB radar can serve as a navigator technique for high and ultrahigh field magnetic resonance imaging and can be beneficial preserving the high resolution capability of this imaging modality. Furthermore it

  16. Non-contact detection of myocardium's mechanical activity by ultrawideband RF-radar and interpretation applying electrocardiography

    Science.gov (United States)

    Thiel, F.; Kreiseler, D.; Seifert, F.

    2009-11-01

    Electromagnetic waves can propagate through the body and are reflected at interfaces between materials with different dielectric properties. Therefore the reason for using ultrawideband (UWB) radar for probing the human body in the frequency range from 100 MHz up to 10 GHz is obvious and suggests an ability to monitor the motion of organs within the human body as well as obtaining images of internal structures. The specific advantages of UWB sensors are high temporal and spatial resolutions, penetration into object, low integral power, and compatibility with established narrowband systems. The sensitivity to ultralow power signals makes them suitable for human medical applications including mobile and continuous noncontact supervision of vital functions. Since no ionizing radiation is used, and due to the ultralow specific absorption rate applied, UWB techniques permit noninvasive sensing with no potential risks. This research aims at the synergetic use of UWB sounding combined with magnetic resonance imaging (MRI) to gain complementary information for improved functional diagnosis and imaging, especially to accelerate and enhance cardiac MRI by applying UWB radar as a noncontact navigator of myocardial contraction. To this end a sound understanding of how myocardial's mechanic is rendered by reflected and postprocessed UWB radar signals must be achieved. Therefore, we have executed the simultaneous acquisition and evaluation of radar signals with signals from a high-resolution electrocardiogram. The noncontact UWB illumination was done from several radiographic standard positions to monitor selected superficial myocardial areas during the cyclic physiological myocardial deformation in three different respiratory states. From our findings we could conclude that UWB radar can serve as a navigator technique for high and ultrahigh field magnetic resonance imaging and can be beneficial preserving the high resolution capability of this imaging modality. Furthermore it

  17. Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation.

    Science.gov (United States)

    Ghosh, Debashis; Chinnaiyan, Arul M

    2009-01-01

    In most analyses of large-scale genomic data sets, differential expression analysis is typically assessed by testing for differences in the mean of the distributions between 2 groups. A recent finding by Tomlins and others (2005) is of a different type of pattern of differential expression in which a fraction of samples in one group have overexpression relative to samples in the other group. In this work, we describe a general mixture model framework for the assessment of this type of expression, called outlier profile analysis. We start by considering the single-gene situation and establishing results on identifiability. We propose 2 nonparametric estimation procedures that have natural links to familiar multiple testing procedures. We then develop multivariate extensions of this methodology to handle genome-wide measurements. The proposed methodologies are compared using simulation studies as well as data from a prostate cancer gene expression study.

  18. Autoimmune hepatitis in a teenage boy: 'overlap' or 'outlier' syndrome--dilemma for internists.

    Science.gov (United States)

    Talukdar, Arunansu; Khanra, Dibbendhu; Mukherjee, Kabita; Saha, Manjari

    2013-02-08

    An 18-year-old boy presented with upper gastrointestinal bleeding and jaundice. Investigations revealed coarse hepatomegaly, splenomegaly and advanced oesophageal varices. Blood reports showed marked rise of alkaline phosphatase and more than twofold rise of transaminases and IgG. Liver histology was suggestive of piecemeal necrosis, interphase hepatitis and bile duct proliferation. Antinuclear antibody was positive in high titre along with positive antismooth muscle antibody and antimitochondrial antibody. The patient was positive for human leukocyte antigen DR3 type. Although an 'overlap' syndrome exists between autoimmune hepatitis (AIH) and primary biliary cirrhosis (PBC), a cholestatic variant of AIH, a rare 'outlier' syndrome could not be excluded in our case. Moreover, 'the chicken or the egg', AIH or PBC, the dilemma for the internists continued. The patient was put on steroid and ursodeoxycholic acid with unsatisfactory response. The existing international criteria for diagnosis of AIH are not generous enough to accommodate its variant forms.

  19. Assessment of the detectability of geo-hazards using Google Earth applied to the Three Parallel Rivers Area, Yunnan province of China

    Science.gov (United States)

    Voermans, Michiel; Mao, Zhun; Baartman, Jantiene EM; Stokes, Alexia

    2017-04-01

    Anthropogenic activities such as hydropower, mining and road construction in mountainous areas can induce and intensify mass wasting geo-hazards (e.g. landslides, gullies, rockslides). This represses local safety and socio-economic development, and endangers biodiversity at larger scale. Until today, data and knowledge to construct geo-hazard databases for further assessments are lacking. This applies in particular to countries with a recently emerged rapid economic growth, where there are no previous hazard documentations and where means to gain data from e.g. intensive fieldwork or VHR satellite imagery and DEM processing are lacking. Google Earth (GE, https://www.google.com/earth/) is a freely available and relatively simple virtual globe, map and geographical information program, which is potentially useful in detecting geo-hazards. This research aimed at (i) testing the capability of Google Earth to detect locations of geo-hazards and (ii) identifying factors affecting the diagnosing quality of the detection, including effects of geo-hazard dimensions, environs setting and professional background and effort of GE users. This was tested on nine geo-hazard sites following road segments in the Three Parallel Rivers Area in the Yunnan province of China, where geo-hazards are frequently occurring. Along each road site, the position and size of each geo-hazard was measured in situ. Next, independent diagnosers with varying professional experience (students, researchers, engineers etc.) were invited to detect geo-hazard occurrence along each of the eight sites via GE. Finally, the inventory and diagnostic data were compared to validate the objectives. Rates of detected geo-hazards from 30 diagnosers ranged from 10% to 48%. No strong correlations were found between the type and size of the geo-hazards and their detection rates. Also the years of expertise of the diagnosers proved not to make a difference, opposite to what may be expected. Meanwhile the amount of time

  20. Influence of the beam energy on the sensitivity of the PIXE methods applied to the detection of Pb in air

    International Nuclear Information System (INIS)

    Caridi, Aurora; Debray, Mario; Hojman, Daniel; Kreiner, A.J.; Santos, Daniel

    1989-01-01

    The air pollution by lead at the downtown area of Buenos Aires city was studied applying the PIXE method. The samples were collected at different seasons of the year. An appreciable reduction of the Pb content was observed on holidays and in summer when there is a lot less of cars in the streets. The influence of the beam energy on the Bremsstrahlung background was studied in order to optimize the sensitivity of the method. The C-12 beam energy was varied from 54 MeV to 30 MeV. The maximum Bremsstrahlung energy of secondary electrons decreased from 10 keV to 5.5 keV. Simultaneously the background radiation was appreciably reduced in the whole spectrum for the lowest beam energy whereas the values for the cross section of X ray production in the Pb L shell were no substantially modified. It is concluded that it is possible to analyze those elements whose characteristic X rays have an energy larger than 5.5 keV. (Author) [es

  1. Estimation of Signal Coherence Threshold and Concealed Spectral Lines Applied to Detection of Turbofan Engine Combustion Noise

    Science.gov (United States)

    Miles, Jeffrey Hilton

    2010-01-01

    Combustion noise from turbofan engines has become important, as the noise from sources like the fan and jet are reduced. An aligned and un-aligned coherence technique has been developed to determine a threshold level for the coherence and thereby help to separate the coherent combustion noise source from other noise sources measured with far-field microphones. This method is compared with a statistics based coherence threshold estimation method. In addition, the un-aligned coherence procedure at the same time also reveals periodicities, spectral lines, and undamped sinusoids hidden by broadband turbofan engine noise. In calculating the coherence threshold using a statistical method, one may use either the number of independent records or a larger number corresponding to the number of overlapped records used to create the average. Using data from a turbofan engine and a simulation this paper shows that applying the Fisher z-transform to the un-aligned coherence can aid in making the proper selection of samples and produce a reasonable statistics based coherence threshold. Examples are presented showing that the underlying tonal and coherent broad band structure which is buried under random broadband noise and jet noise can be determined. The method also shows the possible presence of indirect combustion noise. Copyright 2011 Acoustical Society of America. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the Acoustical Society of America.

  2. SERS as an analytical tool in environmental science: The detection of sulfamethoxazole in the nanomolar range by applying a microfluidic cartridge setup.

    Science.gov (United States)

    Patze, Sophie; Huebner, Uwe; Liebold, Falk; Weber, Karina; Cialla-May, Dana; Popp, Juergen

    2017-01-01

    Sulfamethoxazole (SMX) is a commonly applied antibiotic for treating urinary tract infections; however, allergic reactions and skin eczema are known side effects that are observed for all sulfonamides. Today, this molecule is present in drinking and surface water sources. The allowed concentration in tap water is 2·10 -7  mol L -1 . SMX could unintentionally be ingested by healthy people when drinking contaminated tap water, representing unnecessary drug intake. To assess the quality of tap water, fast, specific and sensitive detection methods are required, in which consequence measures for improving the purification of water might be initiated in the short term. Herein, the quantitative detection of SMX down to environmentally and physiologically relevant concentrations in the nanomolar range by employing surface-enhanced Raman spectroscopy (SERS) and a microfluidic cartridge system is presented. By applying surface-water samples as matrices, the detection of SMX down to 2.2·10 -9  mol L -1 is achieved, which illustrates the great potential of our proposed method in environmental science. Copyright © 2016 Elsevier B.V. All rights reserved.

  3. Using a cross-model loadings plot to identify protein spots causing 2-DE gels to become outliers in PCA

    DEFF Research Database (Denmark)

    Kristiansen, Luise Cederkvist; Jacobsen, Susanne; Jessen, Flemming

    2010-01-01

    The multivariate method PCA is an exploratory tool often used to get an overview of multivariate data, such as the quantified spot volumes of digitized 2-DE gels. PCA can reveal hidden structures present in the data, and thus enables identification of potential outliers and clustering. Based on PCA...

  4. Novelty detection for breast cancer image classification

    Science.gov (United States)

    Cichosz, Pawel; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał; Oleszkiewicz, Witold

    2016-09-01

    Using classification learning algorithms for medical applications may require not only refined model creation techniques and careful unbiased model evaluation, but also detecting the risk of misclassification at the time of model application. This is addressed by novelty detection, which identifies instances for which the training set is not sufficiently representative and for which it may be safer to restrain from classification and request a human expert diagnosis. The paper investigates two techniques for isolated instance identification, based on clustering and one-class support vector machines, which represent two different approaches to multidimensional outlier detection. The prediction quality for isolated instances in breast cancer image data is evaluated using the random forest algorithm and found to be substantially inferior to the prediction quality for non-isolated instances. Each of the two techniques is then used to create a novelty detection model which can be combined with a classification model and used at the time of prediction to detect instances for which the latter cannot be reliably applied. Novelty detection is demonstrated to improve random forest prediction quality and argued to deserve further investigation in medical applications.

  5. Detecting the changes in rural communities in Taiwan by applying multiphase segmentation on FORMOSA-2 satellite imagery

    Science.gov (United States)

    Huang, Yishuo

    2015-09-01

    regions containing roads, buildings, and other manmade construction works and the class with high values of NDVI indicates that those regions contain vegetation in good health. In order to verify the processed results, the regional boundaries were extracted and laid down on the given images to check whether the extracted boundaries were laid down on buildings, roads, or other artificial constructions. In addition to the proposed approach, another approach called statistical region merging was employed by grouping sets of pixels with homogeneous properties such that those sets are iteratively grown by combining smaller regions or pixels. In doing so, the segmented NDVI map can be generated. By comparing the areas of the merged classes in different years, the changes occurring in the rural communities of Taiwan can be detected. The satellite imagery of FORMOSA-2 with 2-m ground resolution is employed to evaluate the performance of the proposed approach. The satellite imagery of two rural communities (Jhumen and Taomi communities) is chosen to evaluate environmental changes between 2005 and 2010. The change maps of 2005-2010 show that a high density of green on a patch of land is increased by 19.62 ha in Jhumen community and conversely a similar patch of land is significantly decreased by 236.59 ha in Taomi community. Furthermore, the change maps created by another image segmentation method called statistical region merging generate similar processed results to multiphase segmentation.

  6. Applying ISO 11929:2010 Standard to detection limit calculation in least-squares based multi-nuclide gamma-ray spectrum evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Kanisch, G., E-mail: guenter.kanisch@hanse.net

    2017-05-21

    The concepts of ISO 11929 (2010) are applied to evaluation of radionuclide activities from more complex multi-nuclide gamma-ray spectra. From net peak areas estimated by peak fitting, activities and their standard uncertainties are calculated by weighted linear least-squares method with an additional step, where uncertainties of the design matrix elements are taken into account. A numerical treatment of the standard's uncertainty function, based on ISO 11929 Annex C.5, leads to a procedure for deriving decision threshold and detection limit values. The methods shown allow resolving interferences between radionuclide activities also in case of calculating detection limits where they can improve the latter by including more than one gamma line per radionuclide. The co'mmon single nuclide weighted mean is extended to an interference-corrected (generalized) weighted mean, which, combined with the least-squares method, allows faster detection limit calculations. In addition, a new grouped uncertainty budget was inferred, which for each radionuclide gives uncertainty budgets from seven main variables, such as net count rates, peak efficiencies, gamma emission intensities and others; grouping refers to summation over lists of peaks per radionuclide.

  7. Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets

    Directory of Open Access Journals (Sweden)

    Min-Wei Huang

    2018-01-01

    Full Text Available Many real-world medical datasets contain some proportion of missing (attribute values. In general, missing value imputation can be performed to solve this problem, which is to provide estimations for the missing values by a reasoning process based on the (complete observed data. However, if the observed data contain some noisy information or outliers, the estimations of the missing values may not be reliable or may even be quite different from the real values. The aim of this paper is to examine whether a combination of instance selection from the observed data and missing value imputation offers better performance than performing missing value imputation alone. In particular, three instance selection algorithms, DROP3, GA, and IB3, and three imputation algorithms, KNNI, MLP, and SVM, are used in order to find out the best combination. The experimental results show that that performing instance selection can have a positive impact on missing value imputation over the numerical data type of medical datasets, and specific combinations of instance selection and imputation methods can improve the imputation results over the mixed data type of medical datasets. However, instance selection does not have a definitely positive impact on the imputation result for categorical medical datasets.

  8. Comparative Performance of Four Single Extreme Outlier Discordancy Tests from Monte Carlo Simulations

    Directory of Open Access Journals (Sweden)

    Surendra P. Verma

    2014-01-01

    Full Text Available Using highly precise and accurate Monte Carlo simulations of 20,000,000 replications and 102 independent simulation experiments with extremely low simulation errors and total uncertainties, we evaluated the performance of four single outlier discordancy tests (Grubbs test N2, Dixon test N8, skewness test N14, and kurtosis test N15 for normal samples of sizes 5 to 20. Statistical contaminations of a single observation resulting from parameters called δ from ±0.1 up to ±20 for modeling the slippage of central tendency or ε from ±1.1 up to ±200 for slippage of dispersion, as well as no contamination (δ=0 and ε=±1, were simulated. Because of the use of precise and accurate random and normally distributed simulated data, very large replications, and a large number of independent experiments, this paper presents a novel approach for precise and accurate estimations of power functions of four popular discordancy tests and, therefore, should not be considered as a simple simulation exercise unrelated to probability and statistics. From both criteria of the Power of Test proposed by Hayes and Kinsella and the Test Performance Criterion of Barnett and Lewis, Dixon test N8 performs less well than the other three tests. The overall performance of these four tests could be summarized as N2≅N15>N14>N8.

  9. Outliers in American juvenile justice: the need for statutory reform in North Carolina and New York.

    Science.gov (United States)

    Tedeschi, Frank; Ford, Elizabeth

    2015-05-01

    There is a well-established and growing body of evidence from research that adolescents who commit crimes differ in many regards from their adult counterparts and are more susceptible to the negative effects of adjudication and incarceration in adult criminal justice systems. The age of criminal court jurisdiction in the United States has varied throughout history; yet, there are only two remaining states, New York and North Carolina, that continue to automatically charge 16 year olds as adults. This review traces the statutory history of juvenile justice in these two states with an emphasis on political and social factors that have contributed to their outlier status related to the age of criminal court jurisdiction. The neurobiological, psychological, and developmental aspects of the adolescent brain and personality, and how those issues relate both to a greater likelihood of rehabilitation in appropriate settings and to greater vulnerability in adult correctional facilities, are also reviewed. The importance of raising the age in New York and North Carolina not only lies in protecting incarcerated youths but also in preventing the associated stigma following release. Mental health practitioners are vital to the process of local and national juvenile justice reform. They can serve as experts on and advocates for appropriate mental health care and as experts on the adverse effects of the adult criminal justice system on adolescents.

  10. Mitochondrial DNA heritage of Cres Islanders--example of Croatian genetic outliers.

    Science.gov (United States)

    Jeran, Nina; Havas Augustin, Dubravka; Grahovac, Blaienka; Kapović, Miljenko; Metspalu, Ene; Villems, Richard; Rudan, Pavao

    2009-12-01

    Diversity of mitochondrial DNA (mtDNA) lineages of the Island of Cres was determined by high-resolution phylogenetic analysis on a sample of 119 adult unrelated individuals from eight settlements. The composition of mtDNA pool of this Island population is in contrast with other Croatian and European populations. The analysis revealed the highest frequency of haplogroup U (29.4%) with the predominance of one single lineage of subhaplogroup U2e (20.2%). Haplogroup H is the second most prevalent one with only 27.7%. Other very interesting features of contemporary Island population are extremely low frequency of haplogroup J (only 0.84%), and much higher frequency of haplogroup W (12.6%) comparing to other Croatian and European populations. Especially interesting finding is a strikingly higher frequency of haplogroup N1a (9.24%) presented with African/south Asian branch almost absent in Europeans, while its European sister-branch, proved to be highly prevalent among Neolithic farmers, is present in contemporary Europeans with only 0.2%. Haplotype analysis revealed that only five mtDNA lineages account for almost 50% of maternal genetic heritage of this island and they present supposed founder lineages. All presented findings confirm that genetic drift, especially founder effect, has played significant role in shaping genetic composition of the isolated population of the Island of Cres. Due to presented data contemporary population of Cres Island can be considered as genetic "outlier" among Croatian populations.

  11. Universal Linear Fit Identification: A Method Independent of Data, Outliers and Noise Distribution Model and Free of Missing or Removed Data Imputation

    Science.gov (United States)

    Adikaram, K. K. L. B.; Becker, T.

    2015-01-01

    Data processing requires a robust linear fit identification method. In this paper, we introduce a non-parametric robust linear fit identification method for time series. The method uses an indicator 2/n to identify linear fit, where n is number of terms in a series. The ratio R max of a max − a min and S n − a min *n and that of R min of a max − a min and a max *n − S n are always equal to 2/n, where a max is the maximum element, a min is the minimum element and S n is the sum of all elements. If any series expected to follow y = c consists of data that do not agree with y = c form, R max > 2/n and R min > 2/n imply that the maximum and minimum elements, respectively, do not agree with linear fit. We define threshold values for outliers and noise detection as 2/n * (1 + k 1 ) and 2/n * (1 + k 2 ), respectively, where k 1 > k 2 and 0 ≤ k 1 ≤ n/2 − 1. Given this relation and transformation technique, which transforms data into the form y = c, we show that removing all data that do not agree with linear fit is possible. Furthermore, the method is independent of the number of data points, missing data, removed data points and nature of distribution (Gaussian or non-Gaussian) of outliers, noise and clean data. These are major advantages over the existing linear fit methods. Since having a perfect linear relation between two variables in the real world is impossible, we used artificial data sets with extreme conditions to verify the method. The method detects the correct linear fit when the percentage of data agreeing with linear fit is less than 50%, and the deviation of data that do not agree with linear fit is very small, of the order of ±10−4%. The method results in incorrect detections only when numerical accuracy is insufficient in the calculation process. PMID:26571035

  12. Data Mining for Anomaly Detection

    Science.gov (United States)

    Biswas, Gautam; Mack, Daniel; Mylaraswamy, Dinkar; Bharadwaj, Raj

    2013-01-01

    The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.

  13. An SPSS implementation of the nonrecursive outlier deletion procedure with shifting z score criterion (Van Selst & Jolicoeur, 1994).

    Science.gov (United States)

    Thompson, Glenn L

    2006-05-01

    Sophisticated univariate outlier screening procedures are not yet available in widely used statistical packages such as SPSS. However, SPSS can accept user-supplied programs for executing these procedures. Failing this, researchers tend to rely on simplistic alternatives that can distort data because they do not adjust to cell-specific characteristics. Despite their popularity, these simple procedures may be especially ill suited for some applications (e.g., data from reaction time experiments). A user friendly SPSS Production Facility implementation of the shifting z score criterion procedure (Van Selst & Jolicoeur, 1994) is presented in an attempt to make it easier to use. In addition to outlier screening, optional syntax modules can be added that will perform tedious database management tasks (e.g., restructuring or computing means).

  14. Engaging children in the development of obesity interventions: exploring outcomes that matter most among obesity positive outliers

    OpenAIRE

    Sharifi, Mona; Marshall, Gareth; Goldman, Roberta E.; Cunningham, Courtney; Marshall, Richard; Taveras, Elsie M

    2015-01-01

    Objective To explore outcomes and measures of success that matter most to 'positive outlier' children who improved their body mass index (BMI) despite living in obesogenic neighborhoods. Methods We collected residential address and longitudinal height/weight data from electronic health records of 22,657 children ages 6–12 years in Massachusetts. We defined obesity “hotspots” as zip codes where >15% of children had a BMI ≥95th percentile. Using linear mixed effects models, we gener...

  15. SPATIAL CLUSTER AND OUTLIER IDENTIFICATION OF GEOCHEMICAL ASSOCIATION OF ELEMENTS: A CASE STUDY IN JUIRUI COPPER MINING AREA

    Directory of Open Access Journals (Sweden)

    Tien Thanh NGUYEN

    2016-12-01

    Full Text Available Spatial clusters and spatial outliers play an important role in the study of the spatial distribution patterns of geochemical data. They characterize the fundamental properties of mineralization processes, the spatial distribution of mineral deposits, and ore element concentrations in mineral districts. In this study, a new method for the study of spatial distribution patterns of multivariate data is proposed based on a combination of robust Mahalanobis distance and local Moran’s Ii. In order to construct the spatial matrix, the Moran's I spatial correlogram was first used to determine the range. The robust Mahalanobis distances were then computed for an association of elements. Finally, local Moran’s Ii statistics was used to measure the degree of spatial association and discover the spatial distribution patterns of associations of Cu, Au, Mo, Ag, Pb, Zn, As, and Sb elements including spatial clusters and spatial outliers. Spatial patterns were analyzed at six different spatial scales (2km, 4 km, 6 km, 8 km, 10 km and 12 km for both the raw data and Box-Cox transformed data. The results show that identified spatial cluster and spatial outlier areas using local Moran’s Ii and the robust Mahalanobis accord the objective reality and have a good conformity with known deposits in the study area.

  16. A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers

    Science.gov (United States)

    Liu, Zhuowei; Chen, Shuxin; Wu, Hao; He, Renke; Hao, Lin

    2018-01-01

    In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student’s t distribution as well as approximates the multi-target intensity as a mixture of Student’s t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student’s t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student’s t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers. PMID:29617348

  17. A Global Photoionization Response to Prompt Emission and Outliers: Different Origin of Long Gamma-ray Bursts?

    Science.gov (United States)

    Wang, J.; Xin, L. P.; Qiu, Y. L.; Xu, D. W.; Wei, J. Y.

    2018-03-01

    By using the line ratio C IV λ1549/C II λ1335 as a tracer of the ionization ratio of the interstellar medium (ISM) illuminated by a long gamma-ray burst (LGRB), we identify a global photoionization response of the ionization ratio to the photon luminosity of the prompt emission assessed by either L iso/E peak or {L}iso}/{E}peak}2. The ionization ratio increases with both L iso/E peak and L iso/E 2 peak for a majority of the LGRBs in our sample, although there are a few outliers. The identified dependence of C IV/C II on {L}iso}/{E}peak}2 suggests that the scatter of the widely accepted Amati relation is related to the ionization ratio in the ISM. The outliers tend to have relatively high C IV/C II values as well as relatively high C IV λ1549/Si IV λ1403 ratios, which suggests an existence of Wolf–Rayet stars in the environment of these LGRBs. We finally argue that the outliers and the LGRBs following the identified C IV/C II‑L iso/E peak ({L}iso}/{E}peak}2) correlation might come from different progenitors with different local environments.

  18. A new HPLC method for the detection of iodine applied to natural samples of edible seaweeds and commercial seaweed food products.

    Science.gov (United States)

    Nitschke, Udo; Stengel, Dagmar B

    2015-04-01

    Rich in micronutrients and considered to contain high iodine levels, seaweeds have multiple applications as food/supplements and nutraceuticals with potential health implications. Here, we describe the development and validation of a new analytical method to quantify iodine as iodide (I(-)) using an isocratic HPLC system with UV detection; algal iodine was converted to I(-) via dry alkaline incineration. The method was successfully applied to 19 macroalgal species from three taxonomic groups and five commercially available seaweed food products. Fesh kelps contained highest levels, reaching >1.0% per dry weight (DW), but concentrations differed amongst thallus parts. In addition to kelps, other brown (Fucales: ∼ 0.05% DW) and some red species (∼ 0.05% DW) can also serve as a rich source of iodine; lowest iodine concentrations were detected in green macroalgae (∼ 0.005% DW), implying that quantities recommended for seaweed consumption may require species-specific re-evaluation to reach adequate daily intake levels. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Can fractal methods applied to video tracking detect the effects of deltamethrin pesticide or mercury on the locomotion behavior of shrimps?

    Science.gov (United States)

    Tenorio, Bruno Mendes; da Silva Filho, Eurípedes Alves; Neiva, Gentileza Santos Martins; da Silva, Valdemiro Amaro; Tenorio, Fernanda das Chagas Angelo Mendes; da Silva, Themis de Jesus; Silva, Emerson Carlos Soares E; Nogueira, Romildo de Albuquerque

    2017-08-01

    Shrimps can accumulate environmental toxicants and suffer behavioral changes. However, methods to quantitatively detect changes in the behavior of these shrimps are still needed. The present study aims to verify whether mathematical and fractal methods applied to video tracking can adequately describe changes in the locomotion behavior of shrimps exposed to low concentrations of toxic chemicals, such as 0.15µgL -1 deltamethrin pesticide or 10µgL -1 mercuric chloride. Results showed no change after 1min, 4, 24, and 48h of treatment. However, after 72 and 96h of treatment, both the linear methods describing the track length, mean speed, mean distance from the current to the previous track point, as well as the non-linear methods of fractal dimension (box counting or information entropy) and multifractal analysis were able to detect changes in the locomotion behavior of shrimps exposed to deltamethrin. Analysis of angular parameters of the track points vectors and lacunarity were not sensitive to those changes. None of the methods showed adverse effects to mercury exposure. These mathematical and fractal methods applicable to software represent low cost useful tools in the toxicological analyses of shrimps for quality of food, water and biomonitoring of ecosystems. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Adaptive statistical iterative reconstruction-applied ultra-low-dose CT with radiography- comparable radiation dose: Usefulness for lung nodule detection

    International Nuclear Information System (INIS)

    Yoon, Hyun Jung; Chung, Myung Jin; Hwang, Hye Sun; Lee, Kyung Soo; Moon, Jung Won

    2015-01-01

    To assess the performance of adaptive statistical iterative reconstruction (ASIR)-applied ultra-low-dose CT (ULDCT) in detecting small lung nodules. Thirty patients underwent both ULDCT and standard dose CT (SCT). After determining the reference standard nodules, five observers, blinded to the reference standard reading results, independently evaluated SCT and both subsets of ASIR- and filtered back projection (FBP)-driven ULDCT images. Data assessed by observers were compared statistically. Converted effective doses in SCT and ULDCT were 2.81 ± 0.92 and 0.17 ± 0.02 mSv, respectively. A total of 114 lung nodules were detected on SCT as a standard reference. There was no statistically significant difference in sensitivity between ASIR-driven ULDCT and SCT for three out of the five observers (p = 0.678, 0.735, < 0.01, 0.038, and < 0.868 for observers 1, 2, 3, 4, and 5, respectively). The sensitivity of FBP-driven ULDCT was significantly lower than that of ASIR-driven ULDCT in three out of the five observers (p < 0.01 for three observers, and p = 0.064 and 0.146 for two observers). In jackknife alternative free-response receiver operating characteristic analysis, the mean values of figure-of-merit (FOM) for FBP, ASIR-driven ULDCT, and SCT were 0.682, 0.772, and 0.821, respectively, and there were no significant differences in FOM values between ASIR-driven ULDCT and SCT (p = 0.11), but the FOM value of FBP-driven ULDCT was significantly lower than that of ASIR-driven ULDCT and SCT (p = 0.01 and 0.00). Adaptive statistical iterative reconstruction-driven ULDCT delivering a radiation dose of only 0.17 mSv offers acceptable sensitivity in nodule detection compared with SCT and has better performance than FBP-driven ULDCT

  1. Adaptive statistical iterative reconstruction-applied ultra-low-dose CT with radiography- comparable radiation dose: Usefulness for lung nodule detection

    Energy Technology Data Exchange (ETDEWEB)

    Yoon, Hyun Jung; Chung, Myung Jin; Hwang, Hye Sun; Lee, Kyung Soo [Dept. of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of); Moon, Jung Won [Dept. of Radiology, Kangbuk Samsung Hospital, Seoul (Korea, Republic of)

    2015-10-15

    To assess the performance of adaptive statistical iterative reconstruction (ASIR)-applied ultra-low-dose CT (ULDCT) in detecting small lung nodules. Thirty patients underwent both ULDCT and standard dose CT (SCT). After determining the reference standard nodules, five observers, blinded to the reference standard reading results, independently evaluated SCT and both subsets of ASIR- and filtered back projection (FBP)-driven ULDCT images. Data assessed by observers were compared statistically. Converted effective doses in SCT and ULDCT were 2.81 ± 0.92 and 0.17 ± 0.02 mSv, respectively. A total of 114 lung nodules were detected on SCT as a standard reference. There was no statistically significant difference in sensitivity between ASIR-driven ULDCT and SCT for three out of the five observers (p = 0.678, 0.735, < 0.01, 0.038, and < 0.868 for observers 1, 2, 3, 4, and 5, respectively). The sensitivity of FBP-driven ULDCT was significantly lower than that of ASIR-driven ULDCT in three out of the five observers (p < 0.01 for three observers, and p = 0.064 and 0.146 for two observers). In jackknife alternative free-response receiver operating characteristic analysis, the mean values of figure-of-merit (FOM) for FBP, ASIR-driven ULDCT, and SCT were 0.682, 0.772, and 0.821, respectively, and there were no significant differences in FOM values between ASIR-driven ULDCT and SCT (p = 0.11), but the FOM value of FBP-driven ULDCT was significantly lower than that of ASIR-driven ULDCT and SCT (p = 0.01 and 0.00). Adaptive statistical iterative reconstruction-driven ULDCT delivering a radiation dose of only 0.17 mSv offers acceptable sensitivity in nodule detection compared with SCT and has better performance than FBP-driven ULDCT.

  2. Adaptive Statistical Iterative Reconstruction-Applied Ultra-Low-Dose CT with Radiography-Comparable Radiation Dose: Usefulness for Lung Nodule Detection.

    Science.gov (United States)

    Yoon, Hyun Jung; Chung, Myung Jin; Hwang, Hye Sun; Moon, Jung Won; Lee, Kyung Soo

    2015-01-01

    To assess the performance of adaptive statistical iterative reconstruction (ASIR)-applied ultra-low-dose CT (ULDCT) in detecting small lung nodules. Thirty patients underwent both ULDCT and standard dose CT (SCT). After determining the reference standard nodules, five observers, blinded to the reference standard reading results, independently evaluated SCT and both subsets of ASIR- and filtered back projection (FBP)-driven ULDCT images. Data assessed by observers were compared statistically. Converted effective doses in SCT and ULDCT were 2.81 ± 0.92 and 0.17 ± 0.02 mSv, respectively. A total of 114 lung nodules were detected on SCT as a standard reference. There was no statistically significant difference in sensitivity between ASIR-driven ULDCT and SCT for three out of the five observers (p = 0.678, 0.735, ASIR-driven ULDCT in three out of the five observers (p ASIR-driven ULDCT, and SCT were 0.682, 0.772, and 0.821, respectively, and there were no significant differences in FOM values between ASIR-driven ULDCT and SCT (p = 0.11), but the FOM value of FBP-driven ULDCT was significantly lower than that of ASIR-driven ULDCT and SCT (p = 0.01 and 0.00). Adaptive statistical iterative reconstruction-driven ULDCT delivering a radiation dose of only 0.17 mSv offers acceptable sensitivity in nodule detection compared with SCT and has better performance than FBP-driven ULDCT.

  3. Evaluation of selected biomarkers for the detection of chemical sensitization in human skin: a comparative study applying THP-1, MUTZ-3 and primary dendritic cells in culture.

    Science.gov (United States)

    Hitzler, Manuel; Bergert, Antje; Luch, Andreas; Peiser, Matthias

    2013-09-01

    Dendritic cells (DCs) exhibit the unique capacity to induce T cell differentiation and proliferation, two processes that are crucially involved in allergic reactions. By combining the exclusive potential of DCs as the only professional antigen-presenting cells of the human body with the well known handling advantages of cell lines, cell-based alternative methods aimed at detecting chemical sensitization in vitro commonly apply DC-like cells derived from myeloid cell lines. Here, we present the new biomarkers programmed death-ligand 1 (PD-L1), DC immunoreceptor (DCIR), IL-16, and neutrophil-activating protein-2 (NAP-2), all of which have been detectable in primary human DCs upon exposure to chemical contact allergens. To evaluate the applicability of DC-like cells in the prediction of a chemical's sensitization potential, the expression of cell surface PD-L1 and DCIR was analyzed. In contrast to primary DCs, only minor subpopulations of MUTZ-3 and THP-1 cells presented PD-L1 or DCIR at their surface. After exposure to increasing concentrations of nickel and cinnamic aldehyde, the expression level of PD-L1 and DCIR revealed much stronger affected on monocyte-derived DCs (MoDCs) or Langerhans cells (MoLCs) when compared to THP-1 and MUTZ-3 cells. Applying protein profiler arrays we further identified the soluble factors NAP-2, IL-16, IL-8 and MIP-1α as sensitive biomarkers showing the capacity to discriminate sensitizing from non-sensitizing chemicals or irritants. An allergen-specific release of IL-8 and MIP-1α could be detected in the supernatants of MoDCs and MoLCs and also in MUTZ-3 and THP-1 cells, though at much lower levels. On the protein and transcriptional level, NAP-2 and IL-16 indicated sensitizers most sensitively and specifically in MoDCs. Altogether, we have proven the reciprocal regulated surface molecules PD-L1 and DCIR and the soluble factors MIP-1α, NAP-2 and IL-16 as reliable biomarkers for chemical sensitization. We further show that primary

  4. Determining Type I and Type II Errors when Applying Information Theoretic Change Detection Metrics for Data Association and Space Situational Awareness

    Science.gov (United States)

    Wilkins, M.; Moyer, E. J.; Hussein, Islam I.; Schumacher, P. W., Jr.

    Correlating new detections back to a large catalog of resident space objects (RSOs) requires solving one of three types of data association problems: observation-to-track, track-to-track, or observation-to-observation. The authors previous work has explored the use of various information divergence metrics for solving these problems: Kullback-Leibler (KL) divergence, mutual information, and Bhattacharrya distance. In addition to approaching the data association problem strictly from the metric tracking aspect, we have explored fusing metric and photometric data using Bayesian probabilistic reasoning for RSO identification to aid in our ability to correlate data to specific RS Os. In this work, we will focus our attention on the KL Divergence, which is a measure of the information gained when new evidence causes the observer to revise their beliefs. We can apply the Principle of Minimum Discrimination Information such that new data produces as small an information gain as possible and this information change is bounded by ɛ. Choosing an appropriate value for ɛ for both convergence and change detection is a function of your risk tolerance. Small ɛ for change detection increases alarm rates while larger ɛ for convergence means that new evidence need not be identical in information content. We need to understand what this change detection metric implies for Type I α and Type II β errors when we are forced to make a decision on whether new evidence represents a true change in characterization of an object or is merely within the bounds of our measurement uncertainty. This is unclear for the case of fusing multiple kinds and qualities of characterization evidence that may exist in different metric spaces or are even semantic statements. To this end, we explore the use of Sequential Probability Ratio Testing where we suppose that we may need to collect additional evidence before accepting or rejecting the null hypothesis that a change has occurred. In this work, we

  5. In vivo detection of small tumour lesions by multi-pinhole SPECT applying a (99m)Tc-labelled nanobody targeting the Epidermal Growth Factor Receptor.

    Science.gov (United States)

    Krüwel, Thomas; Nevoltris, Damien; Bode, Julia; Dullin, Christian; Baty, Daniel; Chames, Patrick; Alves, Frauke

    2016-02-25

    The detection of tumours in an early phase of tumour development in combination with the knowledge of expression of tumour markers such as epidermal growth factor receptor (EGFR) is an important prerequisite for clinical decisions. In this study we applied the anti-EGFR nanobody (99m)Tc-D10 for visualizing small tumour lesions with volumes below 100 mm(3) by targeting EGFR in orthotopic human mammary MDA-MB-468 and MDA-MB-231 and subcutaneous human epidermoid A431 carcinoma mouse models. Use of nanobody (99m)Tc-D10 of a size as small as 15.5 kDa enables detection of tumours by single photon emission computed tomography (SPECT) imaging already 45 min post intravenous administration with high tumour uptake (>3% ID/g) in small MDA-MB-468 and A431 tumours, with tumour volumes of 52.5 mm(3) ± 21.2 and 26.6 mm(3) ± 16.7, respectively. Fast blood clearance with a serum half-life of 4.9 min resulted in high in vivo contrast and ex vivo tumour to blood and tissue ratios. In contrast, no accumulation of (99m)Tc-D10 in MDA-MB-231 tumours characterized by a very low expression of EGFR was observed. Here we present specific and high contrast in vivo visualization of small human tumours overexpressing EGFR by preclinical multi-pinhole SPECT shortly after administration of anti-EGFR nanobody (99m)Tc-D10.

  6. In vivo detection of small tumour lesions by multi-pinhole SPECT applying a 99mTc-labelled nanobody targeting the Epidermal Growth Factor Receptor

    Science.gov (United States)

    Krüwel, Thomas; Nevoltris, Damien; Bode, Julia; Dullin, Christian; Baty, Daniel; Chames, Patrick; Alves, Frauke

    2016-01-01

    The detection of tumours in an early phase of tumour development in combination with the knowledge of expression of tumour markers such as epidermal growth factor receptor (EGFR) is an important prerequisite for clinical decisions. In this study we applied the anti-EGFR nanobody 99mTc-D10 for visualizing small tumour lesions with volumes below 100 mm3 by targeting EGFR in orthotopic human mammary MDA-MB-468 and MDA-MB-231 and subcutaneous human epidermoid A431 carcinoma mouse models. Use of nanobody 99mTc-D10 of a size as small as 15.5 kDa enables detection of tumours by single photon emission computed tomography (SPECT) imaging already 45 min post intravenous administration with high tumour uptake (>3% ID/g) in small MDA-MB-468 and A431 tumours, with tumour volumes of 52.5 mm3 ± 21.2 and 26.6 mm3 ± 16.7, respectively. Fast blood clearance with a serum half-life of 4.9 min resulted in high in vivo contrast and ex vivo tumour to blood and tissue ratios. In contrast, no accumulation of 99mTc-D10 in MDA-MB-231 tumours characterized by a very low expression of EGFR was observed. Here we present specific and high contrast in vivo visualization of small human tumours overexpressing EGFR by preclinical multi-pinhole SPECT shortly after administration of anti-EGFR nanobody 99mTc-D10. PMID:26912069

  7. Reliability of cortical lesion detection on double inversion recovery MRI applying the MAGNIMS-Criteria in multiple sclerosis patients within a 16-months period.

    Directory of Open Access Journals (Sweden)

    Tobias Djamsched Faizy

    Full Text Available In patients with multiple sclerosis (MS, Double Inversion Recovery (DIR magnetic resonance imaging (MRI can be used to identify cortical lesions (CL. We sought to evaluate the reliability of CL detection on DIR longitudinally at multiple subsequent time-points applying the MAGNIMs scoring criteria for CLs.26 MS patients received a 3T-MRI (Siemens, Skyra with DIR at 12 time-points (TP within a 16 months period. Scans were assessed in random order by two different raters. Both raters separately marked all CLs on each scan and total lesion numbers were obtained for each scan-TP and patient. After a retrospective re-evaluation, the number of consensus CLs (conL was defined as the total number of CLs, which both raters finally agreed on. CLs volumes, relative signal intensities and CLs localizations were determined. Both ratings (conL vs. non-consensus scoring were compared for further analysis.A total number of n = 334 CLs were identified by both raters in 26 MS patients with a first agreement of both raters on 160 out of 334 of the CLs found (κ = 0.48. After the retrospective re-evaluation, consensus agreement increased to 233 out of 334 CL (κ = 0.69. 93.8% of conL were visible in at least 2 consecutive TP. 74.7% of the conL were visible in all 12 consecutive TP. ConL had greater mean lesion volumes and higher mean signal intensities compared to lesions that were only detected by one of the raters (p<0.05. A higher number of CLs in the frontal, parietal, temporal and occipital lobe were identified by both raters than the number of those only identified by one of the raters (p<0.05.After a first assessment, slightly less than a half of the CL were considered as reliably detectable on longitudinal DIR images. A retrospective re-evaluation notably increased the consensus agreement. However, this finding is narrowed, considering the fact that retrospective evaluation steps might not be practicable in clinical routine. Lesions that were not reliably

  8. A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution

    Directory of Open Access Journals (Sweden)

    Shizhen Zhao

    2018-06-01

    Full Text Available Mobile activity recognition is significant to the development of human-centric pervasive applications including elderly care, personalized recommendations, etc. Nevertheless, the distribution of inertial sensor data can be influenced to a great extent by varying users. This means that the performance of an activity recognition classifier trained by one user’s dataset will degenerate when transferred to others. In this study, we focus on building a personalized classifier to detect four categories of human activities: light intensity activity, moderate intensity activity, vigorous intensity activity, and fall. In order to solve the problem caused by different distributions of inertial sensor signals, a user-adaptive algorithm based on K-Means clustering, local outlier factor (LOF, and multivariate Gaussian distribution (MGD is proposed. To automatically cluster and annotate a specific user’s activity data, an improved K-Means algorithm with a novel initialization method is designed. By quantifying the samples’ informative degree in a labeled individual dataset, the most profitable samples can be selected for activity recognition model adaption. Through experiments, we conclude that our proposed models can adapt to new users with good recognition performance.

  9. Hot spots, cluster detection and spatial outlier analysis of teen birth rates in the U.S., 2003–2012

    OpenAIRE

    Khan, Diba; Rossen, Lauren M.; Hamilton, Brady E.; He, Yulei; Wei, Rong; Dienes, Erin

    2017-01-01

    Teen birth rates have evidenced a significant decline in the United States over the past few decades. Most of the states in the US have mirrored this national decline, though some reports have illustrated substantial variation in the magnitude of these decreases across the U.S. Importantly, geographic variation at the county level has largely not been explored. We used National Vital Statistics Births data and Hierarchical Bayesian space-time interaction models to produce smoothed estimates o...

  10. Detection of Doppler Microembolic Signals Using High Order Statistics

    Directory of Open Access Journals (Sweden)

    Maroun Geryes

    2016-01-01

    Full Text Available Robust detection of the smallest circulating cerebral microemboli is an efficient way of preventing strokes, which is second cause of mortality worldwide. Transcranial Doppler ultrasound is widely considered the most convenient system for the detection of microemboli. The most common standard detection is achieved through the Doppler energy signal and depends on an empirically set constant threshold. On the other hand, in the past few years, higher order statistics have been an extensive field of research as they represent descriptive statistics that can be used to detect signal outliers. In this study, we propose new types of microembolic detectors based on the windowed calculation of the third moment skewness and fourth moment kurtosis of the energy signal. During energy embolus-free periods the distribution of the energy is not altered and the skewness and kurtosis signals do not exhibit any peak values. In the presence of emboli, the energy distribution is distorted and the skewness and kurtosis signals exhibit peaks, corresponding to the latter emboli. Applied on real signals, the detection of microemboli through the skewness and kurtosis signals outperformed the detection through standard methods. The sensitivities and specificities reached 78% and 91% and 80% and 90% for the skewness and kurtosis detectors, respectively.

  11. Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.

    Directory of Open Access Journals (Sweden)

    Tarun Mehra

    Full Text Available Case weights of Diagnosis Related Groups (DRGs are determined by the average cost of cases from a previous billing period. However, a significant amount of cases are largely over- or underfunded. We therefore decided to analyze earning outliers of our hospital as to search for predictors enabling a better grouping under SwissDRG.28,893 inpatient cases without additional private insurance discharged from our hospital in 2012 were included in our analysis. Outliers were defined by the interquartile range method. Predictors for deficit and profit outliers were determined with logistic regressions. Predictors were shortlisted with the LASSO regularized logistic regression method and compared to results of Random forest analysis. 10 of these parameters were selected for quantile regression analysis as to quantify their impact on earnings.Psychiatric diagnosis and admission as an emergency case were significant predictors for higher deficit with negative regression coefficients for all analyzed quantiles (p<0.001. Admission from an external health care provider was a significant predictor for a higher deficit in all but the 90% quantile (p<0.001 for Q10, Q20, Q50, Q80 and p = 0.0017 for Q90. Burns predicted higher earnings for cases which were favorably remunerated (p<0.001 for the 90% quantile. Osteoporosis predicted a higher deficit in the most underfunded cases, but did not predict differences in earnings for balanced or profitable cases (Q10 and Q20: p<0.00, Q50: p = 0.10, Q80: p = 0.88 and Q90: p = 0.52. ICU stay, mechanical and patient clinical complexity level score (PCCL predicted higher losses at the 10% quantile but also higher profits at the 90% quantile (p<0.001.We suggest considering psychiatric diagnosis, admission as an emergency case and admission from an external health care provider as DRG split criteria as they predict large, consistent and significant losses.

  12. Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.

    Science.gov (United States)

    Mehra, Tarun; Müller, Christian Thomas Benedikt; Volbracht, Jörk; Seifert, Burkhardt; Moos, Rudolf

    2015-01-01

    Case weights of Diagnosis Related Groups (DRGs) are determined by the average cost of cases from a previous billing period. However, a significant amount of cases are largely over- or underfunded. We therefore decided to analyze earning outliers of our hospital as to search for predictors enabling a better grouping under SwissDRG. 28,893 inpatient cases without additional private insurance discharged from our hospital in 2012 were included in our analysis. Outliers were defined by the interquartile range method. Predictors for deficit and profit outliers were determined with logistic regressions. Predictors were shortlisted with the LASSO regularized logistic regression method and compared to results of Random forest analysis. 10 of these parameters were selected for quantile regression analysis as to quantify their impact on earnings. Psychiatric diagnosis and admission as an emergency case were significant predictors for higher deficit with negative regression coefficients for all analyzed quantiles (p<0.001). Admission from an external health care provider was a significant predictor for a higher deficit in all but the 90% quantile (p<0.001 for Q10, Q20, Q50, Q80 and p = 0.0017 for Q90). Burns predicted higher earnings for cases which were favorably remunerated (p<0.001 for the 90% quantile). Osteoporosis predicted a higher deficit in the most underfunded cases, but did not predict differences in earnings for balanced or profitable cases (Q10 and Q20: p<0.00, Q50: p = 0.10, Q80: p = 0.88 and Q90: p = 0.52). ICU stay, mechanical and patient clinical complexity level score (PCCL) predicted higher losses at the 10% quantile but also higher profits at the 90% quantile (p<0.001). We suggest considering psychiatric diagnosis, admission as an emergency case and admission from an external health care provider as DRG split criteria as they predict large, consistent and significant losses.

  13. Pilot study for supervised target detection applied to spatially registered multiparametric MRI in order to non-invasively score prostate cancer.

    Science.gov (United States)

    Mayer, Rulon; Simone, Charles B; Skinner, William; Turkbey, Baris; Choykey, Peter

    2018-03-01

    Gleason Score (GS) is a validated predictor of prostate cancer (PCa) disease progression and outcomes. GS from invasive needle biopsies suffers from significant inter-observer variability and possible sampling error, leading to underestimating disease severity ("underscoring") and can result in possible complications. A robust non-invasive image-based approach is, therefore, needed. Use spatially registered multi-parametric MRI (MP-MRI), signatures, and supervised target detection algorithms (STDA) to non-invasively GS PCa at the voxel level. This study retrospectively analyzed 26 MP-MRI from The Cancer Imaging Archive. The MP-MRI (T2, Diffusion Weighted, Dynamic Contrast Enhanced) were spatially registered to each other, combined into stacks, and stitched together to form hypercubes. Multi-parametric (or multi-spectral) signatures derived from a training set of registered MP-MRI were transformed using statistics-based Whitening-Dewhitening (WD). Transformed signatures were inserted into STDA (having conical decision surfaces) applied to registered MP-MRI determined the tumor GS. The MRI-derived GS was quantitatively compared to the pathologist's assessment of the histology of sectioned whole mount prostates from patients who underwent radical prostatectomy. In addition, a meta-analysis of 17 studies of needle biopsy determined GS with confusion matrices and was compared to the MRI-determined GS. STDA and histology determined GS are highly correlated (R = 0.86, p < 0.02). STDA more accurately determined GS and reduced GS underscoring of PCa relative to needle biopsy as summarized by meta-analysis (p < 0.05). This pilot study found registered MP-MRI, STDA, and WD transforms of signatures shows promise in non-invasively GS PCa and reducing underscoring with high spatial resolution. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. Thermal detection thresholds of Aδ- and C-fibre afferents activated by brief CO2 laser pulses applied onto the human hairy skin.

    Directory of Open Access Journals (Sweden)

    Maxim Churyukanov

    Full Text Available Brief high-power laser pulses applied onto the hairy skin of the distal end of a limb generate a double sensation related to the activation of Aδ- and C-fibres, referred to as first and second pain. However, neurophysiological and behavioural responses related to the activation of C-fibres can be studied reliably only if the concomitant activation of Aδ-fibres is avoided. Here, using a novel CO(2 laser stimulator able to deliver constant-temperature heat pulses through a feedback regulation of laser power by an online measurement of skin temperature at target site, combined with an adaptive staircase algorithm using reaction-time to distinguish between responses triggered by Aδ- and C-fibre input, we show that it is possible to estimate robustly and independently the thermal detection thresholds of Aδ-fibres (46.9±1.7°C and C-fibres (39.8±1.7°C. Furthermore, we show that both thresholds are dependent on the skin temperature preceding and/or surrounding the test stimulus, indicating that the Aδ- and C-fibre afferents triggering the behavioural responses to brief laser pulses behave, at least partially, as detectors of a change in skin temperature rather than as pure level detectors. Most importantly, our results show that the difference in threshold between Aδ- and C-fibre afferents activated by brief laser pulses can be exploited to activate C-fibres selectively and reliably, provided that the rise in skin temperature generated by the laser stimulator is well-controlled. Our approach could constitute a tool to explore, in humans, the physiological and pathophysiological mechanisms involved in processing C- and Aδ-fibre input, respectively.

  15. Robust bivariate error detection in skewed data with application to historical radiosonde winds

    KAUST Repository

    Sun, Ying

    2017-01-18

    The global historical radiosonde archives date back to the 1920s and contain the only directly observed measurements of temperature, wind, and moisture in the upper atmosphere, but they contain many random errors. Most of the focus on cleaning these large datasets has been on temperatures, but winds are important inputs to climate models and in studies of wind climatology. The bivariate distribution of the wind vector does not have elliptical contours but is skewed and heavy-tailed, so we develop two methods for outlier detection based on the bivariate skew-t (BST) distribution, using either distance-based or contour-based approaches to flag observations as potential outliers. We develop a framework to robustly estimate the parameters of the BST and then show how the tuning parameter to get these estimates is chosen. In simulation, we compare our methods with one based on a bivariate normal distribution and a nonparametric approach based on the bagplot. We then apply all four methods to the winds observed for over 35,000 radiosonde launches at a single station and demonstrate differences in the number of observations flagged across eight pressure levels and through time. In this pilot study, the method based on the BST contours performs very well.

  16. Robust bivariate error detection in skewed data with application to historical radiosonde winds

    KAUST Repository

    Sun, Ying; Hering, Amanda S.; Browning, Joshua M.

    2017-01-01

    The global historical radiosonde archives date back to the 1920s and contain the only directly observed measurements of temperature, wind, and moisture in the upper atmosphere, but they contain many random errors. Most of the focus on cleaning these large datasets has been on temperatures, but winds are important inputs to climate models and in studies of wind climatology. The bivariate distribution of the wind vector does not have elliptical contours but is skewed and heavy-tailed, so we develop two methods for outlier detection based on the bivariate skew-t (BST) distribution, using either distance-based or contour-based approaches to flag observations as potential outliers. We develop a framework to robustly estimate the parameters of the BST and then show how the tuning parameter to get these estimates is chosen. In simulation, we compare our methods with one based on a bivariate normal distribution and a nonparametric approach based on the bagplot. We then apply all four methods to the winds observed for over 35,000 radiosonde launches at a single station and demonstrate differences in the number of observations flagged across eight pressure levels and through time. In this pilot study, the method based on the BST contours performs very well.

  17. Engaging children in the development of obesity interventions: Exploring outcomes that matter most among obesity positive outliers.

    Science.gov (United States)

    Sharifi, Mona; Marshall, Gareth; Goldman, Roberta E; Cunningham, Courtney; Marshall, Richard; Taveras, Elsie M

    2015-11-01

    To explore outcomes and measures of success that matter most to 'positive outlier' children who improved their body mass index (BMI) despite living in obesogenic neighborhoods. We collected residential address and longitudinal height/weight data from electronic health records of 22,657 children ages 6-12 years in Massachusetts. We defined obesity "hotspots" as zip codes where >15% of children had a BMI ≥95th percentile. Using linear mixed effects models, we generated a BMI z-score slope for each child with a history of obesity. We recruited 10-12 year-olds with negative slopes living in hotspots for focus groups. We analyzed group transcripts and discussed emerging themes in iterative meetings using an immersion/crystallization approach. We reached thematic saturation after 4 focus groups with 21 children. Children identified bullying and negative peer comparisons related to physical appearance, clothing size, and athletic ability as motivating them to achieve a healthier weight, and they measured success as improvement in these domains. Positive relationships with friends and family facilitated both behavior change initiation and maintenance. The perspectives of positive outlier children can provide insight into children's motivations leading to successful obesity management. Child/family engagement should guide the development of patient-centered obesity interventions. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  18. Combined CT-based and image-free navigation systems in TKA reduces postoperative outliers of rotational alignment of the tibial component.

    Science.gov (United States)

    Mitsuhashi, Shota; Akamatsu, Yasushi; Kobayashi, Hideo; Kusayama, Yoshihiro; Kumagai, Ken; Saito, Tomoyuki

    2018-02-01

    Rotational malpositioning of the tibial component can lead to poor functional outcome in TKA. Although various surgical techniques have been proposed, precise rotational placement of the tibial component was difficult to accomplish even with the use of a navigation system. The purpose of this study is to assess whether combined CT-based and image-free navigation systems replicate accurately the rotational alignment of tibial component that was preoperatively planned on CT, compared with the conventional method. We compared the number of outliers for rotational alignment of the tibial component using combined CT-based and image-free navigation systems (navigated group) with those of conventional method (conventional group). Seventy-two TKAs were performed between May 2012 and December 2014. In the navigated group, the anteroposterior axis was prepared using CT-based navigation system and the tibial component was positioned under control of the navigation. In the conventional group, the tibial component was placed with reference to the Akagi line that was determined visually. Fisher's exact probability test was performed to evaluate the results. There was a significant difference between the two groups with regard to the number of outliers: 3 outliers in the navigated group compared with 12 outliers in the conventional group (P image-free navigation systems decreased the number of rotational outliers of tibial component, and was helpful for the replication of the accurate rotational alignment of the tibial component that was preoperatively planned.

  19. STEM - software test and evaluation methods: fault detection using static analysis techniques

    International Nuclear Information System (INIS)

    Bishop, P.G.; Esp, D.G.

    1988-08-01

    STEM is a software reliability project with the objective of evaluating a number of fault detection and fault estimation methods which can be applied to high integrity software. This Report gives some interim results of applying both manual and computer-based static analysis techniques, in particular SPADE, to an early CERL version of the PODS software containing known faults. The main results of this study are that: The scope for thorough verification is determined by the quality of the design documentation; documentation defects become especially apparent when verification is attempted. For well-defined software, the thoroughness of SPADE-assisted verification for detecting a large class of faults was successfully demonstrated. For imprecisely-defined software (not recommended for high-integrity systems) the use of tools such as SPADE is difficult and inappropriate. Analysis and verification tools are helpful, through their reliability and thoroughness. However, they are designed to assist, not replace, a human in validating software. Manual inspection can still reveal errors (such as errors in specification and errors of transcription of systems constants) which current tools cannot detect. There is a need for tools to automatically detect typographical errors in system constants, for example by reporting outliers to patterns. To obtain the maximum benefit from advanced tools, they should be applied during software development (when verification problems can be detected and corrected) rather than retrospectively. (author)

  20. An Applied Physicist Does Econometrics

    Science.gov (United States)

    Taff, L. G.

    2010-02-01

    The biggest problem those attempting to understand econometric data, via modeling, have is that economics has no F = ma. Without a theoretical underpinning, econometricians have no way to build a good model to fit observations to. Physicists do, and when F = ma failed, we knew it. Still desiring to comprehend econometric data, applied economists turn to mis-applying probability theory---especially with regard to the assumptions concerning random errors---and choosing extremely simplistic analytical formulations of inter-relationships. This introduces model bias to an unknown degree. An applied physicist, used to having to match observations to a numerical or analytical model with a firm theoretical basis, modify the model, re-perform the analysis, and then know why, and when, to delete ``outliers'', is at a considerable advantage when quantitatively analyzing econometric data. I treat two cases. One is to determine the household density distribution of total assets, annual income, age, level of education, race, and marital status. Each of these ``independent'' variables is highly correlated with every other but only current annual income and level of education follow a linear relationship. The other is to discover the functional dependence of total assets on the distribution of assets: total assets has an amazingly tight power law dependence on a quadratic function of portfolio composition. Who knew? )

  1. Health system barriers and enablers to early access to breast cancer screening, detection, and diagnosis: a global analysis applied to the MENA region.

    Science.gov (United States)

    Bowser, D; Marqusee, H; El Koussa, M; Atun, R

    2017-11-01

    To identify barriers and enablers that impact access to early screening, detection, and diagnosis of breast cancer both globally and more specifically in the Middle East and North Africa (MENA) region (with a specific focus on Egypt, Jordan, Oman, Saudi Arabia, United Arab Emirates [UAE], and Kuwait) with a specific focus on the health system. A systematic review of literature. We conducted a systematic reviewing using the PRISMA methodology. We searched PubMed, Global Index Medicus, and EMBASE for studies on 'breast cancer', 'breast neoplasm,' or 'screening, early detection, and early diagnosis' as well as key words related to the following barriers: religion, culture, health literacy, lack of knowledge/awareness/understanding, attitudes, fatalism/fear, shame/embarrassment, and physician gender from January 1, 2000 until September 1, 2016. Two independent reviewers screened both titles and abstracts. The application of inclusion and exclusion criteria yielded a final list of articles. A conceptual framework was used to guide the thematic analysis and examine health system barriers and enablers to breast cancer screening at the broader macro health system level, at the health provider level, and the individual level. The analysis was conducted globally and in the MENA region. A total of 11,936 references were identified through the initial search strategy, of which 55 were included in the final thematic analysis. The results found the following barriers and enablers to access to breast cancer screening at the health system level, the health provider level, and the individual level: health system structures such as health insurance and care coordination systems, costs, time concerns, provider characteristics including gender of the provider, quality of care issues, medical concerns, and fear. In addition, the following seven barriers and enablers were identified at the health system or provider level as significantly impacting screening for breast cancer: (1) access

  2. Using the developed cross-flow filtration chip for collecting blood plasma under high flow rate condition and applying the immunoglobulin E detection

    Science.gov (United States)

    Yeh, Chia-Hsien; Hung, Chia-Wei; Wu, Chun-Han; Lin, Yu-Cheng

    2014-09-01

    This paper presents a cross-flow filtration chip for separating blood cells (white blood cells, red blood cells, and platelets) and obtaining blood plasma from human blood. Our strategy is to flow the sample solution in parallel to the membrane, which can generate a parallel shear stress to remove the clogging microparticles on the membrane, so the pure sample solution is obtained in the reservoir. The cross-flow filtration chip includes a cross-flow layer, a Ni-Pd alloy micro-porous membrane, and a reservoir layer. The three layers are packaged in a polymethylmethacrylate (PMMA) frame to create the cross-flow filtration chip. Various dilutions of the blood sample (original, 2 × , 3 × , 5 × , and 10×), pore sizes with different diameters (1 µm, 2 µm, 4 µm, 7 µm, and 10 µm), and different flow rates (1 mL/min, 3 mL/min, 5 mL/min, 7 mL/min, and 10 mL/min) are tested to determine their effects on filtration percentage. The best filtration percentage is 96.2% when the dilution of the blood sample is 10 × , the diameter of pore size of a Ni-Pd alloy micro-porous membrane is 2 µm, and the flow rate is 10 mL/min. Finally, for the clinical tests of the immunoglobulin E (IgE) concentration, the cross-flow filtration chip is used to filter the blood of the allergy patients to obtain the blood plasma. This filtered blood plasma is compared with that obtained using the conventional centrifugation based on the enzyme-linked immunosorbent assay. The results reveal that these two blood separation methods have similar detection trends. The proposed filtration chip has the advantages of low cost, short filtration time, and easy operation and thus can be applied to the separation of microparticles, cells, bacteria, and blood.

  3. Using the developed cross-flow filtration chip for collecting blood plasma under high flow rate condition and applying the immunoglobulin E detection

    International Nuclear Information System (INIS)

    Yeh, Chia-Hsien; Hung, Chia-Wei; Lin, Yu-Cheng; Wu, Chun-Han

    2014-01-01

    This paper presents a cross-flow filtration chip for separating blood cells (white blood cells, red blood cells, and platelets) and obtaining blood plasma from human blood. Our strategy is to flow the sample solution in parallel to the membrane, which can generate a parallel shear stress to remove the clogging microparticles on the membrane, so the pure sample solution is obtained in the reservoir. The cross-flow filtration chip includes a cross-flow layer, a Ni-Pd alloy micro-porous membrane, and a reservoir layer. The three layers are packaged in a polymethylmethacrylate (PMMA) frame to create the cross-flow filtration chip. Various dilutions of the blood sample (original, 2 × , 3 × , 5 × , and 10×), pore sizes with different diameters (1 µm, 2 µm, 4 µm, 7 µm, and 10 µm), and different flow rates (1 mL/min, 3 mL/min, 5 mL/min, 7 mL/min, and 10 mL/min) are tested to determine their effects on filtration percentage. The best filtration percentage is 96.2% when the dilution of the blood sample is 10 × , the diameter of pore size of a Ni-Pd alloy micro-porous membrane is 2 µm, and the flow rate is 10 mL/min. Finally, for the clinical tests of the immunoglobulin E (IgE) concentration, the cross-flow filtration chip is used to filter the blood of the allergy patients to obtain the blood plasma. This filtered blood plasma is compared with that obtained using the conventional centrifugation based on the enzyme-linked immunosorbent assay. The results reveal that these two blood separation methods have similar detection trends. The proposed filtration chip has the advantages of low cost, short filtration time, and easy operation and thus can be applied to the separation of microparticles, cells, bacteria, and blood. (paper)

  4. Applied physics

    International Nuclear Information System (INIS)

    Anon.

    1980-01-01

    The Physics Division research program that is dedicated primarily to applied research goals involves the interaction of energetic particles with solids. This applied research is carried out in conjunction with the basic research studies from which it evolved

  5. Detection of xenobiotic-induced DNA damage by the comet assay applied to human and rat precision-cut liver slices

    NARCIS (Netherlands)

    Plazar, Janja; Hrejac, Irena; Pirih, Primoz; Filipic, Metka; Groothuis, Geny M. M.

    The comet assay is a simple and sensitive method for measuring DNA damage at the level of individual cells and is extensively used in genotoxicity studies. It is commonly applied to cultured cells. The aim of this study was to apply the comet assay for use in fresh liver tissue, where metabolic

  6. Algorithms for Anomaly Detection - Lecture 1

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    The concept of statistical anomalies, or outliers, has fascinated experimentalists since the earliest attempts to interpret data. We want to know why some data points don’t seem to belong with the others: perhaps we want to eliminate spurious or unrepresentative data from our model. Or, the anomalies themselves may be what we are interested in: an outlier could represent the symptom of a disease, an attack on a computer network, a scientific discovery, or even an unfaithful partner. We start with some general considerations, such as the relationship between clustering and anomaly detection, the choice between supervised and unsupervised methods, and the difference between global and local anomalies. Then we will survey the most representative anomaly detection algorithms, highlighting what kind of data each approach is best suited to, and discussing their limitations. We will finish with a discussion of the difficulties of anomaly detection in high-dimensional data and some new directions for anomaly detec...

  7. Algorithms for Anomaly Detection - Lecture 2

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    The concept of statistical anomalies, or outliers, has fascinated experimentalists since the earliest attempts to interpret data. We want to know why some data points don’t seem to belong with the others: perhaps we want to eliminate spurious or unrepresentative data from our model. Or, the anomalies themselves may be what we are interested in: an outlier could represent the symptom of a disease, an attack on a computer network, a scientific discovery, or even an unfaithful partner. We start with some general considerations, such as the relationship between clustering and anomaly detection, the choice between supervised and unsupervised methods, and the difference between global and local anomalies. Then we will survey the most representative anomaly detection algorithms, highlighting what kind of data each approach is best suited to, and discussing their limitations. We will finish with a discussion of the difficulties of anomaly detection in high-dimensional data and some new directions for anomaly detec...

  8. Can the same edge-detection algorithm be applied to on-line and off-line analysis systems? Validation of a new cinefilm-based geometric coronary measurement software

    NARCIS (Netherlands)

    J. Haase (Jürgen); C. di Mario (Carlo); P.W.J.C. Serruys (Patrick); M.M.J.M. van der Linden (Mark); D.P. Foley (David); W.J. van der Giessen (Wim)

    1993-01-01

    textabstractIn the Cardiovascular Measurement System (CMS) the edge-detection algorithm, which was primarily designed for the Philips digital cardiac imaging system (DCI), is applied to cinefilms. Comparative validation of CMS and DCI was performed in vitro and in vivo with intracoronary insertion

  9. Applying spatial analysis tools in public health: an example using SaTScan to detect geographic targets for colorectal cancer screening interventions.

    Science.gov (United States)

    Sherman, Recinda L; Henry, Kevin A; Tannenbaum, Stacey L; Feaster, Daniel J; Kobetz, Erin; Lee, David J

    2014-03-20

    Epidemiologists are gradually incorporating spatial analysis into health-related research as geocoded cases of disease become widely available and health-focused geospatial computer applications are developed. One health-focused application of spatial analysis is cluster detection. Using cluster detection to identify geographic areas with high-risk populations and then screening those populations for disease can improve cancer control. SaTScan is a free cluster-detection software application used by epidemiologists around the world to describe spatial clusters of infectious and chronic disease, as well as disease vectors and risk factors. The objectives of this article are to describe how spatial analysis can be used in cancer control to detect geographic areas in need of colorectal cancer screening intervention, identify issues commonly encountered by SaTScan users, detail how to select the appropriate methods for using SaTScan, and explain how method selection can affect results. As an example, we used various methods to detect areas in Florida where the population is at high risk for late-stage diagnosis of colorectal cancer. We found that much of our analysis was underpowered and that no single method detected all clusters of statistical or public health significance. However, all methods detected 1 area as high risk; this area is potentially a priority area for a screening intervention. Cluster detection can be incorporated into routine public health operations, but the challenge is to identify areas in which the burden of disease can be alleviated through public health intervention. Reliance on SaTScan's default settings does not always produce pertinent results.

  10. When the Plus Sign is a Negative: Challenging and Reinforcing Embodied Stigmas Through Outliers and Counter-Narratives.

    Science.gov (United States)

    Lippert, Alexandra

    2017-11-30

    When individuals become aware of their stigma, they attempt to manage their identity through discourses that both challenge and reinforce power. Identity management is fraught with tensions between the desire to fit normative social constructions and counter the same discourse. This essay explores identity management in the midst of the embodied stigmas concerning unplanned pregnancy during college and raising a biracial son. In doing so, this essay points to the difference between outlier narratives and counter-narratives. The author encourages health communication scholars to explore conditions under which storytelling moves beyond the personal to the political. Emancipatory intent does not guarantee emancipatory outcomes. Storytelling can function therapeutically for individuals while failing to redress forces that constrain human potential and agency.

  11. Reference-free fatigue crack detection using nonlinear ultrasonic modulation under various temperature and loading conditions

    Science.gov (United States)

    Lim, Hyung Jin; Sohn, Hoon; DeSimio, Martin P.; Brown, Kevin

    2014-04-01

    This study presents a reference-free fatigue crack detection technique using nonlinear ultrasonic modulation. When low frequency (LF) and high frequency (HF) inputs generated by two surface-mounted lead zirconate titanate (PZT) transducers are applied to a structure, the presence of a fatigue crack can provide a mechanism for nonlinear ultrasonic modulation and create spectral sidebands around the frequency of the HF signal. The crack-induced spectral sidebands are isolated using a combination of linear response subtraction (LRS), synchronous demodulation (SD) and continuous wavelet transform (CWT) filtering. Then, a sequential outlier analysis is performed on the extracted sidebands to identify the crack presence without referring any baseline data obtained from the intact condition of the structure. Finally, the robustness of the proposed technique is demonstrated using actual test data obtained from simple aluminum plate and complex aircraft fitting-lug specimens under varying temperature and loading variations.

  12. Robust nonhomogeneous training samples detection method for space-time adaptive processing radar using sparse-recovery with knowledge-aided

    Science.gov (United States)

    Li, Zhihui; Liu, Hanwei; Zhang, Yongshun; Guo, Yiduo

    2017-10-01

    The performance of space-time adaptive processing (STAP) may degrade significantly when some of the training samples are contaminated by the signal-like components (outliers) in nonhomogeneous clutter environments. To remove the training samples contaminated by outliers in nonhomogeneous clutter environments, a robust nonhomogeneous training samples detection method using the sparse-recovery (SR) with knowledge-aided (KA) is proposed. First, the reduced-dimension (RD) overcomplete spatial-temporal steering dictionary is designed with the prior knowledge of system parameters and the possible target region. Then, the clutter covariance matrix (CCM) of cell under test is efficiently estimated using a modified focal underdetermined system solver (FOCUSS) algorithm, where a RD overcomplete spatial-temporal steering dictionary is applied. Third, the proposed statistics are formed by combining the estimated CCM with the generalized inner products (GIP) method, and the contaminated training samples can be detected and removed. Finally, several simulation results validate the effectiveness of the proposed KA-SR-GIP method.

  13. Applied Electromagnetics

    Energy Technology Data Exchange (ETDEWEB)

    Yamashita, H; Marinova, I; Cingoski, V [eds.

    2002-07-01

    These proceedings contain papers relating to the 3rd Japanese-Bulgarian-Macedonian Joint Seminar on Applied Electromagnetics. Included are the following groups: Numerical Methods I; Electrical and Mechanical System Analysis and Simulations; Inverse Problems and Optimizations; Software Methodology; Numerical Methods II; Applied Electromagnetics.

  14. Applied Electromagnetics

    International Nuclear Information System (INIS)

    Yamashita, H.; Marinova, I.; Cingoski, V.

    2002-01-01

    These proceedings contain papers relating to the 3rd Japanese-Bulgarian-Macedonian Joint Seminar on Applied Electromagnetics. Included are the following groups: Numerical Methods I; Electrical and Mechanical System Analysis and Simulations; Inverse Problems and Optimizations; Software Methodology; Numerical Methods II; Applied Electromagnetics

  15. DISCOVERY AND ATMOSPHERIC CHARACTERIZATION OF GIANT PLANET KEPLER-12b: AN INFLATED RADIUS OUTLIER

    International Nuclear Information System (INIS)

    Fortney, Jonathan J.; Nutzman, Philip; Demory, Brice-Olivier; Désert, Jean-Michel; Buchhave, Lars A.; Charbonneau, David; Fressin, François; Rowe, Jason; Caldwell, Douglas A.; Jenkins, Jon M.; Marcy, Geoffrey W.; Isaacson, Howard; Howard, Andrew; Knutson, Heather A.; Ciardi, David; Gautier, Thomas N.; Batalha, Natalie M.; Bryson, Stephen T.; Howell, Steve B.; Everett, Mark

    2011-01-01

    We report the discovery of planet Kepler-12b (KOI-20), which at 1.695 ± 0.030 R J is among the handful of planets with super-inflated radii above 1.65 R J . Orbiting its slightly evolved G0 host with a 4.438 day period, this 0.431 ± 0.041 M J planet is the least irradiated within this largest-planet-radius group, which has important implications for planetary physics. The planet's inflated radius and low mass lead to a very low density of 0.111 ± 0.010 g cm –3 . We detect the occultation of the planet at a significance of 3.7σ in the Kepler bandpass. This yields a geometric albedo of 0.14 ± 0.04; the planetary flux is due to a combination of scattered light and emitted thermal flux. We use multiple observations with Warm Spitzer to detect the occultation at 7σ and 4σ in the 3.6 and 4.5 μm bandpasses, respectively. The occultation photometry timing is consistent with a circular orbit at e < 0.01 (1σ) and e < 0.09 (3σ). The occultation detections across the three bands favor an atmospheric model with no dayside temperature inversion. The Kepler occultation detection provides significant leverage, but conclusions regarding temperature structure are preliminary, given our ignorance of opacity sources at optical wavelengths in hot Jupiter atmospheres. If Kepler-12b and HD 209458b, which intercept similar incident stellar fluxes, have the same heavy-element masses, the interior energy source needed to explain the large radius of Kepler-12b is three times larger than that of HD 209458b. This may suggest that more than one radius-inflation mechanism is at work for Kepler-12b or that it is less heavy-element rich than other transiting planets.

  16. Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

    Directory of Open Access Journals (Sweden)

    Luis Martí

    2015-01-01

    Full Text Available Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA, a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.

  17. Detection of boiling by Piety's on-line PSD-pattern recognition algorithm applied to neutron noise signals in the SAPHIR reactor

    International Nuclear Information System (INIS)

    Spiekerman, G.

    1988-09-01

    A partial blockage of the cooling channels of a fuel element in a swimming pool reactor could lead to vapour generation and to burn-out. To detect such anomalies, a pattern recognition algorithm based on power spectra density (PSD) proposed by Piety was further developed and implemented on a PDP 11/23 for on-line applications. This algorithm identifies anomalies by measuring the PSD on the process signal and comparing them with a standard baseline previously formed. Up to 8 decision discriminants help to recognize spectral changes due to anomalies. In our application, to detect boiling as quickly as possible with sufficient sensitivity, Piety's algorithm was modified using overlapped Fast-Fourier-Transform-Processing and the averaging of the PSDs over a large sample of preceding instantaneous PSDs. This processing allows high sensitivity in detecting weak disturbances without reducing response time. The algorithm was tested with simulation-of-boiling experiments where nitrogen in a cooling channel of a mock-up of a fuel element was injected. Void fractions higher than 30 % in the channel can be detected. In the case of boiling, it is believed that this limit is lower because collapsing bubbles could give rise to stronger fluctuations. The algorithm was also tested with a boiling experiment where the reactor coolant flow was actually reduced. The results showed that the discriminant D5 of Piety's algorithm based on neutron noise obtained from the existing neutron chambers of the reactor control system could sensitively recognize boiling. The detection time amounts to 7-30 s depending on the strength of the disturbances. Other events, which arise during a normal reactor run like scrams, removal of isotope elements without scramming or control rod movements and which could lead to false alarms, can be distinguished from boiling. 49 refs., 104 figs., 5 tabs

  18. A practical method to detect the freezing/thawing onsets of seasonal frozen ground in Alaska

    Science.gov (United States)

    Chen, Xiyu; Liu, Lin

    2017-04-01

    Microwave remote sensing can provide useful information about freeze/thaw state of soil at the Earth surface. An edge detection method is applied in this study to estimate the onsets of soil freeze/thaw state transition using L band space-borne radiometer data. The Soil Moisture Active Passive (SMAP) mission has a L band radiometer and can provide daily brightness temperature (TB) with horizontal/vertical polarizations. We use the normalized polarization ratios (NPR) calculated based on the Level-1C TB product of SMAP (spatial resolution: 36 km) as the indicator for soil freeze/thaw state, to estimate the freezing and thawing onsets in Alaska in the year of 2015 and 2016. NPR is calculated based on the difference between TB at vertical and horizontal polarizations. Therefore, it is strongly sensitive to liquid water content change in the soil and independent with the soil temperature. Onset estimation is based on the detection of abrupt changes of NPR in transition seasons using edge detection method, and the validation is to compare estimated onsets with the onsets derived from in situ measurement. According to the comparison, the estimated onsets were generally 15 days earlier than the measured onsets in 2015. However, in 2016 there were 4 days in average for the estimation earlier than the measured, which may be due to the less snow cover. Moreover, we extended our estimation to the entire state of Alaska. The estimated freeze/thaw onsets showed a reasonable latitude-dependent distribution although there are still some outliers caused by the noisy variation of NPR. At last, we also try to remove these outliers and improve the performance of the method by smoothing the NPR time series.

  19. Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings

    NARCIS (Netherlands)

    Maslov, A.; Pechenizkiy, M.; Kärkkäinen, T.; Tähtinen, M.

    2012-01-01

    In this paper we consider the problem of online detection of gradual and abrupt changes in sensor data having high levels of noise and outliers. We propose a simple heuristic method based on the Quantile Index (QI) and study how robust this method is for detecting both gradual and abrupt changes

  20. Real Time Search Algorithm for Observation Outliers During Monitoring Engineering Constructions

    Science.gov (United States)

    Latos, Dorota; Kolanowski, Bogdan; Pachelski, Wojciech; Sołoducha, Ryszard

    2017-12-01

    Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object's behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar).

  1. Real Time Search Algorithm for Observation Outliers During Monitoring Engineering Constructions

    Directory of Open Access Journals (Sweden)

    Latos Dorota

    2017-12-01

    Full Text Available Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object’s behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar.

  2. Final Assessment of Manual Ultrasonic Examinations Applied to Detect Flaws in Primary System Dissimilar Metal Welds at North Anna Power Station

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, Michael T.; Diaz, Aaron A.; Cinson, Anthony D.; Crawford, Susan L.; Prowant, Matthew S.; Doctor, Steven R.

    2014-03-24

    PNNL conducted a technical assessment of the NDE issues and protocols that led to missed detections of several axially oriented flaws in a steam generator primary inlet dissimilar metal weld at North Anna Power Station, Unit 1 (NAPS-1). This particular component design exhibits a significant outside-diameter (OD) taper that is not included as a blind performance demonstration mock-up within the industry’s Performance Demonstration Initiative, administered by EPRI. For this reason, the licensee engaged EPRI to assist in the development of a technical justification to support the basis for a site-specific qualification. The service-induced flaws at NAPS-1 were eventually detected as a result of OD surface machining in preparation for a full structural weld overlay. The machining operation uncovered the existence of two through-wall flaws, based on the observance of primary water leaking from the dissimilar metal weld. A total of five axially oriented flaws were detected in varied locations around the weld circumference. The field volumetric examination that was conducted at NAPS-1 was a non-encoded, real-time manual ultrasonic examination. PNNL conducted both an initial assessment, and subsequently, a more rigorous technical evaluation (reported here), which has identified an array of NDE issues that may have led to the subject missed detections. These evaluations were performed through technical reviews and discussions with NRC staff, EPRI NDE Center personnel, industry and ISI vendor personnel, and ultrasonic transducer manufacturers, and laboratory tests, to better understand the underlying issues at North Anna.

  3. Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain

    Directory of Open Access Journals (Sweden)

    Juan Jose Saucedo-Dorantes

    2016-01-01

    Full Text Available Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral components of the faults. In this work, the influence of vibrations over the system is observed by performing motor current signal analysis to detect the presence of faults. The obtained results show the feasibility of detecting multiple faults in a kinematic chain, making the proposed methodology suitable to be used in the application of industrial machinery diagnosis.

  4. Protein Detection Using the Multiplexed Proximity Extension Assay (PEA) from Plasma and Vaginal Fluid Applied to the Indicating FTA Elute Micro CardTM

    OpenAIRE

    Berggrund, Malin; Ekman, Daniel; Gustavsson, Inger; Sundfeldt, Karin; Olovsson, Matts; Enroth, Stefan; Gyllensten, Ulf

    2016-01-01

    The indicating FTA elute micro cardTM has been developed to collect and stabilize the nucleic acid in biological samples and is widely used in human and veterinary medicine and other disciplines. This card is not recommended for protein analyses, since surface treatment may denature proteins. We studied the ability to analyse proteins in human plasma and vaginal fluid as applied to the indicating FTA elute micro cardTM using the sensitive proximity extension assay (PEA). Among 92 proteins in ...

  5. Fast clustering using adaptive density peak detection.

    Science.gov (United States)

    Wang, Xiao-Feng; Xu, Yifan

    2017-12-01

    Common limitations of clustering methods include the slow algorithm convergence, the instability of the pre-specification on a number of intrinsic parameters, and the lack of robustness to outliers. A recent clustering approach proposed a fast search algorithm of cluster centers based on their local densities. However, the selection of the key intrinsic parameters in the algorithm was not systematically investigated. It is relatively difficult to estimate the "optimal" parameters since the original definition of the local density in the algorithm is based on a truncated counting measure. In this paper, we propose a clustering procedure with adaptive density peak detection, where the local density is estimated through the nonparametric multivariate kernel estimation. The model parameter is then able to be calculated from the equations with statistical theoretical justification. We also develop an automatic cluster centroid selection method through maximizing an average silhouette index. The advantage and flexibility of the proposed method are demonstrated through simulation studies and the analysis of a few benchmark gene expression data sets. The method only needs to perform in one single step without any iteration and thus is fast and has a great potential to apply on big data analysis. A user-friendly R package ADPclust is developed for public use.

  6. Evaluating IMRT and VMAT dose accuracy: Practical examples of failure to detect systematic errors when applying a commonly used metric and action levels

    Energy Technology Data Exchange (ETDEWEB)

    Nelms, Benjamin E. [Canis Lupus LLC, Merrimac, Wisconsin 53561 (United States); Chan, Maria F. [Memorial Sloan-Kettering Cancer Center, Basking Ridge, New Jersey 07920 (United States); Jarry, Geneviève; Lemire, Matthieu [Hôpital Maisonneuve-Rosemont, Montréal, QC H1T 2M4 (Canada); Lowden, John [Indiana University Health - Goshen Hospital, Goshen, Indiana 46526 (United States); Hampton, Carnell [Levine Cancer Institute/Carolinas Medical Center, Concord, North Carolina 28025 (United States); Feygelman, Vladimir [Moffitt Cancer Center, Tampa, Florida 33612 (United States)

    2013-11-15

    Purpose: This study (1) examines a variety of real-world cases where systematic errors were not detected by widely accepted methods for IMRT/VMAT dosimetric accuracy evaluation, and (2) drills-down to identify failure modes and their corresponding means for detection, diagnosis, and mitigation. The primary goal of detailing these case studies is to explore different, more sensitive methods and metrics that could be used more effectively for evaluating accuracy of dose algorithms, delivery systems, and QA devices.Methods: The authors present seven real-world case studies representing a variety of combinations of the treatment planning system (TPS), linac, delivery modality, and systematic error type. These case studies are typical to what might be used as part of an IMRT or VMAT commissioning test suite, varying in complexity. Each case study is analyzed according to TG-119 instructions for gamma passing rates and action levels for per-beam and/or composite plan dosimetric QA. Then, each case study is analyzed in-depth with advanced diagnostic methods (dose profile examination, EPID-based measurements, dose difference pattern analysis, 3D measurement-guided dose reconstruction, and dose grid inspection) and more sensitive metrics (2% local normalization/2 mm DTA and estimated DVH comparisons).Results: For these case studies, the conventional 3%/3 mm gamma passing rates exceeded 99% for IMRT per-beam analyses and ranged from 93.9% to 100% for composite plan dose analysis, well above the TG-119 action levels of 90% and 88%, respectively. However, all cases had systematic errors that were detected only by using advanced diagnostic techniques and more sensitive metrics. The systematic errors caused variable but noteworthy impact, including estimated target dose coverage loss of up to 5.5% and local dose deviations up to 31.5%. Types of errors included TPS model settings, algorithm limitations, and modeling and alignment of QA phantoms in the TPS. Most of the errors were

  7. Group method of data handling and neral networks applied in monitoring and fault detection in sensors in nuclear power plants; Group Method of Data Handling (GMDH) e Redes Neurais na Monitoracao e Deteccao de Falhas em sensores de centrais nucleares

    Energy Technology Data Exchange (ETDEWEB)

    Bueno, Elaine Inacio

    2011-07-01

    The increasing demand in the complexity, efficiency and reliability in modern industrial systems stimulated studies on control theory applied to the development of Monitoring and Fault Detection system. In this work a new Monitoring and Fault Detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and Artificial Neural Networks (ANNs) which was applied to the IEA-R1 research reactor at IPEN. The Monitoring and Fault Detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second part to the process information using ANNs. The GMDH algorithm was used in two different ways: firstly, the GMDH algorithm was used to generate a better database estimated, called matrix{sub z}, which was used to train the ANNs. After that, the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one Theoretical Model and four Models using different sets of reactor variables. After an exhausting study dedicated to the sensors Monitoring, the Fault Detection in sensors was developed by simulating faults in the sensors database using values of 5%, 10%, 15% and 20% in these sensors database. The results obtained using GMDH algorithm in the choice of the best input variables to the ANNs were better than that using only ANNs, thus making possible the use of these methods in the implementation of a new Monitoring and Fault Detection methodology applied in sensors. (author)

  8. Applied mathematics

    CERN Document Server

    Logan, J David

    2013-01-01

    Praise for the Third Edition"Future mathematicians, scientists, and engineers should find the book to be an excellent introductory text for coursework or self-study as well as worth its shelf space for reference." -MAA Reviews Applied Mathematics, Fourth Edition is a thoroughly updated and revised edition on the applications of modeling and analyzing natural, social, and technological processes. The book covers a wide range of key topics in mathematical methods and modeling and highlights the connections between mathematics and the applied and nat

  9. A method to assist in the diagnosis of early diabetic retinopathy: Image processing applied to detection of microaneurysms in fundus images.

    Science.gov (United States)

    Rosas-Romero, Roberto; Martínez-Carballido, Jorge; Hernández-Capistrán, Jonathan; Uribe-Valencia, Laura J

    2015-09-01

    Diabetes increases the risk of developing any deterioration in the blood vessels that supply the retina, an ailment known as Diabetic Retinopathy (DR). Since this disease is asymptomatic, it can only be diagnosed by an ophthalmologist. However, the growth of the number of ophthalmologists is lower than the growth of the population with diabetes so that preventive and early diagnosis is difficult due to the lack of opportunity in terms of time and cost. Preliminary, affordable and accessible ophthalmological diagnosis will give the opportunity to perform routine preventive examinations, indicating the need to consult an ophthalmologist during a stage of non proliferation. During this stage, there is a lesion on the retina known as microaneurysm (MA), which is one of the first clinically observable lesions that indicate the disease. In recent years, different image processing algorithms, which allow the detection of the DR, have been developed; however, the issue is still open since acceptable levels of sensitivity and specificity have not yet been reached, preventing its use as a pre-diagnostic tool. Consequently, this work proposes a new approach for MA detection based on (1) reduction of non-uniform illumination; (2) normalization of image grayscale content to improve dependence of images from different contexts; (3) application of the bottom-hat transform to leave reddish regions intact while suppressing bright objects; (4) binarization of the image of interest with the result that objects corresponding to MAs, blood vessels, and other reddish objects (Regions of Interest-ROIs) are completely separated from the background; (5) application of the hit-or-miss Transformation on the binary image to remove blood vessels from the ROIs; (6) two features are extracted from a candidate to distinguish real MAs from FPs, where one feature discriminates round shaped candidates (MAs) from elongated shaped ones (vessels) through application of Principal Component Analysis (PCA

  10. Applied Enzymology.

    Science.gov (United States)

    Manoharan, Asha; Dreisbach, Joseph H.

    1988-01-01

    Describes some examples of chemical and industrial applications of enzymes. Includes a background, a discussion of structure and reactivity, enzymes as therapeutic agents, enzyme replacement, enzymes used in diagnosis, industrial applications of enzymes, and immobilizing enzymes. Concludes that applied enzymology is an important factor in…

  11. A New Approach for Detection Improvement of the Creutzfeldt-Jakob Disorder through a Specific Surface Chemistry Applied onto Titration Well

    Science.gov (United States)

    Mille, Caroline; Debarnot, Dominique; Zorzi, Willy; Moualij, Benaissa El; Quadrio, Isabelle; Perret-Liaudet, Armand; Coudreuse, Arnaud; Legeay, Gilbert; Poncin-Epaillard, Fabienne

    2012-01-01

    This work illustrates the enhancement of the sensitivity of the ELISA titration for recombinant human and native prion proteins, while reducing other non-specific adsorptions that could increase the background signal and lead to a low sensitivity and false positives. It is achieved thanks to the association of plasma chemistry and coating with different amphiphilic molecules bearing either ionic charges and/or long hydrocarbon chains. The treated support by 3-butenylamine hydrochloride improves the signal detection of recombinant protein, while surface modification with the 3,7-dimethylocta-2,6-dien-1-diamine (geranylamine) enhances the sensitivity of the native protein. Beside the surface chemistry effect, these different results are associated with protein conformation. PMID:25586034

  12. Detection of correct and incorrect measurements in real-time continuous glucose monitoring systems by applying a postprocessing support vector machine.

    Science.gov (United States)

    Leal, Yenny; Gonzalez-Abril, Luis; Lorencio, Carol; Bondia, Jorge; Vehi, Josep

    2013-07-01

    Support vector machines (SVMs) are an attractive option for detecting correct and incorrect measurements in real-time continuous glucose monitoring systems (RTCGMSs), because their learning mechanism can introduce a postprocessing strategy for imbalanced datasets. The proposed SVM considers the geometric mean to obtain a more balanced performance between sensitivity and specificity. To test this approach, 23 critically ill patients receiving insulin therapy were monitored over 72 h using an RTCGMS, and a dataset of 537 samples, classified according to International Standards Organization (ISO) criteria (372 correct and 165 incorrect measurements), was obtained. The results obtained were promising for patients with septic shock or with sepsis, for which the proposed system can be considered as reliable. However, this approach cannot be considered suitable for patients without sepsis.

  13. A New Approach for Detection Improvement of the Creutzfeldt-Jakob Disorder through a Specific Surface Chemistry Applied onto Titration Well

    Directory of Open Access Journals (Sweden)

    Dominique Debarnot

    2012-10-01

    Full Text Available This work illustrates the enhancement of the sensitivity of the ELISA titration for recombinant human and native prion proteins, while reducing other non-specific adsorptions that could increase the background signal and lead to a low sensitivity and false positives. It is achieved thanks to the association of plasma chemistry and coating with different amphiphilic molecules bearing either ionic charges and/or long hydrocarbon chains. The treated support by 3-butenylamine hydrochloride improves the signal detection of recombinant protein, while surface modification with the 3,7-dimethylocta-2,6-dien-1-diamine (geranylamine enhances the sensitivity of the native protein. Beside the surface chemistry effect, these different results are associated with protein conformation.

  14. Increasing the Detection Limit of the Parkinson Disorder through a Specific Surface Chemistry Applied onto Inner Surface of the Titration Well

    Directory of Open Access Journals (Sweden)

    Fabienne Poncin-Epaillard

    2012-04-01

    Full Text Available The main objective of this paper was to illustrate the enhancement of the sensitivity of ELISA titration for neurodegenerative proteins by reducing nonspecific adsorptions that could lead to false positives. This goal was obtained thanks to the association of plasma and wet chemistries applied to the inner surface of the titration well. The polypropylene surface was plasma-activated and then, dip-coated with different amphiphilic molecules. These molecules have more or less long hydrocarbon chains and may be charged. The modified surfaces were characterized in terms of hydrophilic—phobic character, surface chemical groups and topography. Finally, the coated wells were tested during the ELISA titration of the specific antibody capture of the α-synuclein protein. The highest sensitivity is obtained with polar (Θ = 35°, negatively charged and smooth inner surface.

  15. Applied dynamics

    CERN Document Server

    Schiehlen, Werner

    2014-01-01

    Applied Dynamics is an important branch of engineering mechanics widely applied to mechanical and automotive engineering, aerospace and biomechanics as well as control engineering and mechatronics. The computational methods presented are based on common fundamentals. For this purpose analytical mechanics turns out to be very useful where D’Alembert’s principle in the Lagrangian formulation proves to be most efficient. The method of multibody systems, finite element systems and continuous systems are treated consistently. Thus, students get a much better understanding of dynamical phenomena, and engineers in design and development departments using computer codes may check the results more easily by choosing models of different complexity for vibration and stress analysis.

  16. Applied optics

    International Nuclear Information System (INIS)

    Orszag, A.; Antonetti, A.

    1988-01-01

    The 1988 progress report, of the Applied Optics laboratory, of the (Polytechnic School, France), is presented. The optical fiber activities are focused on the development of an optical gyrometer, containing a resonance cavity. The following domains are included, in the research program: the infrared laser physics, the laser sources, the semiconductor physics, the multiple-photon ionization and the nonlinear optics. Investigations on the biomedical, the biological and biophysical domains are carried out. The published papers and the congress communications are listed [fr

  17. Radiographic detection of 100 A thickness variations in 1-μm-thick coatings applied to submillimeter-diameter laser fusion targets

    International Nuclear Information System (INIS)

    Stupin, D.M.

    1986-01-01

    We have developed x-ray radiography to measure thickness variations of coatings on laser fusion targets. Our technique is based on measuring the variation in x-ray transmission through the targets. The simplest targets are hollow glass microshells or microballoons 100 to 500 μm in diameter, that have several layers of metals or plastics, 1 to 100 μm thick. Our goal is to examine these opaque coatings for thickness variations as small as 1% or 0.1%, depending on the type of defect. Using contact radiography we have obtained the desired sensitivity for concentric and elliptical defects of 1%. This percentage corresponds to thickness variations as small as 100 A in a 1-μm-thick coating. For warts and dimples, the desired sensitivity is a function of the area of the defect, and we are developing a system to detect 0.1% thickness variations that cover an area 10 μm by 10 μm. We must use computer analysis of contact radiographs to measure 1% thickness variations in either concentricity or ellipticity. Because this analysis takes so long on our minicomputer, we preselect the radiographs by looking for defects at the 10% level on a video image analysis system

  18. A Fast Multimodal Ectopic Beat Detection Method Applied for Blood Pressure Estimation Based on Pulse Wave Velocity Measurements in Wearable Sensors.

    Science.gov (United States)

    Pflugradt, Maik; Geissdoerfer, Kai; Goernig, Matthias; Orglmeister, Reinhold

    2017-01-14

    Automatic detection of ectopic beats has become a thoroughly researched topic, with literature providing manifold proposals typically incorporating morphological analysis of the electrocardiogram (ECG). Although being well understood, its utilization is often neglected, especially in practical monitoring situations like online evaluation of signals acquired in wearable sensors. Continuous blood pressure estimation based on pulse wave velocity considerations is a prominent example, which depends on careful fiducial point extraction and is therefore seriously affected during periods of increased occurring extrasystoles. In the scope of this work, a novel ectopic beat discriminator with low computational complexity has been developed, which takes advantage of multimodal features derived from ECG and pulse wave relating measurements, thereby providing additional information on the underlying cardiac activity. Moreover, the blood pressure estimations' vulnerability towards ectopic beats is closely examined on records drawn from the Physionet database as well as signals recorded in a small field study conducted in a geriatric facility for the elderly. It turns out that a reliable extrasystole identification is essential to unsupervised blood pressure estimation, having a significant impact on the overall accuracy. The proposed method further convinces by its applicability to battery driven hardware systems with limited processing power and is a favorable choice when access to multimodal signal features is given anyway.

  19. Integrated hierarchical geo-environmental survey strategy applied to the detection and investigation of an illegal landfill: A case study in the Campania Region (Southern Italy).

    Science.gov (United States)

    Di Fiore, Vincenzo; Cavuoto, Giuseppe; Punzo, Michele; Tarallo, Daniela; Casazza, Marco; Guarriello, Silvio Marco; Lega, Massimiliano

    2017-10-01

    This paper describes an approach to detect and investigate the main characteristics of a solid waste landfill through the integration of geological, geographical and geophysical methods. In particular, a multi-temporal analysis of the landfill morphological evolution was carried out using aerial and satellite photos, since there were no geological and geophysical data referring to the study area. Subsequently, a surface geophysical prospection was performed through geoelectric and geomagnetic methods. In particular, the combination of electrical resistivity, induced polarization and magnetic measurements removed some of the uncertainties, generally associated with a separate utilization of these techniques. This approach was successfully tested to support the Prosecutor Office of Salerno (S Italy) during a specific investigation about an illegal landfill. All the collected field data supported the reconstruction of the site-specific history, while the real quarry geometry and site geology were defined. Key elements of novelty of this method are the combination and the integration of different methodological approaches, as the parallel and combined use of satellite, aerial and in-situ collected data, that were validated in a real investigation and that revealed the effectiveness of this strategy. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Previous study for the setting up and optimization of detection of ZnS(Ag) scintillation applied to the measure of alpha radioactivity index

    International Nuclear Information System (INIS)

    Pujol, L.; Suarez-Navarro, J.A.; Montero, M.

    1998-01-01

    The determination of radiological water quality is useful for a wide range of environmental studies. In these cases, the gross alpha activity is one of the parameters to determine. This parameter permits to decide if further radiological analyses are necessary in order to identify and quantify the presence of alpha emitters in water. The usual method for monitoring the gross alpha activity includes sample evaporation to dryness on a disk and counting using ZnS(Ag) scintillation detector. Detector electronics is provided with two components which are adjustable by the user the high-voltage applied to the photomultiplier tubes and the low level discriminator that is used to eliminate the electronic noise. The high-voltage and low level discriminator optimization are convenient in order to reach the best counting conditions. This paper is a preliminary study of the procedure followed for the setting up and optimization of the detector electronics in the laboratories of CEDEX for the measurement of gross alpha activity. (Author)

  1. ¿Se pueden predecir geográficamente los resultados electorales? Una aplicación del análisis de clusters y outliers espaciales

    Directory of Open Access Journals (Sweden)

    Carlos J. Vilalta Perdomo

    2008-01-01

    Full Text Available Los resultados de este estudio demuestran que al aplicar la estadística espacial en la geografía electoral es posible predecir los resultados electorales. Se utilizan los conceptos geográficos de cluster y outlier espaciales, y como variable predictiva la segregación espacial socioeconómica. Las técnicas estadísticas que se emplean son los índices globales y locales de autocorrelación espacial de Moran y el análisis de regresión lineal. Sobre los datos analizados se encuentra: 1 que la Ciudad de México posee clusters espaciales de apoyo electoral y de marginación, 2 outliers espaciales de marginación, 3 que los partidos electorales se excluyen geográficamente, y 4 que sus resultados dependen significativamente de los niveles de segregación espacial en la ciudad.

  2. Type-specific detection of high-risk human papillomavirus (HPV) in self-sampled cervicovaginal cells applied to FTA elute cartridge.

    Science.gov (United States)

    Gustavsson, Inger; Sanner, Karin; Lindell, Monica; Strand, Anders; Olovsson, Matts; Wikström, Ingrid; Wilander, Erik; Gyllensten, Ulf

    2011-08-01

    Most procedures for self-sampling of cervical cells are based on liquid-based media for transportation and storage. An alternative is to use a solid support, such as dry filter paper media. To evaluate if self-sampling of cervicovaginal fluid using a cytobrush (Viba-brush; Rovers Medical Devices B.V., Oss, The Netherlands) and a solid support such as the Whatman Indicating FTA Elute cartridge (GE Healthcare, United Kingdom) can be used for reliable typing of human papillomavirus (HPV), as compared to cervical samples obtained by a physician using a cytobrush and the indicating FTA Elute Micro card and biopsy analysis. A total of 50 women with a previous high-risk (HR) HPV positive test were invited to perform self-sampling using the Viba-brush and the FTA cartridge and thereafter a physician obtained a cervical sample using the cytobrush and a FTA card, together with a cervical biopsy for histology and HPV typing. Detection of HR-HPV types 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58 and 59 was performed using three multiplex real-time polymerase chain reaction (PCR) assays. All samples contained sufficient amounts of genomic DNA and the self-samples yielded on average 3.5 times more DNA than those obtained by the physician. All women that were positive for HR-HPV in the biopsy sample also typed positive both by self-sampling and physician-obtained sampling. For women with a histological diagnosis of cervical intraepithelial neoplasia grades 2-3 (CIN 2-3) all three HPV samples showed 100% concordance. A higher number of women were HPV positive by self-sampling than by physician-obtained sampling or by biopsy analysis. The Viba-brush and the FTA cartridge are suitable for self-sampling of vaginal cells and subsequent HR-HPV typing. Copyright © 2011 Elsevier B.V. All rights reserved.

  3. Developing and Applying Control Charts to Detect Change Water Chemistry Parameters Measured in the Athabasca River Near the Oil Sands: A Tool for Surveillance Monitoring.

    Science.gov (United States)

    Arciszewski, Tim J; Hazewinkel, Rod R; Munkittrick, Kelly R; Kilgour, Bruce W

    2018-05-10

    Control charting is a simple technique to identify change and is well-suited for use in water quality programs. Control charts accounting for co-variation associated with discharge and time were used to explore example and representative variables routinely measured in the Athabasca River near the oil sands area for indications of change, including 5 major ions (chloride, sodium, sulphate, calcium, magnesium), 5 total metals (aluminum, iron, thallium, molybdenum, vanadium) and total suspended solids (TSS). Regression equations developed from reference data (1988-2009) were used to predict observations and calculate residuals from later test observations (2010-2016). Evidence of change was sought in the deviation of residual errors from the test period compared to the patterns expected and defined from probability distributions of the reference residuals using the Odds Ratio. In most cases, the patterns in test residuals were not statistically different from those expected from the reference period, especially when data was examined annually. However, some differences were apparent and more differences were apparent as data accumulated and was analysed over time. In sum, the analyses suggest higher concentrations than predicted in most major ions, but the source of the changes is uncertain. In contrast, most metals were lower than expected and may be related to changing deposition patterns of materials or weathering of minerals during construction activities of the 2000's which influence the reference data used. The analyses also suggest alternative approaches may be necessary to understand change in some variables. Despite this, the results support the use of control charts to detect changes in water chemistry parameters and the value of the tool in surveillance phases of long-term and adaptive monitoring programs. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  4. Robust motion correction and outlier rejection of in vivo functional MR images of the fetal brain and placenta during maternal hyperoxia

    OpenAIRE

    You, Wonsang; Serag, Ahmed; Evangelou, Iordanis E.; Andescavage, Nickie; Limperopoulos, Catherine

    2017-01-01

    Subject motion is a major challenge in functional magnetic resonance imaging studies (fMRI) of the fetal brain and placenta during maternal hyperoxia. We propose a motion correction and volume outlier rejection method for the correction of severe motion artifacts in both fetal brain and placenta. The method is optimized to the experimental design by processing different phases of acquisition separately. It also automatically excludes high-motion volumes and all the missing data are regressed ...

  5. Robust motion correction and outlier rejection of in vivo functional MR images of the fetal brain and placenta during maternal hyperoxia

    OpenAIRE

    You, Wonsang; Serag, Ahmed; Evangelou, Iordanis E.; Andescavage, Nickie; Limperopoulos, Catherine

    2015-01-01

    Subject motion is a major challenge in functional magnetic resonance imaging studies (fMRI) of the fetal brain and placenta during maternal hyperoxia. We propose a motion correction and volume outlier rejection method for the correction of severe motion artifacts in both fetal brain and placenta. The method is optimized to the experimental design by processing different phases of acquisition separately. It also automatically excludes high-motion volumes and all the missing data are regressed ...

  6. Comparison of robustness to outliers between robust poisson models and log-binomial models when estimating relative risks for common binary outcomes: a simulation study.

    Science.gov (United States)

    Chen, Wansu; Shi, Jiaxiao; Qian, Lei; Azen, Stanley P

    2014-06-26

    To estimate relative risks or risk ratios for common binary outcomes, the most popular model-based methods are the robust (also known as modified) Poisson and the log-binomial regression. Of the two methods, it is believed that the log-binomial regression yields more efficient estimators because it is maximum likelihood based, while the robust Poisson model may be less affected by outliers. Evidence to support the robustness of robust Poisson models in comparison with log-binomial models is very limited. In this study a simulation was conducted to evaluate the performance of the two methods in several scenarios where outliers existed. The findings indicate that for data coming from a population where the relationship between the outcome and the covariate was in a simple form (e.g. log-linear), the two models yielded comparable biases and mean square errors. However, if the true relationship contained a higher order term, the robust Poisson models consistently outperformed the log-binomial models even when the level of contamination is low. The robust Poisson models are more robust (or less sensitive) to outliers compared to the log-binomial models when estimating relative risks or risk ratios for common binary outcomes. Users should be aware of the limitations when choosing appropriate models to estimate relative risks or risk ratios.

  7. Applied geodesy

    International Nuclear Information System (INIS)

    Turner, S.

    1987-01-01

    This volume is based on the proceedings of the CERN Accelerator School's course on Applied Geodesy for Particle Accelerators held in April 1986. The purpose was to record and disseminate the knowledge gained in recent years on the geodesy of accelerators and other large systems. The latest methods for positioning equipment to sub-millimetric accuracy in deep underground tunnels several tens of kilometers long are described, as well as such sophisticated techniques as the Navstar Global Positioning System and the Terrameter. Automation of better known instruments such as the gyroscope and Distinvar is also treated along with the highly evolved treatment of components in a modern accelerator. Use of the methods described can be of great benefit in many areas of research and industrial geodesy such as surveying, nautical and aeronautical engineering, astronomical radio-interferometry, metrology of large components, deformation studies, etc

  8. Applied mathematics

    International Nuclear Information System (INIS)

    Nedelec, J.C.

    1988-01-01

    The 1988 progress report of the Applied Mathematics center (Polytechnic School, France), is presented. The research fields of the Center are the scientific calculus, the probabilities and statistics and the video image synthesis. The research topics developed are: the analysis of numerical methods, the mathematical analysis of the physics and mechanics fundamental models, the numerical solution of complex models related to the industrial problems, the stochastic calculus and the brownian movement, the stochastic partial differential equations, the identification of the adaptive filtering parameters, the discrete element systems, statistics, the stochastic control and the development, the image synthesis techniques for education and research programs. The published papers, the congress communications and the thesis are listed [fr

  9. Shallow Transits—Deep Learning. I. Feasibility Study of Deep Learning to Detect Periodic Transits of Exoplanets

    Science.gov (United States)

    Zucker, Shay; Giryes, Raja

    2018-04-01

    Transits of habitable planets around solar-like stars are expected to be shallow, and to have long periods, which means low information content. The current bottleneck in the detection of such transits is caused in large part by the presence of red (correlated) noise in the light curves obtained from the dedicated space telescopes. Based on the groundbreaking results deep learning achieves in many signal and image processing applications, we propose to use deep neural networks to solve this problem. We present a feasibility study, in which we applied a convolutional neural network on a simulated training set. The training set comprised light curves received from a hypothetical high-cadence space-based telescope. We simulated the red noise by using Gaussian Processes with a wide variety of hyper-parameters. We then tested the network on a completely different test set simulated in the same way. Our study proves that very difficult cases can indeed be detected. Furthermore, we show how detection trends can be studied and detection biases quantified. We have also checked the robustness of the neural-network performance against practical artifacts such as outliers and discontinuities, which are known to affect space-based high-cadence light curves. Future work will allow us to use the neural networks to characterize the transit model and identify individual transits. This new approach will certainly be an indispensable tool for the detection of habitable planets in the future planet-detection space missions such as PLATO.

  10. Mixture based outlier filtration

    Czech Academy of Sciences Publication Activity Database

    Pecherková, Pavla; Nagy, Ivan

    2006-01-01

    Roč. 46, č. 2 (2006), s. 30-35 ISSN 1210-2709 R&D Projects: GA MŠk 1M0572; GA MDS 1F43A/003/120 Institutional research plan: CEZ:AV0Z10750506 Keywords : data filtration * system modelling * mixture models Subject RIV: BD - Theory of Information http://library.utia.cas.cz/prace/20060165.pdf

  11. Applying radiation

    International Nuclear Information System (INIS)

    Mallozzi, P.J.; Epstein, H.M.; Jung, R.G.; Applebaum, D.C.; Fairand, B.P.; Gallagher, W.J.; Uecker, R.L.; Muckerheide, M.C.

    1979-01-01

    The invention discloses a method and apparatus for applying radiation by producing X-rays of a selected spectrum and intensity and directing them to a desired location. Radiant energy is directed from a laser onto a target to produce such X-rays at the target, which is so positioned adjacent to the desired location as to emit the X-rays toward the desired location; or such X-rays are produced in a region away from the desired location, and are channeled to the desired location. The radiant energy directing means may be shaped (as with bends; adjustable, if desired) to circumvent any obstruction between the laser and the target. Similarly, the X-ray channeling means may be shaped (as with fixed or adjustable bends) to circumvent any obstruction between the region where the X-rays are produced and the desired location. For producing a radiograph in a living organism the X-rays are provided in a short pulse to avoid any blurring of the radiograph from movement of or in the organism. For altering tissue in a living organism the selected spectrum and intensity are such as to affect substantially the tissue in a preselected volume without injuring nearby tissue. Typically, the selected spectrum comprises the range of about 0.1 to 100 keV, and the intensity is selected to provide about 100 to 1000 rads at the desired location. The X-rays may be produced by stimulated emission thereof, typically in a single direction

  12. SYNTHETIC JET APPLIED TO DETECT POTENTIAL TERRORISTS

    Czech Academy of Sciences Publication Activity Database

    Tesař, Václav; Peszyński, K.

    2010-01-01

    Roč. 5, č. 3 (2010), s. 229-234 ISSN 1231-3998 R&D Projects: GA AV ČR IAA200760705; GA ČR GA101/07/1499 Institutional research plan: CEZ:AV0Z20760514 Keywords : synthetic jets * annular jets * terrorism Subject RIV: BK - Fluid Dynamics

  13. Locally adaptive decision in detection of clustered microcalcifications in mammograms

    Science.gov (United States)

    Sainz de Cea, María V.; Nishikawa, Robert M.; Yang, Yongyi

    2018-02-01

    In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

  14. RE-EXAMINING HIGH ABUNDANCE SLOAN DIGITAL SKY SURVEY MASS-METALLICITY OUTLIERS: HIGH N/O, EVOLVED WOLF-RAYET GALAXIES?

    International Nuclear Information System (INIS)

    Berg, Danielle A.; Skillman, Evan D.; Marble, Andrew R.

    2011-01-01

    We present new MMT spectroscopic observations of four dwarf galaxies representative of a larger sample observed by the Sloan Digital Sky Survey and identified by Peeples et al. as low-mass, high oxygen abundance outliers from the mass-metallicity relation. Peeples showed that these four objects (with metallicity estimates of 8.5 ≤ 12 + log(O/H) ≤ 8.8) have oxygen abundance offsets of 0.4-0.6 dex from the M B luminosity-metallicity relation. Our new observations extend the wavelength coverage to include the [O II] λλ3726, 3729 doublet, which adds leverage in oxygen abundance estimates and allows measurements of N/O ratios. All four spectra are low excitation, with relatively high N/O ratios (N/O ∼> 0.10), each of which tend to bias estimates based on strong emission lines toward high oxygen abundances. These spectra all fall in a regime where the 'standard' strong-line methods for metallicity determinations are not well calibrated either empirically or by photoionization modeling. By comparing our spectra directly to photoionization models, we estimate oxygen abundances in the range of 7.9 ≤ 12 + log (O/H) ≤ 8.4, consistent with the scatter of the mass-metallicity relation. We discuss the physical nature of these galaxies that leads to their unusual spectra (and previous classification as outliers), finding their low excitation, elevated N/O, and strong Balmer absorption are consistent with the properties expected from galaxies evolving past the 'Wolf-Rayet galaxy' phase. We compare our results to the 'main' sample of Peeples and conclude that they are outliers primarily due to enrichment of nitrogen relative to oxygen and not due to unusually high oxygen abundances for their masses or luminosities.

  15. Problems of applied geochemistry

    Energy Technology Data Exchange (ETDEWEB)

    Ovchinnikov, L N

    1983-01-01

    The concept of applied geochemistry was introduced for the first time by A. Ye. Fersman. He linked the branched and complicated questions of geochemistry with specific problems of developing the mineral and raw material base of our country. Geochemical prospecting and geochemistry of mineral raw materials are the most important sections of applied geochemistry. This now allows us the right to view applied geochemistry as a sector of science which applies geochemical methodology, set of geochemical methods of analysis, synthesis, geological interpretation of data based on laws governing theoretical geochemistry to the solution of different tasks of geology, petrology, tectonics, stratigraphy, science of minerals and other geological sciences, and also the technology of mineral raw materials, interrelationships of man and nature (ecogeochemistry, technogeochemistry, agrogeochemistry). The main problem of applied geochemistry, geochemistry of ore fields is the prehistory of ore formation. This is especially important for metallogenic and forecasting constructions, for an understanding of the reasons for the development of fields and the detection of laws governing their distribution, their genetic links with the general geological processes and the products of these processes.

  16. Tourism Demand in Catalonia: detecting external economic factors

    OpenAIRE

    Clavería González, Óscar; Datzira, Jordi

    2009-01-01

    There is a lack of studies on tourism demand in Catalonia. To fill the gap, this paper focuses on detecting the macroeconomic factors that determine tourism demand in Catalonia. We also analyse the relation between these factors and tourism demand. Despite the strong seasonal component and the outliers in the time series of some countries, overnight stays give a better indication of tourism demand in Catalonia than the number of tourists. The degree of linear association between the macroecon...

  17. Automatic EEG spike detection.

    Science.gov (United States)

    Harner, Richard

    2009-10-01

    Since the 1970s advances in science and technology during each succeeding decade have renewed the expectation of efficient, reliable automatic epileptiform spike detection (AESD). But even when reinforced with better, faster tools, clinically reliable unsupervised spike detection remains beyond our reach. Expert-selected spike parameters were the first and still most widely used for AESD. Thresholds for amplitude, duration, sharpness, rise-time, fall-time, after-coming slow waves, background frequency, and more have been used. It is still unclear which of these wave parameters are essential, beyond peak-peak amplitude and duration. Wavelet parameters are very appropriate to AESD but need to be combined with other parameters to achieve desired levels of spike detection efficiency. Artificial Neural Network (ANN) and expert-system methods may have reached peak efficiency. Support Vector Machine (SVM) technology focuses on outliers rather than centroids of spike and nonspike data clusters and should improve AESD efficiency. An exemplary spike/nonspike database is suggested as a tool for assessing parameters and methods for AESD and is available in CSV or Matlab formats from the author at brainvue@gmail.com. Exploratory Data Analysis (EDA) is presented as a graphic method for finding better spike parameters and for the step-wise evaluation of the spike detection process.

  18. A quick method based on SIMPLISMA-KPLS for simultaneously selecting outlier samples and informative samples for model standardization in near infrared spectroscopy

    Science.gov (United States)

    Li, Li-Na; Ma, Chang-Ming; Chang, Ming; Zhang, Ren-Cheng

    2017-12-01

    A novel method based on SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) and Kernel Partial Least Square (KPLS), named as SIMPLISMA-KPLS, is proposed in this paper for selection of outlier samples and informative samples simultaneously. It is a quick algorithm used to model standardization (or named as model transfer) in near infrared (NIR) spectroscopy. The NIR experiment data of the corn for analysis of the protein content is introduced to evaluate the proposed method. Piecewise direct standardization (PDS) is employed in model transfer. And the comparison of SIMPLISMA-PDS-KPLS and KS-PDS-KPLS is given in this research by discussion of the prediction accuracy of protein content and calculation speed of each algorithm. The conclusions include that SIMPLISMA-KPLS can be utilized as an alternative sample selection method for model transfer. Although it has similar accuracy to Kennard-Stone (KS), it is different from KS as it employs concentration information in selection program. This means that it ensures analyte information is involved in analysis, and the spectra (X) of the selected samples is interrelated with concentration (y). And it can be used for outlier sample elimination simultaneously by validation of calibration. According to the statistical data results of running time, it is clear that the sample selection process is more rapid when using KPLS. The quick algorithm of SIMPLISMA-KPLS is beneficial to improve the speed of online measurement using NIR spectroscopy.

  19. The internal structure of eclogite-facies ophiolite complexes: Implications from the Austroalpine outliers within the Zermatt-Saas Zone, Western Alps

    Science.gov (United States)

    Weber, Sebastian; Martinez, Raul

    2016-04-01

    The Western Alpine Penninic domain is a classical accretionary prism that formed after the closure of the Penninic oceans in the Paleogene. Continental and oceanic nappes were telescoped into the Western Alpine stack associated with continent-continent collision. Within the Western Alpine geologic framework, the ophiolite nappes of the Zermatt-Saas Zone and the Tsate Unit are the remnants of the southern branch of the Piemonte-Liguria ocean basin. In addition, a series of continental basement slices reported as lower Austroalpine outliers have preserved an eclogitic high-pressure imprint, and are tectonically sandwiched between these oceanic nappes. Since the outliers occur at an unusual intra-ophiolitic setting and show a polymetamorphic character, this group of continental slices is of special importance for understanding the tectono-metamorphic evolution of Western Alps. Recently, more geochronological data from the Austroalpine outliers have become available that make it possible to establish a more complete picture of their complex geological history. The Lu-Hf garnet-whole rock ages for prograde growth of garnet fall into the time interval of 52 to 62 Ma (Weber et al., 2015, Fassmer et al. 2015), but are consistently higher than the Lu-Hf garnet-whole rock ages from several other locations throughout the Zermatt-Saas zone that range from 52 to 38 Ma (Skora et al., 2015). This discrepancy suggests that the Austroalpine outliers may have been subducted earlier than the ophiolites of the Zermatt-Saas Zone and therefore have been tectonically emplaced into their present intra-ophiolite position. This points to the possibility that the Zermatt-Saas Zone consists of tectonic subunits, which reached their respective pressure peaks over a prolonged time period, approximately 10-20 Ma. The pressure-temperature estimates from several members of the Austroalpine outliers indicate a complex distribution of metamorphic peak conditions, without ultrahigh

  20. Evaluation of the expected moments algorithm and a multiple low-outlier test for flood frequency analysis at streamgaging stations in Arizona

    Science.gov (United States)

    Paretti, Nicholas V.; Kennedy, Jeffrey R.; Cohn, Timothy A.

    2014-01-01

    Flooding is among the costliest natural disasters in terms of loss of life and property in Arizona, which is why the accurate estimation of flood frequency and magnitude is crucial for proper structural design and accurate floodplain mapping. Current guidelines for flood frequency analysis in the United States are described in Bulletin 17B (B17B), yet since B17B’s publication in 1982 (Interagency Advisory Committee on Water Data, 1982), several improvements have been proposed as updates for future guidelines. Two proposed updates are the Expected Moments Algorithm (EMA) to accommodate historical and censored data, and a generalized multiple Grubbs-Beck (MGB) low-outlier test. The current guidelines use a standard Grubbs-Beck (GB) method to identify low outliers, changing the determination of the moment estimators because B17B uses a conditional probability adjustment to handle low outliers while EMA censors the low outliers. B17B and EMA estimates are identical if no historical information or censored or low outliers are present in the peak-flow data. EMA with MGB (EMA-MGB) test was compared to the standard B17B (B17B-GB) method for flood frequency analysis at 328 streamgaging stations in Arizona. The methods were compared using the relative percent difference (RPD) between annual exceedance probabilities (AEPs), goodness-of-fit assessments, random resampling procedures, and Monte Carlo simulations. The AEPs were calculated and compared using both station skew and weighted skew. Streamgaging stations were classified by U.S. Geological Survey (USGS) National Water Information System (NWIS) qualification codes, used to denote historical and censored peak-flow data, to better understand the effect that nonstandard flood information has on the flood frequency analysis for each method. Streamgaging stations were also grouped according to geographic flood regions and analyzed separately to better understand regional differences caused by physiography and climate. The B

  1. Short-term change detection for UAV video

    Science.gov (United States)

    Saur, Günter; Krüger, Wolfgang

    2012-11-01

    In the last years, there has been an increased use of unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. An important application in this context is change detection in UAV video data. Here we address short-term change detection, in which the time between observations ranges from several minutes to a few hours. We distinguish this task from video motion detection (shorter time scale) and from long-term change detection, based on time series of still images taken between several days, weeks, or even years. Examples for relevant changes we are looking for are recently parked or moved vehicles. As a pre-requisite, a precise image-to-image registration is needed. Images are selected on the basis of the geo-coordinates of the sensor's footprint and with respect to a certain minimal overlap. The automatic imagebased fine-registration adjusts the image pair to a common geometry by using a robust matching approach to handle outliers. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed length of shadows, and compression or transmission artifacts. To detect changes in image pairs we analyzed image differencing, local image correlation, and a transformation-based approach (multivariate alteration detection). As input we used color and gradient magnitude images. To cope with local misalignment of image structures we extended the approaches by a local neighborhood search. The algorithms are applied to several examples covering both urban and rural scenes. The local neighborhood search in combination with intensity and gradient magnitude differencing clearly improved the results. Extended image differencing performed better than both the correlation based approach and the multivariate alternation detection. The algorithms are adapted to be used in semi-automatic workflows for the ABUL video exploitation system of Fraunhofer

  2. Remote detection device and detection method therefor

    International Nuclear Information System (INIS)

    Kogure, Sumio; Yoshida, Yoji; Matsuo, Takashiro; Takehara, Hidetoshi; Kojima, Shinsaku.

    1997-01-01

    The present invention provides a non-destructive detection device for collectively, efficiently and effectively conducting maintenance and detection for confirming the integrity of a nuclear reactor by way of a shielding member for shielding radiation rays generated from an objective portion to be detected. Namely, devices for direct visual detection using an under water TV camera as a sensor, an eddy current detection using a coil as a sensor and each magnetic powder flow detection are integrated and applied collectively. Specifically, the visual detection by using the TV camera and the eddy current flaw detection are adopted together. The flaw detection with magnetic powder is applied as a means for confirming the results of the two kinds of detections by other method. With such procedures, detection techniques using respective specific theories are combined thereby enabling to enhance the accuracy for the evaluation of the detection. (I.S.)

  3. Automated rice leaf disease detection using color image analysis

    Science.gov (United States)

    Pugoy, Reinald Adrian D. L.; Mariano, Vladimir Y.

    2011-06-01

    In rice-related institutions such as the International Rice Research Institute, assessing the health condition of a rice plant through its leaves, which is usually done as a manual eyeball exercise, is important to come up with good nutrient and disease management strategies. In this paper, an automated system that can detect diseases present in a rice leaf using color image analysis is presented. In the system, the outlier region is first obtained from a rice leaf image to be tested using histogram intersection between the test and healthy rice leaf images. Upon obtaining the outlier, it is then subjected to a threshold-based K-means clustering algorithm to group related regions into clusters. Then, these clusters are subjected to further analysis to finally determine the suspected diseases of the rice leaf.

  4. Application of surface enhanced Raman scattering and competitive adaptive reweighted sampling on detecting furfural dissolved in transformer oil

    Directory of Open Access Journals (Sweden)

    Weigen Chen

    2018-03-01

    Full Text Available Detecting the dissolving furfural in mineral oil is an essential technical method to evaluate the ageing condition of oil-paper insulation and the degradation of mechanical properties. Compared with the traditional detection method, Raman spectroscopy is obviously convenient and timesaving in operation. This study explored the method of applying surface enhanced Raman scattering (SERS on quantitative analysis of the furfural dissolved in oil. Oil solution with different concentration of furfural were prepared and calibrated by high-performance liquid chromatography. Confocal laser Raman spectroscopy (CLRS and SERS technology were employed to acquire Raman spectral data. Monte Carlo cross validation (MCCV was used to eliminate the outliers in sample set, then competitive adaptive reweighted sampling (CARS was developed to select an optimal combination of informative variables that most reflect the chemical properties of concern. Based on selected Raman spectral features, support vector machine (SVM combined with particle swarm algorithm (PSO was used to set up a furfural quantitative analysis model. Finally, the generalization ability and prediction precision of the established method were verified by the samples made in lab. In summary, a new spectral method is proposed to quickly detect furfural in oil, which lays a foundation for evaluating the ageing of oil-paper insulation in oil immersed electrical equipment.

  5. Application of surface enhanced Raman scattering and competitive adaptive reweighted sampling on detecting furfural dissolved in transformer oil

    Science.gov (United States)

    Chen, Weigen; Zou, Jingxin; Wan, Fu; Fan, Zhou; Yang, Dingkun

    2018-03-01

    Detecting the dissolving furfural in mineral oil is an essential technical method to evaluate the ageing condition of oil-paper insulation and the degradation of mechanical properties. Compared with the traditional detection method, Raman spectroscopy is obviously convenient and timesaving in operation. This study explored the method of applying surface enhanced Raman scattering (SERS) on quantitative analysis of the furfural dissolved in oil. Oil solution with different concentration of furfural were prepared and calibrated by high-performance liquid chromatography. Confocal laser Raman spectroscopy (CLRS) and SERS technology were employed to acquire Raman spectral data. Monte Carlo cross validation (MCCV) was used to eliminate the outliers in sample set, then competitive adaptive reweighted sampling (CARS) was developed to select an optimal combination of informative variables that most reflect the chemical properties of concern. Based on selected Raman spectral features, support vector machine (SVM) combined with particle swarm algorithm (PSO) was used to set up a furfural quantitative analysis model. Finally, the generalization ability and prediction precision of the established method were verified by the samples made in lab. In summary, a new spectral method is proposed to quickly detect furfural in oil, which lays a foundation for evaluating the ageing of oil-paper insulation in oil immersed electrical equipment.

  6. DETECÇÃO DE OUTLIERS NO DESEMPENHO ECONÔMICO-FINANCEIRO DO SPORT CLUB CORINTHIANS PAULISTA NO PERÍODO 2008 A 2010

    Directory of Open Access Journals (Sweden)

    Marke Geisy da Silva Dantas

    2011-12-01

    Full Text Available Os ativos intangíveis permeiam o mercado de futebol onde os principais ativos das entidades futebolísticas são os contratos com os jogadores e os torcedores são considerados usuários importantes da informação contábil, uma vez que fornecem recursos para tais entidades. É dentro desse contexto que o estudo ganha relevância, visando analisar a presença de outliers nas contas do Sport Club Corinthians Paulista, referente aos anos de 2008 e 2009, quando o clube participou da Série B do Campeonato Brasileiro e quando foi efetivada a contratação de Ronaldo, respectivamente. No tocante aos procedimentos metodológicos, essa pesquisa se constitui de um estudo exploratório, demonstrando a utilização do teste de Grubbs para analisar o impacto dos ativos intangíveis sobre as contas do Corinthians, detectando anormalidades nos anos estudados. Os dados foram coletados em sites e artigos que tratavam sobre a mensuração e o enquadramento como ativo dos jogadores de futebol. Para o tratamento dos dados foi utilizada a planilha eletrônica MICROSOFT EXCEL®. Os resultados demonstraram um grande aumento percentual nas contas estudadas na comparação dos anos. Foram encontrados dois outliers em 2008 (Licenciamentos e franquias e Ativo Total, mas, em 2009 foram encontradas cinco contas que ultrapassaram a normalidade (“Licenciamentos e franquias”, “Patrocínio e publicidades”, “Arrecadação de jogos”, “Direitos de TV” e “Premiação em campeonatos”. Em 2010, só a conta “Direitos de TV”.

  7. Supervised detection of anomalous light curves in massive astronomical catalogs

    International Nuclear Information System (INIS)

    Nun, Isadora; Pichara, Karim; Protopapas, Pavlos; Kim, Dae-Won

    2014-01-01

    The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each of the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set, we perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low joint probability. Our method is suitable for exploring massive data sets given that the training process is performed offline. We tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis, we divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates we identified certain known

  8. Supervised Detection of Anomalous Light Curves in Massive Astronomical Catalogs

    Science.gov (United States)

    Nun, Isadora; Pichara, Karim; Protopapas, Pavlos; Kim, Dae-Won

    2014-09-01

    The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each of the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set, we perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low joint probability. Our method is suitable for exploring massive data sets given that the training process is performed offline. We tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis, we divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates we identified certain known

  9. Simultaneous estimation of cross-validation errors in least squares collocation applied for statistical testing and evaluation of the noise variance components

    Science.gov (United States)

    Behnabian, Behzad; Mashhadi Hossainali, Masoud; Malekzadeh, Ahad

    2018-02-01

    The cross-validation technique is a popular method to assess and improve the quality of prediction by least squares collocation (LSC). We present a formula for direct estimation of the vector of cross-validation errors (CVEs) in LSC which is much faster than element-wise CVE computation. We show that a quadratic form of CVEs follows Chi-squared distribution. Furthermore, a posteriori noise variance factor is derived by the quadratic form of CVEs. In order to detect blunders in the observations, estimated standardized CVE is proposed as the test statistic which can be applied when noise variances are known or unknown. We use LSC together with the methods proposed in this research for interpolation of crustal subsidence in the northern coast of the Gulf of Mexico. The results show that after detection and removing outliers, the root mean square (RMS) of CVEs and estimated noise standard deviation are reduced about 51 and 59%, respectively. In addition, RMS of LSC prediction error at data points and RMS of estimated noise of observations are decreased by 39 and 67%, respectively. However, RMS of LSC prediction error on a regular grid of interpolation points covering the area is only reduced about 4% which is a consequence of sparse distribution of data points for this case study. The influence of gross errors on LSC prediction results is also investigated by lower cutoff CVEs. It is indicated that after elimination of outliers, RMS of this type of errors is also reduced by 19.5% for a 5 km radius of vicinity. We propose a method using standardized CVEs for classification of dataset into three groups with presumed different noise variances. The noise variance components for each of the groups are estimated using restricted maximum-likelihood method via Fisher scoring technique. Finally, LSC assessment measures were computed for the estimated heterogeneous noise variance model and compared with those of the homogeneous model. The advantage of the proposed method is the

  10. 基于离群点算法和用电信息采集系统的反窃电研究%Study on the anti-electricity stealing based on outlier algorithm and the electricity information acquisition system

    Institute of Scientific and Technical Information of China (English)

    程超; 张汉敬; 景志敏; 陈明; 矫磊; 杨立新

    2015-01-01

    is drawn based on distance-based outlier detection method. Examples validate that the proposed algorithm and electricity stealing user selection process can fully identify electricity stealing users, providing a new idea to monitor staff for the accurate and timely anti-electricity stealing analysis with massive data of electricity information acquisition system.

  11. Applied plasma physics

    International Nuclear Information System (INIS)

    Anon.

    1979-01-01

    Applied Plasma Physics is a major sub-organizational unit of the Magnetic Fusion Energy (MFE) Program. It includes Fusion Plasma Theory and Experimental Plasma Research. The Fusion Plasma Theory group has the responsibility for developing theoretical-computational models in the general areas of plasma properties, equilibrium, stability, transport, and atomic physics. This group has responsibility for giving guidance to the mirror experimental program. There is a formal division of the group into theory and computational; however, in this report the efforts of the two areas are not separated since many projects have contributions from members of both. Under the Experimental Plasma Research Program we are developing a neutral-beam source, the intense, pulsed ion-neutral source (IPINS), for the generation of a reversed-field configuration on 2XIIB. We are also studying the feasibility of using certain neutron-detection techniques as plasma diagnostics in the next generation of thermonuclear experiments

  12. Applied plasma physics

    International Nuclear Information System (INIS)

    Anon.

    1978-01-01

    Applied Plasma Physics is a major sub-organizational unit of the MFE Program. It includes Fusion Plasma Theory and Experimental Plasma Research. The Fusion Plasma Theory group has the responsibility for developing theoretical-computational models in the general areas of plasma properties, equilibrium, stability, transport, and atomic physics. This group has responsibility for giving guidance to the mirror experimental program. There is a formal division of the group into theory and computational; however, in this report the efforts of the two areas are not separated since many projects have contributions from members of both. Under the Experimental Plasma Research Program, we are developing the intense, pulsed neutral-beam source (IPINS) for the generation of a reversed-field configuration on 2XIIB. We are also studying the feasibility of utilizing certain neutron-detection techniques as plasma diagnostics in the next generation of thermonuclear experiments

  13. Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection

    Directory of Open Access Journals (Sweden)

    Yu Qi

    2014-01-01

    Full Text Available Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To deal with the challenge of the impulsive noises and outliers caused by EMG artifacts in EEG signals, we formulate a robust stacked autoencoder (R-SAE as a part of the network to learn an effective feature. In R-SAE, the maximum correntropy criterion (MCC is proposed to reduce the effect of noise/outliers. Unlike the mean square error (MSE, the output of the new kernel MCC increases more slowly than that of MSE when the input goes away from the center. Thus, the effect of those noises/outliers positioned far away from the center can be suppressed. The proposed method is evaluated on six patients of 33.6 hours of scalp EEG data. Our method achieves a sensitivity of 100% and a specificity of 99%, which is promising for clinical applications.

  14. Gas detection by correlation spectroscopy employing a multimode diode laser.

    Science.gov (United States)

    Lou, Xiutao; Somesfalean, Gabriel; Zhang, Zhiguo

    2008-05-01

    A gas sensor based on the gas-correlation technique has been developed using a multimode diode laser (MDL) in a dual-beam detection scheme. Measurement of CO(2) mixed with CO as an interfering gas is successfully demonstrated using a 1570 nm tunable MDL. Despite overlapping absorption spectra and occasional mode hops, the interfering signals can be effectively excluded by a statistical procedure including correlation analysis and outlier identification. The gas concentration is retrieved from several pair-correlated signals by a linear-regression scheme, yielding a reliable and accurate measurement. This demonstrates the utility of the unsophisticated MDLs as novel light sources for gas detection applications.

  15. An Unsupervised Anomalous Event Detection and Interactive Analysis Framework for Large-scale Satellite Data

    Science.gov (United States)

    LIU, Q.; Lv, Q.; Klucik, R.; Chen, C.; Gallaher, D. W.; Grant, G.; Shang, L.

    2016-12-01

    Due to the high volume and complexity of satellite data, computer-aided tools for fast quality assessments and scientific discovery are indispensable for scientists in the era of Big Data. In this work, we have developed a framework for automated anomalous event detection in massive satellite data. The framework consists of a clustering-based anomaly detection algorithm and a cloud-based tool for interactive analysis of detected anomalies. The algorithm is unsupervised and requires no prior knowledge of the data (e.g., expected normal pattern or known anomalies). As such, it works for diverse data sets, and performs well even in the presence of missing and noisy data. The cloud-based tool provides an intuitive mapping interface that allows users to interactively analyze anomalies using multiple features. As a whole, our framework can (1) identify outliers in a spatio-temporal context, (2) recognize and distinguish meaningful anomalous events from individual outliers, (3) rank those events based on "interestingness" (e.g., rareness or total number of outliers) defined by users, and (4) enable interactively query, exploration, and analysis of those anomalous events. In this presentation, we will demonstrate the effectiveness and efficiency of our framework in the application of detecting data quality issues and unusual natural events using two satellite datasets. The techniques and tools developed in this project are applicable for a diverse set of satellite data and will be made publicly available for scientists in early 2017.

  16. Treatment on outliers in UBJ-SARIMA models for forecasting dengue cases on age groups not eligible for vaccination in Baguio City, Philippines

    Science.gov (United States)

    Magsakay, Clarenz B.; De Vera, Nora U.; Libatique, Criselda P.; Addawe, Rizavel C.; Addawe, Joel M.

    2017-11-01

    Dengue vaccination has become a breakthrough in the fight against dengue infection. This is however not applicable to all ages. Individuals from 0 to 8 years old and adults older than 45 years old remain susceptible to the vector-borne disease dengue. Forecasting future dengue cases accurately from susceptible age groups would aid in the efforts to prevent further increase in dengue infections. For the age groups of individuals not eligible for vaccination, the presence of outliers was observed and was treated using winsorization, square root, and logarithmic transformations to create a SARIMA model. The best model for the age group 0 to 8 years old was found to be ARIMA(13,1,0)(1,0,0)12 with 10 fixed variables using square root transformation with a 95% winsorization, and the best model for the age group older than 45 years old is ARIMA(7,1,0)(1,0,0)12 with 5 fixed variables using logarithmic transformation with 90% winsorization. These models are then used to forecast the monthly dengue cases for Baguio City for the age groups considered.

  17. An assessment of thin cloud detection by applying bidirectional reflectance distribution function model-based background surface reflectance using Geostationary Ocean Color Imager (GOCI): A case study for South Korea

    Science.gov (United States)

    Kim, Hye-Won; Yeom, Jong-Min; Shin, Daegeun; Choi, Sungwon; Han, Kyung-Soo; Roujean, Jean-Louis

    2017-08-01

    In this study, a new assessment of thin cloud detection with the application of bidirectional reflectance distribution function (BRDF) model-based background surface reflectance was undertaken by interpreting surface spectra characterized using the Geostationary Ocean Color Imager (GOCI) over a land surface area. Unlike cloud detection over the ocean, the detection of cloud over land surfaces is difficult due to the complicated surface scattering characteristics, which vary among land surface types. Furthermore, in the case of thin clouds, in which the surface and cloud radiation are mixed, it is difficult to detect the clouds in both land and atmospheric fields. Therefore, to interpret background surface reflectance, especially underneath cloud, the semiempirical BRDF model was used to simulate surface reflectance by reflecting solar angle-dependent geostationary sensor geometry. For quantitative validation, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data were used to make a comparison with the proposed cloud masking result. As a result, the new cloud masking scheme resulted in a high probability of detection (POD = 0.82) compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) (POD = 0.808) for all cloud cases. In particular, the agreement between the CALIPSO cloud product and new GOCI cloud mask was over 94% when detecting thin cloud (e.g., altostratus and cirrus) from January 2014 to June 2015. This result is relatively high in comparison with the result from the MODIS Collection 6 cloud mask product (MYD35).

  18. A novel bi-level meta-analysis approach: applied to biological pathway analysis.

    Science.gov (United States)

    Nguyen, Tin; Tagett, Rebecca; Donato, Michele; Mitrea, Cristina; Draghici, Sorin

    2016-02-01

    The accumulation of high-throughput data in public repositories creates a pressing need for integrative analysis of multiple datasets from independent experiments. However, study heterogeneity, study bias, outliers and the lack of power of available methods present real challenge in integrating genomic data. One practical drawback of many P-value-based meta-analysis methods, including Fisher's, Stouffer's, minP and maxP, is that they are sensitive to outliers. Another drawback is that, because they perform just one statistical test for each individual experiment, they may not fully exploit the potentially large number of samples within each study. We propose a novel bi-level meta-analysis approach that employs the additive method and the Central Limit Theorem within each individual experiment and also across multiple experiments. We prove that the bi-level framework is robust against bias, less sensitive to outliers than other methods, and more sensitive to small changes in signal. For comparative analysis, we demonstrate that the intra-experiment analysis has more power than the equivalent statistical test performed on a single large experiment. For pathway analysis, we compare the proposed framework versus classical meta-analysis approaches (Fisher's, Stouffer's and the additive method) as well as against a dedicated pathway meta-analysis package (MetaPath), using 1252 samples from 21 datasets related to three human diseases, acute myeloid leukemia (9 datasets), type II diabetes (5 datasets) and Alzheimer's disease (7 datasets). Our framework outperforms its competitors to correctly identify pathways relevant to the phenotypes. The framework is sufficiently general to be applied to any type of statistical meta-analysis. The R scripts are available on demand from the authors. sorin@wayne.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e

  19. Detection of anomalous signals in temporally correlated data (Invited)

    Science.gov (United States)

    Langbein, J. O.

    2010-12-01

    Detection of transient tectonic signals in data obtained from large geodetic networks requires the ability to detect signals that are both temporally and spatially coherent. In this report I will describe a modification to an existing method that estimates both the coefficients of temporally correlated noise model and an efficient filter based on the noise model. This filter, when applied to the original time-series, effectively whitens (or flattens) the power spectrum. The filtered data provide the means to calculate running averages which are then used to detect deviations from the background trends. For large networks, time-series of signal-to-noise ratio (SNR) can be easily constructed since, by filtering, each of the original time-series has been transformed into one that is closer to having a Gaussian distribution with a variance of 1.0. Anomalous intervals may be identified by counting the number of GPS sites for which the SNR exceeds a specified value. For example, during one time interval, if there were 5 out of 20 time-series with SNR>2, this would be considered anomalous; typically, one would expect at 95% confidence that there would be at least 1 out of 20 time-series with an SNR>2. For time intervals with an anomalously large number of high SNR, the spatial distribution of the SNR is mapped to identify the location of the anomalous signal(s) and their degree of spatial clustering. Estimating the filter that should be used to whiten the data requires modification of the existing methods that employ maximum likelihood estimation to determine the temporal covariance of the data. In these methods, it is assumed that the noise components in the data are a combination of white, flicker and random-walk processes and that they are derived from three different and independent sources. Instead, in this new method, the covariance matrix is constructed assuming that only one source is responsible for the noise and that source can be represented as a white

  20. Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

    Science.gov (United States)

    Ganapathy, S.; Yogesh, P.; Kannan, A.

    2012-01-01

    Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036