WorldWideScience

Sample records for machining feature relationship

  1. Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

    Directory of Open Access Journals (Sweden)

    Ickwon Choi

    2015-04-01

    Full Text Available The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release. We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

  2. Feature Recognition for Virtual Machining

    OpenAIRE

    Xú, Shixin; Anwer, Nabil; Qiao, Lihong

    2014-01-01

    International audience; Virtual machining uses software tools to simulate machining processes in virtual environments ahead of actual production. This paper proposes that feature recognition techniques can be applied in the course of virtual machining, such as identifying some process problems, and presenting corresponding correcting advices. By comparing with the original CAD model, form errors of the machining features can be found. And then corrections are suggested to process designers. T...

  3. Modeling the Relationship between Vibration Features and Condition Parameters Using Relevance Vector Machines for Health Monitoring of Rolling Element Bearings under Varying Operation Conditions

    Directory of Open Access Journals (Sweden)

    Lei Hu

    2015-01-01

    Full Text Available Rotational speed and load usually change when rotating machinery works. Both this kind of changing operational conditions and machine fault could make the mechanical vibration characteristics change. Therefore, effective health monitoring method for rotating machinery must be able to adjust during the change of operational conditions. This paper presents an adaptive threshold model for the health monitoring of bearings under changing operational conditions. Relevance vector machines (RVMs are used for regression of the relationships between the adaptive parameters of the threshold model and the statistical characteristics of vibration features. The adaptive threshold model is constructed based on these relationships. The health status of bearings can be indicated via detecting whether vibration features exceed the adaptive threshold. This method is validated on bearings running at changing speeds. The monitoring results show that this method is effective as long as the rotational speed is higher than a relative small value.

  4. Elementary epistemological features of machine intelligence

    CERN Document Server

    Horvat, Marko

    2008-01-01

    Theoretical analysis of machine intelligence (MI) is useful for defining a common platform in both theoretical and applied artificial intelligence (AI). The goal of this paper is to set canonical definitions that can assist pragmatic research in both strong and weak AI. Described epistemological features of machine intelligence include relationship between intelligent behavior, intelligent and unintelligent machine characteristics, observable and unobservable entities and classification of intelligence. The paper also establishes algebraic definitions of efficiency and accuracy of MI tests as their quality measure. The last part of the paper addresses the learning process with respect to the traditional epistemology and the epistemology of MI described here. The proposed views on MI positively correlate to the Hegelian monistic epistemology and contribute towards amalgamating idealistic deliberations with the AI theory, particularly in a local frame of reference.

  5. Improved AAG based recognization of machining feature

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The lost information caused by feature interaction is restored by using auxiliary faces(AF)and virtual links(VL).The delta volume of the interacted features represented by concave attachable connected graph (CACG)can be decomposed into several isolated features represented by complete concave adjacency graph (CCAG).We can recognize the features sketchy type by using CCAG as a hint; the exact type of the feature can be attained by deleting the auxiliary faces from the isolated feature.United machining feature(UMF)is used to represent the features that can be machined in the same machining process.It is important to the rationalizing of the process plans and reduce the time costing in machining.An example is given to demonstrate the effectiveness of this method.

  6. Finite State Machine based Vending Machine Controller with Auto-Billing Features

    OpenAIRE

    2012-01-01

    Nowadays, Vending Machines are well known among Japan, Malaysia and Singapore. The quantity of machines in these countries is on the top worldwide. This is due to the modern lifestyles which require fast food processing with high quality. This paper describes the designing of multi select machine using Finite State Machine Model with Auto-Billing Features. Finite State Machine (FSM) modelling is the most crucial part in developing proposed model as this reduces the hardware. In this paper th...

  7. Finite State Machine based Vending Machine Controller with Auto-Billing Features

    Directory of Open Access Journals (Sweden)

    Ana Monga

    2012-04-01

    Full Text Available Nowadays, Vending Machines are well known among Japan, Malaysia and Singapore. The quantity of machines in these countries is on the top worldwide. This is due to the modern lifestyles which require fast food processing with high quality. This paper describes the designing of multi select machine using Finite State Machine Model with Auto-Billing Features. Finite State Machine (FSM modelling is the most crucial part in developing proposed model as this reduces the hardware. In this paper the process of four state (user Selection, Waiting for money insertion, product delivery and servicing has been modelled using MEALY Machine Model. The proposed model is tested using Spartan 3 development board and its performance is compared with CMOS based machine.

  8. Finite State Machine based Vending Machine Controller with Auto-Billing Features

    Directory of Open Access Journals (Sweden)

    Balwinder Singh

    2012-05-01

    Full Text Available Nowadays, Vending Machines are well known among Japan, Malaysia and Singapore. The quantity of machines in these countries is on the top worldwide. This is due to the modern lifestyles which require fast food processing with high quality. This paper describes the designing of multi select machine using Finite State Machine Model with Auto-Billing Features. Finite State Machine (FSM modelling is the most crucial part in developing proposed model as this reduces the hardware. In this paper the process of four state (user Selection, Waiting for money insertion, product delivery and servicing has been modelled using MEALY Machine Model. The proposed model is tested using Spartan 3 development board and its performance is compared with CMOS based machine.

  9. Finite State Machine based Vending Machine Controller with Auto-Billing Features

    CERN Document Server

    Monga, Ana; 10.5121/vlsic.2012.3202

    2012-01-01

    Nowadays, Vending Machines are well known among Japan, Malaysia and Singapore. The quantity of machines in these countries is on the top worldwide. This is due to the modern lifestyles which require fast food processing with high quality. This paper describes the designing of multi select machine using Finite State Machine Model with Auto-Billing Features. Finite State Machine (FSM) modelling is the most crucial part in developing proposed model as this reduces the hardware. In this paper the process of four state (user Selection, Waiting for money insertion, product delivery and servicing) has been modelled using MEALY Machine Model. The proposed model is tested using Spartan 3 development board and its performance is compared with CMOS based machine.

  10. Blue gum gaming machine: an evaluation of responsible gambling features.

    Science.gov (United States)

    Blaszczynski, Alexander; Gainsbury, Sally; Karlov, Lisa

    2014-09-01

    Structural characteristics of gaming machines contribute to persistence in play and excessive losses. The purpose of this study was to evaluate the effectiveness of five proposed responsible gaming features: responsible gaming messages; a bank meter quarantining winnings until termination of play; alarm clock facilitating setting time-reminders; demo mode allowing play without money; and a charity donation feature where residual amounts can be donated rather than played to zero credits. A series of ten modified gaming machines were located in five Australian gambling venues. The sample comprised 300 patrons attending the venue and who played the gaming machines. Participants completed a structured interview eliciting gambling and socio-demographic data and information on their perceptions and experience of play on the index machines. Results showed that one-quarter of participants considered that these features would contribute to preventing recreational gamblers from developing problems. Just under half of the participants rated these effects to be at least moderate or significant. The promising results suggest that further refinements to several of these features could represent a modest but effective approach to minimising excessive gambling on gaming machines.

  11. Feature importance for machine learning redshifts applied to SDSS galaxies

    CERN Document Server

    Hoyle, Ben; Zitlau, Roman; Steiz, Stella; Weller, Jochen

    2014-01-01

    We present an analysis of importance feature selection applied to photometric redshift estimation using the machine learning architecture Random Decision Forests (RDF) with the ensemble learning routine Adaboost. We select a list of 85 easily measured (or derived) photometric quantities (or 'features') and spectroscopic redshifts for almost two million galaxies from the Sloan Digital Sky Survey Data Release 10. After identifying which features have the most predictive power, we use standard artificial Neural Networks (aNN) to show that the addition of these features, in combination with the standard magnitudes and colours, improves the machine learning redshift estimate by 18% and decreases the catastrophic outlier rate by 32%. We further compare the redshift estimate from RDF using the ensemble learning routine Adaboost with those from two different aNNs, and with photometric redshifts available from the SDSS. We find that the RDF requires orders of magnitude less computation time than the aNNs to obtain a m...

  12. An attempt of CNC machining cycle’s application as a tool of the design feature library elaboration

    Directory of Open Access Journals (Sweden)

    Grabowik Cezary

    2017-01-01

    Full Text Available This paper presents a novel approach to a problem of the design feature library elaboration. As a tool of the design feature library development CNC machining cycles were proposed. Because of the great number of commercially available CNC machine controllers, with different CNC machining cycles definitions, it was necessary to make a decision about a research methodological framework, it is the selected CNC machine controller. Taking into account the criterion of popularity as the research framework the selected group of Sinumerik CNC machine controllers was chosen. Presented in the paper idea of the feature library development is based on an assumption saying that it is possible to find a relationship between a particular CNC machining cycle and the simple design feature or even compound design features. Identified, thanks to this assumption, set of the design features could be the base for elaboration of the design feature library. This solution, it is the feature library next gave opportunity for elaboration of the feature based design modelling module (FBDMM working in the SIEMENS NX system environment. Hence, the FBDMM module can support both a designer and CNC machine programmer which is possible due to received in the module modelling paradigm. In FBDMM module the removal feature based modelling technique is received.

  13. Statistical Machine Translation Features with Multitask Tensor Networks

    OpenAIRE

    Setiawan, Hendra; Huang, Zhongqiang; Devlin, Jacob; Lamar, Thomas; Zbib, Rabih; Schwartz, Richard; Makhoul, John

    2015-01-01

    We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT. First, we propose new features based on neural networks to model various non-local translation phenomena. Second, we augment the architecture of the neural network with tensor layers that capture important higher-order interaction among the network units. Third, we apply multitask learning to estimate the neural network parameters joi...

  14. Sensitivity of Support Vector Machine Classification to Various Training Features

    Directory of Open Access Journals (Sweden)

    Fuling Bian

    2013-07-01

    Full Text Available Remote sensing image classification is one of the most important techniques in image interpretation, which can be used for environmental monitoring, evaluation and prediction. Many algorithms have been developed for image classification in the literature. Support vector machine (SVM is a kind of supervised classification that has been widely used recently. The classification accuracy produced by SVM may show variation depending on the choice of training features. In this paper, SVM was used for land cover classification using Quickbird images. Spectral and textural features were extracted for the classification and the results were analyzed thoroughly. Results showed that the number of features employed in SVM was not the more the better. Different features are suitable for different type of land cover extraction. This study verifies the effectiveness and robustness of SVM in the classification of high spatial resolution remote sensing images.    

  15. An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction

    Directory of Open Access Journals (Sweden)

    Dongju Chen

    2016-01-01

    Full Text Available This paper presents a new identification method to identify the main errors of the machine tool in time-frequency domain. The low- and high-frequency signals of the workpiece surface are decomposed based on the Daubechies wavelet transform. With power spectral density analysis, the main features of the high-frequency signal corresponding to the imbalance of the spindle system are extracted from the surface topography of the workpiece in the frequency domain. With the cross-correlation analysis method, the relationship between the guideway error of the machine tool and the low-frequency signal of the surface topography is calculated in the time domain.

  16. Exploring the relationship between fractal features and bacterial essential genes

    Science.gov (United States)

    Yong-Ming, Yu; Li-Cai, Yang; Qian, Zhou; Lu-Lu, Zhao; Zhi-Ping, Liu

    2016-06-01

    Essential genes are indispensable for the survival of an organism in optimal conditions. Rapid and accurate identifications of new essential genes are of great theoretical and practical significance. Exploring features with predictive power is fundamental for this. Here, we calculate six fractal features from primary gene and protein sequences and then explore their relationship with gene essentiality by statistical analysis and machine learning-based methods. The models are applied to all the currently available identified genes in 27 bacteria from the database of essential genes (DEG). It is found that the fractal features of essential genes generally differ from those of non-essential genes. The fractal features are used to ascertain the parameters of two machine learning classifiers: Naïve Bayes and Random Forest. The area under the curve (AUC) of both classifiers show that each fractal feature is satisfactorily discriminative between essential genes and non-essential genes individually. And, although significant correlations exist among fractal features, gene essentiality can also be reliably predicted by various combinations of them. Thus, the fractal features analyzed in our study can be used not only to construct a good essentiality classifier alone, but also to be significant contributors for computational tools identifying essential genes. Project supported by the Shandong Provincial Natural Science Foundation, China (Grant No. ZR2014FM022).

  17. Image Retrieval via Relevance Vector Machine with Multiple Features

    Directory of Open Access Journals (Sweden)

    Zemin Liu

    2014-05-01

    Full Text Available With the fast development of computer network technique, there is large amount of image information every day. Researchers have paid more and more attention to the problem of how users quickly retrieving and identifying the images that they may interest. Meanwhile, with the rapid development of artificial intelligence and pattern recognition techniques, it provides people with new thought on the study on complex image retrieval while it’s very difficult for traditional machine learning method to get ideal retrieval results. For this reason, we in this paper propose a new approach for image retrieval based on multiple types of image features and relevance vector machine (RVM. The proposed method, termed as MF-RVM, integrates the informative cures of features and the discrimination ability of RVM. The retrieval experiment is conducted on COREL image library which is collected from internet. The experimental results show that the proposed method can significantly improve the performance for image retrieval, so MF-RVM presented in this paper has very high practicability in image retrieval.

  18. Discriminative feature-rich models for syntax-based machine translation.

    Energy Technology Data Exchange (ETDEWEB)

    Dixon, Kevin R.

    2012-12-01

    This report describes the campus executive LDRD %E2%80%9CDiscriminative Feature-Rich Models for Syntax-Based Machine Translation,%E2%80%9D which was an effort to foster a better relationship between Sandia and Carnegie Mellon University (CMU). The primary purpose of the LDRD was to fund the research of a promising graduate student at CMU; in this case, Kevin Gimpel was selected from the pool of candidates. This report gives a brief overview of Kevin Gimpel's research.

  19. Novel Automatic Filter-Class Feature Selection for Machine Learning Regression

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Hallam, John; Jørgensen, Bo Nørregaard

    2017-01-01

    With the increased focus on application of Big Data in all sectors of society, the performance of machine learning becomes essential. Efficient machine learning depends on efficient feature selection algorithms. Filter feature selection algorithms are model-free and therefore very fast, but require...... model in the feature selection process. PCA is often used in machine learning litterature and can be considered the default feature selection method. RDESF outperformed PCA in both experiments in both prediction error and computational speed. RDESF is a new step into filter-based automatic feature...

  20. Novel Automatic Filter-Class Feature Selection for Machine Learning Regression

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Hallam, John; Jørgensen, Bo Nørregaard

    2016-01-01

    With the increased focus on application of Big Data in all sectors of society, the performance of machine learning becomes essential. Efficient machine learning depends on efficient feature selection algorithms. Filter feature selection algorithms are model-free and therefore very fast, but require...... model in the feature selection process. PCA is often used in machine learning litterature and can be considered the default feature selection method. RDESF outperformed PCA in both experiments in both prediction error and computational speed. RDESF is a new step into filter-based automatic feature...

  1. A multi-perspective dynamic feature concept in adaptive NC machining of complex freeform surfaces

    OpenAIRE

    Liu, Xu; Li, Yingguang; Gao, James

    2016-01-01

    This paper presents a new concept of feature for freeform surface machining that defines the changes in feature status during real manufacturing situations which have not been sufficiently addressed by current international standards and previous research in feature technology. These changes are multi-perspective, including (i) changes in depth-of-cut: the geometry of a feature in the depth-of-cut direction changes during different machining operations such as roughing, semi-finishing and fin...

  2. LSTM Neural Reordering Feature for Statistical Machine Translation

    OpenAIRE

    Cui, Yiming; Wang, Shijin; Li, Jianfeng

    2015-01-01

    Artificial neural networks are powerful models, which have been widely applied into many aspects of machine translation, such as language modeling and translation modeling. Though notable improvements have been made in these areas, the reordering problem still remains a challenge in statistical machine translations. In this paper, we present a novel neural reordering model that directly models word pairs and alignment. By utilizing LSTM recurrent neural networks, much longer context could be ...

  3. Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.

    Science.gov (United States)

    Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V; Neil, Greg J; Black, Sue

    2016-01-01

    Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.

  4. Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

    Science.gov (United States)

    2016-01-01

    Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications. PMID:27806075

  5. NEW FEATURES OF EXPERIMENTAL MACHINE HYDRAULIC DRIVE FINE TUNING

    Directory of Open Access Journals (Sweden)

    M. I. Zhylevich

    2011-01-01

    Full Text Available The paper considers new methods for  honing and functional testing of machine hydraulic drives: a method for evaluation of friction surface running-in ability and a functional test method for an unsteady temperature regime. Possibilities of their experimental realization are described in the paper

  6. Improvement of Machine Translation Evaluation by Simple Linguistically Motivated Features

    Institute of Scientific and Technical Information of China (English)

    Mu-Yun Yang; Shu-Qi Sun; Jun-Guo Zhu; Sheng Li; Tie-Jun Zhao; Xiao-Ning Zhu

    2011-01-01

    Adopting the regression SVM framework, this paper proposes a linguistically motivated feature engineering strategy to develop an MT evaluation metric with a better correlation with human assessments. In contrast to current practices of "greedy" combination of all available features, six features are suggested according to the human intuition for translation quality. Then the contribution of linguistic features is examined and analyzed via a hill-climbing strategy. Experiments indicate that, compared to either the SVM-ranking model or the previous attempts on exhaustive linguistic features, the regression SVM model with six linguistic information based features generalizes across different datasets better, and augmenting these linguistic features with proper non-linguistic metrics can achieve additional improvements.

  7. NEW FEATURE SELECTION METHOD IN MACHINE FAULT DIAGNOSIS

    Institute of Scientific and Technical Information of China (English)

    Wang Xinfeng; Qiu Jing; Liu Guanjun

    2005-01-01

    Aiming to deficiency of the filter and wrapper feature selection methods, a new method based on composite method of filter and wrapper method is proposed. First the method filters original features to form a feature subset which can meet classification correctness rate, then applies wrapper feature selection method select optimal feature subset. A successful technique for solving optimization problems is given by genetic algorithm (GA). GA is applied to the problem of optimal feature selection. The composite method saves computing time several times of the wrapper method with holding the classification accuracy in data simulation and experiment on bearing fault feature selection. So this method possesses excellent optimization property, can save more selection time, and has the characteristics of high accuracy and high efficiency.

  8. Method of determining the process applied for feature machining : experimental validation of a slot

    OpenAIRE

    Martin, Patrick; D'ACUNTO, Alain

    2007-01-01

    International audience; In this paper, we will be evaluating the "manufacturability" levels for several machining processes of "slot" feature. Using the STEP standard, we will identify the slot feature characteristics. Then, using the ascendant generation of process method, we will define the associated milling process. The expertise is based on a methodology relative to the experience plans carried out during the formalization and systematic evaluation of the machining process associated wit...

  9. Using machine learning to model dose-response relationships.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R; Nallamothu, Brahmajee K

    2016-12-01

    Establishing the relationship between various doses of an exposure and a response variable is integral to many studies in health care. Linear parametric models, widely used for estimating dose-response relationships, have several limitations. This paper employs the optimal discriminant analysis (ODA) machine-learning algorithm to determine the degree to which exposure dose can be distinguished based on the distribution of the response variable. By framing the dose-response relationship as a classification problem, machine learning can provide the same functionality as conventional models, but can additionally make individual-level predictions, which may be helpful in practical applications like establishing responsiveness to prescribed drug regimens. Using data from a study measuring the responses of blood flow in the forearm to the intra-arterial administration of isoproterenol (separately for 9 black and 13 white men, and pooled), we compare the results estimated from a generalized estimating equations (GEE) model with those estimated using ODA. Generalized estimating equations and ODA both identified many statistically significant dose-response relationships, separately by race and for pooled data. Post hoc comparisons between doses indicated ODA (based on exact P values) was consistently more conservative than GEE (based on estimated P values). Compared with ODA, GEE produced twice as many instances of paradoxical confounding (findings from analysis of pooled data that are inconsistent with findings from analyses stratified by race). Given its unique advantages and greater analytic flexibility, maximum-accuracy machine-learning methods like ODA should be considered as the primary analytic approach in dose-response applications. © 2016 John Wiley & Sons, Ltd.

  10. Learning features for tissue classification with the classification restricted Boltzmann machine

    NARCIS (Netherlands)

    G. van Tulder (Gijs); M. de Bruijne (Marleen)

    2014-01-01

    markdownabstract__Abstract__ Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue

  11. An Approach with Support Vector Machine using Variable Features Selection on Breast Cancer Prognosis

    Directory of Open Access Journals (Sweden)

    Sandeep Chaurasia

    2013-09-01

    Full Text Available Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of machine learning. In this paper we have used an approach by using support vector machine classifier to construct a model that is useful for the breast cancer survivability prediction. We have used both 5 cross and 10 cross validation of variable selection on input feature vectors and the performance measurement through bio-learning class performance while measuring AUC, specificity and sensitivity. The performance of the SVM is much better than the other machine learning classifier.

  12. Operator functional state classification using least-square support vector machine based recursive feature elimination technique.

    Science.gov (United States)

    Yin, Zhong; Zhang, Jianhua

    2014-01-01

    This paper proposed two psychophysiological-data-driven classification frameworks for operator functional states (OFS) assessment in safety-critical human-machine systems with stable generalization ability. The recursive feature elimination (RFE) and least square support vector machine (LSSVM) are combined and used for binary and multiclass feature selection. Besides typical binary LSSVM classifiers for two-class OFS assessment, two multiclass classifiers based on multiclass LSSVM-RFE and decision directed acyclic graph (DDAG) scheme are developed, one used for recognizing the high mental workload and fatigued state while the other for differentiating overloaded and base-line states from the normal states. Feature selection results have revealed that different dimensions of OFS can be characterized by specific set of psychophysiological features. Performance comparison studies show that reasonable high and stable classification accuracy of both classification frameworks can be achieved if the RFE procedure is properly implemented and utilized.

  13. Extracting invariable fault features of rotating machines with multi-ICA networks

    Institute of Scientific and Technical Information of China (English)

    焦卫东; 杨世锡; 吴昭同

    2003-01-01

    This paper proposes novel multi-layer neural networks based on Independent Component Analysis for feature extraction of fault modes. By the use of ICA, invariable features embedded in multi-channel vibration measurements under different operating conditions (rotating speed and/or load) can be captured together.Thus, stable MLP classifiers insensitive to the variation of operation conditions are constructed. The successful results achieved by selected experiments indicate great potential of ICA in health condition monitoring of rotating machines.

  14. A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

    Science.gov (United States)

    D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

    1993-01-01

    A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

  15. Machine-assisted discovery of relationships in astronomy

    CERN Document Server

    Graham, Matthew J; Mahabal, Ashish A; Donalek, Ciro; Drake, Andrew J

    2013-01-01

    High-volume feature-rich data sets are becoming the bread-and-butter of 21st century astronomy but present significant challenges to scientific discovery. In particular, identifying scientifically significant relationships between sets of parameters is non-trivial. Similar problems in biological and geosciences have led to the development of systems which can explore large parameter spaces and identify potentially interesting sets of associations. In this paper, we describe the application of automated discovery systems of relationships to astronomical data sets, focussing on an evolutionary programming technique and an information-theory technique. We demonstrate their use with classical astronomical relationships - the Hertzsprung-Russell diagram and the fundamental plane of elliptical galaxies. We also show how they work with the issue of binary classification which is relevant to the next generation of large synoptic sky surveys, such as LSST. We find that comparable results to more familiar techniques, s...

  16. A novel method for machine performance degradation assessment based on fixed cycle features test

    Science.gov (United States)

    Liao, Linxia; Lee, Jay

    2009-10-01

    This paper presents a novel machine performance degradation scheme based on fixed cycle features test (FCFT). Instead of monitoring the machine under constant working load, FCFT introduces a new testing method which obtains data during the transient periods of different working loads. A novel performance assessment method based on those transient data without failure history is proposed. Wavelet packet analysis (WPA) is applied to extract features which capture the dynamic characteristics from the non-stationary vibration data. Principal component analysis (PCA) is used to reduce the dimension of the feature space. Gaussian mixture model (GMM) is utilized to approximate the density distribution of the lower-dimensional feature space which consists of the major principal components. The performance index of the machine is calculated based on the overlap between the distribution of the baseline feature space and that of the testing feature space. Bayesian information criterion (BIC) is used to determine the number of mixtures for the GMM and a density boosting method is applied to achieve better accuracy of the distribution estimation. A case study for a chiller system performance assessment is used as an example to validate the effectiveness of the proposed method.

  17. Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

    Science.gov (United States)

    Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

    2015-04-01

    With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

  18. Automatic Extraction of Three Dimensional Prismatic Machining Features from CAD Model

    Directory of Open Access Journals (Sweden)

    B.V. Sudheer Kumar

    2011-12-01

    Full Text Available Machining features recognition provides the necessary platform for the computer aided process planning (CAPP and plays a key role in the integration of computer aided design (CAD and computer aided manufacturing (CAM. This paper presents a new methodology for extracting features from the geometrical data of the CAD Model present in the form of Virtual Reality Modeling Language (VRML files. First, the point cloud is separated into the available number of horizontal cross sections. Each cross section consists of a 2D point cloud. Then, a collection of points represented by a set of feature points is derived for each slice, describing the cross section accurately, and providing the basis for a feature-extraction. These extracted manufacturing features, gives the necessary information regarding the manufacturing activities tomanufacture the part. Software in Microsoft Visual C++ environment is developed to recognize the features, where geometric information of the part isextracted from the CAD model. By using this data, anoutput file i.e., text file is generated, which gives all the machinable features present in the part. This process has been tested on various parts and successfully extracted all the features

  19. Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jia Uddin

    2014-01-01

    Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

  20. Representing Topological Relationships Among Heterogeneous Geometry-Collection Features

    Institute of Scientific and Technical Information of China (English)

    Zhi-Nong Zhong; Ning Jing; Luo Chen; Qiu-Yun Wu

    2004-01-01

    Topological relationships between two spatial features represent important knowledge in Geographical Information Systems (GIS). In the last few years, many models that represent topological relationships have been proposed. But these models cannot represent the topological relationships between heterogeneous geometrycollection features, which are composed of different dimensional geometries. In this paper, the formal definition of regular heterogeneous geometrycollection and regularization rules are given. Based on the spatial model, two methods for representing topological relationships between these complex features are proposed. The first method is Dimensionally Extended Nine-Intersection Model Based on Components (DE-9IMBC) that extends Dimensionally Extended Nine-Intersection Model (DE-9IM) and takes into account the topological relationships between components of these complex features. The advantage of DE-9IMBC is that a large number of different topological relationships can be checked. The second method extends the definitions of topological relationships in Open Geodata Interoperability Specification (OpenGIS), and redefines the seven named topological relationships:{Disjoint, Touches, Within, Crosses, Overlaps, Contains and Equal}, to represent the topological relationships between heterogeneous geometrycollection features. It is proven that the seven extended topological relationships are complete and mutually exclusive, and they are suitable for being embedded in spatial query languages.

  1. Representing Topological Relationships Among Heterogeneous Geometry-Collection Features

    Institute of Scientific and Technical Information of China (English)

    Zhi-NongZhong[; NingJing; LuoChen; Qiu-YunWu

    2004-01-01

    Topological relationships between two spatial features represent important knowledge in Geographical Information Systems (GIS). In the last few years, many models that represent topological relationships have been proposed. But these models cannot represent the topological relationships between heterogeneous geometrycollection features, which are composed of different dimensional geometries. In this paper, the formal definition of regular heterogeneous geometrycollection and regularization rules are given. Based on the spatial model, two methods for representing topological relationships between these complex features are proposed. The first method is Dimensionally Extended Nine-Intersection Model Based on Components (DE-9IMBC) that extends Dimensionally Extended Nine-Intersection Model (DE-9IM) and takes into account the topological relationships between components of these complex features. The advantage of DE-9IMBC is that a large number of different topological relationships can be checked. The second method extends the definitions of topological relationships in Open Geodata Interoperability Specification (OpenGIS), and redefines the seven named topological relationships: {Disjoint, Touches, Within, Crosses, Overlaps, Contains and Equal}, to represent the topological relationships between heterogeneous geometrycollection features. It is proven that the seven extended topological relationships are complete and mutually exclusive, and they are suitable for being embedded in spatial query languages.

  2. Impact of Machine-Translated Text on Entity and Relationship Extraction

    Science.gov (United States)

    2014-12-01

    Impact of Machine-Translated Text on Entity and Relationship Extraction by Mark R Mittrick and John T Richardson ARL-TN-0649 December...2014 Impact of Machine-Translated Text on Entity and Relationship Extraction Mark R Mittrick and John T Richardson Computational and...

  3. Feature Subset Selection for Hot Method Prediction using Genetic Algorithm wrapped with Support Vector Machines

    Directory of Open Access Journals (Sweden)

    S. Johnson

    2011-01-01

    Full Text Available Problem statement: All compilers have simple profiling-based heuristics to identify and predict program hot methods and also to make optimization decisions. The major challenge in the profile-based optimization is addressing the problem of overhead. The aim of this work is to perform feature subset selection using Genetic Algorithms (GA to improve and refine the machine learnt static hot method predictive technique and to compare the performance of the new models against the simple heuristics. Approach: The relevant features for training the predictive models are extracted from an initial set of randomly selected ninety static program features, with the help of the GA wrapped with the predictive model using the Support Vector Machine (SVM, a Machine Learning (ML algorithm. Results: The GA-generated feature subsets containing thirty and twenty nine features respectively for the two predictive models when tested on MiBench predict Long Running Hot Methods (LRHM and frequently called hot methods (FCHM with the respective accuracies of 71% and 80% achieving an increase of 19% and 22%. Further, inlining of the predicted LRHM and FCHM improve the program performance by 3% and 5% as against 4% and 6% with Low Level Virtual Machines (LLVM default heuristics. When intra-procedural optimizations (IPO are performed on the predicted hot methods, this system offers a performance improvement of 5% and 4% as against 0% and 3% by LLVM default heuristics on LRHM and FCHM respectively. However, we observe an improvement of 36% in certain individual programs. Conclusion: Overall, the results indicate that the GA wrapped with SVM derived feature reduction improves the hot method prediction accuracy and that the technique of hot method prediction based optimization is potentially useful in selective optimization.

  4. Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection

    Directory of Open Access Journals (Sweden)

    Abdelniser Moomen

    2016-04-01

    Full Text Available Nondestructive Testing (NDT assessment of materials’ health condition is useful for classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures. Performing structural health testing for coated/uncoated metallic or dielectric materials with the same testing equipment requires a testing method that can work on metallics and dielectrics such as microwave testing. Reducing complexity and expenses associated with current diagnostic practices of microwave NDT of structural health requires an effective and intelligent approach based on feature selection and classification techniques of machine learning. Current microwave NDT methods in general based on measuring variation in the S-matrix over the entire operating frequency ranges of the sensors. For instance, assessing the health of metallic structures using a microwave sensor depends on the reflection or/and transmission coefficient measurements as a function of the sweeping frequencies of the operating band. The aim of this work is reducing sweeping frequencies using machine learning feature selection techniques. By treating sweeping frequencies as features, the number of top important features can be identified, then only the most influential features (frequencies are considered when building the microwave NDT equipment. The proposed method of reducing sweeping frequencies was validated experimentally using a waveguide sensor and a metallic plate with different cracks. Among the investigated feature selection techniques are information gain, gain ratio, relief, chi-squared. The effectiveness of the selected features were validated through performance evaluations of various classification models; namely, Nearest Neighbor, Neural Networks, Random Forest, and Support Vector Machine. Results showed good crack classification accuracy rates after employing feature selection algorithms.

  5. Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection.

    Science.gov (United States)

    Moomen, Abdelniser; Ali, Abdulbaset; Ramahi, Omar M

    2016-04-19

    Nondestructive Testing (NDT) assessment of materials' health condition is useful for classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures. Performing structural health testing for coated/uncoated metallic or dielectric materials with the same testing equipment requires a testing method that can work on metallics and dielectrics such as microwave testing. Reducing complexity and expenses associated with current diagnostic practices of microwave NDT of structural health requires an effective and intelligent approach based on feature selection and classification techniques of machine learning. Current microwave NDT methods in general based on measuring variation in the S-matrix over the entire operating frequency ranges of the sensors. For instance, assessing the health of metallic structures using a microwave sensor depends on the reflection or/and transmission coefficient measurements as a function of the sweeping frequencies of the operating band. The aim of this work is reducing sweeping frequencies using machine learning feature selection techniques. By treating sweeping frequencies as features, the number of top important features can be identified, then only the most influential features (frequencies) are considered when building the microwave NDT equipment. The proposed method of reducing sweeping frequencies was validated experimentally using a waveguide sensor and a metallic plate with different cracks. Among the investigated feature selection techniques are information gain, gain ratio, relief, chi-squared. The effectiveness of the selected features were validated through performance evaluations of various classification models; namely, Nearest Neighbor, Neural Networks, Random Forest, and Support Vector Machine. Results showed good crack classification accuracy rates after employing feature selection algorithms.

  6. Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

    Science.gov (United States)

    Polat, Huseyin; Danaei Mehr, Homay; Cetin, Aydin

    2017-04-01

    As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

  7. Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

    Science.gov (United States)

    Huang, Haiping

    2017-05-01

    Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

  8. A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

    Science.gov (United States)

    Torija, Antonio J; Ruiz, Diego P

    2015-02-01

    The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Learning features for tissue classification with the classification restricted Boltzmann machine

    DEFF Research Database (Denmark)

    van Tulder, Gijs; de Bruijne, Marleen

    2014-01-01

    Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce...... the convolutional classification RBM, a combination of the existing convolutional RBM and classification RBM, and use it for discriminative feature learning. We evaluate the classification accuracy of convolutional and non-convolutional classification RBMs on two lung CT problems. We find that RBM-learned features...... outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy....

  10. Relationship between Serum Testosterone Levels and Features of ...

    African Journals Online (AJOL)

    Relationship between Serum Testosterone Levels and Features of the Metabolic Syndrome Defining Criteria in Patients with Type 2 Diabetes Mellitus. ... (TDS) and increased risk of development of the metabolic syndrome – a well recognized ...

  11. Application of higher order spectral features and support vector machines for bearing faults classification.

    Science.gov (United States)

    Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat

    2015-01-01

    Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.

  12. Complex extreme learning machine applications in terahertz pulsed signals feature sets.

    Science.gov (United States)

    Yin, X-X; Hadjiloucas, S; Zhang, Y

    2014-11-01

    This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed

  13. Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

    Science.gov (United States)

    Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

    2016-07-01

    We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

  14. Unsupervised Feature Learning Classification With Radial Basis Function Extreme Learning Machine Using Graphic Processors.

    Science.gov (United States)

    Lam, Dao; Wunsch, Donald

    2017-01-01

    Ever-increasing size and complexity of data sets create challenges and potential tradeoffs of accuracy and speed in learning algorithms. This paper offers progress on both fronts. It presents a mechanism to train the unsupervised learning features learned from only one layer to improve performance in both speed and accuracy. The features are learned by an unsupervised feature learning (UFL) algorithm. Then, those features are trained by a fast radial basis function (RBF) extreme learning machine (ELM). By exploiting the massive parallel computing attribute of modern graphics processing unit, a customized compute unified device architecture (CUDA) kernel is developed to further speed up the computing of the RBF kernel in the ELM. Results tested on Canadian Institute for Advanced Research and Mixed National Institute of Standards and Technology data sets confirm the UFL RBF ELM achieves high accuracy, and the CUDA implementation is up to 20 times faster than CPU and the naive parallel approach.

  15. Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction

    Directory of Open Access Journals (Sweden)

    Jie Zhang

    2017-04-01

    Full Text Available Device-free localization (DFL is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF DFL system, radio transmitters (RTs and radio receivers (RXs are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN, support vector machine (SVM, back propagation neural network (BPNN, as well as the well-known radio tomographic imaging (RTI DFL approach.

  16. Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction.

    Science.gov (United States)

    Zhang, Jie; Xiao, Wendong; Zhang, Sen; Huang, Shoudong

    2017-04-17

    Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach.

  17. The relationship between 2D static features and 2D dynamic features used in gait recognition

    Science.gov (United States)

    Alawar, Hamad M.; Ugail, Hassan; Kamala, Mumtaz; Connah, David

    2013-05-01

    In most gait recognition techniques, both static and dynamic features are used to define a subject's gait signature. In this study, the existence of a relationship between static and dynamic features was investigated. The correlation coefficient was used to analyse the relationship between the features extracted from the "University of Bradford Multi-Modal Gait Database". This study includes two dimensional dynamic and static features from 19 subjects. The dynamic features were compromised of Phase-Weighted Magnitudes driven by a Fourier Transform of the temporal rotational data of a subject's joints (knee, thigh, shoulder, and elbow). The results concluded that there are eleven pairs of features that are considered significantly correlated with (pgait signature using latent data.

  18. Epileptic EEG classification based on extreme learning machine and nonlinear features.

    Science.gov (United States)

    Yuan, Qi; Zhou, Weidong; Li, Shufang; Cai, Dongmei

    2011-09-01

    The automatic detection and classification of epileptic EEG are significant in the evaluation of patients with epilepsy. This paper presents a new EEG classification approach based on the extreme learning machine (ELM) and nonlinear dynamical features. The theory of nonlinear dynamics has been a powerful tool for understanding brain electrical activities. Nonlinear features extracted from EEG signals such as approximate entropy (ApEn), Hurst exponent and scaling exponent obtained with detrended fluctuation analysis (DFA) are employed to characterize interictal and ictal EEGs. The statistics indicate that the differences of those nonlinear features between interictal and ictal EEGs are statistically significant. The ELM algorithm is employed to train a single hidden layer feedforward neural network (SLFN) with EEG nonlinear features. The experiments demonstrate that compared with the backpropagation (BP) algorithm and support vector machine (SVM), the performance of the ELM is better in terms of training time and classification accuracy which achieves a satisfying recognition accuracy of 96.5% for interictal and ictal EEG signals. Copyright © 2011 Elsevier B.V. All rights reserved.

  19. Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

    Directory of Open Access Journals (Sweden)

    Liao Li

    2010-10-01

    Full Text Available Abstract Background Protein-protein interaction (PPI plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs, based on domains represented as interaction profile hidden Markov models (ipHMM where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB. Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD. Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure, an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on

  20. AFREET: HUMAN-INSPIRED SPATIO-SPECTRAL FEATURE CONSTRUCTION FOR IMAGE CLASSIFICATION WITH SUPPORT VECTOR MACHINES

    Energy Technology Data Exchange (ETDEWEB)

    S. PERKINS; N. HARVEY

    2001-02-01

    The authors examine the task of pixel-by-pixel classification of the multispectral and grayscale images typically found in remote-sensing and medical applications. Simple machine learning techniques have long been applied to remote-sensed image classification, but almost always using purely spectral information about each pixel. Humans can often outperform these systems, and make extensive use of spatial context to make classification decisions. They present AFREET: an SVM-based learning system which attempts to automatically construct and refine spatio-spectral features in a somewhat human-inspired fashion. Comparisons with traditionally used machine learning techniques show that AFREET achieves significantly higher performance. The use of spatial context is particularly useful for medical imagery, where multispectral images are still rare.

  1. Application of the Disruption Predictor Feature Developer to developing a machine-portable disruption predictor

    Science.gov (United States)

    Parsons, Matthew; Tang, William; Feibush, Eliot

    2016-10-01

    Plasma disruptions pose a major threat to the operation of tokamaks which confine a large amount of stored energy. In order to effectively mitigate this damage it is necessary to predict an oncoming disruption with sufficient warning time to take mitigative action. Machine learning approaches to this problem have shown promise but require further developments to address (1) the need for machine-portable predictors and (2) the availability of multi-dimensional signal inputs. Here we demonstrate progress in these two areas by applying the Disruption Predictor Feature Developer to data from JET and NSTX, and discuss topics of focus for ongoing work in support of ITER. The author is also supported under the Fulbright U.S. Student Program as a graduate student in the department of Nuclear, Plasma and Radiological Engineering at the University of Illinois at Urbana-Champaign.

  2. Pipeline leakage recognition based on the projection singular value features and support vector machine

    Energy Technology Data Exchange (ETDEWEB)

    Liang, Wei; Zhang, Laibin; Mingda, Wang; Jinqiu, Hu [College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing, (China)

    2010-07-01

    The negative wave pressure method is one of the processes used to detect leaks on oil pipelines. The development of new leakage recognition processes is difficult because it is practically impossible to collect leakage pressure samples. The method of leakage feature extraction and the selection of the recognition model are also important in pipeline leakage detection. This study investigated a new feature extraction approach Singular Value Projection (SVP). It projects the singular value to a standard basis. A new pipeline recognition model based on the multi-class Support Vector Machines was also developed. It was found that SVP is a clear and concise recognition feature of the negative pressure wave. Field experiments proved that the model provided a high recognition accuracy rate. This approach to pipeline leakage detection based on the SVP and SVM has a high application value.

  3. Feature Selection by Merging Sequential Bidirectional Search into Relevance Vector Machine in Condition Monitoring

    Institute of Scientific and Technical Information of China (English)

    ZHANG Kui; DONG Yu; BALL Andrew

    2015-01-01

    For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique(feature selection methods) are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types: filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.

  4. Feature selection by merging sequential bidirectional search into relevance vector machine in condition monitoring

    Science.gov (United States)

    Zhang, Kui; Dong, Yu; Ball, Andrew

    2015-11-01

    For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique(feature selection methods) are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types: filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.

  5. Soft Computing, Machine Intelligence and Data Mining: Features, Applications and Prospects

    Institute of Scientific and Technical Information of China (English)

    Sankar K. Pal

    2006-01-01

    Different components of soft computing (e.g., fuzzy logic, artificial neural networks, rough sets and genetic algorithms) and machine intelligence and their relevance to data mining and knowledge discovery from pattern recognition points of view are explained. Different features of these tools are explained conceptually. Ways of integrating different tools for application specific merits are described. Problems like case (prototype) generation, rule generation, and classification are considered in general. Some of such integrations are explained along with their merits and suitability for data mining with real life applications. Significance of granular computing through rough sets is given emphasis. Finally, the application in bioinformatics and webmining are discussed.

  6. Feature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains.

    Directory of Open Access Journals (Sweden)

    Ariadne Barbosa Gonçalves

    Full Text Available The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer vision based automatic pollen classifiers. A first baseline human and computer performance for this dataset has been established using 805 pollen images of 23 pollen types. In order to access the computer performance, a combination of three feature extractors and four machine learning techniques has been implemented, fine tuned and tested. The results of these tests are also presented in this paper.

  7. BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar

    2011-08-17

    Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

  8. Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

    Directory of Open Access Journals (Sweden)

    Mauricio Holguín-Londoño

    2016-01-01

    Full Text Available Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM. This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

  9. Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part I—Model Development

    Directory of Open Access Journals (Sweden)

    Roque Calvo

    2016-09-01

    Full Text Available The development of an error compensation model for coordinate measuring machines (CMMs and its integration into feature measurement is presented. CMMs are widespread and dependable instruments in industry and laboratories for dimensional measurement. From the tip probe sensor to the machine display, there is a complex transformation of probed point coordinates through the geometrical feature model that makes the assessment of accuracy and uncertainty measurement results difficult. Therefore, error compensation is not standardized, conversely to other simpler instruments. Detailed coordinate error compensation models are generally based on CMM as a rigid-body and it requires a detailed mapping of the CMM’s behavior. In this paper a new model type of error compensation is proposed. It evaluates the error from the vectorial composition of length error by axis and its integration into the geometrical measurement model. The non-explained variability by the model is incorporated into the uncertainty budget. Model parameters are analyzed and linked to the geometrical errors and uncertainty of CMM response. Next, the outstanding measurement models of flatness, angle, and roundness are developed. The proposed models are useful for measurement improvement with easy integration into CMM signal processing, in particular in industrial environments where built-in solutions are sought. A battery of implementation tests are presented in Part II, where the experimental endorsement of the model is included.

  10. Near-Surface Crevasse Detection in Ice Sheets using Feature-Based Machine Learning

    Science.gov (United States)

    Ray, L.; Walker, B.; Lever, J.; Arcone, S. A.

    2015-12-01

    In 2014, a team of Dartmouth, CRREL, and University of Maine researchers conducted the first of three annual ground-penetrating radar surveys of the McMurdo Shear Zone using robot-towed instruments. This survey provides over 100 transects of a 5.7 km x 5.0 km grid spanning the width of the shear zone at spacing of approximately 50 m. Transect direction was orthogonal to ice flow. Additionally, a dense 200 m x 200 m grid was surveyed at 10 m spacing in both the N-S and W-E directions. Radar settings provided 20 traces/sec, which combined with an average robot speed of 1.52 m/s, provides a trace every 7.6 cm. The robot towed two antenna units at 400 MHz and 200 MHz center frequencies, with the former penetrating to approximately 19 m. We establish boundaries for the shear zone over the region surveyed using the 400 MHz antenna data, and we geo-locate crevasses using feature-based machine learning classification of GPR traces into one of three classes - 1) firn, 2) distinct crevasses, and 3) less distinct or deeper features originating within the 19 m penetration depth. Distinct crevasses feature wide, hyperbolic reflections with strike angles of 35-40° to transect direction and clear voids. Less distinct or deeper features range from broad diffraction patterns with no clear void to overlapping diffractions extending tens of meters in width with or without a clear void. The classification is derived from statistical features of unprocessed traces and thus provides a computationally efficient means for eventual real-time classification of GPR traces. Feature-based classification is shown to be insensitive to artifacts related to rolling or pitching motion of the instrument sled and also provides a means of assessing crevasse width and depth. In subsequent years, we will use feature-based classification to estimate ice flow and evolution of individual crevasses.

  11. Specific Features of Chip Making and Work-piece Surface Layer Formation in Machining Thermal Coatings

    Directory of Open Access Journals (Sweden)

    V. M. Yaroslavtsev

    2016-01-01

    Full Text Available A wide range of unique engineering structural and performance properties inherent in metallic composites characterizes wear- and erosion-resistant high-temperature coatings made by thermal spraying methods. This allows their use both in manufacturing processes to enhance the wear strength of products, which have to operate under the cyclic loading, high contact pressures, corrosion and high temperatures and in product renewal.Thermal coatings contribute to the qualitative improvement of the technical level of production and product restoration using the ceramic composite materials. However, the possibility to have a significantly increased product performance, reduce their factory labour hours and materials/output ratio in manufacturing and restoration is largely dependent on the degree of the surface layer quality of products at their finishing stage, which is usually provided by different kinds of machining.When machining the plasma-sprayed thermal coatings, a removing process of the cut-off layer material is determined by its distinctive features such as a layered structure, high internal stresses, low ductility material, high tendency to the surface layer strengthening and rehardening, porosity, high abrasive properties, etc. When coatings are machined these coating properties result in specific characteristics of chip formation and conditions for formation of the billet surface layer.The chip formation of plasma-sprayed coatings was studied at micro-velocities using an experimental tool-setting microscope-based setup, created in BMSTU. The setup allowed simultaneous recording both the individual stages (phases of the chip formation process and the operating force factors.It is found that formation of individual chip elements comes with the multiple micro-cracks that cause chipping-off the small particles of material. The emerging main crack in the cut-off layer of material leads to separation of the largest chip element. Then all the stages

  12. A machine-learning approach for predicting palmitoylation sites from integrated sequence-based features.

    Science.gov (United States)

    Li, Liqi; Luo, Qifa; Xiao, Weidong; Li, Jinhui; Zhou, Shiwen; Li, Yongsheng; Zheng, Xiaoqi; Yang, Hua

    2017-02-01

    Palmitoylation is the covalent attachment of lipids to amino acid residues in proteins. As an important form of protein posttranslational modification, it increases the hydrophobicity of proteins, which contributes to the protein transportation, organelle localization, and functions, therefore plays an important role in a variety of cell biological processes. Identification of palmitoylation sites is necessary for understanding protein-protein interaction, protein stability, and activity. Since conventional experimental techniques to determine palmitoylation sites in proteins are both labor intensive and costly, a fast and accurate computational approach to predict palmitoylation sites from protein sequences is in urgent need. In this study, a support vector machine (SVM)-based method was proposed through integrating PSI-BLAST profile, physicochemical properties, [Formula: see text]-mer amino acid compositions (AACs), and [Formula: see text]-mer pseudo AACs into the principal feature vector. A recursive feature selection scheme was subsequently implemented to single out the most discriminative features. Finally, an SVM method was implemented to predict palmitoylation sites in proteins based on the optimal features. The proposed method achieved an accuracy of 99.41% and Matthews Correlation Coefficient of 0.9773 for a benchmark dataset. The result indicates the efficiency and accuracy of our method in prediction of palmitoylation sites based on protein sequences.

  13. Microcanonical Annealing and Threshold Accepting for Parameter Determination and Feature Selection of Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Seyyid Ahmed Medjahed

    2016-12-01

    Full Text Available Support vector machine (SVM is a popular classification technique with many diverse applications. Parameter determination and feature selection significantly influences the classification accuracy rate and the SVM model quality. This paper proposes two novel approaches based on: Microcanonical Annealing (MA-SVM and Threshold Accepting (TA-SVM to determine the optimal value parameter and the relevant features subset, without reducing SVM classification accuracy. In order to evaluate the performance of MA-SVM and TA-SVM, several public datasets are employed to compute the classification accuracy rate. The proposed approaches were tested in the context of medical diagnosis. Also, we tested the approaches on DNA microarray datasets used for cancer diagnosis. The results obtained by the MA-SVM and TA-SVM algorithms are shown to be superior and have given a good performance in the DNA microarray data sets which are characterized by the large number of features. Therefore, the MA-SVM and TA-SVM approaches are well suited for parameter determination and feature selection in SVM.

  14. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

    Science.gov (United States)

    Li, Qiang; Zhao, Xuehua; Cai, ZhenNao; Tong, Changfei; Liu, Wenbin; Tian, Xin

    2017-01-01

    In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts. PMID:28246543

  15. PROSODIC FEATURE BASED TEXT DEPENDENT SPEAKER RECOGNITION USING MACHINE LEARNING ALGORITHMS

    Directory of Open Access Journals (Sweden)

    Sunil Agrawal

    2010-10-01

    Full Text Available Most of us are aware of the fact that voices of different individuals do not sound alike. The ability of recognizing a person solely from his voice is known as speaker recognition. Speaker recognition can not only assist in building better access control systems and security apparatus, it can be a useful tool in many other areas such as forensic speech analysis. The choice of features plays an important role in the performance of ML algorithm. Here we propose prosodic features based text dependent speaker recognition where the prosodic features can be extracted through linear predictive coding. Formants are efficient parameters to characterize a speaker’s voice. Formants are combined with their corresponding amplitudes, fundamental frequency, duration of speech utterance and energy ofthe windowed section. This feature vector is input to machine learning (ML algorithms for recognition. We investigate the performance of four ML algorithms namely MLP, RBFN, C4.5 decision tree, and BayesNet. Out of these ML algorithms, C4.5 decision tree performance is consistent. MLP performs better for gender recognition and experimental results show that RBFN gives better performance for increased population size.

  16. Digital Library ImageRetrieval usingScale Invariant Feature and Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Hongtao Zhang

    2014-10-01

    Full Text Available With the advance of digital library, the digital content develops with rich information connotation. Traditional information retrieval methods based on external characteristic and text description are unable to sufficientlyreveal and express the substance and semantic relation of multimedia information, and unable to fully reveal and describe the representative characteristics of information. Because of the abundant connotation of image content and the people’s abstract subjectivity in studying image content, the visual feature of the image is difficult to be described by key words. Therefore, this method not always can meet people’s needs, and the study of digital library image retrieval technique based on content is important to both academic research and application. At present, image retrieval methods are mainly based on the text and content, etc. But these existing algorithms have shortages, such as large errors and slow speeds. Motivated by the above fact, we in this paper propose a new approach based on relevance vector machine (RVM. The proposed approach first extracts the patch-level scale invariant image feature (SIFT, and then constructs the global features for images. The image feature is then delivered into RVM for retrieval. We evaluate the proposed approach on Corel dataset. The experimental result shows that the proposed method in this text has high accuracy when retrieves images.

  17. A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data

    Directory of Open Access Journals (Sweden)

    Rabia Aziz

    2016-06-01

    Full Text Available Feature (gene selection and classification of microarray data are the two most interesting machine learning challenges. In the present work two existing feature selection/extraction algorithms, namely independent component analysis (ICA and fuzzy backward feature elimination (FBFE are used which is a new combination of selection/extraction. The main objective of this paper is to select the independent components of the DNA microarray data using FBFE to improve the performance of support vector machine (SVM and Naïve Bayes (NB classifier, while making the computational expenses affordable. To show the validity of the proposed method, it is applied to reduce the number of genes for five DNA microarray datasets namely; colon cancer, acute leukemia, prostate cancer, lung cancer II, and high-grade glioma. Now these datasets are then classified using SVM and NB classifiers. Experimental results on these five microarray datasets demonstrate that gene selected by proposed approach, effectively improve the performance of SVM and NB classifiers in terms of classification accuracy. We compare our proposed method with principal component analysis (PCA as a standard extraction algorithm and find that the proposed method can obtain better classification accuracy, using SVM and NB classifiers with a smaller number of selected genes than the PCA. The curve between the average error rate and number of genes with each dataset represents the selection of required number of genes for the highest accuracy with our proposed method for both the classifiers. ROC shows best subset of genes for both the classifier of different datasets with propose method.

  18. Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

    Science.gov (United States)

    Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

    2012-07-01

    Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

  19. Feature-matching pattern-based support vector machines for robust peptide mass fingerprinting.

    Science.gov (United States)

    Li, Youyuan; Hao, Pei; Zhang, Siliang; Li, Yixue

    2011-12-01

    Peptide mass fingerprinting, regardless of becoming complementary to tandem mass spectrometry for protein identification, is still the subject of in-depth study because of its higher sample throughput, higher level of specificity for single peptides and lower level of sensitivity to unexpected post-translational modifications compared with tandem mass spectrometry. In this study, we propose, implement and evaluate a uniform approach using support vector machines to incorporate individual concepts and conclusions for accurate PMF. We focus on the inherent attributes and critical issues of the theoretical spectrum (peptides), the experimental spectrum (peaks) and spectrum (masses) alignment. Eighty-one feature-matching patterns derived from cleavage type, uniqueness and variable masses of theoretical peptides together with the intensity rank of experimental peaks were proposed to characterize the matching profile of the peptide mass fingerprinting procedure. We developed a new strategy including the participation of matched peak intensity redistribution to handle shared peak intensities and 440 parameters were generated to digitalize each feature-matching pattern. A high performance for an evaluation data set of 137 items was finally achieved by the optimal multi-criteria support vector machines approach, with 491 final features out of a feature vector of 35,640 normalized features through cross training and validating a publicly available "gold standard" peptide mass fingerprinting data set of 1733 items. Compared with the Mascot, MS-Fit, ProFound and Aldente algorithms commonly used for MS-based protein identification, the feature-matching patterns algorithm has a greater ability to clearly separate correct identifications and random matches with the highest values for sensitivity (82%), precision (97%) and F1-measure (89%) of protein identification. Several conclusions reached via this research make general contributions to MS-based protein identification. Firstly

  20. 基于支持向量机的特征选择%Feature Selection Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    葛敏敏; 范丽亚

    2011-01-01

    主要研究了基于支持向量机的特征选择方法——特征权法,通过对两组数据进行试验,说明了特征权法在分类效果上优于F-得分法和支持向量机.%This paper is devoted to study a feature election method based on support vector machine feature weight. Experiments with two kinds of data taken from UCI machine learning repository show that feature weight method is superior to F-score method and SVM on

  1. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Science.gov (United States)

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  2. Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

    Science.gov (United States)

    Szantoi, Zoltan; Escobedo, Francisco J; Abd-Elrahman, Amr; Pearlstine, Leonard; Dewitt, Bon; Smith, Scot

    2015-05-01

    Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (α<0.05). Findings show that using multiple window sizes provided the best results. First-ordertexture featuresalso provided computational advantages and results that were not significantly different fromthose usingsecond-order texture features.

  3. Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques.

    Science.gov (United States)

    Amin, Hafeez Ullah; Malik, Aamir Saeed; Ahmad, Rana Fayyaz; Badruddin, Nasreen; Kamel, Nidal; Hussain, Muhammad; Chooi, Weng-Tink

    2015-03-01

    This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.

  4. Two applications of small feature dimensional measurements on a coordinate measuring machine with a fiber probe

    Science.gov (United States)

    Stanfield, Eric; Muralikrishnan, Bala; Doiron, Ted; Zheng, Alan; Orandi, Shahram; Duquette, David

    2013-10-01

    This paper describes two applications of dimensional measurements performed using a contact fiber probe on a commercial coordinate measuring machine (CMM). Both examples involve artifacts that serve as reference standards and contain features in the 100 μm to 500 μm range. The first application involves measuring the spacing between features, either holes or rectangular prisms, on a cylinder that is approximately the size of a finger. The artifact, referred to as the fingerprint target, serves as a standard for verifying the performance of fingerprint scanners. The second application involves measuring the volume of small three-dimensional features such as cylinders and rectangular prisms that rise from a plate. This artifact is referred to as the volume target in this paper; these targets serve as volume standards for manufacturers and users of solder paste inspection systems. In each case, the measurement challenges presented by these artifacts are discussed and the measurand, the measurement plan, error sources, and uncertainty budget are described.

  5. Machine Learning Approaches to Classification of Seafloor Features from High Resolution Sonar Data

    Science.gov (United States)

    Smith, D. G.; Ed, L.; Sofge, D.; Elmore, P. A.; Petry, F.

    2014-12-01

    Navigation charts provide topographic maps of the seafloor created from swaths of sonar data. Converting sonar data to a topographic map is a manual, labor-intensive process that can be greatly assisted by contextual information obtained from automated classification of geomorphological structures. Finding structures such as seamounts can be challenging, as there are no established rules that can be used for decision-making. Often times, it is a determination that is made by human expertise. A variety of feature metrics may be useful for this task and we use a large number of metrics relevant to the task of finding seamounts. We demonstrate this ability in locating seamounts by two related machine learning techniques. As well as achieving good accuracy in classification, the human-understandable set of metrics that are most important for the results are discussed.

  6. FEATURE RANKING BASED NESTED SUPPORT VECTOR MACHINE ENSEMBLE FOR MEDICAL IMAGE CLASSIFICATION.

    Science.gov (United States)

    Varol, Erdem; Gaonkar, Bilwaj; Erus, Guray; Schultz, Robert; Davatzikos, Christos

    2012-01-01

    This paper presents a method for classification of structural magnetic resonance images (MRI) of the brain. An ensemble of linear support vector machine classifiers (SVMs) is used for classifying a subject as either patient or normal control. Image voxels are first ranked based on the voxel wise t-statistics between the voxel intensity values and class labels. Then voxel subsets are selected based on the rank value using a forward feature selection scheme. Finally, an SVM classifier is trained on each subset of image voxels. The class label of a test subject is calculated by combining individual decisions of the SVM classifiers using a voting mechanism. The method is applied for classifying patients with neurological diseases such as Alzheimer's disease (AD) and autism spectrum disorder (ASD). The results on both datasets demonstrate superior performance as compared to two state of the art methods for medical image classification.

  7. Extending the features of RBMK refuelling machine simulator with a training tool based on virtual reality

    Energy Technology Data Exchange (ETDEWEB)

    Khoudiakov, M.; Slonimsky, V.; Mitrofanov, S. (and others)

    2004-07-01

    should include a training methodology, simulation models/ malfunctions and VR-models to support the maintenance personnel. That work is to be based on a design and creation of a multi-machine computer complex, software and information support (Data base) development, and developing anew and/or up-grade the technology system models and training support methodology. The paper gives the background for developing the training system, the features and the structure of the system in addition to the current status in the development process. The final system will be delivered to LNPP in November 2004. (Author)

  8. Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines.

    Directory of Open Access Journals (Sweden)

    Sascha Gruss

    Full Text Available The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient's report on the pain sensation. Verbal scales, visual analog scales (VAS or numeric rating scales (NRS count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity.In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity.We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography.The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.

  9. Support vector machine model for diagnosing pneumoconiosis based on wavelet texture features of digital chest radiographs.

    Science.gov (United States)

    Zhu, Biyun; Chen, Hui; Chen, Budong; Xu, Yan; Zhang, Kuan

    2014-02-01

    This study aims to explore the classification ability of decision trees (DTs) and support vector machines (SVMs) to discriminate between the digital chest radiographs (DRs) of pneumoconiosis patients and control subjects. Twenty-eight wavelet-based energy texture features were calculated at the lung fields on DRs of 85 healthy controls and 40 patients with stage I and stage II pneumoconiosis. DTs with algorithm C5.0 and SVMs with four different kernels were trained by samples with two combinations of the texture features to classify a DR as of a healthy subject or of a patient with pneumoconiosis. All of the models were developed with fivefold cross-validation, and the final performances of each model were compared by the area under receiver operating characteristic (ROC) curve. For both SVM (with a radial basis function kernel) and DT (with algorithm C5.0), areas under ROC curves (AUCs) were 0.94 ± 0.02 and 0.86 ± 0.04 (P = 0.02) when using the full feature set and 0.95 ± 0.02 and 0.88 ± 0.04 (P = 0.05) when using the selected feature set, respectively. When built on the selected texture features, the SVM with a polynomial kernel showed a higher diagnostic performance with an AUC value of 0.97 ± 0.02 than SVMs with a linear kernel, a radial basis function kernel and a sigmoid kernel with AUC values of 0.96 ± 0.02 (P = 0.37), 0.95 ± 0.02 (P = 0.24), and 0.90 ± 0.03 (P = 0.01), respectively. The SVM model with a polynomial kernel built on the selected feature set showed the highest diagnostic performance among all tested models when using either all the wavelet texture features or the selected ones. The model has a good potential in diagnosing pneumoconiosis based on digital chest radiographs.

  10. Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

    Directory of Open Access Journals (Sweden)

    Yeom, Ha-Neul

    2014-09-01

    Full Text Available In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

  11. Machine medical ethics

    CERN Document Server

    Pontier, Matthijs

    2015-01-01

    The essays in this book, written by researchers from both humanities and sciences, describe various theoretical and experimental approaches to adding medical ethics to a machine in medical settings. Medical machines are in close proximity with human beings, and getting closer: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. In such contexts, machines are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for e...

  12. Text and Language-Independent Speaker Recognition Using Suprasegmental Features and Support Vector Machines

    Science.gov (United States)

    Bajpai, Anvita; Pathangay, Vinod

    In this paper, presence of the speaker-specific suprasegmental information in the Linear Prediction (LP) residual signal is demonstrated. The LP residual signal is obtained after removing the predictable part of the speech signal. This information, if added to existing speaker recognition systems based on segmental and subsegmental features, can result in better performing combined system. The speaker-specific suprasegmental information can not only be perceived by listening to the residual, but can also be seen in the form of excitation peaks in the residual waveform. However, the challenge lies in capturing this information from the residual signal. Higher order correlations among samples of the residual are not known to be captured using standard signal processing and statistical techniques. The Hilbert envelope of residual is shown to further enhance the excitation peaks present in the residual signal. A speaker-specific pattern is also observed in the autocorrelation sequence of the Hilbert envelope, and further in the statistics of this autocorrelation sequence. This indicates the presence of the speaker-specific suprasegmental information in the residual signal. In this work, no distinction between voiced and unvoiced sounds is done for extracting these features. Support Vector Machine (SVM) is used to classify the patterns in the variance of the autocorrelation sequence for the speaker recognition task.

  13. Improved residue contact prediction using support vector machines and a large feature set

    Directory of Open Access Journals (Sweden)

    Baldi Pierre

    2007-04-01

    Full Text Available Abstract Background Predicting protein residue-residue contacts is an important 2D prediction task. It is useful for ab initio structure prediction and understanding protein folding. In spite of steady progress over the past decade, contact prediction remains still largely unsolved. Results Here we develop a new contact map predictor (SVMcon that uses support vector machines to predict medium- and long-range contacts. SVMcon integrates profiles, secondary structure, relative solvent accessibility, contact potentials, and other useful features. On the same test data set, SVMcon's accuracy is 4% higher than the latest version of the CMAPpro contact map predictor. SVMcon recently participated in the seventh edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7 experiment and was evaluated along with seven other contact map predictors. SVMcon was ranked as one of the top predictors, yielding the second best coverage and accuracy for contacts with sequence separation >= 12 on 13 de novo domains. Conclusion We describe SVMcon, a new contact map predictor that uses SVMs and a large set of informative features. SVMcon yields good performance on medium- to long-range contact predictions and can be modularly incorporated into a structure prediction pipeline.

  14. Intelligent Video Object Classification Scheme using Offline Feature Extraction and Machine Learning based Approach

    Directory of Open Access Journals (Sweden)

    Chandra Mani Sharma

    2012-01-01

    Full Text Available Classification of objects in video stream is important because of its application in many emerging areas such as visual surveillance, content based video retrieval and indexing etc. The task is far more challenging because the video data is of heavy and highly variable nature. The processing of video data is required to be in real-time. This paper presents a multiclass object classification technique using machine learning approach. Haar-like features are used for training the classifier. The feature calculation is performed using Integral Image representation and we train the classifier offline using a Stage-wise Additive Modeling using a Multiclass Exponential loss function (SAMME. The validity of the method has been verified from the implementation of a real-time human-car detector. Experimental results show that the proposed method can accurately classify objects, in video, into their respective classes. The proposed object classifier works well in outdoor environment in presence of moderate lighting conditions and variable scene background. The proposed technique is compared, with other object classification techniques, based on various performance parameters.

  15. A Generalizable Brain-Computer Interface (BCI Using Machine Learning for Feature Discovery.

    Directory of Open Access Journals (Sweden)

    Ewan S Nurse

    Full Text Available This work describes a generalized method for classifying motor-related neural signals for a brain-computer interface (BCI, based on a stochastic machine learning method. The method differs from the various feature extraction and selection techniques employed in many other BCI systems. The classifier does not use extensive a-priori information, resulting in reduced reliance on highly specific domain knowledge. Instead of pre-defining features, the time-domain signal is input to a population of multi-layer perceptrons (MLPs in order to perform a stochastic search for the best structure. The results showed that the average performance of the new algorithm outperformed other published methods using the Berlin BCI IV (2008 competition dataset and was comparable to the best results in the Berlin BCI II (2002-3 competition dataset. The new method was also applied to electroencephalography (EEG data recorded from five subjects undertaking a hand squeeze task and demonstrated high levels of accuracy with a mean classification accuracy of 78.9% after five-fold cross-validation. Our new approach has been shown to give accurate results across different motor tasks and signal types as well as between subjects.

  16. Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features

    Science.gov (United States)

    Radüntz, Thea; Scouten, Jon; Hochmuth, Olaf; Meffert, Beate

    2017-08-01

    Objective. Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent component analysis (ICA) is a widely used method for eliminating various artifacts from recordings. However, evaluating and classifying the calculated independent components (IC) as artifact or EEG is not fully automated at present. Approach. In this study, we propose a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. Main results. We compared the performance of our classifiers with the visual classification results given by experts. The best result with an accuracy rate of 95% was achieved using features obtained by range filtering of the topoplots and IC power spectra combined with an artificial neural network. Significance. Compared with the existing automated solutions, our proposed method is not limited to specific types of artifacts, electrode configurations, or number of EEG channels. The main advantages of the proposed method is that it provides an automatic, reliable, real-time capable, and practical tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal.

  17. Integration of Error Compensation of Coordinate Measuring Machines into Feature Measurement: Part II—Experimental Implementation

    Directory of Open Access Journals (Sweden)

    Roque Calvo

    2016-10-01

    Full Text Available Coordinate measuring machines (CMM are main instruments of measurement in laboratories and in industrial quality control. A compensation error model has been formulated (Part I. It integrates error and uncertainty in the feature measurement model. Experimental implementation for the verification of this model is carried out based on the direct testing on a moving bridge CMM. The regression results by axis are quantified and compared to CMM indication with respect to the assigned values of the measurand. Next, testing of selected measurements of length, flatness, dihedral angle, and roundness features are accomplished. The measurement of calibrated gauge blocks for length or angle, flatness verification of the CMM granite table and roundness of a precision glass hemisphere are presented under a setup of repeatability conditions. The results are analysed and compared with alternative methods of estimation. The overall performance of the model is endorsed through experimental verification, as well as the practical use and the model capability to contribute in the improvement of current standard CMM measuring capabilities.

  18. Histogram of Intensity Feature Extraction for Automatic Plastic Bottle Recycling System Using Machine Vision

    Directory of Open Access Journals (Sweden)

    Suzaimah Ramli

    2008-01-01

    Full Text Available Currently, many recycling activities adopt manual sorting for plastic recycling that relies on plant personnel who visually identify and pick plastic bottles as they travel along the conveyor belt. These bottles are then sorted into the respective containers. Manual sorting may not be a suitable option for recycling facilities of high throughput. It has also been noted that the high turnover among sorting line workers had caused difficulties in achieving consistency in the plastic separation process. As a result, an intelligent system for automated sorting is greatly needed to replace manual sorting system. The core components of machine vision for this intelligent sorting system is the image recognition and classification. In this research, the overall plastic bottle sorting system is described. Additionally, the feature extraction algorithm used is discussed in detail since it is the core component of the overall system that determines the success rate. The performance of the proposed feature extractions were evaluated in terms of classification accuracy and result obtained showed an accuracy of more than 80%.

  19. Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

    Directory of Open Access Journals (Sweden)

    Park Jinho

    2012-06-01

    Full Text Available Abstract Background Myocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome in electrocardiogram (ECG more accurately and automatically can prevent it from developing into a catastrophic disease. To this end, we propose a new method, which employs wavelets and simple feature selection. Methods For training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in 90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method based on the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used for differentiating ST episodes from normal: 1 the area between QRS offset and T-peak points, 2 the normalized and signed sum from QRS offset to effective zero voltage point, and 3 the slope from QRS onset to offset point. We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiers to those features. Results We evaluated the algorithm by kernel density estimation (KDE and support vector machine (SVM methods. Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemic ST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively. The SVM classifier detects 355 ischemic ST episodes. Conclusions We proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removing baseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and feature extraction from morphology of ECG waveforms explicitly. It was shown that the number of selected features were sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposed KDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require any numerical

  20. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

    Science.gov (United States)

    Shan, Juan; Alam, S Kaisar; Garra, Brian; Zhang, Yingtao; Ahmed, Tahira

    2016-04-01

    This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions.

  1. Identifying TF-MiRNA Regulatory Relationships Using Multiple Features.

    Directory of Open Access Journals (Sweden)

    Mingyu Shao

    Full Text Available MicroRNAs are known to play important roles in the transcriptional and post-transcriptional regulation of gene expression. While intensive research has been conducted to identify miRNAs and their target genes in various genomes, there is only limited knowledge about how microRNAs are regulated. In this study, we construct a pipeline that can infer the regulatory relationships between transcription factors and microRNAs from ChIP-Seq data with high confidence. In particular, after identifying candidate peaks from ChIP-Seq data, we formulate the inference as a PU learning (learning from only positive and unlabeled examples problem. Multiple features including the statistical significance of the peaks, the location of the peaks, the transcription factor binding site motifs, and the evolutionary conservation are derived from peaks for training and prediction. To further improve the accuracy of our inference, we also apply a mean reciprocal rank (MRR-based method to the candidate peaks. We apply our pipeline to infer TF-miRNA regulatory relationships in mouse embryonic stem cells. The experimental results show that our approach provides very specific findings of TF-miRNA regulatory relationships.

  2. Depth-based human fall detection via shape features and improved extreme learning machine.

    Science.gov (United States)

    Ma, Xin; Wang, Haibo; Xue, Bingxia; Zhou, Mingang; Ji, Bing; Li, Yibin

    2014-11-01

    Falls are one of the major causes leading to injury of elderly people. Using wearable devices for fall detection has a high cost and may cause inconvenience to the daily lives of the elderly. In this paper, we present an automated fall detection approach that requires only a low-cost depth camera. Our approach combines two computer vision techniques-shape-based fall characterization and a learning-based classifier to distinguish falls from other daily actions. Given a fall video clip, we extract curvature scale space (CSS) features of human silhouettes at each frame and represent the action by a bag of CSS words (BoCSS). Then, we utilize the extreme learning machine (ELM) classifier to identify the BoCSS representation of a fall from those of other actions. In order to eliminate the sensitivity of ELM to its hyperparameters, we present a variable-length particle swarm optimization algorithm to optimize the number of hidden neurons, corresponding input weights, and biases of ELM. Using a low-cost Kinect depth camera, we build an action dataset that consists of six types of actions (falling, bending, sitting, squatting, walking, and lying) from ten subjects. Experimenting with the dataset shows that our approach can achieve up to 91.15% sensitivity, 77.14% specificity, and 86.83% accuracy. On a public dataset, our approach performs comparably to state-of-the-art fall detection methods that need multiple cameras.

  3. Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning

    Directory of Open Access Journals (Sweden)

    Arif Mehmood

    2017-01-01

    Full Text Available This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. Bibliometric research uses the evaluation criteria to find relevancy of articles instead. Researchers mostly face relevancy issues while searching articles. Therefore, they classify the articles manually. However, manual classification is burdensome due to time needed for evaluation. The proposed model performs automatic essay evaluation using multi-text features and ensemble machine learning. The proposed method is implemented in two data sets: a Kaggle short answer data set for essay scoring that includes four ranges of disciplines (Science, Biology, English, and English language Arts, and a bibliometric data set having IoT (Internet of Things and non-IoT classes. The efficacy of the model is measured against the Tandalla and AutoP approach using Cohen’s kappa. The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has better performance than those of earlier approaches.

  4. Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.

    Science.gov (United States)

    Shim, Miseon; Hwang, Han-Jeong; Kim, Do-Won; Lee, Seung-Hwan; Im, Chang-Hwan

    2016-10-01

    Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine learning techniques applied to EEG biomarkers. Although a number of studies showed that source-level EEG features can potentially be applied to the differential diagnosis of schizophrenia, most studies have used only sensor-level EEG features such as ERP peak amplitude and power spectrum for machine learning-based diagnosis of schizophrenia. In this study, we used both sensor-level and source-level features extracted from EEG signals recorded during an auditory oddball task for the classification of patients with schizophrenia and healthy controls. EEG signals were recorded from 34 patients with schizophrenia and 34 healthy controls while each subject was asked to attend to oddball tones. Our results demonstrated higher classification accuracy when source-level features were used together with sensor-level features, compared to when only sensor-level features were used. In addition, the selected sensor-level features were mostly found in the frontal area, and the selected source-level features were mostly extracted from the temporal area, which coincide well with the well-known pathological region of cognitive processing in patients with schizophrenia. Our results suggest that our approach would be a promising tool for the computer-aided diagnosis of schizophrenia.

  5. Artificial immune system based on adaptive clonal selection for feature selection and parameters optimisation of support vector machines

    Science.gov (United States)

    Sadat Hashemipour, Maryam; Soleimani, Seyed Ali

    2016-01-01

    Artificial immune system (AIS) algorithm based on clonal selection method can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure along with the feature selection significantly impacts on the classification accuracy rate. In this study, AIS based on Adaptive Clonal Selection (AISACS) algorithm has been used to optimise the SVM parameters and feature subset selection without degrading the SVM classification accuracy. Several public datasets of University of California Irvine machine learning (UCI) repository are employed to calculate the classification accuracy rate in order to evaluate the AISACS approach then it was compared with grid search algorithm and Genetic Algorithm (GA) approach. The experimental results show that the feature reduction rate and running time of the AISACS approach are better than the GA approach.

  6. Feature Based Machining Process Planning Modeling and Integration for Life Cycle Engineering

    Institute of Scientific and Technical Information of China (English)

    LIU Changyi

    2006-01-01

    Machining process data is the core of computer aided process planning application systems. It is also provides essential content for product life cycle engineering. The character of CAPP that supports product LCE and virtual manufacturing is analyzed. The structure and content of machining process data concerning green manufacturing is also examined. A logic model of Machining Process Data has been built based on an object oriented approach, using UML technology and a physical model of machining process data that utilizes XML technology. To realize the integration of design and process, an approach based on graph-based volume decomposition was apposed. Instead, to solve the problem of generation in the machining process, case-based reasoning and rule-based reasoning have been applied synthetically. Finally, the integration framework and interface that deal with the CAPP integration with CAD, CAM, PDM, and ERP are discussed.

  7. Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

    Science.gov (United States)

    Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

    2017-08-04

    Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the

  8. Diagnosis of Alzheimer's Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

    Science.gov (United States)

    Lama, Ramesh Kumar; Gwak, Jeonghwan; Park, Jeong-Seon

    2017-01-01

    Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer's disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

  9. Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

    Directory of Open Access Journals (Sweden)

    Ramesh Kumar Lama

    2017-01-01

    Full Text Available Alzheimer’s disease (AD is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR images to discriminate AD, mild cognitive impairment (MCI, and healthy control (HC subjects using a support vector machine (SVM, an import vector machine (IVM, and a regularized extreme learning machine (RELM. The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

  10. Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning

    Directory of Open Access Journals (Sweden)

    Mohammad H. Alomari

    2013-07-01

    Full Text Available In this paper, we propose an automated computer platform for the purpose of classifying Electroencephalography (EEG signals associated with left and right hand movements using a hybrid system that uses advanced feature extraction techniques and machine learning algorithms. It is known that EEG represents the brain activity by the electrical voltage fluctuations along the scalp, and Brain-Computer Interface (BCI is a device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. In our research work, we aspired to find the best feature extraction method that enables the differentiation between left and right executed fist movements through various classification algorithms. The EEG dataset used in this research was created and contributed to PhysioNet by the developers of the BCI2000 instrumentation system. Data was preprocessed using the EEGLAB MATLAB toolbox and artifacts removal was done using AAR. Data was epoched on the basis of Event-Related (De Synchronization (ERD/ERS and movement-related cortical potentials (MRCP features. Mu/beta rhythms were isolated for the ERD/ERS analysis and delta rhythms were isolated for the MRCP analysis. The Independent Component Analysis (ICA spatial filter was applied on related channels for noise reduction and isolation of both artifactually and neutrally generated EEG sources. The final feature vector included the ERD, ERS, and MRCP features in addition to the mean, power and energy of the activations of the resulting Independent Components (ICs of the epoched feature datasets. The datasets were inputted into two machine-learning algorithms: Neural Networks (NNs and Support Vector Machines (SVMs. Intensive experiments were carried out and optimum classification performances of 89.8 and 97.1 were obtained using NN and SVM, respectively. This research shows that this method of feature extraction

  11. Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

    Science.gov (United States)

    Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

    2016-05-15

    Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases.

  12. 小样本问题的算法比较%Feature Selection Based on Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    张先荣; 范丽亚

    2011-01-01

    This paper is devoted to study a leature election method based on support vector machine feature weight. Experiments with two kinds of data taken from UCI machine learning repository show that feature weight method is superior to F-score method and SVM on%将不相关线性判别分析(ULDA)和零空间线性判别分析(NLDA)两种思想结合起来,提出了处理小样本问题的六种算法,并通过实验说明了这六种算法的分类有效性.

  13. Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Muhammad Naveed Iqbal Qureshi

    2017-09-01

    Full Text Available Multimodal features of structural and functional magnetic resonance imaging (MRI of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001 accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

  14. A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

    Science.gov (United States)

    Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

    2014-01-01

    Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

  15. Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

    Directory of Open Access Journals (Sweden)

    Steren Chabert

    2017-01-01

    Full Text Available Cerebral aneurysm is a cerebrovascular disorder characterized by a bulging in a weak area in the wall of an artery that supplies blood to the brain. It is relevant to understand the mechanisms leading to the apparition of aneurysms, their growth and, more important, leading to their rupture. The purpose of this study is to study the impact on aneurysm rupture of the combination of different parameters, instead of focusing on only one factor at a time as is frequently found in the literature, using machine learning and feature extraction techniques. This discussion takes relevance in the context of the complex decision that the physicians have to take to decide which therapy to apply, as each intervention bares its own risks, and implies to use a complex ensemble of resources (human resources, OR, etc. in hospitals always under very high work load. This project has been raised in our actual working team, composed of interventional neuroradiologist, radiologic technologist, informatics engineers and biomedical engineers, from Valparaiso public Hospital, Hospital Carlos van Buren, and from Universidad de Valparaíso – Facultad de Ingeniería and Facultad de Medicina. This team has been working together in the last few years, and is now participating in the implementation of an “interdisciplinary platform for innovation in health”, as part of a bigger project leaded by Universidad de Valparaiso (PMI UVA1402. It is relevant to emphasize that this project is made feasible by the existence of this network between physicians and engineers, and by the existence of data already registered in an orderly manner, structured and recorded in digital format. The present proposal arises from the description in nowadays literature that the actual indicators, whether based on morphological description of the aneurysm, or based on characterization of biomechanical factor or others, these indicators were shown not to provide sufficient information in order

  16. Automatic Detection of Diabetes Diagnosis using Feature Weighted Support Vector Machines based on Mutual Information and Modified Cuckoo Search

    CERN Document Server

    Giveki, Davar; Bahmanyar, GholamReza; Khademian, Younes

    2012-01-01

    Diabetes is a major health problem in both developing and developed countries and its incidence is rising dramatically. In this study, we investigate a novel automatic approach to diagnose Diabetes disease based on Feature Weighted Support Vector Machines (FW-SVMs) and Modified Cuckoo Search (MCS). The proposed model consists of three stages: Firstly, PCA is applied to select an optimal subset of features out of set of all the features. Secondly, Mutual Information is employed to construct the FWSVM by weighting different features based on their degree of importance. Finally, since parameter selection plays a vital role in classification accuracy of SVMs, MCS is applied to select the best parameter values. The proposed MI-MCS-FWSVM method obtains 93.58% accuracy on UCI dataset. The experimental results demonstrate that our method outperforms the previous methods by not only giving more accurate results but also significantly speeding up the classification procedure.

  17. Using machine learning to classify image features from canine pelvic radiographs

    DEFF Research Database (Denmark)

    McEvoy, Fintan; Amigo Rubio, Jose Manuel

    2013-01-01

    As the number of images per study increases in the field of veterinary radiology, there is a growing need for computer-assisted diagnosis techniques. The purpose of this study was to evaluate two machine learning statistical models for automatically identifying image regions that contain the canine...

  18. Multi-script handwritten character recognition : Using feature descriptors and machine learning

    NARCIS (Netherlands)

    Surinta, Olarik

    2016-01-01

    Handwritten character recognition plays an important role in transforming raw visual image data obtained from handwritten documents using for example scanners to a format which is understandable by a computer. It is an important application in the field of pattern recognition, machine learning and a

  19. Multi-script handwritten character recognition : Using feature descriptors and machine learning

    NARCIS (Netherlands)

    Surinta, Olarik

    2016-01-01

    Handwritten character recognition plays an important role in transforming raw visual image data obtained from handwritten documents using for example scanners to a format which is understandable by a computer. It is an important application in the field of pattern recognition, machine learning and

  20. Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

    Directory of Open Access Journals (Sweden)

    Peek Andrew S

    2007-06-01

    Full Text Available Abstract Background RNA interference (RNAi is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid

  1. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

    Science.gov (United States)

    Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

    2017-07-18

    Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

  2. Using Trajectory Clusters to Define the Most Relevant Features for Transient Stability Prediction Based on Machine Learning Method

    Directory of Open Access Journals (Sweden)

    Luyu Ji

    2016-11-01

    Full Text Available To achieve rapid real-time transient stability prediction, a power system transient stability prediction method based on the extraction of the post-fault trajectory cluster features of generators is proposed. This approach is conducted using data-mining techniques and support vector machine (SVM models. First, the post-fault rotor angles and generator terminal voltage magnitudes are considered as the input vectors. Second, we construct a high-confidence dataset by extracting the 27 trajectory cluster features obtained from the chosen databases. Then, by applying a filter–wrapper algorithm for feature selection, we obtain the final feature set composed of the eight most relevant features for transient stability prediction, called the global trajectory clusters feature subset (GTCFS, which are validated by receiver operating characteristic (ROC analysis. Comprehensive simulations are conducted on a New England 39-bus system under various operating conditions, load levels and topologies, and the transient stability predicting capability of the SVM model based on the GTCFS is extensively tested. The experimental results show that the selected GTCFS features improve the prediction accuracy with high computational efficiency. The proposed method has distinct advantages for transient stability prediction when faced with incomplete Wide Area Measurement System (WAMS information, unknown operating conditions and unknown topologies and significantly improves the robustness of the transient stability prediction system.

  3. Support vector machine-based feature extractor for L/H transitions in JETa)

    Science.gov (United States)

    González, S.; Vega, J.; Murari, A.; Pereira, A.; Ramírez, J. M.; Dormido-Canto, S.; Jet-Efda Contributors

    2010-10-01

    Support vector machines (SVM) are machine learning tools originally developed in the field of artificial intelligence to perform both classification and regression. In this paper, we show how SVM can be used to determine the most relevant quantities to characterize the confinement transition from low to high confinement regimes in tokamak plasmas. A set of 27 signals is used as starting point. The signals are discarded one by one until an optimal number of relevant waveforms is reached, which is the best tradeoff between keeping a limited number of quantities and not loosing essential information. The method has been applied to a database of 749 JET discharges and an additional database of 150 JET discharges has been used to test the results obtained.

  4. A NOVEL FEATURE SET FOR RECOGNITION OF SIMILAR SHAPED HANDWRITTEN HINDI CHARACTERS USING MACHINE LEARNING

    OpenAIRE

    Sheetal Dabra; Sunil Agrawal; Rama Krishna Challa

    2011-01-01

    The growing need of handwritten Hindi character recognition in Indian offices such as passport, railway etc, has made it a vital area of research. Similar shaped characters are more prone to misclassification. In this paper four Machine Learning (ML) algorithms namely Bayesian Network, Radial Basis Function Network (RBFN), Multilayer Perceptron (MLP), and C4.5 Decision Tree are used for recognition of Similar Shaped Handwritten Hindi Characters (SSHHC) and their performance is ...

  5. Brain cells in the avian 'prefrontal cortex' code for features of slot-machine-like gambling.

    Directory of Open Access Journals (Sweden)

    Damian Scarf

    Full Text Available Slot machines are the most common and addictive form of gambling. In the current study, we recorded from single neurons in the 'prefrontal cortex' of pigeons while they played a slot-machine-like task. We identified four categories of neurons that coded for different aspects of our slot-machine-like task. Reward-Proximity neurons showed a linear increase in activity as the opportunity for a reward drew near. I-Won neurons fired only when the fourth stimulus of a winning (four-of-a-kind combination was displayed. I-Lost neurons changed their firing rate at the presentation of the first nonidentical stimulus, that is, when it was apparent that no reward was forthcoming. Finally, Near-Miss neurons also changed their activity the moment it was recognized that a reward was no longer available, but more importantly, the activity level was related to whether the trial contained one, two, or three identical stimuli prior to the display of the nonidentical stimulus. These findings not only add to recent neurophysiological research employing simulated gambling paradigms, but also add to research addressing the functional correspondence between the avian NCL and primate PFC.

  6. Integrated Features by Administering the Support Vector Machine (SVM of Translational Initiations Sites in Alternative Polymorphic Contex

    Directory of Open Access Journals (Sweden)

    Nurul Arneida Husin

    2012-04-01

    Full Text Available Many algorithms and methods have been proposed for classification problems in bioinformatics. In this study, the discriminative approach in particular support vector machines (SVM is employed to recognize the studied TIS patterns. The applied discriminative approach is used to learn about some discriminant functions of samples that have been labelled as positive or negative. After learning, the discriminant functions are employed to decide whether a new sample is true or false. In this study, support vector machines (SVM is employed to recognize the patterns for studied translational initiation sites in alternative weak context. The method has been optimized with the best parameters selected; c=100, E=10-6 and ex=2 for non linear kernel function. Results show that with top 5 features and non linear kernel, the best prediction accuracy achieved is 95.8%. J48 algorithm is applied to compare with SVM with top 15 features and the results show a good prediction accuracy of 95.8%. This indicates that the top 5 features selected by the IGR method and that are performed by SVM are sufficient to use in the prediction of TIS in weak contexts.

  7. Computer-aided classification of Alzheimer's disease based on support vector machine with combination of cerebral image features in MRI

    Science.gov (United States)

    Jongkreangkrai, C.; Vichianin, Y.; Tocharoenchai, C.; Arimura, H.; Alzheimer's Disease Neuroimaging Initiative

    2016-03-01

    Several studies have differentiated Alzheimer's disease (AD) using cerebral image features derived from MR brain images. In this study, we were interested in combining hippocampus and amygdala volumes and entorhinal cortex thickness to improve the performance of AD differentiation. Thus, our objective was to investigate the useful features obtained from MRI for classification of AD patients using support vector machine (SVM). T1-weighted MR brain images of 100 AD patients and 100 normal subjects were processed using FreeSurfer software to measure hippocampus and amygdala volumes and entorhinal cortex thicknesses in both brain hemispheres. Relative volumes of hippocampus and amygdala were calculated to correct variation in individual head size. SVM was employed with five combinations of features (H: hippocampus relative volumes, A: amygdala relative volumes, E: entorhinal cortex thicknesses, HA: hippocampus and amygdala relative volumes and ALL: all features). Receiver operating characteristic (ROC) analysis was used to evaluate the method. AUC values of five combinations were 0.8575 (H), 0.8374 (A), 0.8422 (E), 0.8631 (HA) and 0.8906 (ALL). Although “ALL” provided the highest AUC, there were no statistically significant differences among them except for “A” feature. Our results showed that all suggested features may be feasible for computer-aided classification of AD patients.

  8. Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries

    Directory of Open Access Journals (Sweden)

    Shengyu Liu

    2015-12-01

    Full Text Available Semantic features are very important for machine learning-based drug name recognition (DNR systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts. Building large-scale drug dictionaries is a time-consuming task and adding new drugs to existing drug dictionaries immediately after they are developed is also a challenge. In recent years, word embeddings that contain rich latent semantic information of words have been widely used to improve the performance of various natural language processing tasks. However, they have not been used in DNR systems. Compared to the semantic features based on drug dictionaries, the advantage of word embeddings lies in that learning them is unsupervised. In this paper, we investigate the effect of semantic features based on word embeddings on DNR and compare them with semantic features based on three drug dictionaries. We propose a conditional random fields (CRF-based system for DNR. The skip-gram model, an unsupervised algorithm, is used to induce word embeddings on about 17.3 GigaByte (GB unlabeled biomedical texts collected from MEDLINE (National Library of Medicine, Bethesda, MD, USA. The system is evaluated on the drug-drug interaction extraction (DDIExtraction 2013 corpus. Experimental results show that word embeddings significantly improve the performance of the DNR system and they are competitive with semantic features based on drug dictionaries. F-score is improved by 2.92 percentage points when word embeddings are added into the baseline system. It is comparative with the improvements from semantic features based on drug dictionaries. Furthermore, word embeddings are complementary to the semantic features based on drug dictionaries. When both word embeddings and semantic features based on drug dictionaries are added, the system achieves the best performance with an F-score of 78.37%, which outperforms the best system of the DDIExtraction 2013

  9. Decision forests for machine learning classification of large, noisy seafloor feature sets

    Science.gov (United States)

    Lawson, Ed; Smith, Denson; Sofge, Donald; Elmore, Paul; Petry, Frederick

    2017-02-01

    Extremely randomized trees (ET) classifiers, an extension of random forests (RF) are applied to classification of features such as seamounts derived from bathymetry data. This data is characterized by sparse training data from by large noisy features sets such as often found in other geospatial data. A variety of feature metrics may be useful for this task and we use a large number of metrics relevant to the task of finding seamounts. The major significant results to be described include: an outstanding seamount classification accuracy of 97%; an automated process to produce the most useful classification features that are relevant to geophysical scientists (as represented by the feature metrics); demonstration that topography provides the most important data representation for classification. As well as achieving good accuracy in classification, the human-understandable set of metrics generated by the classifier that are most relevant for the results are discussed.

  10. Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition

    Directory of Open Access Journals (Sweden)

    Yu-Xiang Zhao

    2016-06-01

    Full Text Available In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters.

  11. Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition

    Science.gov (United States)

    Zhao, Yu-Xiang; Chou, Chien-Hsing

    2016-01-01

    In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters. PMID:27314346

  12. Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition.

    Science.gov (United States)

    Zhao, Yu-Xiang; Chou, Chien-Hsing

    2016-06-14

    In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters.

  13. Brief report: relationships between facets of impulsivity and borderline personality features.

    Science.gov (United States)

    Peters, Jessica R; Upton, Brian T; Baer, Ruth A

    2013-08-01

    Relationships between specific borderline personality disorder (BPD) features and facets of impulsivity (negative and positive urgency, premeditation, perseverance, and sensation seeking) were examined in a sample of 227 undergraduate students, oversampled to include many with elevations on a measure of borderline features. Most facets of impulsivity were positively correlated with borderline features, except for sensation seeking, which showed a mixed pattern of relationships with specific BPD features. In regression models, negative urgency was the strongest predictor of all BPD features scales, including affective instability, identity problems, negative relationships, and self-harm. Premeditation, positive urgency, and sensation seeking demonstrated incremental validity over negative urgency in predicting some BPD features; however, significant beta weights were negative for sensation seeking, suggesting that it may be protective or adaptive for BPD, unlike other forms of impulsivity. This study provides evidence for variation in how types of impulsivity contribute to different BPD features and demonstrates the importance of examining BPD features on the subscale level.

  14. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Maolong Xi

    2016-01-01

    Full Text Available This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO for cancer feature gene selection, coupling support vector machine (SVM for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV. Finally, the BQPSO coupling SVM (BQPSO/SVM, binary PSO coupling SVM (BPSO/SVM, and genetic algorithm coupling SVM (GA/SVM are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

  15. Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine.

    Science.gov (United States)

    Xi, Maolong; Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun

    2016-01-01

    This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

  16. Comparative study of shape, intensity and texture features and support vector machine for white blood cell classification

    Directory of Open Access Journals (Sweden)

    Mehdi Habibzadeh

    2013-04-01

    Full Text Available The complete blood count (CBC is widely used test for counting and categorizing various peripheral particles in the blood. The main goal of the paper is to count and classify white blood cells (leukocytes in microscopic images into five major categories using features such as shape, intensity and texture features. The first critical step of counting and classification procedure involves segmentation of individual cells in cytological images of thin blood smears. The quality of segmentation has significant impact on the cell type identification, but poor quality, noise, and/or low resolution images make segmentation less reliable. We analyze the performance of our system for three different sets of features and we determine that the best performance is achieved by wavelet features using the Dual-Tree Complex Wavelet Transform (DT-CWT which is based on multi-resolution characteristics of the image. These features are combined with the Support Vector Machine (SVM which classifies white blood cells into their five primary types. This approach was validated with experiments conducted on digital normal blood smear images with low resolution.

  17. Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes

    Directory of Open Access Journals (Sweden)

    Divya Tomar

    2015-01-01

    Full Text Available There is a necessity for analysis of a large amount of data in many fields such as healthcare, business, industries, and agriculture. Therefore, the need of the feature selection (FS technique for the researchers is quite evident in many fields of science, especially in computer science. Furthermore, an effective FS technique that is best suited to a particular learning algorithm is of great help for the researchers. Hence, this paper proposes a hybrid feature selection (HFS based efficient disease diagnostic model for Breast Cancer, Hepatitis, and Diabetes. A HFS is an efficient method that combines the positive aspects of both Filter and Wrapper FS approaches. The proposed model adopts weighted least squares twin support vector machine (WLSTSVM as a classification approach, sequential forward selection (SFS as a search strategy, and correlation feature selection (CFS to evaluate the importance of each feature. This model not only selects relevant feature subset but also efficiently deals with the data imbalance problem. The effectiveness of the HFS based WLSTSVM approach is examined on three well-known disease datasets taken from UCI repository with the help of predictive accuracy, sensitivity, specificity, and geometric mean. The experiment confirms that our proposed HFS based WLSTSVM disease diagnostic model can result in positive outcomes.

  18. Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

    Directory of Open Access Journals (Sweden)

    Mustafa Serter Uzer

    2013-01-01

    Full Text Available This paper offers a hybrid approach that uses the artificial bee colony (ABC algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

  19. Testing Occam's razor to characterize high-order connectivity in pore networks of granular media: Feature selection in machine learning

    Science.gov (United States)

    van der Linden, Joost; Tordesillas, Antoinette; Narsilio, Guillermo

    2017-06-01

    A perennial challenge for the characterization and modelling of phenomena involving granular media is that the internal connectivity of, and interactions between, the pores and the particles exhibit hallmarks of complexity: multi-scale and nonlinear interactions that lead to a plethora of patterns at the mesoscale, including fluid flow patterns that ultimately render a permeability of the granular media at the macroscale. A multitude of physical parameters exist to characterize geometry and structure, including pore/particle shape, volume and surface area, while a rich class of complex network parameters quantifies internal connectivity of the pore and particles in the material. A large collection of such variables is likely to exhibit a high degree of redundancy. Here we demonstrate how to use feature selection in machine learning theory to identify the most informative and non-redundant, yet parsimonious set of features that optimally characterizes the interstitial flow properties of porous, granular media, e.g., permeability, from high resolution data.

  20. Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

    Directory of Open Access Journals (Sweden)

    Tiannan Ma

    2016-12-01

    Full Text Available Accurate forecasting of icing thickness has great significance for ensuring the security and stability of the power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on the fireworks algorithm and weighted least square support vector machine (W-LSSVM. The method of the fireworks algorithm is employed to select the proper input features with the purpose of eliminating redundant influence. In addition, the aim of the W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through simulation experiments using real-world icing data from the monitoring center of the key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

  1. New tools of relationship marketing for innovative projects of machine-building enterprises: crowdsourcing and crowdfunding

    Directory of Open Access Journals (Sweden)

    N.B. Kolotova

    2015-09-01

    Full Text Available The aim of the article. The aim of the article is justification of theoretical provisions for determining the nature and use of relationship marketing in innovative projects of machine-building enterprises. The results of the analysis. Today there are a lot of reasonable thoughts on the fundamental principles of relationship marketing concept origin. Generally, in our opinion, relationship marketing is a communication strategy intended to keep existing customers and to attract potential customers and key business partners. The peculiarity of the current development stage of information technologies is the widespread use of the following specific tools for development and commercialization of innovative projects: crowdsourcing and crowdfunding. These tools have already become a widely implemented in world practice and are vivid examples of a new interaction among subjects of innovation activity. Crowdsourcing and crowdfunding are relationship marketing tools by which a reduction of commercial risk innovative products is provided through attracting consumers in the processes of creating, testing, financing and promotion of new products, and so on. These tools use profound social nature of man, the desire to be «trailed» to something new (interesting, fashionable, promising, etc.. Marketing benefits of using crowdsourcing and crowdfunding for innovative projects are: - identifying of consumer preferences in the design stage of a new product; - the possibility to reduce the financial, intellectual and time spent on research, design, marketing research, etc.; - receiving of prior order and (or the advance payment for the projected product; - increasing customer loyalty, obtaining active «promoters», project’s «attorneys» as the clients; - the possibility of renewed interest in existing projects (food; - increasing interest in the innovative project on the part of other market entities (partners, investors, etc. on the basis of existing demand

  2. Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

    CERN Document Server

    Tangaro, Sabina; Brescia, Massimo; Cavuoti, Stefano; Chincarini, Andrea; Errico, Rosangela; Inglese, Paolo; Longo, Giuseppe; Maglietta, Rosalia; Tateo, Andrea; Riccio, Giuseppe; Bellotti, Roberto

    2015-01-01

    Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations and therefore be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach, for each voxel a number of local features were calculated. In this paper we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) Sequential Forward Selection and (iii) Sequential Backward Elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects...

  3. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

    Science.gov (United States)

    Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

    2013-03-01

    Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  4. Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features

    Directory of Open Access Journals (Sweden)

    Adrian Ion-Mărgineanu

    2017-07-01

    Full Text Available Purpose: The purpose of this study is classifying multiple sclerosis (MS patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features.Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS, 30 Relapse Remitting (RR, 17 Primary Progressive (PP, and 28 Secondary Progressive (SP] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale, conventional magnetic resonance imaging and spectroscopic imaging. We extract N-acetyl-aspartate (NAA, Choline (Cho, and Creatine (Cre concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA, Support Vector Machines with gaussian kernel (SVM-rbf, and Random Forests.Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71–72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85% after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87% after training LDA and SVM

  5. Man-machine interface in a submarine command and weapon control system: features and design experience

    Directory of Open Access Journals (Sweden)

    Johan H. Aas

    1989-01-01

    Full Text Available Important man-machine interface (MMI issues concerning a submarine command and weapon control system (CWCS such as crew organization, automation level and decision support are discussed in this paper. Generic submarine CWCS functions and operating conditions are outlined. Detailed, dynamic and real-time prototypes were used to support the MMI design. The prototypes are described and experience with detailed prototyping is discussed. Some of the main interaction principles are summarized and a restricted example of the resulting design is given. Our design experience and current work have been used to outline future perspectives of MMI design in naval CWCSs. The need for both formal and experimental approaches is emphasized.

  6. The Machine Recognition for Population Feature of Wheat Images Based on BP Neural Network

    Institute of Scientific and Technical Information of China (English)

    LI Shao-kun; SUO Xing-mei; BAI Zhong-ying; QI Zhi-li; Liu Xiao-hong; GAO Shi-ju; ZHAO Shuang-ning

    2002-01-01

    Recognition and analysis of dynamic information about population images during wheat growth periods can be taken for the base of quantitative diagnosis for wheat growth. A recognition system based on self-learning BP neural network for feature data of wheat population images, such as total green areas and leaves areas was designed in this paper. In addition, some techniques to create favorable conditions for image recognition was discussed, which were as follows: (1) The method of collecting images by a digital camera and assistant equipment under natural conditions in fields. (2) An algorithm of pixei labeling was used to segment image and extract feature. (3)A high pass filter based on Laplacian was used to strengthen image information. The results showed that the ANN system was availability for image recognition of wheat population feature.

  7. SOME FEATURES OF THE RELATIONSHIP OF CIVIL LIABILITY OF PUBLIC ENTITIES

    OpenAIRE

    2014-01-01

    The article deals with the civil liability of public entities as a kind of civil legal relationships. We have analyzed the features of this relationship in comparison with a common understanding of civil liability legal relationship, which is not complicated with public-law entity

  8. Finite State Machine with Adaptive Electromyogram (EMG) Feature Extraction to Drive Meal Assistance Robot

    Science.gov (United States)

    Zhang, Xiu; Wang, Xingyu; Wang, Bei; Sugi, Takenao; Nakamura, Masatoshi

    Surface electromyogram (EMG) from elbow, wrist and hand has been widely used as an input of multifunction prostheses for many years. However, for patients with high-level limb deficiencies, muscle activities in upper-limbs are not strong enough to be used as control signals. In this paper, EMG from lower-limbs is acquired and applied to drive a meal assistance robot. An onset detection method with adaptive threshold based on EMG power is proposed to recognize different muscle contractions. Predefined control commands are output by finite state machine (FSM), and applied to operate the robot. The performance of EMG control is compared with joystick control by both objective and subjective indices. The results show that FSM provides the user with an easy-performing control strategy, which successfully operates robots with complicated control commands by limited muscle motions. The high accuracy and comfortableness of the EMG-control meal assistance robot make it feasible for users with upper limbs motor disabilities.

  9. Classifying Cyst and Tumor Lesion Using Support Vector Machine Based on Dental Panoramic Images Texture Features

    OpenAIRE

    Nurtanio, Ingrid

    2013-01-01

    Dental radiographs are essential in diagnosing the pathology of the jaw. However, similar radiographic appearance of jaw lesions causes difficulties in differentiating cyst from tumor. Therefore, we conducted a development of computer-aided classification system for cyst and tumor lesions in dental panoramic images. The proposed system consists of feature extraction based on texture using the first-order statistics texture (FO), Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run ...

  10. Practical data mining and machine learning for optics applications: introduction to the feature issue.

    Science.gov (United States)

    Abdulla, Ghaleb; Awwal, Abdul; Borne, Kirk; Ho, Tin Kam; Vestrand, W Thomas

    2011-08-01

    Data mining algorithms utilize search techniques to explore hidden patterns and correlations in the data, which otherwise require a tremendous amount of human time to explore. This feature issue explores the use of such techniques to help understand the data, build better simulators, explain outlier behavior, and build better predictive models. We hope that this issue will spur discussions and expose a set of tools that can be useful to the optics community.

  11. Application of Multi-task Sparse Lasso Feature Extraction and Support Vector Machine Regression in the Stellar Atmospheric Parameterization

    Science.gov (United States)

    Gao, Wei; Li, Xiang-ru

    2017-07-01

    The multi-task learning takes the multiple tasks together to make analysis and calculation, so as to dig out the correlations among them, and therefore to improve the accuracy of the analyzed results. This kind of methods have been widely applied to the machine learning, pattern recognition, computer vision, and other related fields. This paper investigates the application of multi-task learning in estimating the stellar atmospheric parameters, including the surface temperature (Teff), surface gravitational acceleration (lg g), and chemical abundance ([Fe/H]). Firstly, the spectral features of the three stellar atmospheric parameters are extracted by using the multi-task sparse group Lasso algorithm, then the support vector machine is used to estimate the atmospheric physical parameters. The proposed scheme is evaluated on both the Sloan stellar spectra and the theoretical spectra computed from the Kurucz's New Opacity Distribution Function (NEWODF) model. The mean absolute errors (MAEs) on the Sloan spectra are: 0.0064 for lg (Teff /K), 0.1622 for lg (g/(cm · s-2)), and 0.1221 dex for [Fe/H]; the MAEs on the synthetic spectra are 0.0006 for lg (Teff /K), 0.0098 for lg (g/(cm · s-2)), and 0.0082 dex for [Fe/H]. Experimental results show that the proposed scheme has a rather high accuracy for the estimation of stellar atmospheric parameters.

  12. Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment.

    Science.gov (United States)

    Torres-Valencia, Cristian A; Álvarez, Mauricio A; Orozco-Gutiérrez, Alvaro A

    2014-01-01

    Human emotion recognition (HER) allows the assessment of an affective state of a subject. Until recently, such emotional states were described in terms of discrete emotions, like happiness or contempt. In order to cover a high range of emotions, researchers in the field have introduced different dimensional spaces for emotion description that allow the characterization of affective states in terms of several variables or dimensions that measure distinct aspects of the emotion. One of the most common of such dimensional spaces is the bidimensional Arousal/Valence space. To the best of our knowledge, all HER systems so far have modelled independently, the dimensions in these dimensional spaces. In this paper, we study the effect of modelling the output dimensions simultaneously and show experimentally the advantages in modeling them in this way. We consider a multimodal approach by including features from the Electroencephalogram and a few physiological signals. For modelling the multiple outputs, we employ a multiple output regressor based on support vector machines. We also include an stage of feature selection that is developed within an embedded approach known as Recursive Feature Elimination (RFE), proposed initially for SVM. The results show that several features can be eliminated using the multiple output support vector regressor with RFE without affecting the performance of the regressor. From the analysis of the features selected in smaller subsets via RFE, it can be observed that the signals that are more informative into the arousal and valence space discrimination are the EEG, Electrooculogram/Electromiogram (EOG/EMG) and the Galvanic Skin Response (GSR).

  13. 基于改进SVM的特征选择%Based on Modified Support Vector Machines Feature Selection

    Institute of Scientific and Technical Information of China (English)

    陈振洲; 邹丽珊

    2007-01-01

    本文在仔细分析特征选择思想的基础上,将特征选择过程嵌入到学习机里面,提出了一种基于改进支持向量机的特征选择算法(Feature selection via Modified Support Vector Machines),该方法通过对特征的权重进行排序来实现特征选择.利用可以将特征选择过程和学习过程有机地统一起来,实验表明,与其它方法比较,该方法能够达到比较好的效果.

  14. Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Yang Zhang

    2015-11-01

    Full Text Available Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for online capacity estimation. First, six novel features are extracted from cyclic charge/discharge cycles and used as indirect health indicators. An adaptive multi-kernel relevance machine (MKRVM based on accelerated particle swarm optimization algorithm is used to determine the optimal parameters of MKRVM and characterize the relationship between extracted features and battery capacity. The overall estimation process comprises offline and online stages. A supervised learning step in the offline stage is established for model verification to ensure the generalizability of MKRVM for online application. Cross-validation is further conducted to validate the performance of the proposed model. Experiment and comparison results show the effectiveness, accuracy, efficiency, and robustness of the proposed approach for online capacity estimation of lithium-ion batteries.

  15. An analysis of feature relevance in the classification of astronomical transients with machine learning methods

    CERN Document Server

    D'Isanto, Antonio; Brescia, Massimo; Donalek, Ciro; Longo, Giuseppe; Riccio, Giuseppe; Djorgovski, Stanislav G

    2016-01-01

    The exploitation of present and future synoptic (multi-band and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) and K-Nearest Neighbors, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extra-galactic objects and identification of supernovae.

  16. Virtual machines placement algorithm based on resource utilization feature-matching%基于资源特征匹配的虚拟机放置算法

    Institute of Scientific and Technical Information of China (English)

    冯伟; 陈静怡; 吴杰

    2012-01-01

    VMP-RUFM (virtual machines placement algorithm based on resource utilization feature-matching) is proposed to address the problem of resource inefficient utilization in data centers. Considering of the feature of performance and access mode of virtual machine application, the algorithm models feature expression of resource utilization. After that, the method selects the associated collection of virtual machines of which resource utilization feature matches the resource allocation of physical machine. The experimental results show that this approach can effectively optimize the matching degree between resource utilization of virtual machines and resource allocation of relevant physical machine.%针对目前数据中心的资源低效利用问题,提出了一种基于资源消耗特征匹配的虚拟机放置算法VMP-RUFM (virtual machines placement algorithm based on resource utilization feature-matching).算法在虚拟机应用的性能表现和访问模式两个层面上,建立虚拟机资源特征模型,进而选择资源消耗特征与物理机资源配置相匹配的虚拟机集合.实验结果表明,该算法对满足条件的虚拟机进行关联后,能够显著优化虚拟机整体资源消耗和对应物理机资源配置的匹配程度.

  17. ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix

    Institute of Scientific and Technical Information of China (English)

    Ya-tao ZHANG; Cheng-yu LIU; Shou-shui WEI; Chang-zhi WEI; Fei-fei LIU

    2014-01-01

    We propose a systematic ECG quality classification method based on a kernel support vector machine (KSVM) and genetic algorithm (GA) to determine whether ECGs collected via mobile phone are acceptable or not. This method includes mainly three modules, i.e., lead-fall detection, feature extraction, and intelligent classification. First, lead-fall detection is executed to make the initial classification. Then the power spectrum, baseline drifts, amplitude difference, and other time-domain features for ECGs are analyzed and quantified to form the feature matrix. Finally, the feature matrix is assessed using KSVM and GA to determine the ECG quality classification results. A Gaussian radial basis function (GRBF) is employed as the kernel function of KSVM and its performance is compared with that of the Mexican hat wavelet function (MHWF). GA is used to determine the optimal parameters of the KSVM classifier and its performance is compared with that of the grid search (GS) method. The performance of the proposed method was tested on a database from PhysioNet/Computing in Cardiology Challenge 2011, which includes 1500 12-lead ECG recordings. True positive (TP), false positive (FP), and classification accuracy were used as the assessment indices. For training database set A (1000 recordings), the optimal results were obtained using the combination of lead-fall, GA, and GRBF methods, and the corresponding results were:TP 92.89%, FP 5.68%, and classification accuracy 94.00%. For test database set B (500 recordings), the optimal results were also obtained using the combination of lead-fall, GA, and GRBF methods, and the classification accuracy was 91.80%.

  18. Features of borderline personality disorder, perceived childhood emotional invalidation, and dysfunction within current romantic relationships.

    Science.gov (United States)

    Selby, Edward A; Braithwaite, Scott R; Joiner, Thomas E; Fincham, Frank D

    2008-12-01

    The mechanisms through which current romantic relationship dysfunction develops in individuals with borderline personality disorder (BPD) symptoms are still unclear. One possible pathway may be childhood experiences of emotional invalidation by parents, which may result in the development of poor social problem-solving skills or cognitive responses such as splitting, which impair current romantic relationships. This study examines the relationship between features of BPD and current romantic relationship dysfunction, and demonstrates that perceived emotional invalidation by parents during childhood mediates the relationship between BPD features and current romantic relationship dysfunction. Structural equations modeling was used to test the hypothesized model in 758 young adults in an ethnically diverse community sample. The proposed model fit the data well; perceived childhood emotional invalidation partially mediated the relationship between features of BPD and romantic relationship dysfunction, even when controlling for the presence of a major depressive episode in the last year. The findings of this study suggest that individuals with features of BPD experience relationship dysfunction that cannot be accounted for by comorbid depression and that perceived childhood emotional invalidation may contribute to these problems. Copyright 2008 APA, all rights reserved.

  19. Relationship between elevated plantar pressure of toes and forefoot and gait features in diabetic patients.

    Science.gov (United States)

    Amemiya, Ayumi; Noguchi, Hiroshi; Oe, Makoto; Takehara, Kimie; Yamada, Amika; Ohashi, Yumiko; Ueki, Kohjiro; Kadowaki, Takashi; Mori, Taketoshi; Sanada, Hiromi

    2013-01-01

    This cross-sectional observational study is to reveal what kind of gait feature is relevant to elevated segment and its plantar pressure for prevention of diabetic foot ulcers. In 57 diabetic patients, the relationship between elevated plantar pressure and gait features was analyzed. To conduct this investigation, a simultaneous measurement system of plantar pressure and gait features was constructed. Plantar pressure distribution was measured by F-scan with customized footwear, and gait features were mainly measured using wireless motion sensors attached to the sacrum and feet. Several gait features of small rolling during the mid-stance phase were relevant to the elevated plantar pressure.

  20. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Lili Chen

    2017-01-01

    Full Text Available Preterm birth (PTB is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT and extreme learning machine (ELM. For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs using empirical mode decomposition (EMD. Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

  1. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

    Science.gov (United States)

    Hao, Yaru

    2017-01-01

    Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group. PMID:28316639

  2. Understanding world soils: Machine Learning as a framework for analyzing global soil-landscape relationships

    Science.gov (United States)

    Hengl, Tomislav; Mendes de Jesus, Jorge

    2016-04-01

    Soil information is an increasingly important input to global geochemical modelling, hydrological modelling, spatial planning and agricultural extension. Soil remains one of the least developed environmental layers globally with data available only at coarse resolutions and with limited accuracy. In 2013/2014 ISRIC - World Soil Information has released a Global Soil Information system (SoilGrids1km) and an app to serve 3D soil information globally in near real time (DOI: 10.1371/journal.pone.0105992)). At the time, this system was a proof of concept demonstrating that global compilations of soil profiles can be used in an automated framework to produce complete and consistent spatial predictions of soil properties and classes. It was primarily been based on linear statistical modelling, which resulted in a limited fitting success. Global models fit to large, noisy data, can often result in significant oversmoothing of the measured variation. In year 2015, focus of the SoilGrids project has shifted towards improving data quality primarily considering of spatial detail and attribute accuracy. Initial testing using African soil data (DOI: 10.1371/journal.pone.0125814) has shown that the key to improving accuracy might lay in using Machine learning techniques such as random forests, neural networks and similar that are able to better represent complex, often non-linear soil-landscape relationships. In 2015 we have fitted machine learning using larger global compilations of soil profiles (about 150,000 points) and covariates at 250 m spatial resolution (about 150 covariates; mainly MODIS seasonal land products, SRTM DEM derivatives, climatic images, lithological and land cover and landform maps) and extracted more significant global soil-landscape relationships (R-square ranging from 0.42 to 0.83). Our results show that the key predictors for mapping soil classes are most commonly hydrological DEM parameters and climatic data; for soil texture fractions lithology and

  3. Understanding the Growth of ESL Paragraph Writing Skills and Its Relationships with Linguistic Features

    Science.gov (United States)

    Aryadoust, Vahid

    2016-01-01

    This study sought to examine the development of paragraph writing skills of 116 English as a second language university students over the course of 12 weeks and the relationship between the linguistic features of students' written texts as measured by Coh-Metrix--a computational system for estimating textual features such as cohesion and…

  4. Understanding the Growth of ESL Paragraph Writing Skills and Its Relationships with Linguistic Features

    Science.gov (United States)

    Aryadoust, Vahid

    2016-01-01

    This study sought to examine the development of paragraph writing skills of 116 English as a second language university students over the course of 12 weeks and the relationship between the linguistic features of students' written texts as measured by Coh-Metrix--a computational system for estimating textual features such as cohesion and…

  5. Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

    2016-12-16

    This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm (2). The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

  6. Does skull shape mediate the relationship between objective features and subjective impressions about the face?

    Science.gov (United States)

    Marečková, Klára; Chakravarty, M Mallar; Huang, Mei; Lawrence, Claire; Leonard, Gabriel; Perron, Michel; Pike, Bruce G; Richer, Louis; Veillette, Suzanne; Pausova, Zdenka; Paus, Tomáš

    2013-10-01

    In our previous work, we described facial features associated with a successful recognition of the sex of the face (Marečková et al., 2011). These features were based on landmarks placed on the surface of faces reconstructed from magnetic resonance (MR) images; their position was therefore influenced by both soft tissue (fat and muscle) and bone structure of the skull. Here, we ask whether bone structure has dissociable influences on observers' identification of the sex of the face. To answer this question, we used a novel method of studying skull morphology using MR images and explored the relationship between skull features, facial features, and sex recognition in a large sample of adolescents (n=876; including 475 adolescents from our original report). To determine whether skull features mediate the relationship between facial features and identification accuracy, we performed mediation analysis using bootstrapping. In males, skull features mediated fully the relationship between facial features and sex judgments. In females, the skull mediated this relationship only after adjusting facial features for the amount of body fat (estimated with bioimpedance). While body fat had a very slight positive influence on correct sex judgments about male faces, there was a robust negative influence of body fat on the correct sex judgments about female faces. Overall, these results suggest that craniofacial bone structure is essential for correct sex judgments about a male face. In females, body fat influences negatively the accuracy of sex judgments, and craniofacial bone structure alone cannot explain the relationship between facial features and identification of a face as female.

  7. 基于最大加工体的特征模型转换方法%Feature Model Conversion Based on Max Machining Body

    Institute of Scientific and Technical Information of China (English)

    刘景; 朱英; 陈正鸣

    2011-01-01

    An incremental intermediate-model-based approach to convert design feature model to machining feature model is presented for the class of rough machining parts produced by milling or turning operation. A new concept named Max Machining Body (MMB) is proposed. The proposed method consists of three steps. Firstly, all the basic MMBs are generated incrementally based on the design feature history. Secondly, new MMBs are generated by merging the basic MMBs according to the types of their original features. Lastly, the intermediate model, which is composed of all the MMBs and their relative machining parameters, is converted to the machining feature model incrementally based on the machining priority rules and the user interaction strategies. The examples show that the proposed method can generate multiple meaningful machining interpretations, thus the user can obtain a reasonable machining feature interpretation automatically or conveniently.%针对需要铣削或车削的粗加工零件,提出一种基于中间模型的、从设计特征模型向加工特征模型的逐步转换方法.在提出最大加工体概念的基础上,基于设计特征历史逐步生成基本最大加工体;并根据基本最大加工体的来源特征类型合并产生新的最大加工体,所有最大加工体及其加工参数构成了中间模型;最后基于加工优先规则和用户交互策略实现中间模型向加工特征模型的逐步转换.实例结果表明,该方法能够生成有意义的多种加工解释,用户可以自动或者方便地获得合理的加工特征解释.

  8. A new model of flavonoids affinity towards P-glycoprotein: genetic algorithm-support vector machine with features selected by a modified particle swarm optimization algorithm.

    Science.gov (United States)

    Cui, Ying; Chen, Qinggang; Li, Yaxiao; Tang, Ling

    2017-02-01

    Flavonoids exhibit a high affinity for the purified cytosolic NBD (C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationship (QSAR) models were developed using support vector machines (SVMs). A novel method coupling a modified particle swarm optimization algorithm with random mutation strategy and a genetic algorithm coupled with SVM was proposed to simultaneously optimize the kernel parameters of SVM and determine the subset of optimized features for the first time. Using DRAGON descriptors to represent compounds for QSAR, three subsets (training, prediction and external validation set) derived from the dataset were employed to investigate QSAR. With excluding of the outlier, the correlation coefficient (R(2)) of the whole training set (training and prediction) was 0.924, and the R(2) of the external validation set was 0.941. The root-mean-square error (RMSE) of the whole training set was 0.0588; the RMSE of the cross-validation of the external validation set was 0.0443. The mean Q(2) value of leave-many-out cross-validation was 0.824. With more informations from results of randomization analysis and applicability domain, the proposed model is of good predictive ability, stability.

  9. Relationship of goat milk flow emission variables with milking routine, milking parameters, milking machine characteristics and goat physiology.

    Science.gov (United States)

    Romero, G; Panzalis, R; Ruegg, P

    2017-04-10

    The aim of this paper was to study the relationship between milk flow emission variables recorded during milking of dairy goats with variables related to milking routine, goat physiology, milking parameters and milking machine characteristics, to determine the variables affecting milking performance and help the goat industry pinpoint farm and milking practices that improve milking performance. In total, 19 farms were visited once during the evening milking. Milking parameters (vacuum level (VL), pulsation ratio and pulsation rate, vacuum drop), milk emission flow variables (milking time, milk yield, maximum milk flow (MMF), average milk flow (AVMF), time until 500 g/min milk flow is established (TS500)), doe characteristics of 8 to 10 goats/farm (breed, days in milk and parity), milking practices (overmilking, overstripping, pre-lag time) and milking machine characteristics (line height, presence of claw) were recorded on every farm. The relationships between recorded variables and farm were analysed by a one-way ANOVA analysis. The relationships of milk yield, MMF, milking time and TS500 with goat physiology, milking routine, milking parameters and milking machine design were analysed using a linear mixed model, considering the farm as the random effect. Farm was significant (Pvariables. Milk emission flow variables were similar to those recommended in scientific studies. Milking parameters were adequate in most of the farms, being similar to those recommended in scientific studies. Few milking parameters and milking machine characteristics affected the tested variables: average vacuum level only showed tendency on MMF, and milk pipeline height on TS500. Milk yield (MY) was mainly affected by parity, as the interaction of days in milk with parity was also significant. Milking time was mainly affected by milk yield and breed. Also significant were parity, the interaction of days in milk with parity and overstripping, whereas overmilking showed a slight tendency

  10. Wavelet Correlation Feature Scale Entropy and Fuzzy Support Vector Machine Approach for Aeroengine Whole-Body Vibration Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Cheng-Wei Fei

    2013-01-01

    Full Text Available In order to correctly analyze aeroengine whole-body vibration signals, Wavelet Correlation Feature Scale Entropy (WCFSE and Fuzzy Support Vector Machine (FSVM (WCFSE-FSVM method was proposed by fusing the advantages of the WCFSE method and the FSVM method. The wavelet coefficients were known to be located in high Signal-to-Noise Ratio (S/N or SNR scales and were obtained by the Wavelet Transform Correlation Filter Method (WTCFM. This method was applied to address the whole-body vibration signals. The WCFSE method was derived from the integration of the information entropy theory and WTCFM, and was applied to extract the WCFSE values of the vibration signals. Among the WCFSE values, the WFSE1 and WCFSE2 values on the scale 1 and 2 from the high band of vibration signal were believed to acceptably reflect the vibration feature and were selected to construct the eigenvectors of vibration signals as fault samples to establish the WCFSE-FSVM model. This model was applied to aeroengine whole-body vibration fault diagnosis. Through the diagnoses of four vibration fault modes and the comparison of the analysis results by four methods (SVM, FSVM, WESE-SVM, WCFSE-FSVM, it is shown that the WCFSE-FSVM method is characterized by higher learning ability, higher generalization ability and higher anti-noise ability than other methods in aeroengine whole-vibration fault analysis. Meanwhile, this present study provides a useful insight for the vibration fault diagnosis of complex machinery besides an aeroengine.

  11. Feature selection for speech emotion recognition in Spanish and Basque: on the use of machine learning to improve human-computer interaction.

    Directory of Open Access Journals (Sweden)

    Andoni Arruti

    Full Text Available Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

  12. Feature selection for speech emotion recognition in Spanish and Basque: on the use of machine learning to improve human-computer interaction.

    Science.gov (United States)

    Arruti, Andoni; Cearreta, Idoia; Alvarez, Aitor; Lazkano, Elena; Sierra, Basilio

    2014-01-01

    Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

  13. Machining Feature Recognition Based on Surface Clustering%基于表面聚类优化的加工特征识别方法

    Institute of Scientific and Technical Information of China (English)

    汤岑书; 褚学宁; 孙习武; 苏於梁

    2009-01-01

    To realize the effective integration of CAD and CAPP system, an approach of machining feature recognition was proposed based on the generation and clustering of surface machining methods. Three kinds of information models, such as manufacturing resource, machined surface and machining method were built. A concept of cutting mode was proposed and used for generating the machining methods of part surfaces. Aiming at minimizing the number of tool type and the number of setups used, an optimal model for surface clustering was established to select the best machining method for every surface. Surfaces which can be simultaneously machined by a common type of tool in the same setup were then recognized as a machining feature. Finally, an example part was used to test the validity and effectiveness of the approach proposed, and the final results illustrate that the approach can effectively solve some problems such as the recognition of intersecting features which are difficult to traditional feature recognition approaches.%为了实现计算机辅助设计与计算机辅助工艺设计系统的有效集成,提出了以表面加工方法生成和聚类优化为基础的加工特征识别新方法.基于加工资源、加工表面和加工方法3类信息模型,引出了切削模式概念和表面加工方法生成的原理.以刀具种类数和零件装夹次数最少为目标,建立了表面聚类优化模型,为加工表面选择最优加工方法,并把可用同类刀具、在同一装夹下连续加工的一组表面聚为一个加工特征.通过实例测试,验证了该方法的正确性和有效性.

  14. Relationship between blood pressure and psychological features of experience and behaviour among teachers

    OpenAIRE

    Marcus Stueck; Thomas Rigotti; Juliet Roudini; Edgar Galindo; Dian S. Utami

    2016-01-01

    Background Relationships between psychological features and psychophysical parameters, such as blood pressure, have a high relevance in research on coping with stress. We want to investigate the correlation between blood pressure and these psychological features. Participants and procedure We investigated 79 teachers from high schools and secondary schools in and around Leipzig, Germany. Using the systolic blood pressure as an indicator, we formed three groups: hypotensive...

  15. Clinical and histopathological features and relationship of Barrett esophagus and its related adenocarcinoma

    Institute of Scientific and Technical Information of China (English)

    陈慧

    2014-01-01

    Objective To explore the clinical and histopathological features of Barrett esophagus and its related adenocarcinoma as well as the relationship between them.Methods From ajanuary 2002 to January 2012,the clinical data of 35 patients with Barrett esophagus,850 patients with esophagus cancer and 218 patients with esophageal-gastric junction cancer were collected,and the histopathological features of all the patients and the followup in patients with Barrett esophagus were retrospectively

  16. Appraisals of daily romantic relationship experiences in individuals with borderline personality disorder features.

    Science.gov (United States)

    Bhatia, Vickie; Davila, Joanne; Eubanks-Carter, Catherine; Burckell, Lisa A

    2013-06-01

    The current study examined the relationship between borderline personality disorder (BPD) features and appraisals of daily romantic relationship experiences. The sample included 114 ethnically diverse, young adult dating couples (total N = 228). Participants completed a 14-day daily diary study and reported negative impact and emotional loss to their romantic partner in response to daily positive and negative self-initiated and partner-initiated romantic experiences. Results indicated that BPD features, even when controlling for relationship satisfaction, total number of relationship experiences, and depressive symptoms, were associated with reporting greater negative impact and greater emotional loss to both partner-initiated negative and positive experiences. BPD features were generally not associated with reporting greater negative impact and emotional loss in response to self-initiated negative and positive experiences. The results suggest that individuals with BPD features have a negative interpretation bias to both negative and positive experiences and the effect is generally specific to partner-initiated experiences. Negative appraisals may be one mechanism underlying interpersonal dysfunction in those with BPD features and interventions that directly assess and target these cognitive biases may help improve individual well-being and overall couple functioning. PsycINFO Database Record (c) 2013 APA, all rights reserved.

  17. Filter-based feature selection and support vector machine for false positive reduction in computer-aided mass detection in mammograms

    Science.gov (United States)

    Nguyen, V. D.; Nguyen, D. T.; Nguyen, T. D.; Phan, V. A.; Truong, Q. D.

    2015-02-01

    In this paper, a method for reducing false positive in computer-aided mass detection in screening mammograms is proposed. A set of 32 features, including First Order Statistics (FOS) features, Gray-Level Occurrence Matrix (GLCM) features, Block Difference Inverse Probability (BDIP) features, and Block Variation of Local Correlation coefficients (BVLC) are extracted from detected Regions-Of-Interest (ROIs). An optimal subset of 8 features is selected from the full feature set by mean of a filter-based Sequential Backward Selection (SBS). Then, Support Vector Machine (SVM) is utilized to classify the ROIs into massive regions or normal regions. The method's performance is evaluated using the area under the Receiver Operating Characteristic (ROC) curve (AUC or AZ). On a dataset consisting about 2700 ROIs detected from mini-MIAS database of mammograms, the proposed method achieves AZ=0.938.

  18. The relationship study between image features and detection probability based on psychology experiments

    Science.gov (United States)

    Lin, Wei; Chen, Yu-hua; Wang, Ji-yuan; Gao, Hong-sheng; Wang, Ji-jun; Su, Rong-hua; Mao, Wei

    2011-04-01

    Detection probability is an important index to represent and estimate target viability, which provides basis for target recognition and decision-making. But it will expend a mass of time and manpower to obtain detection probability in reality. At the same time, due to the different interpretation of personnel practice knowledge and experience, a great difference will often exist in the datum obtained. By means of studying the relationship between image features and perception quantity based on psychology experiments, the probability model has been established, in which the process is as following.Firstly, four image features have been extracted and quantified, which affect directly detection. Four feature similarity degrees between target and background were defined. Secondly, the relationship between single image feature similarity degree and perception quantity was set up based on psychological principle, and psychological experiments of target interpretation were designed which includes about five hundred people for interpretation and two hundred images. In order to reduce image features correlativity, a lot of artificial synthesis images have been made which include images with single brightness feature difference, images with single chromaticity feature difference, images with single texture feature difference and images with single shape feature difference. By analyzing and fitting a mass of experiments datum, the model quantitys have been determined. Finally, by applying statistical decision theory and experimental results, the relationship between perception quantity with target detection probability has been found. With the verification of a great deal of target interpretation in practice, the target detection probability can be obtained by the model quickly and objectively.

  19. The Polish Cyborg. A Reflection on the Relationship between Man and Machine in Early Polish Modernism

    Directory of Open Access Journals (Sweden)

    Emiliano Ranocchi

    2016-12-01

    Full Text Available Far from being enthusiastic “modernolatry” of Italian futurism, Polish futurism demonstrates an attitude of ambivalence toward modernity. This is particularly evident in the Polish approach to that very synecdoche of modernity which is the machine. In his essay of 1923, the leader of the group, Bruno Jasieński, compares the fetishistic cult of the machine, which characterizes the Italian approach, with the utilitarian one of the Russians, exemplified by a quote from Majakovskij. To these two propositions, as a sort of Hegelian synthesis, he adds a Polish one consisting in the conception of the machine as a prosthesis, a continuation of the human body. Thereby he introduces an idea later known as “cyborg”. The category of cyborg is also useful to understand the work of another today almost forgotten Polish writer of the Twenties, Jerzy Sosnkowski. He was the author of a short novel, A Car, You and Me (Love of Machines, in which a whole chapter concerns the chief character’s dystopian nightmare wherein machines take control over the world. The third section of the essay deals with the idea of man a machine – an old, 18th century conception, which became actual anew in the 20th century and whose traces we can find among others in a well-known poem by Tytus Czyżewski. Thirty years before N. Wiener, Polish modernists seem to have sensed the social, political and anthropological implications of the mechanization of work.

  20. 基于加工特征的铣削力预测研究%Numerical prediction of milling forces based on machining feature

    Institute of Scientific and Technical Information of China (English)

    赵凯; 刘战强

    2014-01-01

    航空发动机广泛采用钛合金薄壁结构,薄壁件在铣削加工过程中受铣削力的影响易于产生加工变形,影响加工质量。为减少加工变形,提高加工质量,需对铣削加工过程中的铣削力进行预测。为此,以Johnson-Cook本构方程为基础,考虑材料热力学动态性能和断裂准则对铣削力的影响,建立了基于加工特征的钛合金Ti-6Al-4V铣削力预测模型。首先,利用UG/Open工具模块对UG软件进行二次开发,创建了零件加工特征知识库。然后,利用Deform-3D仿真软件对材料本构模型、切屑分离和切屑断裂准则等进行描述,建立钛合金Ti-6Al-4V铣削加工有限元模型,对铣削力进行预测。铣削力实验证明了预测模型的可行性。最后,利用建立的有限元模型研究了工件曲率半径对铣削力的影响。结果表明,圆弧内轮廓铣削过程中的铣削力较大,圆弧外轮廓铣削过程中的铣削力较小。%Thin-walled structures with titanium alloy are widely used in aircraft engine .However , deflection induced by cutting force will reduce the finished part accuracy due to the lower rigidy of thin-walled structure .The amount of cutting force must be accurately predicted to improve the machining precision of thin-walled parts.A numerical prediction model for milling force of Ti-6Al-4V titanium alloy is developed based on the Johnson-Cook constitutive equation ,UG geometric modeling and Deform-3D fi-nite element simulation .The effects of the material properties and fracture criterion on the milling force are considered during sim -ulation modeling.Firstly,the knowledge-base of features for aero-engine components is created by UG software .Then,the finite el-ement model of milling process is established by modeling the material constitutive relationship ,chip fracture and material separa-tion criteria.The machining experiments are conducted to validate the feasibility of the proposed

  1. Machine Process Capability Information Through Six Sigma

    Energy Technology Data Exchange (ETDEWEB)

    Lackner, M.F.

    1998-03-13

    A project investigating details concerning machine process capability information and its accessibility has been conducted. The thesis of the project proposed designing a part (denoted as a machine capability workpiece) based on the major machining features of a given machine. Parts are machined and measured to gather representative production, short-term variation. The information is utilized to predict the expected defect rate, expressed in terms of a composite sigma level process capability index, for a production part. Presently, decisions concerning process planning, particularly what machine will statistically produce the minimum amount of defects based on machined features and associated tolerances, are rarely made. Six sigma tools and methodology were employed to conduct this investigation at AlliedSignal FM and T. Tools such as the thought process map, factor relationship diagrams, and components of variance were used. This study is progressing toward completion. This research study was an example of how machine process capability information may be gathered for milling planar faces (horizontal) and slot features. The planning method used to determine where and how to gather variation for the part to be designed is known as factor relationship diagramming. Components-of-variation is then applied to the gathered data to arrive at the contributing level of variation illustrated within the factor relationship diagram. The idea of using this capability information beyond process planning to the other business enterprise operations is proposed.

  2. A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea

    Science.gov (United States)

    Vasu, Nikhil N.; Lee, Seung-Rae

    2016-06-01

    An ever-increasing trend of extreme rainfall events in South Korea owing to climate change is causing shallow landslides and debris flows in mountains that cover 70% of the total land area of the nation. These catastrophic, gravity-driven processes cost the government several billion KRW (South Korean Won) in losses in addition to fatalities every year. The most common type of landslide observed is the shallow landslide, which occurs at 1-3 m depth, and may mobilize into more catastrophic flow-type landslides. Hence, to predict potential landslide areas, susceptibility maps are developed in a geographical information system (GIS) environment utilizing available morphological, hydrological, geotechnical, and geological data. Landslide susceptibility models were developed using 163 landslide points and an equal number of nonlandslide points in Mt. Woomyeon, Seoul, and 23 landslide conditioning factors. However, because not all of the factors contribute to the determination of the spatial probability for landslide initiation, and a simple filter or wrapper-based approach is not efficient in identifying all of the relevant features, a feedback-loop-based hybrid algorithm was implemented in conjunction with a learning scheme called an extreme learning machine, which is based on a single-layer, feed-forward network. Validation of the constructed susceptibility model was conducted using a testing set of landslide inventory data through a prediction rate curve. The model selected 13 relevant conditioning factors out of the initial 23; and the resulting susceptibility map shows a success rate of 85% and a prediction rate of 89.45%, indicating a good performance, in contrast to the low success and prediction rate of 69.19% and 56.19%, respectively, as obtained using a wrapper technique.

  3. Students' Demand for Smartphones: Structural Relationships of Product Features, Brand Name, Product Price and Social Infuence

    Science.gov (United States)

    Suki, Norazah Mohd

    2013-01-01

    Purpose: The study aims to examine structural relationships of product features, brand name, product price and social influence with demand for Smartphones among Malaysian students'. Design/methodology/approach: Data collected from 320 valid pre-screened university students studying at the pubic higher learning institution in Federal Territory of…

  4. Students' Demand for Smartphones: Structural Relationships of Product Features, Brand Name, Product Price and Social Infuence

    Science.gov (United States)

    Suki, Norazah Mohd

    2013-01-01

    Purpose: The study aims to examine structural relationships of product features, brand name, product price and social influence with demand for Smartphones among Malaysian students'. Design/methodology/approach: Data collected from 320 valid pre-screened university students studying at the pubic higher learning institution in Federal Territory of…

  5. Investigating Relationships between Features of Learning Designs and Student Learning Outcomes

    Science.gov (United States)

    McNaught, Carmel; Lam, Paul; Cheng, Kin Fai

    2012-01-01

    This article reports a study of eLearning in 21 courses in Hong Kong universities that had a blended design of face-to-face classes combined with online learning. The main focus of the study was to examine possible relationships between features of online learning designs and student learning outcomes. Data-collection strategies included expert…

  6. A Retrieval Algorithm of Sheet Metal Parts Based on Relationships of Features

    Institute of Scientific and Technical Information of China (English)

    WANG Dawei; YAN Guangrong; LEI Yi; ZHANG Jiaying

    2012-01-01

    With the rapid increase in the number of three-dimensional (3D) models each year,to quickly and easily find the part desired has become a big challenge of enterprises.Meanwhile,many methods and algorithms have been proposed for part retrieval.However,most of the existing methods are designed for mechanical parts,and can not be well worked for sheet metal part retrieval.An approach to feature-based retrieval of sheet metal parts is presented.Firstly,the features frequently used in sheet metal part design are chosen as the "key words" in retrieval.Based on those features,a relative position model is built to express the different relationships of the features in 3D space.Secondly,a description method of the model is studied.With the description method the relative position of features in sheet metal parts can be expressed by four location description matrices.Thirdly,based on the relative position model and location description matrices,the equivalent definition of relationships of two feature groups is given which is the basis to calculate the similarity of two sheet metal parts.Next,the formula of retrieval algorithm for sheet metal parts is given.Finally,a prototype system is developed to test and verify the effectiveness of the retrieval method suggested.Experiments verify that the new method is able to meet the requirements of searching sheet metal parts and possesses potentials in practical application.

  7. Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI.

    Science.gov (United States)

    Qureshi, Muhammad Naveed Iqbal; Oh, Jooyoung; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

    2017-01-01

    Structural and functional MRI unveil many hidden properties of the human brain. We performed this multi-class classification study on selected subjects from the publically available attention deficit hyperactivity disorder ADHD-200 dataset of patients and healthy children. The dataset has three groups, namely, ADHD inattentive, ADHD combined, and typically developing. We calculated the global averaged functional connectivity maps across the whole cortex to extract anatomical atlas parcellation based features from the resting-state fMRI (rs-fMRI) data and cortical parcellation based features from the structural MRI (sMRI) data. In addition, the preprocessed image volumes from both of these modalities followed an ANOVA analysis separately using all the voxels. This study utilized the average measure from the most significant regions acquired from ANOVA as features for classification in addition to the multi-modal and multi-measure features of structural and functional MRI data. We extracted most discriminative features by hierarchical sparse feature elimination and selection algorithm. These features include cortical thickness, image intensity, volume, cortical thickness standard deviation, surface area, and ANOVA based features respectively. An extreme learning machine performed both the binary and multi-class classifications in comparison with support vector machines. This article reports prediction accuracy of both unimodal and multi-modal features from test data. We achieved 76.190% (p multi-class settings as well as 92.857% (p multi-modal group analysis approach with multi-measure features may improve the accuracy of the ADHD differential diagnosis.

  8. From consciousness to computation: a spectrum of theories of consciousness and selected salient features germane to the development of thinking machines

    OpenAIRE

    2013-01-01

    This study investigated the field of consciousness to isolate concepts that might be useful in producing thinking machines, potentially with full consciousness. Questions that informed the research were: Is it possible to identify “successful” theories of consciousness? Can there be a set of salient features that would be useful in the evaluation of theories of consciousness? A literature survey identifies ways in which enduring problems in discussing intelligence, cognition and conscious...

  9. Principal Components of Superhigh-Dimensional Statistical Features and Support Vector Machine for Improving Identification Accuracies of Different Gear Crack Levels under Different Working Conditions

    Directory of Open Access Journals (Sweden)

    Dong Wang

    2015-01-01

    Full Text Available Gears are widely used in gearbox to transmit power from one shaft to another. Gear crack is one of the most frequent gear fault modes found in industry. Identification of different gear crack levels is beneficial in preventing any unexpected machine breakdown and reducing economic loss because gear crack leads to gear tooth breakage. In this paper, an intelligent fault diagnosis method for identification of different gear crack levels under different working conditions is proposed. First, superhigh-dimensional statistical features are extracted from continuous wavelet transform at different scales. The number of the statistical features extracted by using the proposed method is 920 so that the extracted statistical features are superhigh dimensional. To reduce the dimensionality of the extracted statistical features and generate new significant low-dimensional statistical features, a simple and effective method called principal component analysis is used. To further improve identification accuracies of different gear crack levels under different working conditions, support vector machine is employed. Three experiments are investigated to show the superiority of the proposed method. Comparisons with other existing gear crack level identification methods are conducted. The results show that the proposed method has the highest identification accuracies among all existing methods.

  10. Relationship between Chinese Learning Motivation types and demographic features among Danish Students

    DEFF Research Database (Denmark)

    Zhang, Chun

    motivation types are generalized from the exploratory factor analysis : (1)pragmatic orientation; (2) integrative motivation; (3) individual self-confidence; (4) attitudinal motivation; (5) social-cultural interest; (6)learning situational level. Bearing the motivational model of Csizer and Dornyei (2005......The purpose of this study is to investigate the relationship between Chinese learning motivation types and the various demographic features among students at lower and upper secondary schools in Denmark. The basis of the analysis is survey data collected in Denmark from 204 students from 6 upper......) in mind, the motivational types in Chinese learning demonstrate the distinct features of the context. Theoretical and pedagogical implications for the findings are discussed....

  11. An AAG Based Method of Machining Feature Recognition%基于属性邻接图的制造特征识别方法

    Institute of Scientific and Technical Information of China (English)

    刘文剑; 顾琳; 常伟; 杨乐民

    2001-01-01

    制造特征的识别是实现CAD/CAPP/CAM集成的关键技术,既要将特征交互的区域合理分解,又要避免在识别过程中把单个特征不恰当地分解为两个或多个特征。本文使用辅助面、延伸面相结合的方法,对零件的属性邻接图表示进行扩展,同时引入了联合加工特征的概念及识别方法,使扩展后得到的图被分解后能够真实地表示零件的制造特征,从而使加工方法的推理更确切、更快捷。%The recognition of machining features is the key technology in the integration of CAD/CAPP/CAM.How to decompose the interacted zone into different features is the core issue of the recognition of machining features.This paper extends the attributed adjacency graph(AAG) of the part with the connection of assistant face and extended face.We can get the exact type of the feature by matching the sub-graph divided from the NAAG.We also introduced united machining feature (UMF) to make the reasoning more reasonable.

  12. Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification.

    Science.gov (United States)

    Liu, Feng; Wee, Chong-Yaw; Chen, Huafu; Shen, Dinggang

    2014-01-01

    Previous studies have demonstrated that the use of integrated information from multi-modalities could significantly improve diagnosis of Alzheimer's Disease (AD). However, feature selection, which is one of the most important steps in classification, is typically performed separately for each modality, which ignores the potentially strong inter-modality relationship within each subject. Recent emergence of multi-task learning approach makes the joint feature selection from different modalities possible. However, joint feature selection may unfortunately overlook different yet complementary information conveyed by different modalities. We propose a novel multi-task feature selection method to preserve the complementary inter-modality information. Specifically, we treat feature selection from each modality as a separate task and further impose a constraint for preserving the inter-modality relationship, besides separately enforcing the sparseness of the selected features from each modality. After feature selection, a multi-kernel support vector machine (SVM) is further used to integrate the selected features from each modality for classification. Our method is evaluated using the baseline PET and MRI images of subjects obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our method achieves a good performance, with an accuracy of 94.37% and an area under the ROC curve (AUC) of 0.9724 for AD identification, and also an accuracy of 78.80% and an AUC of 0.8284 for mild cognitive impairment (MCI) identification. Moreover, the proposed method achieves an accuracy of 67.83% and an AUC of 0.6957 for separating between MCI converters and MCI non-converters (to AD). These performances demonstrate the superiority of the proposed method over the state-of-the-art classification methods.

  13. Exploring the relationships among service quality features, perceived value and customer satisfaction

    OpenAIRE

    Azman Ismail; Muhammad Madi Bin Abdullah; Sebastian K. Francis

    2009-01-01

    The purpose of this paper is to explore the relationships among service quality features (responsiveness, assurance, and empathy), perceived value and customer satisfaction in the context of Malaysia. The empirical data are drawn from 102 members of an academic staff of a Malaysian public institution of higher learning using a survey questionnaire. The results indicate three important findings: firstly, the interaction between perceived value and responsiveness was not signific...

  14. Relationship between clinicopathological features and mucin phenotypes of advanced gastric adenocarcinoma

    Institute of Scientific and Technical Information of China (English)

    Fumiaki; Toki; Atsushi; Takahashi; Ryusuke; Aihara; Kyoichi; Ogata; Hiroyuki; Ando; Tetsuro; Ohno; Erito; Mochiki; Hiroyuki; Kuwano

    2010-01-01

    AIM: To investigate a relationship between the clinicopathological features and mucin phenotypes in advanced gastric adenocarcinoma (AGA). METHODS: Immunohistochemical staining was performed to determine the mucin phenotypes in 38 patients with differentiated adenocarcinomas (DACs), 9 with signet-ring cell carcinomas (SIGs), and 48 with other diffuse-type adenocarcinomas (non-SIGs) of AGA. The mucin phenotypes were classified into 4 types: gastric (G), gastrointestinal (GI), intestinal, and unclassified. RE...

  15. Internet-Based Weight Control: The Relationship Between Web Features and Weight Loss

    OpenAIRE

    Krukowski, Rebecca A.; Harvey-Berino, Jean; Ashikaga, Takamaru; Thomas, Colleen S.; Micco, Nicci

    2008-01-01

    Internet-based weight control programs have been showing promising results; however, as of yet, it is unclear which website components are critical for producing and maintaining weight loss. The aim of this study is to examine the utilization patterns of a weight control website and the relationship of the Web features to weight loss and maintenance. One hundred and twenty three (N = 123) participants took part in a 12-month behavioral weight control program over the Internet and their websit...

  16. Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Joonwhoan Lee

    2013-06-01

    Full Text Available Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+ facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.

  17. Relationship between udder morphology traits, alveolar and cisternal milk compartments and machine milking performances of dairy camels (Camelus dromedarius

    Directory of Open Access Journals (Sweden)

    M. Ayadi

    2013-07-01

    Full Text Available A total of 22 dairy dromedary camels under intensive conditions in late lactation (275±24 days were used to study the relationship between external and internal udder morphology and machine milking performances. Measurements of udder and teat morphology were obtained immediately before milking and in duplicate. Individual milk yield, lag time and total milking time were recorded during milking, and milk samples were collected and analyzed for milk composition thereafter. Cisternal and alveolar milk volumes and composition were evaluated at 9 h milking interval. Results revealed that dairy camels had well developed udders and milk veins, with medium sized teats. On average, milk yield as well as milk fat and protein contents were 4.80±0.50 L d-1, 2.61±0.16% and 3.08±0.05%, respectively. The low fat values observed indicated incomplete milk letdown during machine milking. Lag time, and total milking time were 3.0±0.3, and 120.0±8.9s, on average, respectively. Positive correlations (p<0.05 were observed between milk yield and udder depth (r=0.37, distance between teats (r=0.57 and milk vein diameter (r=0.28, while a negative correlation was found with udder height (r=-0.25, p<0.05. Cisternal milk accounted for 11% of the total udder milk. Positive correlations were observed between total milk yield and volume of alveolar milk (r=0.98; p<0.001 as well as with volume of cisternal milk (r=0.63, p<0.05. Despite the low udder milk storage capacity observed in dairy camels, our study concluded that the evaluated dromedary sample had adequate udder morphology for machine milking. Finally, positive relationships were detected between milk yield and udder morphology traits of dairy camels.

  18. An Evolutionary Machine Learning Framework for Big Data Sequence Mining

    Science.gov (United States)

    Kamath, Uday Krishna

    2014-01-01

    Sequence classification is an important problem in many real-world applications. Unlike other machine learning data, there are no "explicit" features or signals in sequence data that can help traditional machine learning algorithms learn and predict from the data. Sequence data exhibits inter-relationships in the elements that are…

  19. An Evolutionary Machine Learning Framework for Big Data Sequence Mining

    Science.gov (United States)

    Kamath, Uday Krishna

    2014-01-01

    Sequence classification is an important problem in many real-world applications. Unlike other machine learning data, there are no "explicit" features or signals in sequence data that can help traditional machine learning algorithms learn and predict from the data. Sequence data exhibits inter-relationships in the elements that are…

  20. SU-F-R-08: Can Normalization of Brain MRI Texture Features Reduce Scanner-Dependent Effects in Unsupervised Machine Learning?

    Energy Technology Data Exchange (ETDEWEB)

    Ogden, K; O’Dwyer, R [SUNY Upstate Medical University, Syracuse, NY (United States); Bradford, T [Syracuse University, Syracuse, NY (United States); Cussen, L [Rochester Institute of Technology, Rochester, NY (United States)

    2016-06-15

    Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysis was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.

  1. 基于图的混合加工特征识别方法%Hybrid Recognition of Machining Features Based on Graph

    Institute of Scientific and Technical Information of China (English)

    李大磊; 陈广飞; 尹跃峰

    2013-01-01

    Since traditional feature recognition method based on graph is difficult to recognize intersecting features and features with variable topology,this paper presents a method of hybrid feature recognition based on graph.Firstly,merged faces are split by creating split lines in order to separate intersecting feature.Secondly,the method establishes the extended attribute adjacency graph again,which is decomposed into several minimal condition sub-graphs (MCSG).Finally,according to boundary pattern of quadric features and features consist of planes,constructs separately feature recognition knowledge bases,and recognizes features by using the knowledge tree and reasoning.The results show the method can separate reasonably intersecting feature and recognize effectively machining features.%针对传统的基于图的特征识别方法难以识别相交特征和拓扑不固定特征的问题,提出了一种基于图的混合加工特征识别方法.该方法首先利用插入分割线分割贴合的面的方法拆分相交特征,然后重构扩展属性邻接图,从中分解出最小条件子图,最后根据二次曲面特征、由平面组成的特征的边界模式,分别建立相应的特征识别知识库,并应用知识树通过推理识别特征.验证结果表明该方法能合理地拆分相交特征,有效地识别常见的加工特征.

  2. Classification of basal cell carcinoma in human skin using machine learning and quantitative features captured by polarization sensitive optical coherence tomography.

    Science.gov (United States)

    Marvdashti, Tahereh; Duan, Lian; Aasi, Sumaira Z; Tang, Jean Y; Ellerbee Bowden, Audrey K

    2016-09-01

    We report the first fully automated detection of basal cell carcinoma (BCC), the most commonly occurring type of skin cancer, in human skin using polarization-sensitive optical coherence tomography (PS-OCT). Our proposed automated procedure entails building a machine-learning based classifier by extracting image features from the two complementary image contrasts offered by PS-OCT, intensity and phase retardation (PR), and selecting a subset of features that yields a classifier with the highest accuracy. Our classifier achieved 95.4% sensitivity and specificity, validated by leave-one-patient-out cross validation (LOPOCV), in detecting BCC in human skin samples collected from 42 patients. Moreover, we show the superiority of our classifier over the best possible classifier based on features extracted from intensity-only data, which demonstrates the significance of PR data in detecting BCC.

  3. Bidirectional Relationships and Disconnects between NAFLD and Features of the Metabolic Syndrome.

    Science.gov (United States)

    Wainwright, Patrick; Byrne, Christopher D

    2016-03-11

    Non-alcoholic fatty liver disease (NAFLD) represents a wide spectrum of liver disease from simple steatosis, to steatohepatitis, (both with and without liver fibrosis), cirrhosis and end-stage liver failure. NAFLD also increases the risk of hepatocellular carcinoma (HCC) and both HCC and end stage liver disease may markedly increase risk of liver-related mortality. NAFLD is increasing in prevalence and is presently the second most frequent indication for liver transplantation. As NAFLD is frequently associated with insulin resistance, central obesity, dyslipidaemia, hypertension and hyperglycaemia, NAFLD is often considered the hepatic manifestation of the metabolic syndrome. There is growing evidence that this relationship between NAFLD and metabolic syndrome is bidirectional, in that NAFLD can predispose to metabolic syndrome features, which can in turn exacerbate NAFLD or increase the risk of its development in those without a pre-existing diagnosis. Although the relationship between NAFLD and metabolic syndrome is frequently bidirectional, recently there has been much interest in genotype/phenotype relationships where there is a disconnect between the liver disease and metabolic syndrome features. Such potential examples of genotypes that are associated with a dissociation between liver disease and metabolic syndrome are patatin-like phospholipase domain-containing protein-3 (PNPLA3) (I148M) and transmembrane 6 superfamily member 2 protein (TM6SF2) (E167K) genotypes. This review will explore the bidirectional relationship between metabolic syndrome and NAFLD, and will also discuss recent insights from studies of PNPLA3 and TM6SF2 genotypes that may give insight into how and why metabolic syndrome features and liver disease are linked in NAFLD.

  4. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2014-01-01

    Full Text Available In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL algorithm and support vector machine (SVM. We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

  5. Relationship between hyperhomocysteinemia and carotid plaque features in high-risk stroke population

    Institute of Scientific and Technical Information of China (English)

    Zhen Lu; Yu-fen Wang; Wen-jun Li; Jun Wang

    2016-01-01

    Objective:To analyze the relationship between hyperhomocysteinemia and carotid plaque features in high-risk stroke population.Methods:A total of 116 cases of high-risk stroke treated in our hospital from March 2014 to September 2015 were included in study and divided into stable plaque group 32 cases, unstable plaque group 45 cases and mixed plaque group 39 cases according to plaque features after carotid artery ultrasonography. Differences in serum levels of homocysteine (Hcy), adhesion molecule, hypersensitive C-reactive protein, lipid, cell fibronectin, and so on were compared among groups, and the correlation between serum Hcy and plaque feature-related indicators was further analyzed.Results: Serum Hcy, sVCAM-1, sICAM-1, hs-CRP, TC, TG, LDL-C and c-Fn values of unstable plaque group were significantly higher than those of stable plaque group and mixed plaque group, and HDL-C value was significantly lower than that of stable plaque group and mixed plaque group (P<0.05); serum Hcy levels in high-risk stroke population were positively correlated with sVCAM-1, sICAM-1, hs-CRP, TC, TG, LDL-C and c-Fn values, and negatively correlated with HDL-C value.Conclusions:Hyperhomocysteinemia can promote the instability of carotid plaque features in high-risk stroke population, and is a high-risk factor of stroke.

  6. Combining cluster analysis, feature selection and multiple support vector machine models for the identification of human ether-a-go-go related gene channel blocking compounds.

    Science.gov (United States)

    Nisius, Britta; Göller, Andreas H; Bajorath, Jürgen

    2009-01-01

    Blockade of the human ether-a-go-go related gene potassium channel is regarded as a major cause of drug toxicity and associated with severe cardiac side-effects. A variety of in silico models have been reported to aid in the identification of compounds blocking the human ether-a-go-go related gene channel. Herein, we present a classification approach for the detection of diverse human ether-a-go-go related gene blockers that combines cluster analysis of training data, feature selection and support vector machine learning. Compound learning sets are first divided into clusters of similar molecules. For each cluster, independent support vector machine models are generated utilizing preselected MACCS structural keys as descriptors. These models are combined to predict human ether-a-go-go related gene inhibition of our large compound data set with consistent experimental measurements (i.e. only patch clamp measurements on mammalian cell lines). Our combined support vector machine model achieves a prediction accuracy of 85% on this data set and performs better than alternative methods used for comparison. We also find that structural keys selected on the basis of statistical criteria are associated with molecular substructures implicated in human ether-a-go-go related gene channel binding.

  7. Data driven analysis of rain events: feature extraction, clustering, microphysical /macro physical relationship

    Science.gov (United States)

    Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

    2017-04-01

    The study of rain time series records is mainly carried out using rainfall rate or rain accumulation parameters estimated on a fixed integration time (typically 1 min, 1 hour or 1 day). In this study we used the concept of rain event. In fact, the discrete and intermittent natures of rain processes make the definition of some features inadequate when defined on a fixed duration. Long integration times (hour, day) lead to mix rainy and clear air periods in the same sample. Small integration time (seconds, minutes) will lead to noisy data with a great sensibility to detector characteristics. The analysis on the whole rain event instead of individual short duration samples of a fixed duration allows to clarify relationships between features, in particular between macro physical and microphysical ones. This approach allows suppressing the intra-event variability partly due to measurement uncertainties and allows focusing on physical processes. An algorithm based on Genetic Algorithm (GA) and Self Organising Maps (SOM) is developed to obtain a parsimonious characterisation of rain events using a minimal set of variables. The use of self-organizing map (SOM) is justified by the fact that it allows to map a high dimensional data space in a two-dimensional space while preserving as much as possible the initial space topology in an unsupervised way. The obtained SOM allows providing the dependencies between variables and consequently removing redundant variables leading to a minimal subset of only five features (the event duration, the rain rate peak, the rain event depth, the event rain rate standard deviation and the absolute rain rate variation of order 0.5). To confirm relevance of the five selected features the corresponding SOM is analyzed. This analysis shows clearly the existence of relationships between features. It also shows the independence of the inter-event time (IETp) feature or the weak dependence of the Dry percentage in event (Dd%e) feature. This confirms

  8. The relationship between self-reported borderline personality features and prospective illness course in bipolar disorder.

    Science.gov (United States)

    Riemann, Georg; Weisscher, Nadine; Post, Robert M; Altshuler, Lori; McElroy, Susan; Frye, Marc A; Keck, Paul E; Leverich, Gabriele S; Suppes, Trisha; Grunze, Heinz; Nolen, Willem A; Kupka, Ralph W

    2017-09-25

    Although bipolar disorder (BD) and borderline personality disorder (BPD) share clinical characteristics and frequently co-occur, their interrelationship is controversial. Especially, the differentiation of rapid cycling BD and BPD can be troublesome. This study investigates the relationship between borderline personality features (BPF) and prospective illness course in patients with BD, and explores the effects of current mood state on self-reported BPF profiles. The study included 375 patients who participated in the former Stanley Foundation Bipolar Network. All patients met DSM-IV criteria for bipolar-I disorder (n = 294), bipolar-II disorder (n = 72) or bipolar disorder NOS (n = 9). BPF were assessed with the self-rated Personality Diagnostic Questionnaire. Illness course was based on 1-year clinician rated prospective daily mood ratings with the life chart methodology. Regression analyses were used to estimate the relationships among these variables. Although correlations were weak, results showed that having more BPF at baseline is associated with a higher episode frequency during subsequent 1-year follow-up. Of the nine BPF, affective instability, impulsivity, and self-mutilation/suicidality showed a relationship to full-duration as well as brief episode frequency. In contrast all other BPF were not related to episode frequency. Having more BPF was associated with an unfavorable illness course of BD. Affective instability, impulsivity, and self-mutilation/suicidality are associated with both rapid cycling BD and BPD. Still, many core features of BPD show no relationship to rapid cycling BD and can help in the differential diagnosis.

  9. Twitter location (sometimes matters: Exploring the relationship between georeferenced tweet content and nearby feature classes

    Directory of Open Access Journals (Sweden)

    Stefan Hahmann

    2014-12-01

    Full Text Available In this paper, we investigate whether microblogging texts (tweets produced on mobile devices are related to the geographical locations where they were posted. For this purpose, we correlate tweet topics to areas. In doing so, classified points of interest from OpenStreetMap serve as validation points. We adopted the classification and geolocation of these points to correlate with tweet content by means of manual, supervised, and unsupervised machine learning approaches. Evaluation showed the manual classification approach to be highest quality, followed by the supervised method, and that the unsupervised classification was of low quality. We found that the degree to which tweet content is related to nearby points of interest depends upon topic (that is, upon the OpenStreetMap category. A more general synthesis with prior research leads to the conclusion that the strength of the relationship of tweets and their geographic origin also depends upon geographic scale (where smaller scale correlations are more significant than those of larger scale.

  10. Features and machine learning classification of connected speech samples from patients with autopsy proven Alzheimer's disease with and without additional vascular pathology.

    Science.gov (United States)

    Rentoumi, Vassiliki; Raoufian, Ladan; Ahmed, Samrah; de Jager, Celeste A; Garrard, Peter

    2014-01-01

    Mixed vascular and Alzheimer-type dementia and pure Alzheimer's disease are both associated with changes in spoken language. These changes have, however, seldom been subjected to systematic comparison. In the present study, we analyzed language samples obtained during the course of a longitudinal clinical study from patients in whom one or other pathology was verified at post mortem. The aims of the study were twofold: first, to confirm the presence of differences in language produced by members of the two groups using quantitative methods of evaluation; and secondly to ascertain the most informative sources of variation between the groups. We adopted a computational approach to evaluate digitized transcripts of connected speech along a range of language-related dimensions. We then used machine learning text classification to assign the samples to one of the two pathological groups on the basis of these features. The classifiers' accuracies were tested using simple lexical features, syntactic features, and more complex statistical and information theory characteristics. Maximum accuracy was achieved when word occurrences and frequencies alone were used. Features based on syntactic and lexical complexity yielded lower discrimination scores, but all combinations of features showed significantly better performance than a baseline condition in which every transcript was assigned randomly to one of the two classes. The classification results illustrate the word content specific differences in the spoken language of the two groups. In addition, those with mixed pathology were found to exhibit a marked reduction in lexical variation and complexity compared to their pure AD counterparts.

  11. Application of Multi-task Sparse Group Lasso Feature Extraction and Support Vector Machine Regression in the Stellar Atmospheric Parametrization

    Science.gov (United States)

    Gao, W.; Li, X. R.

    2016-07-01

    The multi-task learning puts the multiple tasks together to analyse and calculate for discovering the correlation between them, which can improve the accuracy of analysis results. This kind of methods have been widely studied in machine learning, pattern recognition, computer vision, and other related fields. This paper investigates the application of multi-task learning in estimating the effective temperature (T_{eff}), surface gravity (lg g), and chemical abundance ([Fe/H]). Firstly, the spectral characteristics of the three atmospheric physical parameters are extracted by using the multi-task Sparse Group Lasso algorithm, and then the support vector machine is used to estimate the atmospheric physical parameters. The proposed scheme is evaluated on both Sloan stellar spectra and theoretical spectra computed from Kurucz's New Opacity Distribution Function (NEWODF) model. The mean absolute errors (MAEs) on the Sloan spectra are: 0.0064 for lg (T_{eff}/K), 0.1622 for lg (g/(cm\\cdot s^{-2})), and 0.1221 dex for [Fe/H]; The MAEs on synthetic spectra are 0.0006 for lg (T_{eff}/K), 0.0098 for lg (g/(cm\\cdot s^{-2})), and 0.0082 dex for [Fe/H]. Experimental results show that the proposed scheme is excellent for atmospheric parameter estimation.

  12. Relationship between expression of EGFR in gastric cancer tissue and clinicopathological features

    Institute of Scientific and Technical Information of China (English)

    Ming Gao; Xiu-Ju Liang; Zi-Sen Zhang; Wang Ma; Zhi-Wei Chang; Ming-Zhi Zhang

    2013-01-01

    Objective: To investigate the relationship between the expression of epidermal growth factor receptor (EGFR) in gastric cancer and the clinicopathological features and prognosis. Methods: A total of 78 paraffin specimens of gastric cancer operation were collected. The immunohistochemical method was used to detect the expression of EGFR in 78 cases of gastric cancer and 20 cases of adjacent normal tissue. The relationship between the high expression of EGFR and clinicopathological features was analyzed. Results: EGFR positive expression rate in the 78 cases of gastric cancer tissue was 57.7 %( 45/78), while EGFR was not expressed in 20 cases of adjacent normal tissue. The high EGFR expression was positively correlated with the position of gastric cancer, tumor size, cell differentiation, invasive depth, lymph node metastasis and TNM staging, yet having no obvious relation with gender or age. Conclusions: EGFR expression level in gastric cancer is closely related to the incidence and development of gastric cancer, which can provide a theoretical basis for the targeted therapy for gastric cancer with EGFR as the target.

  13. A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection

    Science.gov (United States)

    Liu, Tong; Hu, Liang; Ma, Chao; Wang, Zhi-Yan; Chen, Hui-Ling

    2015-04-01

    In this paper, a novel hybrid method, which integrates an effective filter maximum relevance minimum redundancy (MRMR) and a fast classifier extreme learning machine (ELM), has been introduced for diagnosing erythemato-squamous (ES) diseases. In the proposed method, MRMR is employed as a feature selection tool for dimensionality reduction in order to further improve the diagnostic accuracy of the ELM classifier. The impact of the type of activation functions, the number of hidden neurons and the size of the feature subsets on the performance of ELM have been investigated in detail. The effectiveness of the proposed method has been rigorously evaluated against the ES disease dataset, a benchmark dataset, from UCI machine learning database in terms of classification accuracy. Experimental results have demonstrated that our method has achieved the best classification accuracy of 98.89% and an average accuracy of 98.55% via 10-fold cross-validation technique. The proposed method might serve as a new candidate of powerful methods for diagnosing ES diseases.

  14. Relationship of child abuse with personality features and high risk behaviors in adolescents

    Directory of Open Access Journals (Sweden)

    Mehdi Ghezelseflo

    2015-05-01

    Full Text Available Background: Children are one of the most vulnerable groups of the society and are constantly threatened by different people in their family or society. The aim of this study was investigating the correlation of child abuse with personality features and high risk behavior in high school students of Islamshahr, Iran. Methods: This study cross-sectional analytical was conducted on the high school girls and boys of Islamshahr in spring 2014.528 students were selected by cluster random sampling among 4 high schools (two female and two male high schools. Childhood trauma questionnaire, NEO-Five Factor Inventory and Youth Risk-Taking Scale were used for data collection. Data were analyzed by independence t-test, Pearson's correlation coefficient and multiple linear regression. Results: The results of independence t-test indicated significant differences between girls and boys in terms of child abuse and high risk experience (t=-2.16,p=0.03 and t=-5.03, P=0.001, respectively. Also, the results demonstrated a significant relationship between child abuse and personality characteristics, high risk behavior and all its subscales (P<0.05. The findings of multiple linear regressionindicated that child abuse could explain 14% total risk-taking, 25% neurotic personality feature , 14% extroversion, 10% agreeableness, 1% flexibility and 13% conscientiousness (P<0.05. Conclusion: According to the research findings, appropriate behavior with children is of great importance. Therefore, child abuse would form inappropriate personality features and increase risk behaviors among children.

  15. The essential features of personality disorder in DSM-5: the relationship between criteria A and B.

    Science.gov (United States)

    Hentschel, Annett G; Pukrop, Ralf

    2014-05-01

    The essential features of the general criteria for personality disorder in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), are based on impairments in self and interpersonal functioning (criterion A) and pathological personality traits (criterion B). The current study investigated the relationship between criteria A and B in a German psychiatric sample (N = 149). Criterion A was measured by the General Assessment of Personality Disorder (GAPD); criterion B, by the Dimensional Assessment of Personality Pathology (DAPP) and the Revised NEO Personality Inventory (NEO-PI-R). There was a significant relationship between the GAPD, the DAPP, and the NEO-PI-R. The DAPP and NEO-PI-R domains increased the predictive validity of the GAPD (by 7.5% and 14.6%, respectively). The GAPD increased the variance explained by the DAPP by 1.5% and by the NEO-PI-R by 6.5%. The results suggest a substantial relationship between criteria A and B. Criterion B shows incremental validity over criterion A but criterion A only in part over criterion B. Future research should investigate whether it is possible to assess functional impairment apart from personality traits.

  16. Features of Parent-Child Relationship of Mothers with Teenage Children in the Conditions of Late Motherhood

    National Research Council Canada - National Science Library

    Zakharova E.I

    2015-01-01

    ...’ parent position, who had children at different periods of adulthood (middle, late). The aim of the study was to investigate the features of the parent-child relationship of mothers with teenage children in the conditions of late motherhood...

  17. Effect of landscape features on the relationship between Ixodes ricinus ticks and their small mammal hosts.

    Science.gov (United States)

    Perez, Grégoire; Bastian, Suzanne; Agoulon, Albert; Bouju, Agnès; Durand, Axelle; Faille, Frédéric; Lebert, Isabelle; Rantier, Yann; Plantard, Olivier; Butet, Alain

    2016-01-15

    The consequences of land use changes are among the most cited causes of emerging infectious diseases because they can modify the ecology and transmission of pathogens. This is particularly true for vector-borne diseases which depend on abiotic (e.g. climate) and biotic conditions (i.e. hosts and vectors). In this study, we investigated how landscape features affect the abundances of small mammals and Ixodes ricinus ticks, and how they influence their relationship. From 2012 to 2014, small mammals and questing I. ricinus ticks were sampled in spring and autumn in 24 sites located in agricultural and forest landscapes in Brittany, France. We tested the effects of landscape features (composition and configuration) on the abundances of small mammal species and immature ticks and their relationship. Additionally, we quantified the larval tick burden of small mammals in 2012 to better describe this relationship. The nymph abundance was positively influenced by the larval occurrence and the wood mouse Apodemus sylvaticus abundance the previous spring because they hosted tenfold more larvae than the bank vole Myodes glareolus. The bank vole abundance in spring and autumn had a negative and positive effect, respectively, on the nymph abundance. In agricultural landscapes, wood mice were positively influenced by woodland cover and woodland/hedgerow-grassland ecotone, whereas bank voles showed the opposite or non-significant responses to these landscape variables. The woodland cover had a positive effect on immature ticks. The landscape configuration, likely by affecting the landscape connectivity, influences the small mammal communities in permanent habitats. Our study showed that the wood mouse, due to its dominance and to its tolerance to ticks, feeds a substantial proportion of larvae. The acquired resistance to ticks in the bank vole can reduce its role as a trophic resource over time. The nymph abundance seems indirectly influenced by landscape features via their

  18. Building Support Vector Machine with Reduced Feature Complexity%构造特征复杂性减低的支持向量机

    Institute of Scientific and Technical Information of China (English)

    宇缨

    2007-01-01

    支持向量机(SVM)较一般的机器学习方法显示出更好的泛化能力.然而,在实际的数据中经常存在着大量冗余、噪声或者不可靠的特征,这严重影响到SVM的性能.因此,有必要减低特征复杂性以获取更好的SVM结果.本文提出了一种基于遗传算法(GA)的嵌入式框架下的特征优化算法,以构造改进SVM.针对选择的UCI成人数据库的实验表明,与原始的SVM相比,提出的改进SVM方法获得了更少的支持向量数目和更好的分类精度.%Support Vector Machine (SVM) has revealed better generalization than conventional machine learning methods. However, in the real data there often exist a large number of redundant, noisy or unreliable features to deteriorate the function of SVM strongly. So to reduce the feature complexity, it is necessary to improve the performance of SVM for better results. A method to build modified SVM, which is based on embedded methods for feature optimization using Genetic Algorithm (GA),is proposed in this paper. The experimental results on selected UCI Adult data base show that compared with original SVM classifier, the number of support vector decreases and better classification results are achieved based on our modified SVM.

  19. 可制造性驱动的三维CAD模型相交制造特征识别方法%Manufacturability Driven Interacting Machining Feature Recognition Algorithms for 3D CAD Models

    Institute of Scientific and Technical Information of China (English)

    黄瑞; 张树生; 白晓亮

    2013-01-01

    To realize the effective integration of CAD,CAPP,and CAM system,we present a manufacturability driven interacting machining feature recognition method for 3D CAD models.Firstly,the accessibility cone for each machining face is computed based on heuristic rules.Machining region subgraphs are then constructed by machining face clustering method considering manufacturing semantics.With the dimension semantic information,interacting machining features are finally recognized based on the machining region subgraph that is used as a feature hint.The approach proposed is implemented and tested by hundreds of mechanical parts.Preliminary results show that the method can effectively realize machining feature recognition for complex interacting machining features and complex parts,and the efficiency can meet the requirement of engineering application.%为了实现CAD/CAPP/CAM系统的有效集成,提出一种可制造性驱动的三维CAD模型相交制造特征识别方法.首先通过启发式规则对加工面进行可达性分析,计算加工面可行刀具轴向空间;然后采用融合制造语义的加工面聚类算法构建加工区域子图;最后以加工区域子图为制造特征痕迹,结合标注语义信息对加工区域子图进行优化合并,从而实现制造特征的识别.实验结果表明,该方法能够有效地实现复杂相交制造特征和复杂零件的制造特征识别,制造特征识别性能可满足工程应用中的需求.

  20. Machine vision: an incremental learning system based on features derived using fast Gabor transforms for the identification of textural objects

    Science.gov (United States)

    Clark, Richard M.; Adjei, Osei; Johal, Harpal

    2001-11-01

    This paper proposes a fast, effective and also very adaptable incremental learning system for identifying textures based on features extracted from Gabor space. The Gabor transform is a useful technique for feature extraction since it exhibits properties that are similar to biologically visual sensory systems such as those found in the mammalian visual cortex. Although two-dimensional Gabor filters have been applied successfully to a variety of tasks such as text segmentation, object detection and fingerprint analysis, the work of this paper extends previous work by incorporating incremental learning to facilitate easier training. The proposed system transforms textural images into Gabor space and a non-linear threshold function is then applied to extract feature vectors that bear signatures of the textural images. The mean and variance of each training group is computed followed by a technique that uses the Kohonen network to cluster these features. The centers of these clusters form the basis of an incremental learning paradigm that allows new information to be integrated into the existing knowledge. A number of experiments are conducted for real-time identification or discrimination of textural images.

  1. [Immunomorphologic features of epithelial-stromal relationships at hyperplasia and endometrial carcinoma].

    Science.gov (United States)

    Bantysh, B B; Paukov, v S; Kogan, E A

    2012-01-01

    The results of a immunomorphologic comprehensive study of epithelial-stromal relationships in the uterus hyperplasia and endometrial cancer suggest that the suppressor gene of cancer (PTEN) plays a key role in the process of neoplastic transformation of endometrial hyperplasia and adenocarcinoma development. For the first time the existence of two highly differentiated endometrial adenocarcinoma immunophenotype were detected The first one is a PTEN-negative endometrial aedenocarcinoma, characterized by an almost complete inhibition of tumor suppressor gene PTEN in the epithelium of the glands and stromal cell of the tumor The second type is a PTEN-positive endometrial adenocarcinoma, in which epithelial and stromal tumor suppressor gene PTEN activity has retained Based on these results we have formulated a hypothesis about the different types of endometrial hyperplasia morphogenesis and its possible transfer to cervical cancer associated with features of tumor suppressor gene PTEN.

  2. Relationship between H.Pylori infection and clinicopathological features and prognosis of gastric cancer

    Directory of Open Access Journals (Sweden)

    Wang Guo-Qiang

    2010-07-01

    Full Text Available Abstract Background Aimed to assess the relationship between H.Pylori and the clinicopathological features and prognosis of gastric cancer by quantitative detection of H.Pylori. Methods 157 patients were enrolled, all patients had a record of clinicopathological parameters. Specimens including the tumor and non-neoplastic were detected for H.Pylori by Real-Time PCR and analyzed clinical data retrospectively. Variables independently affecting prognosis were investigated by means of multivariate analysis using the Cox proportional hazards model. Results H.Pylori infection was greater in non-neoplastic tissue than the tumor tissue (p Conclusions H.Pylori infection status and its copies were related to N staging. The OS and RFS in patients with positive H.Pylori status has no significant difference from the patients with negative H.Pylori status.

  3. Model-Based Comparison of Deep Brain Stimulation Array Functionality with Varying Number of Radial Electrodes and Machine Learning Feature Sets.

    Science.gov (United States)

    Teplitzky, Benjamin A; Zitella, Laura M; Xiao, YiZi; Johnson, Matthew D

    2016-01-01

    Deep brain stimulation (DBS) leads with radially distributed electrodes have potential to improve clinical outcomes through more selective targeting of pathways and networks within the brain. However, increasing the number of electrodes on clinical DBS leads by replacing conventional cylindrical shell electrodes with radially distributed electrodes raises practical design and stimulation programming challenges. We used computational modeling to investigate: (1) how the number of radial electrodes impact the ability to steer, shift, and sculpt a region of neural activation (RoA), and (2) which RoA features are best used in combination with machine learning classifiers to predict programming settings to target a particular area near the lead. Stimulation configurations were modeled using 27 lead designs with one to nine radially distributed electrodes. The computational modeling framework consisted of a three-dimensional finite element tissue conductance model in combination with a multi-compartment biophysical axon model. For each lead design, two-dimensional threshold-dependent RoAs were calculated from the computational modeling results. The models showed more radial electrodes enabled finer resolution RoA steering; however, stimulation amplitude, and therefore spatial extent of the RoA, was limited by charge injection and charge storage capacity constraints due to the small electrode surface area for leads with more than four radially distributed electrodes. RoA shifting resolution was improved by the addition of radial electrodes when using uniform multi-cathode stimulation, but non-uniform multi-cathode stimulation produced equivalent or better resolution shifting without increasing the number of radial electrodes. Robust machine learning classification of 15 monopolar stimulation configurations was achieved using as few as three geometric features describing a RoA. The results of this study indicate that, for a clinical-scale DBS lead, more than four radial

  4. Microtopographic Features of Metal Surfaces Machined via Micro-Ploughing%微犁削成形金属表面的微观形貌特征

    Institute of Scientific and Technical Information of China (English)

    王清辉; 张小明; 郑旭; 黄祥; 李静蓉

    2012-01-01

    In this paper, the microtopography of metal surfaces machined via the micro-ploughing is analyzed by combining the evaluation methods of roughness and fractal dimension. Then, the topographical features of micro groove surfaces and the effects of machining parameters on these features are investigated. The results show that the distributions of fractal dimension and roughness of contour profiles in different positions on the micro-ploughing groove surfaces are subject to some specific statistical laws, that there is a relatively strong positive correlation between the average fractal dimension and the average roughness, and that, in a certain range of manufacturing parameters , an increasing groove depth and a decreasing machining feed may cause an increase in average fractal dimension and roughness. In addition, based on the experimental investigation, some digital models are constructed with an improved W-M fractal function to describe the micro-ploughing groove surfaces with specific topographical features, which are beneficial to the initiative design and simulation of functional surface structures fabricated via micro-ploughing.%综合粗糙度和分形维数两种评价方法对微犁削成形金属表面的微观形貌进行分析,研究微沟槽表面形貌特征及加工参数对其的影响规律.研究表明:微犁削沟槽表面不同位置的轮廓分形维数和轮廓粗糙度均符合特定统计规律,且轮廓分形维数平均值和轮廓粗糙度平均值存在较强的正相关性;在一定的加工参数范围内,槽深的增大和加工进给量的减小使沟槽表面轮廓分形维数和轮廓粗糙度的均值上升.文中还基于实验数据,利用改进的W-M分形函数建立了描述上述形貌特征的微犁削沟槽表面数字化模型,为微犁削成形表面功能结构的仿真与主动设计奠定了基础.

  5. SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features

    Energy Technology Data Exchange (ETDEWEB)

    Lafata, K; Ren, L; Wu, Q; Kelsey, C; Hong, J; Cai, J; Yin, F [Duke University Medical Center, Durham, NC (United States)

    2016-06-15

    Purpose: To develop a data-mining methodology based on quantum clustering and machine learning to predict expected dosimetric endpoints for lung SBRT applications based on patient-specific anatomic features. Methods: Ninety-three patients who received lung SBRT at our clinic from 2011–2013 were retrospectively identified. Planning information was acquired for each patient, from which various features were extracted using in-house semi-automatic software. Anatomic features included tumor-to-OAR distances, tumor location, total-lung-volume, GTV and ITV. Dosimetric endpoints were adopted from RTOG-0195 recommendations, and consisted of various OAR-specific partial-volume doses and maximum point-doses. First, PCA analysis and unsupervised quantum-clustering was used to explore the feature-space to identify potentially strong classifiers. Secondly, a multi-class logistic regression algorithm was developed and trained to predict dose-volume endpoints based on patient-specific anatomic features. Classes were defined by discretizing the dose-volume data, and the feature-space was zero-mean normalized. Fitting parameters were determined by minimizing a regularized cost function, and optimization was performed via gradient descent. As a pilot study, the model was tested on two esophageal dosimetric planning endpoints (maximum point-dose, dose-to-5cc), and its generalizability was evaluated with leave-one-out cross-validation. Results: Quantum-Clustering demonstrated a strong separation of feature-space at 15Gy across the first-and-second Principle Components of the data when the dosimetric endpoints were retrospectively identified. Maximum point dose prediction to the esophagus demonstrated a cross-validation accuracy of 87%, and the maximum dose to 5cc demonstrated a respective value of 79%. The largest optimized weighting factor was placed on GTV-to-esophagus distance (a factor of 10 greater than the second largest weighting factor), indicating an intuitively strong

  6. Comparison of Different Machine Learning Algorithms for Lithological Mapping Using Remote Sensing Data and Morphological Features: A Case Study in Kurdistan Region, NE Iraq

    Science.gov (United States)

    Othman, Arsalan; Gloaguen, Richard

    2015-04-01

    Topographic effects and complex vegetation cover hinder lithology classification in mountain regions based not only in field, but also in reflectance remote sensing data. The area of interest "Bardi-Zard" is located in the NE of Iraq. It is part of the Zagros orogenic belt, where seven lithological units outcrop and is known for its chromite deposit. The aim of this study is to compare three machine learning algorithms (MLAs): Maximum Likelihood (ML), Support Vector Machines (SVM), and Random Forest (RF) in the context of a supervised lithology classification task using Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite, its derived, spatial information (spatial coordinates) and geomorphic data. We emphasize the enhancement in remote sensing lithological mapping accuracy that arises from the integration of geomorphic features and spatial information (spatial coordinates) in classifications. This study identifies that RF is better than ML and SVM algorithms in almost the sixteen combination datasets, which were tested. The overall accuracy of the best dataset combination with the RF map for the all seven classes reach ~80% and the producer and user's accuracies are ~73.91% and 76.09% respectively while the kappa coefficient is ~0.76. TPI is more effective with SVM algorithm than an RF algorithm. This paper demonstrates that adding geomorphic indices such as TPI and spatial information in the dataset increases the lithological classification accuracy.

  7. The Short-Term Power Load Forecasting Based on Sperm Whale Algorithm and Wavelet Least Square Support Vector Machine with DWT-IR for Feature Selection

    Directory of Open Access Journals (Sweden)

    Jin-peng Liu

    2017-07-01

    Full Text Available Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.

  8. IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.

    Directory of Open Access Journals (Sweden)

    N Lance Hepler

    2014-09-01

    Full Text Available Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab, determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license, documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.

  9. IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.

    Science.gov (United States)

    Hepler, N Lance; Scheffler, Konrad; Weaver, Steven; Murrell, Ben; Richman, Douglas D; Burton, Dennis R; Poignard, Pascal; Smith, Davey M; Kosakovsky Pond, Sergei L

    2014-09-01

    Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.

  10. Investigation of the relationship between dermoscopic features and histopathological prognostic indicators in patients with cutaneous melanoma

    Directory of Open Access Journals (Sweden)

    Özlem Özbağçıvan

    2015-09-01

    Full Text Available Background and Design: Dermoscopy has an important role in the diagnosis of melanoma nowadays. Dermoscopic findings of melanoma had been associated with Breslow thickness and invasion status in previous studies but the relationship between dermatoscopic findings and other histopathological prognostic indicators has not been investigated until today. In this study, our aim is to investigate the relationship between dermatoscopic findings and histopathologic prognostic indicators such as Breslow thickness, invasion status, mitotic rate, lymphovascular invasion (LVI, ulceration and regression in patients who had been diagnosed with melanoma due to their clinical, dermatoscopic and histopatological findings. Materials and Methods: Dermoscopic and histopathological findings of 47 cases of melanoma who applied to our clinic between the years 2000 and 2014 were evaluated. The relationship between the dermoscopic findings which had been reported to be observed in melanomas in previous research and the histopathologic prognostic indicators such as Breslow thickness, invasion status, mitotic rate, lymphovascular invasion, ulceration and regression were investigated. Results: Irregular dots/globules, atypical pigment network, multifocal hypopigmentation, radial streaks and moth-eaten borders have been associated with good prognostic indicators whereas comedo like openings, regular blotch, exophytic papillary structures, dotted, glomerular, lineer irregular vessels, pink/red and blue/gray colors were associated with poor prognostic indicators. Additionally some dermatoscopic findings which are more observed in benign lesions such as multiple milia-like cysts, comedo like openings, moth-eaten borders, regular blotch, exophytic papillary structures and finger print areas have been observed in melanomas in our study. Conclusion: Many dermoscopic findings have demonstrated statistically significant association with the histopathological prognostic indicators

  11. Recent Advances on Permanent Magnet Machines

    Institute of Scientific and Technical Information of China (English)

    诸自强

    2012-01-01

    This paper overviews advances on permanent magnet(PM) brushless machines over last 30 years,with particular reference to new and novel machine topologies.These include current states and trends for surface-mounted and interior PM machines,electrically and mechanically adjusted variable flux PM machines including memory machine,hybrid PM machines which uniquely integrate PM technology into induction machines,switched and synchronous reluctance machines and wound field machines,Halbach PM machines,dual-rotor PM machines,and magnetically geared PM machines,etc.The paper highlights their features and applications to various market sectors.

  12. 2D Quantitative Structure-Property Relationship Study of Mycotoxins by Multiple Linear Regression and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Fereshteh Shiri

    2010-08-01

    Full Text Available In the present work, support vector machines (SVMs and multiple linear regression (MLR techniques were used for quantitative structure–property relationship (QSPR studies of retention time (tR in standardized liquid chromatography–UV–mass spectrometry of 67 mycotoxins (aflatoxins, trichothecenes, roquefortines and ochratoxins based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and genetic algorithm method of variable selection, the most relevant descriptors were selected to build QSPR models. MLRand SVMs methods were employed to build QSPR models. The robustness of the QSPR models was characterized by the statistical validation and applicability domain (AD. The prediction results from the MLR and SVM models are in good agreement with the experimental values. The correlation and predictability measure by r2 and q2 are 0.931 and 0.932, repectively, for SVM and 0.923 and 0.915, respectively, for MLR. The applicability domain of the model was investigated using William’s plot. The effects of different descriptors on the retention times are described.

  13. Relationship between lymphovascular invasion and clinicopathological features of papillary thyroid carcinoma

    Directory of Open Access Journals (Sweden)

    Atakan Sezer

    2017-05-01

    Full Text Available Lymphovascular invasion (LVI is an important prognostic factor in various solid tumors, however, data on the association between LVI and thyroid carcinomas are limited. In this study, we evaluated the relationship between LVI and clinicopathological features of papillary thyroid carcinoma (PTC. Six hundred seventy-eight patients diagnosed with PTC between 2012 and 2015 were included into the study. Patients were classified based on the presence or absence of LVI. Gender, age, ultrasonography (US, tumor size and multifocality, BRAFV600E mutation, perineural and capsular invasion, extrathyroid extension (ETE, nodal metastasis, and recurrences were evaluated, and risk analysis was performed for each parameter. The number of patients with LVI [LVI (+] was 63, while the number of patients without LVI [LVI (-] was 615. The female/male ratio was 564/114. LVI was present in 18.4% of male patients and in 7.4 % of female patients. In the age group between 17-25 years LVI was detected in 6/13 patients, and this result was statistically significant compared to other age groups (p = 0.004. Suspicious lymph nodes upon US, perineural or capsular invasion, ETE, tumor size, and nodal metastasis were significantly more frequent in LVI (+ group (p < 0.001. The frequency of BRAFV600E mutation was also significantly higher in LVI (+ group (p < 0.001. Overall, the presence of LVI was associated with gender, tumor size, age, lymph node metastasis, pathological lymph nodes, perineural and capsular invasion, ETE, and BRAFV600E mutation. These results suggest that in PTC patients undergoing thyroidectomy, the presence of LVI should be considered as an indicator of aggressive clinicopathological features and those patients should be followed up carefully for recurrences and metastasis.

  14. CLIMATIC FEATURES OF SEA TEMPERATURE OF WARM POOL AND RELATIONSHIP WITH SST OF ITS ADJACENT REGIONS

    Institute of Scientific and Technical Information of China (English)

    任小波; 周秀骥; 陈隆勋

    2001-01-01

    In this paper, climatic features of sea temperature of western Pacific warm pool and the relationship with sea surface temperature (SST) of its adjacent regions are analyzed based on the observed sea temperature on vertical cross section along 137°E in western Pacific, the monthly mean SST of Xisha Station in South China Sea and the global monthly mean SST with resolution of 1°× 1° (U. K./GISST2.2). The results indicate that (1) in a sense of correlation, SST of western Pacific warm pool can represent its sea subsurface temperature from surface to 200 m-depth level in winter, and it can only represent sea temperature from surface to 70 m depth in summer. The sea subsurface temperature anomaly of warm pool may be more suitable for representing thermal regime of western Pacific warm pool. The sea subsurface temperature of warm pool has a characteristic of quasi-biennial oscillation. (2) Warm pool and Kuroshio current are subject to different ocean current systems. (3) Furthermore, the relationship between SST of Xisha Station and SST of warm pool has a characteristic of negative correlation in winter and positive correlation in summer, and a better lag negative correlation of SST of Xisha Station with sea subsurface temperature of warm pool exists. (4) Additionally, oscillation structure of sea temperature like "a seesaw" exists in between warm pool and Regions Nino3 and Nino4. January (June) maximum(minimum) sea subsurface temperature anomaly of warm pool may serve as a strong signal that indicates maturity phase (development phase) of La Nina (El Nino) event, it also acts as a strong signal which reveals variations of SST of Regions Nino3 and Nino4.

  15. An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach

    Directory of Open Access Journals (Sweden)

    Chao Ma

    2014-01-01

    Full Text Available A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM, has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC curve (AUC, f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.

  16. RELATIONSHIP AMONG BRAIN HEMISPHERIC DOMINANCE, ATTITUDE TOWARDS L1 AND L2, GENDER, AND LEARNING SUPRASEGMENTAL FEATURES

    Directory of Open Access Journals (Sweden)

    Mohammad Hadi Mahmoodi

    2016-07-01

    Full Text Available Oral skills are important components of language competence. To have good and acceptable listening and speaking, one must have good pronunciation, which encompasses segmental and suprasegmental features. Despite extensive studies on the role of segmental features and related issues in listening and speaking, there is paucity of research on the role of suprasegmental features in the same domain. Conducting studies which aim at shedding light on the issues related to learning suprasegmental features can help language teachers and learners in the process of teaching/learning English as a foreign language. To this end, this study was designed to investigate the relationship among brain hemispheric dominance, gender, attitudes towards L1 and L2, and learning suprasegmental features in Iranian EFL learners. First, 200 Intermediate EFL learners were selected from different English language teaching institutes in Hamedan and Isfahan, two provinces in Iran, as the sample. Prior to the main stage of the study, Oxford Placement Test (OPT was used to homogenize the proficiency level of all the participants. Then, the participants were asked to complete the Edinburgh Handedness Questionnaire to determine their dominant hemisphere. They were also required to answer two questionnaires regarding their attitudes towards L1 and L2. Finally, the participants took suprasegmental features test. The results of the independent samples t-tests indicated left-brained language learners’ superiority in observing and learning suprasegmental features. It was also found that females are better than males in producing suprasegmental features. Furthermore, the results of Pearson Product Moment Correlations indicated that there is significant relationship between attitude towards L2 and learning suprasegmental features. However, no significant relationship was found between attitude towards L1 and learning English suprasegmental features. The findings of this study can

  17. Exploring the relationships among service quality features, perceived value and customer satisfaction

    Directory of Open Access Journals (Sweden)

    Azman Ismail

    2009-07-01

    Full Text Available The purpose of this paper is to explore the relationships among service quality features (responsiveness, assurance, and empathy, perceived value and customer satisfaction in the context of Malaysia. The empirical data are drawn from 102 members of an academic staff of a Malaysian public institution of higher learning using a survey questionnaire. The results indicate three important findings: firstly, the interaction between perceived value and responsiveness was not significantly correlated with customer satisfaction. Secondly, the interaction between perceived value and assurance also did not correlate significantly with customer satisfaction. Thirdly, the interaction between perceived value and empathy correlated significantly with customer satisfaction. Thus the results demonstrate that perceived value had increased the effect of empathy on customer satisfaction, but it had not increased the effect of responsiveness and assurance on customer satisfaction. In sum, this study confirms that perceived value act as a partial moderating variable in the service quality models of the organizational sample. In addition, implications and limitations of this study, as well as directions for future research are discussed.

  18. Time feature of Chinese military personnel’s suicide ideation and its relationship with psychosomatic health

    Directory of Open Access Journals (Sweden)

    Li-yi ZHANG

    2012-07-01

    Full Text Available Objective To investigate the time feature of Chinese military personnel's suicide ideation and its relationship with psychosomatic health to provide scientific basis for formulation of mental health policy and intervention of related psychological crisis. Methods By random cluster sampling, a total of 11 362 military personnel including army, navy and air-force (1100 in 1980s, 8000 in 1990s, 2262 in year 2000 were tested by Chinese Psychosomatic Health Scale (CPSHS. SPSS statistic 17.0 program was used for data analysis, i.e., χ2-test, T-test and stepwise regression analysis. Results The incidence rate of military personnel's suicide ideation in the three decades from 1980 to 2000 was 10.27%, 7.09% and 2.83% respectively, which revealed a decreasing trend (P 0.05. Suicide ideation was selected into the regression equation of mental health, physical health, and total psychosomatic health scores, which could positively predict the level of military personnel's psychosomatic health (P=0.05 or 0.01. Conclusions Military personnel's suicide ideation presents a decreasing trend; the psychosomatic health of military personnel who have suicide ideation is worse than that of personnel without suicide ideation.

  19. The relationship between electronic gaming machine accessibility and police-recorded domestic violence: A spatio-temporal analysis of 654 postcodes in Victoria, Australia, 2005-2014.

    Science.gov (United States)

    Markham, Francis; Doran, Bruce; Young, Martin

    2016-08-01

    An emerging body of research has documented an association between problem gambling and domestic violence in a range of study populations and locations. Yet little research has analysed this relationship at ecological scales. This study investigates the proposition that gambling accessibility and the incidence of domestic violence might be linked. The association between police-recorded domestic violence and electronic gaming machine accessibility is described at the postcode level. Police recorded family incidents per 10,000 and domestic-violence related physical assault offenses per 10,000 were used as outcome variables. Electronic gaming machine accessibility was measured as electronic gaming machines per 10,000 and gambling venues per 100,000. Bayesian spatio-temporal mixed-effects models were used to estimate the associations between gambling accessibility and domestic violence, using annual postcode-level data in Victoria, Australia between 2005 and 2014, adjusting for a range of covariates. Significant associations of policy-relevant magnitudes were found between all domestic violence and EGM accessibility variables. Postcodes with no electronic gaming machines were associated with 20% (95% credibility interval [C.I.]: 15%, 24%) fewer family incidents per 10,000 and 30% (95% C.I.: 24%, 35%) fewer domestic-violence assaults per 10,000, when compared with postcodes with 75 electronic gaming machine per 10,000. The causal relations underlying these associations are unclear. Quasi-experimental research is required to determine if reducing gambling accessibility is likely to reduce the incidence of domestic violence.

  20. Dysfunctional responses to emotion mediate the cross-sectional relationship between rejection sensitivity and borderline personality features.

    Science.gov (United States)

    Peters, Jessica R; Smart, Laura M; Baer, Ruth A

    2015-04-01

    A growing body of evidence has tied borderline personality disorder (BPD) to heightened sensitivity to rejection; however, mechanisms through which rejection sensitivity contributes to BPD features have not been identified. Rejection may lead to the dysfunctional emotion regulation strategies common in BPD, such as impulsive responses to distress, anger rumination, difficulties engaging in goal-oriented behavior, nonacceptance of emotions, and low emotional clarity. The present study used self-report measures and bootstrapping procedures to investigate the role of difficulties in emotional regulation in the relationship between rejection sensitivity and borderline personality features in a cross-sectional sample of 410 undergraduates. Difficulties in emotion regulation accounted for significant variance in the relationships between rejection sensitivity and BPD features, with varying sets of deficits in emotion regulation skills accounting for associations with specific BPD features. Potential clinical implications and the need for replication in longitudinal studies are discussed.

  1. Quantified Relationship between Shaft Drilling Parameters and Drilling Machine Performances%钻井参数与钻机性能间的量化关系

    Institute of Scientific and Technical Information of China (English)

    曹钧; 芦伟

    2015-01-01

    A quantified relationship between shaft drilling parameters and drilling machine performan -ces always is a weak link in a research on the shaft drilling engineering. With a quantified analysis ,a introduction on a conception of a rock breaking specific power and a derivation calculation conducted , a quantified relationship between the shaft drilling parameters and drilling machine performances was provided.In the engineering practices ,the research on the quantified relationship could be applied to optimize the parameter design on the shaft drilling technique and to improve the actual application effi ‐ciency of the drilling machine.Based on the variation conditions of the shaft drilling technical parame ‐ters ,the level of the drilling machine performances played could be predicted ,the drilling machine acci‐dent occurred could be prevented and the construction target could be scientifically and rationally real ‐ized.%钻井参数与钻机性能间的量化关系问题,一直是钻井工程研究中的薄弱环节。通过量化分析,引入破岩比功概念,并对其进行推导计算,给出了钻井参数与钻机性能间的量化关系。在工程实践中,可通过研究该量化关系,优化钻井工艺参数设计,提高钻机实际使用效率;并可根据钻井工艺参数的变化情况,预测钻机性能发挥水平,防止钻机故障发生,实现科学合理的施工目标。

  2. Relationships between nesting populations of wading birds and habitat features along the Atlantic Coast

    Science.gov (United States)

    Erwin, R.M.; Spendelow, J.A.; Geissler, P.H.; Williams, B.K.; Whitman, William R.; Meredith, William H.

    1987-01-01

    Using previously published atlas data for 122 mixed-species wading bird colonies on islands along the Atlantic coast (Maine to Florida, 1976-77), we examined relationships between population sizes of 11 species of egrets, herons, ibises, and wood storks (Mycteria americana) and nine habitat variables. On nautical charts, we measured four island characteristics (area, length, width, shape), three isolation factors (distances to nearest island, mainland, and a water barrier),, and two variables related to potential feeding habitat within 5 km of the center of the colony (wetland area and land-water interface, i.e., the linear distance between the marsh/upland and all water bodies within the same 5-km radius). One univariable and five multivariable .procedures were used to determine which habitat features were best related to population size .(all species combined). Multicollinearity problems among the variables limited interpretation for most procedures. Both univariable and the multivariable procedures indicated that land-water interface was the most important of the nine variables, but for all models, less than 10% of the total variance was explained (rz is less than 0.10). The size of the colony was not related to the amount of wetland area (within 5-km).per se. Colony data showed better 'structure' when examined on the basis of geographic and disturbance gradients. Population sizes of colonies near man-altered habitats were compared with those surrounded by relatively natural habitats in three geographic zones: north, middle, and south. Significant differences were found in colony size among the three zones (south largest) and between disturbance types. Surprisingly, in all three zones, colonies near man-altered areas were larger on average than those near more natural habitats in this region. A possible reason for this difference is suggested.

  3. Relationship between Hyperuricemia and Haar-Like Features on Tongue Images

    Directory of Open Access Journals (Sweden)

    Yan Cui

    2015-01-01

    Full Text Available Objective. To investigate differences in tongue images of subjects with and without hyperuricemia. Materials and Methods. This population-based case-control study was performed in 2012-2013. We collected data from 46 case subjects with hyperuricemia and 46 control subjects, including results of biochemical examinations and tongue images. Symmetrical Haar-like features based on integral images were extracted from tongue images. T-tests were performed to determine the ability of extracted features to distinguish between the case and control groups. We first selected features using the common criterion P<0.05, then conducted further examination of feature characteristics and feature selection using means and standard deviations of distributions in the case and control groups. Results. A total of 115,683 features were selected using the criterion P<0.05. The maximum area under the receiver operating characteristic curve (AUC of these features was 0.877. The sensitivity of the feature with the maximum AUC value was 0.800 and specificity was 0.826 when the Youden index was maximized. Features that performed well were concentrated in the tongue root region. Conclusions. Symmetrical Haar-like features enabled discrimination of subjects with and without hyperuricemia in our sample. The locations of these discriminative features were in agreement with the interpretation of tongue appearance in traditional Chinese and Western medicine.

  4. The Relationships between Ball Throwing Velocity and Physical-Psychomotor Features for Talent Identification in Physical Education

    Science.gov (United States)

    Karadenizli, Zeynep Inci

    2016-01-01

    The aim of this study is to investigate the relationships between ball throwing velocity (BTV), and physical features and anaerobic power (AP) for talent identification in team handball players. Players (n: 54) at 21,91 ± 4,94 age, training experience 11,19 ± 4,46 years participated voluntarily to study. These players consist of 54 Turkish…

  5. Using machine learning to classify image features from canine pelvic radiographs: evaluation of partial least squares discriminant analysis and artificial neural network models.

    Science.gov (United States)

    McEvoy, Fintan J; Amigo, José M

    2013-01-01

    As the number of images per study increases in the field of veterinary radiology, there is a growing need for computer-assisted diagnosis techniques. The purpose of this study was to evaluate two machine learning statistical models for automatically identifying image regions that contain the canine hip joint on ventrodorsal pelvis radiographs. A training set of images (120 of the hip and 80 from other regions) was used to train a linear partial least squares discriminant analysis (PLS-DA) model and a nonlinear artificial neural network (ANN) model to classify hip images. Performance of the models was assessed using a separate test image set (36 containing hips and 20 from other areas). Partial least squares discriminant analysis model achieved a classification error, sensitivity, and specificity of 6.7%, 100%, and 89%, respectively. The corresponding values for the ANN model were 8.9%, 86%, and 100%. Findings indicated that statistical classification of veterinary images is feasible and has the potential for grouping and classifying images or image features, especially when a large number of well-classified images are available for model training. © 2012 Veterinary Radiology & Ultrasound.

  6. Decomposition of forging dies for machining planning

    CERN Document Server

    Tapie, Laurent; Anselmetti, Bernard

    2009-01-01

    This paper will provide a method to decompose forging dies for machining planning in the case of high speed machining finishing operations. This method lies on a machining feature approach model presented in the following paper. The two main decomposition phases, called Basic Machining Features Extraction and Process Planning Generation, are presented. These two decomposition phases integrates machining resources models and expert machining knowledge to provide an outstanding process planning.

  7. Decomposition of forging dies for machining planning

    OpenAIRE

    Tapie, Laurent; Mawussi, Kwamiwi; Anselmetti, Bernard

    2009-01-01

    International audience; This paper will provide a method to decompose forging dies for machining planning in the case of high speed machining finishing operations. This method lies on a machining feature approach model presented in the following paper. The two main decomposition phases, called Basic Machining Features Extraction and Process Planning Generation, are presented. These two decomposition phases integrates machining resources models and expert machining knowledge to provide an outs...

  8. Research on the Relationship between Large Printing Machinery and Man- machine Engineering%大型印刷机械人机工程学关系探讨

    Institute of Scientific and Technical Information of China (English)

    徐岩; 宋君

    2012-01-01

    Taking the incompatibility between human and machine in printing machinery as the inspiration, it analyzed the content of the man-machine engineering of printing machinery, discussed the human-machine interactions in printing machinery. And on that basis, with the four-color offset press as the carrier, based on ergonomics and related national standards, it discussed the relationship between printing machinery and ergonomics from three aspects: human-computer interface, operating space and color and proposes ergonomics design principles of large printing machine.%以我国大型印刷装备仍存在人和机器交互不协调性为启示,分析了印刷机械人机工程学的内容,论述了印刷机械中存在的人机交互关系。在此基础上,以四色胶印机为裁体,以人机工程学和相关国家标准为理论基础,从人机界面、操作空间和色彩3个方面对印刷机械人机工程学关系进行探讨,提出了大型印刷机械人机工程学关系设计基础原则。

  9. [Re-signification of the human in the context of the "ciborgzation": a look at the human being-machine relationship in intensive care].

    Science.gov (United States)

    Vargas, Mara Ambrosina de O; Meyer, Dagmar Estermann

    2005-06-01

    This study discusses the human being-machine relationship in the process called "cyborgzation" of the nurse who works in intensive care, based on post-structuralist Cultural Studies and highlighting Haraway's concept of cyborg. In it, manuals used by nurses in Intensive Care Units have been examined as cultural texts. This cultural analysis tries to decode the various senses of "human" and "machine", with the aim of recognizing processes that turn nurses into cyborgs. The argument is that intensive care nurses fall into a process of "technology embodiment" that turns the body-professional into a hybrid that makes possible to disqualify, at the same time, notions such as machine and body "proper", since it is the hybridization between one and the other that counts there. Like cyborgs, intensive care nurses learn to "be with" the machine, and this connection limits the specificity of their actions. It is suggested that processes of "cyborgzation" such as this are useful for questioning - and to deal with in different ways - the senses of "human" and "humanity" that support a major part of knowledge/action in health.

  10. 基于制造特征的三轴高速铣削数控自动编程技术%MACHINING-FEATURE BASED 3-AXIS AUTOMATIC NC PROGRAMMING FOR HIGH-SPEED MILLING

    Institute of Scientific and Technical Information of China (English)

    孙全平; 汪通悦; 廖文和; 何宁

    2007-01-01

    运用面向对象技术,描述了待加工件的制造特征.利用模糊最大隶属原则,实现了加工区域几何制造特征的识别.以高速加工工艺数据库和范例库为支撑,采用IFTHEN规则和模糊匹配方法,提取出了适合高速铣削加工的工艺信息.提出了以切削时间短、加工成本低、表面质量高为目标的工艺方案寻优模型,该模型有助于形成成功的加工范例.依据已有加工范例和提取的工艺信息,实现了3轴高速铣削加工的自动编程.%Machining-features of the workplace are described by using of the object-oriented (O-O) technology. Geometrical machining-features are recognized in the given cut region by using the maximum membership priciple about the fuzzy set. Depending on the IF-THEN rule and the fuzzy matching method, the rough information of the machining-process for high-speed milling (HSM) is extracted based on the database of machining-process for HSM. The optimization model of machining-process scheme is established to obtain shorter cut time, lower cost or higher surface quality. It is helpful to form successful cases for HSM. NC programming for HSM is realized according to optimized machining-process data from HSM cases selected by the optimization model and the extracted information of machining-process.

  11. Quantitative structure-property relationships of electroluminescent materials: Artificial neural networks and support vector machines to predict electroluminescence of organic molecules

    Indian Academy of Sciences (India)

    Alana Fernandes Golin; Ricardo Stefani

    2013-12-01

    Electroluminescent compounds are extensively used as materials for application in OLED. In order to understand the chemical features related to electroluminescence of such compounds, QSPR study based on neural network model and support vector machine was developed on a series of organic compounds commonly used in OLED development. Radial-basis function-SVM model was able to predict the electroluminescence with good accuracy ( = 0.90). Moreover, RMSE of support vector machine model is approximately half of RMSE observed for artificial neural networks model, which is significant from the point of view of model precision, as the dataset is very small. Thus, support vector machine is a good method to build QSPR models to predict the electroluminescence of materials when applied to small datasets. It was observed that descriptors related to chemical bonding and electronic structure are highly correlated with electroluminescence properties. The obtained results can help in understating the structural features related to the electroluminescence, and supporting the development of new electroluminescent materials.

  12. Building a Relationship between Elements of Product Form Features and Vocabulary Assessment Models

    Science.gov (United States)

    Lo, Chi-Hung

    2016-01-01

    Based on the characteristic feature parameterization and the superiority evaluation method (SEM) in extension engineering, a product-shape design method was proposed in this study. The first step of this method is to decompose the basic feature components of a product. After that, the morphological chart method is used to segregate the ideas so as…

  13. Neither separate nor equivalent : Relationships between feature representations within bound objects

    NARCIS (Netherlands)

    Morey, Candice C.; Guerard, Katherine; Tremblay, Sebastien

    2013-01-01

    Evidence suggests that binding, or encoding a feature with respect to other features in time and space, can convey cognitive advantages. However, evidence across many kinds of stimuli and paradigms presents a mixed picture, alternatively showing cognitive costs or cognitive advantages associated

  14. Pattern recognition & machine learning

    CERN Document Server

    Anzai, Y

    1992-01-01

    This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

  15. Distinctiveness of Adolescent and Emerging Adult Romantic Relationship Features in Predicting Externalizing Behavior Problems

    Science.gov (United States)

    van Dulmen, Manfred H. M.; Goncy, Elizabeth A.; Haydon, Katherine C.; Collins, W. Andrew

    2008-01-01

    Romantic relationship involvement has repeatedly been associated with the incidence of externalizing behavior problems, but little is known about the nature and developmental significance of this relation. The current study extends previous research by investigating whether and through what processes romantic relationships distinctively predict…

  16. When Machines Design Machines!

    DEFF Research Database (Denmark)

    2011-01-01

    Until recently we were the sole designers, alone in the driving seat making all the decisions. But, we have created a world of complexity way beyond human ability to understand, control, and govern. Machines now do more trades than humans on stock markets, they control our power, water, gas...... and food supplies, manage our elevators, microclimates, automobiles and transport systems, and manufacture almost everything. It should come as no surprise that machines are now designing machines. The chips that power our computers and mobile phones, the robots and commercial processing plants on which we...... depend, all are now largely designed by machines. So what of us - will be totally usurped, or are we looking at a new symbiosis with human and artificial intelligences combined to realise the best outcomes possible. In most respects we have no choice! Human abilities alone cannot solve any of the major...

  17. Machining strategy choice: performance VIEWER

    CERN Document Server

    Tapie, Laurent; Anselmetti, Bernard

    2009-01-01

    Nowadays high speed machining (HSM) machine tool combines productivity and part quality. So mould and die maker invested in HSM. Die and mould features are more and more complex shaped. Thus, it is difficult to choose the best machining strategy according to part shape. Geometrical analysis of machining features is not sufficient to make an optimal choice. Some research show that security, technical, functional and economical constrains must be taken into account to elaborate a machining strategy. During complex shape machining, production system limits induce feed rate decreases, thus loss of productivity, in some part areas. In this paper we propose to analyse these areas by estimating tool path quality. First we perform experiments on HSM machine tool to determine trajectory impact on machine tool behaviour. Then, we extract critical criteria and establish models of performance loss. Our work is focused on machine tool kinematical performance and numerical controller unit calculation capacity. We implement...

  18. The features of embodiment the love relationships in ukrainian art culture

    Directory of Open Access Journals (Sweden)

    T. O. Kabanets

    2014-03-01

    The topicality of this scientific research is determined by the substantial changes in all areas of social life that also relate to relationship of love. The Great material for such study can be provided by the artistic culture, as it is the historical and cultural source, which is fixing world view, attitude of certain social time. During centuries the spiritual experience of mankind were accumulating in various art products and the artistic embodiment of the phenomenon of love took the one of the primary places of it. The author has observed the artistic representation of the relationship of love from the beginnings of Ukrainian nation to the examples of modern author’s art. The study determined the model of a loving relationship which focuses on the institutionalization in marriage and social­moral control of premarital relations as traditionally inherent in Ukrainian national culture. Analysis of the implementation of loving relationships in contemporary Ukrainian art culture shows that interpersonal relationships presented in the art products differs from the traditional norms and values of the Ukrainian culture. Furthermore this trend is amplified. This situation can provoke controversy in understanding of the phenomenon of love and further rethinking of prevailing cultural attitudes in society, models and examples of behavior in an intimate relationship.

  19. The effect of specifi c relationship between material and coating on tribological and protective features of the product

    Directory of Open Access Journals (Sweden)

    B. Sovilj

    2012-01-01

    Full Text Available Today, parts and tools are increasingly made of composite materials. Realization of specifi c connection between basic material and coating is very important. The quality of coating on products, in terms of wear and resistance to destruction, has a large impact on productivity and reliability of production processes, in particular their life. In this paper, based on experimental investigations, the effect of specific relationship between the base material and coating on tribological and protective features of the product is analyzed.

  20. Debugging the virtual machine

    Energy Technology Data Exchange (ETDEWEB)

    Miller, P.; Pizzi, R.

    1994-09-02

    A computer program is really nothing more than a virtual machine built to perform a task. The program`s source code expresses abstract constructs using low level language features. When a virtual machine breaks, it can be very difficult to debug because typical debuggers provide only low level machine implementation in formation to the software engineer. We believe that the debugging task can be simplified by introducing aspects of the abstract design into the source code. We introduce OODIE, an object-oriented language extension that allows programmers to specify a virtual debugging environment which includes the design and abstract data types of the virtual machine.

  1. Electrical machines & drives

    CERN Document Server

    Hammond, P

    1985-01-01

    Containing approximately 200 problems (100 worked), the text covers a wide range of topics concerning electrical machines, placing particular emphasis upon electrical-machine drive applications. The theory is concisely reviewed and focuses on features common to all machine types. The problems are arranged in order of increasing levels of complexity and discussions of the solutions are included where appropriate to illustrate the engineering implications. This second edition includes an important new chapter on mathematical and computer simulation of machine systems and revised discussions o

  2. Relationships among maladaptive cognitive content, dysfunctional cognitive processes, and borderline personality features.

    Science.gov (United States)

    Geiger, Paul J; Peters, Jessica R; Sauer-Zavala, Shannon E; Baer, Ruth A

    2013-08-01

    Previous research has demonstrated that maladaptive cognitive content, including dysfunctional attitudes and negative automatic thoughts, is associated with emotional distress. Similarly, dysfunctional cognitive processes, including thought suppression and rumination, have been shown to intensify psychological difficulties. Although maladaptive cognitive content and dysfunctional processes have been linked to borderline personality disorder (BPD), most research has been conducted with Axis I disorders. This study examined the incremental validity of dysfunctional cognitive content and processes in predicting BPD symptom severity, controlling for trait negative affect, in a sample of undergraduate students (N = 85), including many with high levels of BPD features. Although nearly all variables were significantly correlated with BPD features, final regression models suggest that rumination and thought suppression are stronger independent predictors of BPD features than automatic thoughts, dysfunctional attitudes, and trait negative affect. These results suggest the importance of targeting thought suppression and rumination in BPD.

  3. Different Confidence-Accuracy Relationships for Feature-Based and Familiarity-Based Memories

    Science.gov (United States)

    Reinitz, Mark Tippens; Peria, William J.; Seguin, Julie Anne; Loftus, Geoffrey R.

    2011-01-01

    Participants studied naturalistic pictures presented for varying brief durations and then received a recognition test on which they indicated whether each picture was old or new and rated their confidence. In 1 experiment they indicated whether each "old"/"new" response was based on memory for a specific feature in the picture…

  4. Different Confidence-Accuracy Relationships for Feature-Based and Familiarity-Based Memories

    Science.gov (United States)

    Reinitz, Mark Tippens; Peria, William J.; Seguin, Julie Anne; Loftus, Geoffrey R.

    2011-01-01

    Participants studied naturalistic pictures presented for varying brief durations and then received a recognition test on which they indicated whether each picture was old or new and rated their confidence. In 1 experiment they indicated whether each "old"/"new" response was based on memory for a specific feature in the picture…

  5. Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods.

    Science.gov (United States)

    Kolb, Brian; Lentz, Levi C; Kolpak, Alexie M

    2017-04-26

    Modern ab initio methods have rapidly increased our understanding of solid state materials properties, chemical reactions, and the quantum interactions between atoms. However, poor scaling often renders direct ab initio calculations intractable for large or complex systems. There are two obvious avenues through which to remedy this problem: (i) develop new, less expensive methods to calculate system properties, or (ii) make existing methods faster. This paper describes an open source framework designed to pursue both of these avenues. PROPhet (short for PROPerty Prophet) utilizes machine learning techniques to find complex, non-linear mappings between sets of material or system properties. The result is a single code capable of learning analytical potentials, non-linear density functionals, and other structure-property or property-property relationships. These capabilities enable highly accurate mesoscopic simulations, facilitate computation of expensive properties, and enable the development of predictive models for systematic materials design and optimization. This work explores the coupling of machine learning to ab initio methods through means both familiar (e.g., the creation of various potentials and energy functionals) and less familiar (e.g., the creation of density functionals for arbitrary properties), serving both to demonstrate PROPhet's ability to create exciting post-processing analysis tools and to open the door to improving ab initio methods themselves with these powerful machine learning techniques.

  6. Cue-Reactive Altered State of Consciousness Mediates the Relationship Between Problem-Gambling Severity and Cue-Reactive Urge in Poker-Machine Gamblers.

    Science.gov (United States)

    Tricker, Christopher; Rock, Adam J; Clark, Gavin I

    2016-06-01

    In order to enhance our understanding of the nature of poker-machine problem-gambling, a community sample of 37 poker-machine gamblers (M age = 32 years, M PGSI = 5; PGSI = Problem Gambling Severity Index) were assessed for urge to gamble (responses on a visual analogue scale) and altered state of consciousness (assessed by the Altered State of Awareness dimension of the Phenomenology of Consciousness Inventory) at baseline, after a neutral cue, and after a gambling cue. It was found that (a) problem-gambling severity (PGSI score) predicted increase in urge (from neutral cue to gambling cue, controlling for baseline; sr (2) = .19, p = .006) and increase in altered state of consciousness (from neutral cue to gambling cue, controlling for baseline; sr (2) = .57, p gambling cue) mediated the relationship between problem-gambling severity and increase in urge (from neutral cue to gambling cue; κ(2) = .40, 99 % CI [.08, .71]). These findings suggest that cue-reactive altered state of consciousness is an important component of cue-reactive urge in poker-machine problem-gamblers.

  7. Machine vision assisted analysis of structure-localization relationships in a combinatorial library of prospective bioimaging probes

    OpenAIRE

    Shedden, Kerby; Li, Qian; Liu, Fangyi; Chang, Young Tae; Rosania, Gus R.

    2009-01-01

    With a combinatorial library of bioimaging probes, it is now possible to use machine vision to analyze the contribution of different building blocks of the molecules to their cell-associated visual signals. For athis purpose, cell-permeant, fluorescent styryl molecules were synthesized by condensation of 168 aldehyde with 8 pyridinium/quinolinium building blocks. Images of cells incubated with fluorescent molecules were acquired with a high content screening instrument. Chemical and image fea...

  8. Relationship between solids flux and froth features in batch flotation of sulphide ore

    Institute of Scientific and Technical Information of China (English)

    YANG Xiao-sheng; Aldrich Chris

    2005-01-01

    The froth features in the batch flotation of a sulphide ore were investigated by using the digital image parameters of the froth, the small number emphasis(Nsne), the average grey level(Dagl) and the instability number(Nins), under different conditions of impeller speeds and aeration rates. It is found that the value of Nsne is strongly dependent on the average bubble size of the froth and Dagl on the volume fraction of solid in the froth, and the froth features during the batch flotation are influenced by impeller speed and aeration rate. A kinetic model of the concentrate solid flux was developed which relates the flotation process to the image parameters, Nsne and Dagl of the froth and predictions are well consistent with the experimental data.

  9. Relationship between the imaging features and pathologic alteration in hepatoma of rats

    Institute of Scientific and Technical Information of China (English)

    Jia-He Yang; Tian-Geng You; Nan Li; Qi-Jun Qian; Ping Wang; Zhen-Lin Yan; Meng-Chao Wu

    2003-01-01

    AIM: The imaging features of MRI and DSA, using the modelsof implanted and induced hepatoma, were investigated in rats.METHODS: CBRH3 cancer cells were implanted for differentliver site of rat liver and the diethylnitrosoamine was givenorally to rats in order to induce liver cancer. Both experimentalgroups were detected by magnetic resonance imaging (MRI),digital subtraction angiography (DSA) and morphologic assay.RESULTS: Hypointensity on T1WI and homogenous highsignal intensity on T2WI in MRI, and ring-like abnormal stainon DSA were found in implanted cancer. Induced cancersappeared as homogeneous or heterogeneous hypointensityon T1WI (10 cases), and equal or slight high intensity onT2WI (8 cases), but some as hypointensity on T2WI (2 cases).CONCLUSION: The imaging features of implanted cancerswere similar to that of human liver metastases. Therefore, itcould serve as an experimental model of human liver metastatictumor. The imaging feature of induced cancers, whereas, weresimilar to that of human primary liver cancer. It could be useas an experimental model of human primary liver cancer.

  10. The relationship between monetary policy and bank lending behavior and the influence of bank specific features on this relationship in the banks listed on the Tehran Stock Exchange

    Directory of Open Access Journals (Sweden)

    Ayub Ghasemian

    2016-03-01

    Full Text Available This paper is trying to investigate how monetary policy affects the banks’ loan portfolios and answer the question of whether special bank’s features influence the lending response to a monetary policy. To this end, we use Iran’s bank loan aggregated series and bank’s size and capital structure data. We use the growth rate of M2 as the indicators of Irans' monetary policy. Using Vector error correction model (VECM and quarterly data for the period 2007:Q1 to 2014:Q4, the main hypothesis be examined. The result show that there is a bidirectional causal relationship between the M2 (as monetary policy index and lending behavior of banks listed on the Tehran Stock Exchange. It was also observed that the banks' capital structure as one of the banks specific feature variables have a negative impact on bank lending behavior in accepted banks in Tehran Stock Exchange.

  11. Analysis of relationships between peptide/MHC structural features and naive T cell frequency in humans.

    Science.gov (United States)

    Reiser, Jean-Baptiste; Legoux, François; Gras, Stéphanie; Trudel, Eric; Chouquet, Anne; Léger, Alexandra; Le Gorrec, Madalen; Machillot, Paul; Bonneville, Marc; Saulquin, Xavier; Housset, Dominique

    2014-12-15

    The structural rules governing peptide/MHC (pMHC) recognition by T cells remain unclear. To address this question, we performed a structural characterization of several HLA-A2/peptide complexes and assessed in parallel their antigenicity, by analyzing the frequency of the corresponding Ag-specific naive T cells in A2(+) and A2(-) individuals, as well as within CD4(+) and CD8(+) subsets. We were able to find a correlation between specific naive T cell frequency and peptide solvent accessibility and/or mobility for a subset of moderately prominent peptides. However, one single structural parameter of the pMHC complexes could not be identified to explain each peptide antigenicity. Enhanced pMHC antigenicity was associated with both highly biased TRAV usage, possibly reflecting favored interaction between particular pMHC complexes and germline TRAV loops, and peptide structural features allowing interactions with a broad range of permissive CDR3 loops. In this context of constrained TCR docking mode, an optimal peptide solvent exposed surface leading to an optimal complementarity with TCR interface may constitute one of the key features leading to high frequency of specific T cells. Altogether our results suggest that frequency of specific T cells depends on the fine-tuning of several parameters, the structural determinants governing TCR-pMHC interaction being just one of them. Copyright © 2014 by The American Association of Immunologists, Inc.

  12. Relationship between Clinical and Immunological Features of Thyroid Autoimmunity and Ophthalmopathy during Pregnancy

    Directory of Open Access Journals (Sweden)

    Jack R. Wall

    2015-01-01

    Full Text Available Problem. Clinical features of Graves’ hyperthyroidism (GH generally improve during pregnancy and rebound in the postpartum period. It is unclear whether the ophthalmopathy that is associated with GH and, less often, Hashimoto’s thyroiditis (HT changes in parallel with the thyroid associated antibody reactions and clinical features or runs a different course. Method of Study. We retrospectively studied 19 patients with autoimmune thyroid disease over 22 pregnancies: 9 pregnancies with GH and 13 with HT. Ophthalmopathy was defined by NOSPECS class. Results. Thyroid peroxidase (TPO and thyroglobulin (Tg antibody titres decreased during pregnancy and rose in the postpartum period. During pregnancy, 5 patients with GH and 4 patients with HT developed mild ophthalmopathy and two patients with GH and HT developed new upper eyelid retraction (UER. In the postpartum period, eye scores improved in 3 patients with GH and 3 with HT, remained stable in two and 5 patients, respectively, and worsened in 2 patients with GH and one with HT. Conclusions. In patients with mild to moderate eye signs associated with GH and HT, the orbital and thyroid reactions ran different courses during pregnancy. Since no patient had severe ophthalmopathy, we cannot draw definitive conclusions from this preliminary study.

  13. 快速数控编程系统的制造特征构建研究%Research on Reconstruction of Machining Feature for Rapid NC Programming System

    Institute of Scientific and Technical Information of China (English)

    李铁钢; 付春林; 于天彪; 王宛山

    2012-01-01

    Aiming at recognition and reconstruction of features from different 3D CAD models, this paper presented the implementation process of feature recognition. Firstly, an attributed adjacency graph (AAG) created of geometry and topology in STEP file by lexical analysis. According to the analyses on machining features of structural parts in terms of the NC programming cutting logical, the machining feature will be recognized and reconstructed. The machining feature are provided by XML, which can be used by CAM system. Case study validates the proposed method, which improves efficiency and quality of NC programming of structural part.%针对快速数控编程系统中不同CAD模型的特征识别和构建,论述了基于STEP文件的特征识别技术及其实现过程:首先利用词法分析器解析STEP中性文件,按照STEP的文件拓扑结构生成属性邻接图(AAG);在总结典型结构件拓扑特征基础上,结合数控编程切削逻辑,以切削级为基础进行特征识别和特征构建;最后以XML形式构造制造特征森林以供CAM系统使用.实例证明了文中方法的有效性,提高了结构件数控编程的效率和质量.

  14. The structure and features of a large-scale three-roller bending machine from America%一种美制大型三辊卷板机的结构与特点

    Institute of Scientific and Technical Information of China (English)

    赵学; 王吉龙; 顾富生

    2011-01-01

    The bending principle, technical characteristics and mechanical structure features of a three roller bending machine from America have been emphasizedly introduced in the text. The bending process and driving mode of the machine have been analyzed as well as the difference of the structure and characteristics comparing with the domestic machines. The machine has advantages as high precision and loading capacity, good damping property and easy operation, which is the ideal equipment for bending the plate with big thickness.%重点介绍了一种美制大型三辊卷板机的卷板原理、技术性能及机械结构特点.分析了该卷板机的卷制工艺、传动方式以及与其他一些国产三辊卷板机的结构和性能上的不同.该卷板机具有卷板精度高、承载能力强、抗振性能好、操作维护方便等优点,是卷制厚重板材的理想设备.

  15. Concomitant amyotrophic lateral sclerosis and paraclinical laboratory features of multiple sclerosis: coincidence or causal relationship?

    Science.gov (United States)

    Borisow, Nadja; Meyer, Thomas; Paul, Friedemann

    2013-01-23

    We report a 55-year-old patient, presenting with paresis, muscle atrophy and dysarthria, all symptoms accordable to definite amyotrophic lateral sclerosis (ALS). However, MRI and cerebrospinal fluid show abnormalities typical of multiple sclerosis (MS). On the basis of this case report, we discuss possible overlaps between both diseases by comparing clinical and paraclinical features including laboratory, radiological and electrophysiological diagnostics. As genetic, as well as environmental, factors are assumed to be involved in the development of both the diseases, literature is reviewed according to similar cases, results of autopsies and possible parallels in pathogenesis. In summary, based on the data currently available, the hypothesis of ALS being a neurodegenerative multisystem disorder, a common pathophysiological pathway or, alternatively, a random comorbidity of ALS and MS in this patient has to be discussed.

  16. Relationship between external and histologic features of progressive stages of caries in the occlusal fossa

    DEFF Research Database (Denmark)

    Ekstrand, K R; Kuzmina, I; Bjørndal, L;

    1995-01-01

    The material comprised 140 extracted maxillary third molars. The central fossa area was examined with a stereomicroscope (SM) (x16) and macroscopically (M) under standardized conditions after cleaning and air-drying. Signs of caries were classified using a detailed scoring system involving 12 (SM...... and the internal enamel and dentine reactions. The data did not support routine usage of radiographic examination for occlusal caries diagnosis.......The material comprised 140 extracted maxillary third molars. The central fossa area was examined with a stereomicroscope (SM) (x16) and macroscopically (M) under standardized conditions after cleaning and air-drying. Signs of caries were classified using a detailed scoring system involving 12 (SM...... highly correlated (rs = 0.90). Dentinal changes were also highly correlated with enamel changes (rs = 0.85). The histologic classifications in conjunction with the macroscopical observations made it possible to demonstrate a clear relationship between the external degree of caries progression...

  17. The Learning Feature of Deep Knowledge and Its Relationship With Exercise

    Directory of Open Access Journals (Sweden)

    Ming Hung Lin

    2014-05-01

    Full Text Available Nine Principles for Deep Knowledge of Habitual Domains (HDs have been identified as an effective approach to expanding and enriching an individual’s HD, or in a broader sense, to improving learning. The purpose of this study was to examine the Principle for Deep Knowledge Survey (PDKS and the correlations between the PDKS and other individual variables, such as gender, body mass index (BMI, and exercise routines. Seven hundred eighty-five industrial high school students completed the questionnaire. Overall, the results suggested that the psychometric properties of the PDKS were acceptable and also showed a significant relationship between gender and the Principles of Contrasting and Complementing and Cracking and Ripping. In addition, the Principles of Alternating, Changing and Transforming, and Void had a positive correlation with the variable of frequency of exercise. The results showed that exercise could be a mediator in expanding the competence of deep knowledge to improve learning.

  18. 三维工艺设计中基于加工特征的工序模型生成技术%Generation of Intermediate Process Model Based on Machining Features in 3D Process Planning

    Institute of Scientific and Technical Information of China (English)

    2013-01-01

    In the 3D process design of machining part, according to machining features such as shape, dimensional tolerance and so on, then carry out process design and process planning, and generate entity modal of every working process from the blank part to the final part. Geometric topology structure and manufacturing process information are combined to set a feature definition and classification system for machining. The concept of intermediate process model and the model recovery method automatically-generated in an intermediate process model is put forward on the basis of feature recognition and 3D process design technology. According to process method and process parameter of each machining feature, automatically generate intermediate process model based on part processing rout. The test results proved that the method offers reference to achieve 3D model-based machining process design.%  在机械加工零件三维工艺设计中,需要根据零件的形状、尺寸公差等识别加工特征,按照加工特征进行工艺设计和工艺路线规划,生成从零件毛坯到最终零件的各个工序的实体模型。结合零件的几何拓扑结构和制造工艺信息,建立一套面向机械加工的特征定义和分类体系,在特征识别和三维工艺设计技术的基础上,提出中间工序模型的概念和中间工序模型自动生成的模型恢复方法,根据各个加工特征的工艺方法和工艺参数,按照零件的加工路线自动生成中间工序模型。实例验证结果证明,该方法可为实现基于三维模型的机加工艺设计提供参考。

  19. An approach to error elimination for multi-axis CNC machining and robot manipulation

    Institute of Scientific and Technical Information of China (English)

    XIONG; CaiHua

    2007-01-01

    The geometrical accuracy of a machined feature on a workpiece during machining processes is mainly affected by the kinematic chain errors of multi-axis CNC machines and robots, locating precision of fixtures, and datum errors on the workpiece. It is necessary to find a way to minimize the feature errors on the workpiece. In this paper, the kinematic chain errors are transformed into the displacements of the workpiece. The relationship between the kinematic chain errors and the displacements of the position and orientation of the workpiece is developed. A mapping model between the displacements of workpieces and the datum errors, and adjustments of fixtures is established. The suitable sets of unit basis twists for each of the commonly encountered types of feature and the corresponding locating directions are analyzed, and an error elimination (EE) method of the machined feature is formulated. A case study is given to verify the EE method.

  20. Development of Correlations for Windage Power Losses Modeling in an Axial Flux Permanent Magnet Synchronous Machine with Geometrical Features of the Magnets

    Directory of Open Access Journals (Sweden)

    Alireza Rasekh

    2016-11-01

    Full Text Available In this paper, a set of correlations for the windage power losses in a 4 kW axial flux permanent magnet synchronous machine (AFPMSM is presented. In order to have an efficient machine, it is necessary to optimize the total electromagnetic and mechanical losses. Therefore, fast equations are needed to estimate the windage power losses of the machine. The geometry consists of an open rotor–stator with sixteen magnets at the periphery of the rotor with an annular opening in the entire disk. Air can flow in a channel being formed between the magnets and in a small gap region between the magnets and the stator surface. To construct the correlations, computational fluid dynamics (CFD simulations through the frozen rotor (FR method are performed at the practical ranges of the geometrical parameters, namely the gap size distance, the rotational speed of the rotor, the magnet thickness and the magnet angle. Thereafter, two categories of formulations are defined to make the windage losses dimensionless based on whether the losses are mainly due to the viscous forces or the pressure forces. At the end, the correlations can be achieved via curve fittings from the numerical data. The results reveal that the pressure forces are responsible for the windage losses for the side surfaces in the air-channel, whereas for the surfaces facing the stator surface in the gap, the viscous forces mainly contribute to the windage losses. Additionally, the results of the parametric study demonstrate that the overall windage losses in the machine escalate with an increase in either the rotational Reynolds number or the magnet thickness ratio. By contrast, the windage losses decrease once the magnet angle ratio enlarges. Moreover, it can be concluded that the proposed correlations are very useful tools in the design and optimizations of this type of electrical machine.

  1. MRI features of ovarian fibromas: emphasis on their relationship to the ovary

    Energy Technology Data Exchange (ETDEWEB)

    Oh, S.N. [Department of Radiology, Kangnam St. Mary' s Hospital, College of Medicine, Catholic University of Korea, Seocho-Ku, Seoul (Korea, Republic of); Rha, S.E. [Department of Radiology, Kangnam St. Mary' s Hospital, College of Medicine, Catholic University of Korea, Seocho-Ku, Seoul (Korea, Republic of)], E-mail: serha@catholic.ac.kr; Byun, J.Y.; Lee, Y.J. [Department of Radiology, Kangnam St. Mary' s Hospital, College of Medicine, Catholic University of Korea, Seocho-Ku, Seoul (Korea, Republic of); Jung, S.E. [Department of Radiology, St. Mary' s Hospital, College of Medicine, Catholic University of Korea, Youngdungpo-gu, Seoul (Korea, Republic of); Jung, C.K. [Department of Hospital Pathology, Kangnam St. Mary' s Hospital, College of Medicine, Catholic University of Korea, Seocho-Ku, Seoul (Korea, Republic of); Kim, M.R. [Department of Obstetrics and gynecology, Kangnam St. Mary' s Hospital, College of Medicine, The Catholic University of Korea, Seocho-Ku, Seoul (Korea, Republic of)

    2008-05-15

    Aim: To evaluate the magnetic resonance (MR) imaging features of ovarian fibromas, emphasizing the presence and shape of the ovary on the same side of the fibroma. Materials and methods: MR images from 23 patients with 24 histologically proven ovarian fibromas were reviewed by two radiologists. Eleven were pre-menopausal and 12 were postmenopausal. The presence and shape of the ovarian tissue on the same side of the fibroma were evaluated on T2-weighted MR images. Results: In 11 (46%) of the 24 ovarian fibromas, the ipsilateral ovary was detected on T2-weighted images. The ovary was crescent-shaped along the periphery of the fibroma in six (55%) of 11 fibromas and had a normal, oval shape in five (45%). Of these five tumours, the ovary was connected to the fibromas by a pedicle-like structure in three and was closely attached to the periphery of the fibromas in two. The ipsilateral ovary was detected in 10 (83%) of 12 fibromas in pre-menopausal patients, and in one (8%) of 12 fibromas in postmenopausal patients. There was a statistically significant difference (p = 0.001) in the presence of detectable ipsilateral ovary between pre-menopausal and postmenopausal women. Conclusions: Detection of the remaining ovary on the same side as the fibroma is not unusual on MRI, especially in pre-menopausal women, and the shape of the ovary may be normal in cases of fibromas with exophytic growth from the periphery of the ovary.

  2. [Relationship between vasculogenic mimicry and clinic pathological features in laryngeal carcinoma].

    Science.gov (United States)

    Feng, Yan; Wang, Binquan; Liang, Gang; Wen, Shuxin; Sun, Ruifang

    2015-12-01

    To investigate the presence of vasculogenic mimicry in laryngeal squamous cell carcino- ma and explore its clinical significance. The presence of vasculogenic mimicry and expression of endotheli- um-dependent vessel in 138 laryngeal squamous cell carcinomas cases were detected by the immunohistochemistry and tissue microarray. Metlab software was used to evaluate the relationship among vasculogenic mimicry, mi- crovessel density and clinic pathological parameters in laryngeal carcinoma. We found vasculogenic mimicry in 32 (26.23%) of 122 laryngeal carcinoma samples. The mean of microvessel density is 12.61 per high-power field. The vasculogenic mimicry and expression of endothelium-dependent vessel were not significantly related to patient age or gender, tumor location, pathology grade, T stage or N stage (P > 0.05). However, the vasculo- genic mimicry and the mean of microvessel density were a little higher in patients older than 60, with poorly differ- entiated and patients with N₁₋₃ stage. Vasculogenic mimicry was positively correlatedwith microvessel density (r = 0.1927, P mimicry can occur in laryngeal carcinoma. Moreover, vasculogenic mimicry may be associated with recurrence and metastasis in laryngeal carcinoma.

  3. Work productivity in rheumatoid arthritis: relationship with clinical and radiological features.

    Science.gov (United States)

    Chaparro Del Moral, Rafael; Rillo, Oscar Luis; Casalla, Luciana; Morón, Carolina Bru; Citera, Gustavo; Cocco, José A Maldonado; Correa, María de Los Ángeles; Buschiazzo, Emilio; Tamborenea, Natalia; Mysler, Eduardo; Tate, Guillermo; Baños, Andrea; Herscovich, Natalia

    2012-01-01

    Objective. To assess the relationship between work productivity with disease activity, functional capacity, life quality and radiological damage in patients with rheumatoid arthritis (RA). Methods. The study included consecutive employed patients with RA (ACR'87), aged over 18. Demographic, disease-related, and work-related variables were determined. The reduction of work productivity was assessed by WPAI-RA. Results. 90 patients were evaluated, 71% women. Age average is 50 years old, DAS28 4, and RAQoL 12. Median SENS is 18 and HAQ-A 0.87. Mean absenteeism was of 14%, presenting an average of 6.30 work hours wasted weekly. The reduction in performance at work or assistance was of 38.4% and the waste of productivity was of 45%. Assistance correlated with DAS28 (r = 0.446; P productivity was noticed in higher levels of activity and functional disability. Patients with SENS > 18 showed lower work productivity than those with SENS productivity were HAQ-A and RAQoL. Conclusion. RA patients with higher disease severity showed higher work productivity compromise.

  4. Work Productivity in Rheumatoid Arthritis: Relationship with Clinical and Radiological Features

    Directory of Open Access Journals (Sweden)

    Rafael Chaparro del Moral

    2012-01-01

    Full Text Available Objective. To assess the relationship between work productivity with disease activity, functional capacity, life quality and radiological damage in patients with rheumatoid arthritis (RA. Methods. The study included consecutive employed patients with RA (ACR'87, aged over 18. Demographic, disease-related, and work-related variables were determined. The reduction of work productivity was assessed by WPAI-RA. Results. 90 patients were evaluated, 71% women. Age average is 50 years old, DAS28 4, and RAQoL 12. Median SENS is 18 and HAQ-A 0.87. Mean absenteeism was of 14%, presenting an average of 6.30 work hours wasted weekly. The reduction in performance at work or assistance was of 38.4% and the waste of productivity was of 45%. Assistance correlated with DAS28 (r = 0.446; P 18 showed lower work productivity than those with SENS < 18 (50 versus 34; P=0.04. In multiple regression analysis, variables associated with reduction of total work productivity were HAQ-A and RAQoL. Conclusion. RA patients with higher disease severity showed higher work productivity compromise.

  5. Cenozoic Subsidence Features of Beitang Sag and Relationship with Tectonic Evolution

    Institute of Scientific and Technical Information of China (English)

    Zhang Tingting; Wang Hua; Yue Yong; Huang Chuanyan; Zhang Liwei

    2009-01-01

    Based on the application of the EBM basin modeling software and 2-D seismic profiles, the Paleogene and Neogene shubsidence histories of the Beitang(北塘) sag are simulated with the backstripping technique,and the relationship between subsidence character and tectonic revolution is discussed.Moreover,the result of the basin modeling reveals that the subsidence history of the Beitang sag has the characteristics of several geological periods,and these succeeding periods have shown certain inheritance and difference characteristics.At the early (Es3) and middle (Es2-Es1) rifting periods,the subsidence reaction of the Beitang sag was mainly in the charge of tectonic activity,while at the late (Ng-Nm+Q) rifting period-post rifting period and post rifting subsidence-acceleration period-the subsidence type is mainly that of thermal subsidence or regional depression effect; from the beginning of the subsidence history to the end,the reason for the basin subsidence has changed from tectonic activity to non-tectonic activity.

  6. Relationship between electrocardiographic features and distribution of hypertrophy in patients with hypertrophic cardiomyopathy

    Energy Technology Data Exchange (ETDEWEB)

    Sato, Tetsuya; Nakamura, Kazufumi; Yamanari, Hiroshi; Ohe, Tohru [Okayama Univ. (Japan). School of Medicine; Yoshinouchi, Takeo

    1998-07-01

    To evaluate the relationship between the distribution of hypertrophy and the electrocardiographic findings in patients with hypertrophic cardiomyopathy (HCM), 54 HCM patients were studied using magnetic resonance imaging. The patients were divided into 4 groups according to hypertrophic patterns: hypertrophy only at the apex (group I, n=12); hypertrophy in both the apex and base (group II, n=20); hypertrophy only at the base with asymmetric septal hypertrophy (ASH) (group IIIa, n=17); and hypertrophy only at the base without ASH (group IIIb, n=5). Abnormal Q waves in leads II, III and aV{sub F} were found in 1/12, 3/20, 10/17 and 0/5, respectively, and in leads I and aV{sub L} they were found in 1/12, 8/20, 4/17 and 1/5, respectively. The largest negative T waves (mm) were found in group I (group I vs group II vs group IIIa vs group IIIb: 15.2{+-}5.3, 8.2{+-}6.1, 1.6{+-}2.0, 0.8{+-}1.3, respectively). The largest positive T waves (mm) were identified in group IIIb (3.8{+-}3.0, 6.8{+-}3.2, 5.8{+-}3.6, 9.3{+-}2.1, respectively). The presence of abnormal Q waves reflected regional hypertrophy in HCM patients but the configuration of T waves represented the difference in the localization of hypertrophy between the basal and apical segments. (author)

  7. Introduction to machine learning.

    Science.gov (United States)

    Baştanlar, Yalin; Ozuysal, Mustafa

    2014-01-01

    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.

  8. Characterising the relationship between weather extremes in Europe and synoptic circulation features

    Directory of Open Access Journals (Sweden)

    S. Pfahl

    2014-02-01

    Full Text Available Extreme weather events in Europe are closely linked to anomalies of the atmospheric circulation and in particular to circulation features like cyclones and atmospheric blocking. In this study, this linkage is systematically characterised with the help of conditional cyclone and blocking frequencies during precipitation, wind gust and temperature extremes at various locations in Europe. Such conditional frequency fields can serve as a dynamical fingerprint of the extreme events and yield insights into their most important physical driving mechanisms. Precipitation extremes over the ocean and over flat terrain are shown to be closely related to cyclones in the vicinity and the associated dynamical lifting. For extreme precipitation over complex terrain, cyclone anomalies are found at more remote locations, favouring the flow of moist air towards the topography. Wind gust extremes are associated with cyclone and blocking anomalies in opposite directions, with the cyclones occurring mostly over the North and Baltic Seas for extreme events in central Europe. This setting is associated with pronounced surface pressure gradients and thus high near-surface wind velocities. Hot temperature extremes in northern and central Europe typically occur in the vicinity of a blocking anticyclone, where subsidence and radiative forcing are strong. Over southern Europe, blocking anomalies are shifted more to the north or northeast, indicating a more important role of warm air advection. Large-scale flow conditions for cold extremes are similar at many locations in Europe, with blocking anomalies over the North Atlantic and northern Europe and cyclone anomalies southeast of the cold extreme, both contributing to the advection of cold air masses. This characterisation of synoptic-scale forcing mechanisms can be helpful for better understanding and anticipating weather extremes and their long-term changes.

  9. Programmed death ligand 1 expression in hepatocellular carcinoma: Relationship With clinical and pathological features.

    Science.gov (United States)

    Calderaro, Julien; Rousseau, Benoît; Amaddeo, Giuliana; Mercey, Marion; Charpy, Cécile; Costentin, Charlotte; Luciani, Alain; Zafrani, Elie-Serge; Laurent, Alexis; Azoulay, Daniel; Lafdil, Fouad; Pawlotsky, Jean-Michel

    2016-12-01

    The prognosis of hepatocellular carcinoma (HCC) remains poor, with only one third of patients eligible for curative treatments and very limited survival benefits with the use of sorafenib, the current standard of care for advanced disease. Recently, agents targeting the programmed death ligand 1 (PD-L1)/programmed death receptor 1 (PD-1) immune checkpoint were shown to display impressive antitumor activity in various solid or hematological malignancies, including HCC. PD-L1 immunohistochemical expression is thought to represent a biomarker predictive of drug sensitivity. Here, we investigated PD-L1 expression in a series of 217 HCCs and correlated our results with clinical and histological features and immunohistochemical markers (PD-1, cytokeratin 19, glutamine synthetase, and β-catenin expression). PD-L1 expression by neoplastic cells was significantly associated with common markers of tumor aggressiveness (high serum alpha-fetoprotein levels, P = 0.038; satellite nodules, P < 0.001; macrovascular invasion, P < 0.001; microvascular invasion, P < 0.001; poor differentiation, P < 0.001) and with the progenitor subtype of HCC (cytokeratin 19 expression, P = 0.031). High PD-L1 expression by inflammatory cells from the tumor microenvironment also correlated with high serum alpha-fetoprotein levels (P < 0.001), macrovascular invasion (P = 0.001), poor differentiation (P = 0.001), high PD-1 expression (P < 0.001), and the so-called lymphoepithelioma-like histological subtype of HCC (P = 0.003).

  10. Features of Parent-Child Relationship of Mothers with Teenage Children in the Conditions of Late Motherhood

    Directory of Open Access Journals (Sweden)

    Zakharova E.I.,

    2015-02-01

    Full Text Available The author's attention is attracted by one of the features of modern Russian family: the tendency to increase the frequency of childbirth by women of older reproductive age. The article presents the results of a comparative analysis of the mothers’ parent position, who had children at different periods of adulthood (middle, late. The aim of the study was to investigate the features of the parent-child relationship of mothers with teenage children in the conditions of late motherhood. Mothers of adolescents who participated in the study were divided into two groups: "young" mothers who gave birth to the first child before the age of 30 years, and "late" mothers who gave birth to their first child after being 30 years old. It turned out that the strategies of education and interaction between the "young" and "late" mothers, reflecting the value orientation of personality, are significantly different. Focusing on the emotional closeness with the child and creativity, education strategy of "late" mothers has a high emotional involvement, soft and inconsistent parenting. The features of maternal parenting strategies are adequately reflected by the teenagers who follow their mothers in priority of the values of family and work, or material well-being and the pursuit of hedonistic values.

  11. Perceptual Visual Distortions in Adult Amblyopia and Their Relationship to Clinical Features

    Science.gov (United States)

    Piano, Marianne E. F.; Bex, Peter J.; Simmers, Anita J.

    2015-01-01

    Purpose Develop a paradigm to map binocular perceptual visual distortions in adult amblyopes and visually normal controls, measure their stability over time, and determine the relationship between strength of binocular single vision and distortion magnitude. Methods Perceptual visual distortions were measured in 24 strabismic, anisometropic, or microtropic amblyopes (interocular acuity difference ≥ 0.200 logMAR or history of amblyopia treatment) and 10 controls (mean age 27.13 ± 10.20 years). The task was mouse-based target alignment on a stereoscopic liquid crystal display monitor, measured binocularly five times during viewing dichoptically through active shutter glasses, amblyopic eye viewing cross-hairs, fellow eye viewing single target dots (16 locations within central 5°), and five times nondichoptically, with all stimuli visible to either eye. Measurements were repeated over time (1 week, 1 month) in eight amblyopic subjects, evaluating test–retest reliability. Measurements were also correlated against logMAR visual acuity, horizontal prism motor fusion range, Frisby/Preschool Randot stereoacuity, and heterophoria/heterotropia prism cover test measurement. Results Sixty-seven percent (16/24) of amblyopes had significant perceptual visual distortions under dichoptic viewing conditions compared to nondichoptic viewing conditions and dichoptic control group performance. Distortions correlated with the strength of motor fusion (r = −0.417, P = 0.043) and log stereoacuity (r = 0.492, P = 0.015), as well as near angle of heterotropic/heterophoric deviation (r = 0.740, P amblyopia depth (r = 0.405, P = 0.049). Global distortion index (GDI, mean displacement) remained, overall, consistent over time (median change in GDI between baseline and 1 week = −0.03°, 1 month = −0.08°; x-axis Z = 4.4256, P amblyopia depth, and larger angles of ocular deviation. Assessment of distortions may be relevant for recent perceptual learning paradigms specifically

  12. [Relationship between prevalent features of central obesity and clustering of cardiometabolic diseases among Chinese elder people].

    Science.gov (United States)

    Jiang, Yong; Zhang, Mei; Li, Yi-chong; Li, Xiao-yan; Wang, Li-min; Zhao, Wen-hua

    2013-09-01

    To study the relationship between prevalence of central obesity and clustering of cardiometabolic diseases among Chinese elder people over 60 years old. A complex multistage stratified sampling survey on chronic diseases was conducted in 162 surveillance points, 31 provinces, China in 2010 by China CDC. The survey included face-to-face interview, physical measurement (body height, weight, waist circumference (WC) and blood pressure) and laboratory test (blood sugar, blood lipid and hemoglobin A1C), to collect the information about the prevalence of the risk factors as smoking, drinking, diet and physical activities and the prevalence of hypertension, diabetes and dyslipidemia. The survey selected 19 966 subjects who were over 60 years old. Central obesity was defined as WC ≥ 85 cm in males or ≥ 80 cm in females. The prevalence of central obesity among the elder people over 60 years old in different districts and populations was calculated; and the proportion of cardiometabolic diseases in groups of different WC was then analyzed. The prevalence of central obesity among elderly population over 60 years old was 48.6% (95%CI:46.1%-51.2%), including 39.7% (95%CI:37.2%-42.2%) males and 57.3% (95%CI:54.5%-60.1%) females. The proportion of females was higher than that of males (χ(2) = 474.63, P central obesity among elderly men. There was no significant association among females. The higher the family income, the higher the prevalence of central obesity. The prevalence of central obesity was 59.2% in urban area, which was much higher than that in rural area (43.5%) (χ(2) = 50.06, P central obese. The proportion of cardiometabolic diseases among central obesity was significantly higher than that among non-obese population. We should pay more concern about them in the future prevention and control of chronic diseases.

  13. Relationship between imaging and pathological features and clinical factors in surgical cases of temporal lobe epilepsy

    Energy Technology Data Exchange (ETDEWEB)

    Uesugi, Hideji; Matsuda, Hiroshi; Onuma, Teiichi [National Hospital for Mental, Nervous and Muscular Disorders, National Center of Neurology and Psychiatry, Kodaira, Tokyo (Japan); Shimizu, Hiroyuki; Arai, Nobutaka; Nakayama, Hiroshi; Maehara, Taketoshi; Yanashita, Akira

    1998-03-01

    The relationships between imaging, pathology and presumed causes in surgical cases of temporal lobe epilepsy (TLE) was studied. The subject was 62 patients. MRI, PET and SPECT were performed. Hematoxylin and eosin was used for pathological judgement. On MRI, mesial temporal sclerosis (MTS) was detected in 48 of 52 patients (92%); 32 (62%) had high-signal intensity on T2-weighted images; 31 (60%) had atrophy {l_brace}23 (44%) had high-signal intensity on T2+atrophy{r_brace}; 5 (10%) had calcified lesions; and 2 (4%) had cystic lesions. On PET and SPECT, abnormal cerebral blood flow was noted in 33 of 36 (92%). On pathological findings (61 cases), Ammon`s horn sclerosis (AHS), tumors, gliosis in lateral temporal and meningeal inflammatory finding were detected in 42 (69%), 10 (23%) and 8 (13%) cases, respectively, whereas 2 showed no abnormalities. The 2 patients with normal pathology showed both high-signal intensity and atrophy on MRI. The presumed causes of TLE were encephalitis/meningitis and/or suspected of these diseases in 15 patients (24%), injuries at birth in 5 (8%), and none in 42 (68%). The presumed causes in the 43 patients with AHS were encephalitis/meningitis in 11, injuries at birth in 3, and none in 29. Of the 15 patients in whom encephalitis/meningitis was estimated as the causes of TLE, only 6 (40%) had pathological evidence of meningeal inflammatory finding. Of the 42 patients in whom cause could not be determined, 2 had pathological evidence of meningeal inflammatory finding. (K.H.)

  14. [The Relationship between Violence and Clinical Features, Insight and Cognitive Functions in Patients with Schizophrenia].

    Science.gov (United States)

    Köşger, Ferdi; Eşsizoğlu, Altan; Sönmez, İpek; Güleç, Gülcan; Genek, Müge; Akarsu, Özlem

    2016-01-01

    We aimed to investigate the predictive factors of violent behavior in schizophrenia and the relationship between violent behavior and insight and cognitive functions in this study. 68 patients diagnosed with schizophrenia were separated into two groups; with a history of violent behavior (n = 30) and without (n = 38). Both group swere administered the Positiveand Negative Syndrome Scale (PANSS), Buss-Perry Aggression Questionnaire, Schedule for Assessing the Three Components of Insight, California Verbal Learning Test (CVLT), Trail Making Test, Wisconsin Card Sorting Test, and Stroop Test. Male gender, the number of hospitalizations, incompliance with the treatment, alcohol and substance abuse, the number of suicide attempts, the mean score of PANSS positive symptoms, PANSS general symptoms and PANSS total were significantly higher in patients with schizophrenia with a history of violent behavior, compared to non-violent group. Long delayed response subsection of CVLT mean score was lower in patients with violent behavior. Incompliance with the treatment (OR:5.927, p=0.041), alcohol and substance abuse (OR:21.089, p=0.000), and PANSS total score (OR:1.053, p=0.011)were identified as predictive factors of violent behavior in patients with schizophrenia. Lack of insight and executive function impairment are the core symptoms of schizophrenia and not seems to be associated with violent behavior in patients with schizophrenia. Impairment of memory may be associated with violent behavior in patients with schizophrenia. Incompliance with treatment, alcohol and substance abuse, and the severity of positive symptoms are important factors in predicting violence behavior in patients with schizophrenia.

  15. Relationship Between Blood Fibrinogen Concentration and Pathological Features of Cancer Patients: A 139-case Clinical Study

    Directory of Open Access Journals (Sweden)

    Da-Yong Lu

    2007-01-01

    Full Text Available Angiogenesis and coagulation are among the most consistent host responses to the presence of a malignant tissue. Pathological angiogenesis and coagulation are often occurred in patients with solid tumors, especially in the occurrence of neoplasm metastasis and as targets for anti-metastatic drugs such as antiangiogenesis agents, coagulation-mediated agents and anticancer drugs. Since fibrinogen (Fib is the most abundant and key haemostatic protein taking part in angiogenesis and coagulation, its biological and pathophysiological roles in cancer patients are intriguing. To continue foundational and translational research on Fib-related cancer pathogenesis, a phase II survey of 139 patients was carried out at the Central Hospital of Jing-An district and Shanghai University, Shanghai, China. The mean BFC of the cancer patients in this survey was overall about 35-50% greater than that in the normal population. This study showed that the mean BFC was higher in patients with long-distance metastases (N1M1 patients than in patients with no sign of long-distance metastases (N0M0 patients. Mean BFCs were 4.42 g/L (n= 21 in patients with lung cancer, 4.36 g/L, and in patients with hepatic cancer (n=5, and 4.63 g/L in patients with stomach cancer (n=8, all higher than the average value of the cancer patients overall (4.16 g/L. However, patients with bowel and colon cancers 3.79 g/L (n=16 showed lower than them. BFC levels increased with increasing cancer duration (latency > 1 year. There was a slight decrease in BFC after one or two treatment cycles, but a more marked decrease after surgery. We propose that the BFC level in cancer patients may be influenced by and related to many aspects of cancer progression such as metastatic conditions, tumor origins, patient’s pathological stage and disease latency. As an important first-hand pathologic-therapeutics relationship study, it provides evidence for the potentiality of a new approach of Fib-targeted as

  16. The Relationship of Forest Fires Detected by MODIS and SRTM Derived Topographic Features in Central Siberia

    Science.gov (United States)

    Ranson, Jon K.; Kovacs, Katalin; Kharuk, Viatcheslav; Burke, Erin

    2006-01-01

    improved Shuttle Radar Topographic Mission (SRTM) version 2 data at 100 m resolution, the distribution of hot spots was examined by elevation, slope and aspect as well as by forest type. The results show that more forest area burns at lower elevations but a larger percentage of the available forest area burns at higher elevations. This is probably because steep slopes occur at higher elevations. Fires are only more common on slopes with a southern exposure if the slope is steeper than 15 degrees. The next step in this study will be to monitor areas where the risk of fire is high (steep slopes with a southern exposure) and to refine this method by incorporating anthropogenic features for more accurate fire disturbance monitoring.

  17. A level set methodology for predicting the effect of mask wear on surface evolution of features in abrasive jet micro-machining

    Science.gov (United States)

    Burzynski, T.; Papini, M.

    2012-07-01

    A previous implementation of narrow-band level set methodology developed by the authors was extended to allow for the modelling of mask erosive wear in abrasive jet micro-machining (AJM). The model permits the prediction of the surface evolution of both the mask and the target simultaneously, by representing them as a hybrid and continuous mask-target surface. The model also accounts for the change in abrasive mass flux incident to both the target surface and, for the first time, the eroding mask edge, that is brought about by the presence of the mask edge itself. The predictions of the channel surface and eroded mask profiles were compared with measurements on channels machined in both glass and poly-methyl-methacrylate (PMMA) targets at both normal and oblique incidence, using tempered steel and elastomeric masks. A much better agreement between the predicted and measured profiles was found when mask wear was taken into account. Mask wear generally resulted in wider and deeper glass target profiles and wider PMMA target profiles, respectively, when compared to cases where no mask wear was present. This work has important implications for the AJM of complex MEMS and microfluidic devices that require longer machining times.

  18. Machine Translation

    Institute of Scientific and Technical Information of China (English)

    张严心

    2015-01-01

    As a kind of ancillary translation tool, Machine Translation has been paid increasing attention to and received different kinds of study by a great deal of researchers and scholars for a long time. To know the definition of Machine Translation and to analyse its benefits and problems are significant for translators in order to make good use of Machine Translation, and helpful to develop and consummate Machine Translation Systems in the future.

  19. 基于特征加工元的复杂箱体类零件工艺路线优化%Process Route Optimization of Complex Housing-type Parts Based on Feature Machining Element

    Institute of Scientific and Technical Information of China (English)

    徐立云; 史楠; 段建国; 李爱平

    2013-01-01

    针对工艺设计过程中工艺路线的优化问题,通过分析复杂箱体类零件特征,并将其细分为加工元,在考虑优化过程中存在的问题和相关工艺约束的基础上,将工艺路线的优化转化为加工元的优先排序.以机床、夹具和刀具变换次数最少建立目标优化模型,利用改进的遗传算法进行求解,避免了遗传算法“早熟”的缺陷.以某型号缸体为研究对象验证该改进算法的有效性,结果表明该算法具有很好的收敛性.%According to the current problems of process route optimization during the process design, the complex part features were divided into several feature machining elements,then the optimization of process route was changed to the priority of feature machining elements considering the problems in the process of optimization and related process constraints. An objective function was built by minimizing the transform of machine tools,fixtures and cutting tools,using an improved genetic algorithm to avoid the premature defects of the genetic algorithm. Finally, a case of cylinder block was illustrated to verify the efficiency of the hybrid algorithm, and the results show that the improved al gorithm has a good convergence.

  20. WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lee, J; Nishikawa, R [University of Pittsburgh, Pittsburgh, PA (United States); Reiser, I [The University of Chicago, Chicago, IL (United States); Boone, J [UC Davis Medical Center, Sacramento, CA (United States)

    2015-06-15

    Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

  1. Computer-aided Selection System for Cutting Tools and Parameters Based on Machining Features%面向加工特征的刀具和切削参数计算机辅助选择系统

    Institute of Scientific and Technical Information of China (English)

    郝传海; 刘战强; 任小平; 万熠

    2012-01-01

    Cutting tool manufacturers are facing increasing demands to supply a comprehensive advice service with relation to selection of appropriate tools and cutting parameters for a widely variety of part materials and machining features. The central element for process planning is to select the appropriate cutting tools and machining parameters, too. However, the main attention has been only paid on the part materials. It causes the mismatches between workpieces and tools. This study is to describe the development of a computer - aided selection system for cutting tools and cutting parameters based on machining features (FTCPS), which is designed to cover different component shapes including turning, milling, drilling as well as boring operation features. The kinematic link between machined surface feature with a simple icon based interface being used to input data records, and a relational database combined with data - driven method and rule - based decision logic is used to select cutting tool geometry and machining parameters for a range of machining operations. The system also utilizes mathematical model to calculate processing conditions (machining time in single path, cutting power, maximum harshness, etc. ). Process planning is completed in the end. By turning tools and turning parameters selection for example , the result shows the realization method of system. FTCPS will help the designers and manufacturing planners to select optimal set of cutting tools and cutting conditions.%切削刀具制造商面临围绕大量工件材料和加工特征为客户提供合理刀具和切削参数的现状,切削工艺规划的核心步骤也是刀具和切削参数的确定.确定刀具和切削参数一般多从零件材料角度出发,可能导致工件与刀具不匹配.文中提出面向加工特征的刀具和切削参数计算机辅助选择系统的开发.系统包括车削特征、铣削特征、钻削和镗削加工特征,系统利用特征

  2. Sustainable machining

    CERN Document Server

    2017-01-01

    This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

  3. Automatic generation of NC program for cylinder-type parts based on machining features%基于加工特征的缸体类零件数控程序自动生成

    Institute of Scientific and Technical Information of China (English)

    杨鹏宇; 曹忠亮; 张楠; 张旭堂

    2011-01-01

    根据企业的产品特点和现有的制造资源,提出了一种新的缸体类的加工特征模型.通过该模型的使用,在设计数控工艺时就能根据加工特征的种类,以模块化的方式进行数控程序的生成,很好地解决了传统工艺和数控工序的集成问题.同时以加工特征为单元,采用参数化技术实现数控程序的派生式生成,在三维环境下进行动态仿真,提高了数控程序设计效率和质量,从而实现了数控程序的模块化设计,提高了数控工艺设计的柔性和自适应能力.该方法在企业的工艺自动化系统中得到了验征.%According to the characteristic of products and the manufacturing resources in some enterprise, a new type of cylinder block machining feature model is proposed. Through the use of the model in the design process, the CNC program is generated by modular approach according to CNC machining features. CNC machining feature classification base process design method is a good solution to the integration issues of the traditional processes and CNC processes. With the machining feature as unit, parametric programming technique is applied to the NC code generation as well as in three-dimensional environment for dynamic simulation, which improves the efficiency of the CNC program design and quality, realizes the modular design of the CNC program and improves the NC process design flexibility and adaptive capacity. The proposed scheme is practically demonstrated by the enterprise system.

  4. Feature based man-hour forecasting model for aircraft structure parts NC machining%基于特征的飞机结构件数控加工工时预测模型

    Institute of Scientific and Technical Information of China (English)

    刘长青; 李迎光; 王伟; 林勇

    2011-01-01

    针对目前数控加工工时预测方法不能兼顾精度和效率的问题,通过分析飞机结构件的结构特点和工艺特点,基于加工特征属性提炼工时的影响因素,提出了一种基于特征的两级结构工时预测模型。首先依据加工特征属性的数据类型把加工特征分为枚举型和数值型,然后以枚举型特征属性作为分类器的输入构建模型第一级结构,数值型特征属性作为反向传播神经网络的输入构建模型第二级结构。基于该模型开发的系统已经在某大型数控企业得到了良好的应用,效率高且误差在10%以内。%Existing prediction methods for Numerical Control(NC) machining man-hours couldn't taken precision and efficiency into consideration simultaneously,by analzing the structure and process characteristics of aircraft structural parts,a feature-based two-level structure man-hour foresting model was proposed in view of man-hour influencing factors exteacted by machining feature attributes.Firstly,the machining features were classified into enumerative type and numerical value type according to machining feature attributes.Then,enumerative value type attribute was used as input of the classifier to establish first level of the model and numerical value type attribute was used as the input of back propagation neural network to set up second level of the model.The system developed based on this model was applied in a large NC enterprise with high efficiency and the error was within 10%.

  5. Relationship between Features of Desks and Chairs and Prevalence of Skeletal Disorders in Primary School Students in Abadan

    Directory of Open Access Journals (Sweden)

    Yadollah Zakeri

    2016-11-01

    Full Text Available BackgroundSitting on inappropriate benches, as well as the poor posture (body position during the years of growth, can lead to spinal disorders, fatigue and discomfort in students. This study aimed to investigate the relationship between features of desks and chairs and prevalence of some musculoskeletal disorders in primary school students in Abadan.Materials and MethodsThis cross-sectional study was conducted in 2015 in the city of Abadan- South West of Iran; for which, 383 primary school students were selected and studied through cluster sampling method. Data were collected by the checkered board and researcher-made questionnaire. Features and dimensions of desks and chairs of students were recorded and evaluated based on their condition (being standard or not. Statistical analysis was conducted using SPSS, version 22; and then, descriptive statistics and Chi-square test were conducted.ResultsStudy results showed that about 56.1% of the desks and chairs in under study schools were non-standard. It found that drooping shoulder (85.4% and scoliosis (81.7% were the more prevalent disorders and back straight (1.6% was the least frequent disorder. There was a significant relationship between the variable of non-standard desks and chairs and prevalence of drooping shoulders (P=0.001, scoliosis (P= 0.04, kyphosis (P=0.007 and lordosis (P=0.002 disorders in students.ConclusionThe non-standard-sized desks and chairs increase the prevalence of skeletal disorders in schoolchildren. Therefore, it is essential to pay attention to design and build standard classroom desks and chairs, which are best, adjust to students’ physics.

  6. 印尼 ASAHAN No.1水电站清污机设计特点%Design Features of Trashrack Cleaning Machine, ASAHAN No.1 Hydropower Station, Indonesia

    Institute of Scientific and Technical Information of China (English)

    刘大宏; 张继雄; 杨鹏隆

    2012-01-01

      Through introduction to design features and operation briefing of transhrack cleaning machine of ASAHAN No.1 Hydropower Station, Indonesia, presenting that arrangement and type selection of trashrack cleaning machine at power intake are quite important for safety operation and economic benefit of the station.Proper arrangement and reasonable design of the trashrack cleaning machine not only result in convenient operation management but also create excellent economic benefit.%  通过对印尼Asahan No.1水电站清污机设计特点及运行概况介绍,指出电站取水口清污设备的布置及选型对电站的安全运行和经济效益十分重要,清污设备布置恰当、设计合理不仅给电站的运行管理带来方便,更能创造出良好的经济效益.

  7. Novel Switched Flux Permanent Magnet Machine Topologies

    Institute of Scientific and Technical Information of China (English)

    诸自强

    2012-01-01

    This paper overviews various switched flux permanent magnet machines and their design and performance features,with particular emphasis on machine topologies with reduced magnet usage or without using magnet,as well as with variable flux capability.

  8. An Investigation of the Relationship Between Automated Machine Translation Evaluation Metrics and User Performance on an Information Extraction Task

    Science.gov (United States)

    2007-01-01

    variable relationships is appropriate for many applications such as ecological and social science studies. I derive two distinct expressions yielding two...search for other domains of applicability. Datasets which are highly cross-classified are often available in several applications such as ecology ...Validation of MT Evaluation Metrics Across Languages,” In Proceedings of the Language Resources and Evaluation Conference (LREC ’02), Las Palmas , Canary

  9. Evaluation of the Relationship Between Quantitative Ultrasound Parameters and Pain and Demographic Features in Pre and Postmenopausal Women

    Directory of Open Access Journals (Sweden)

    Erdal Yücel

    2015-12-01

    Full Text Available Osteoporosis is a systemic metabolic disease which is characterized by low bone mass and microarchitectural damage of bone tissue resulting in increased bone fragility. History, physical examination, laboratory investigations and different imaging technics are used in diagnosis of osteoporosis. Quantitative ultrasound (QUS is an alternative method for diagnosis of osteoporosis and evaluation of fracture risk. In this study we aimed to evaluate the association between quantitative ultrasound values and pain and demographic features in pre- and postmenopausal women. One hundred voluntary women aged over 40 years who were admitted to hospital in one day were included. Eight of these were excluded for different reasons. Demographic features and pain parameters were inquired. Quantitative ultrasound evaluation was performed with Hologic Sahara Clinical Bone Sonometer equipment. Speed of sound (SOS and broadband ultrasound attenuation (BUA values and stiffness parameters were used for evaluation. Twenty four (26.1% of the objects were premenopausal and 68 (73.9% were postmenopausal. 24 (100% of premenopausal objects and 57 (% 83.8 of postmenopausal objects had pain (p=0.061. In evaluation with QUS, mean BUA values were 67.9 ± 13.5 in premenopausal and 60.0 ± 15.8 in postmenopausal women (p=0.026. Mean stiffness values were found 91.2 ± 13.6 in premenopausal and 80.1 ± 17.6 in postmenopausal women (p=0.013. In all of the subjects, QUS parameters were found negatively corraleted with age, while no relationship was found with occupation, education level, body mass index (BMI and pain. Consequently, we found negative correlation between age and QUS parameters, but for other demographic features there was no correlation. This study will be more sensitive and specific if performed on more patients and supported by other measurement methods.

  10. Relationship between Full-Field Digital Mammographic Features and Clinicopathologic Characteristics in 176 Cases with Breast Cancer

    Institute of Scientific and Technical Information of China (English)

    Zhe Sun; Hongwei Liang; Huimian Xu

    2005-01-01

    OBJECTIVE Different mammographic features are probably predictive of different prognosis. However, ambiguity still exists in understanding the relationship between them. In resent years, digital mammography has been available for clinical use which has led to a revolution in the resolving of images and an increase in early-stage breast cancer detection.Based on the above knowledge, this study was performed to evaluate the relationship between full-field digital mammographic features and clinicopathologic characteristics in breast cancer.METHODS Digital mammograms of 176 patients with pathologically proven breast cancer were reviewed. Also, clinical and pathologic records (histological types and axillary lymph nodes status) were retrospectively examined.RESULTS Most of the patients with a solitary microcalcification were young women under the age of 50(84.4%), but the majority of the patients with microcalcifications complicated by a mass were elderly women. Microcalcifications detected by mammography occurred frequently in ductal carcinoma in situ (28.1%) and in early invasive carcinoma (15.6%). Breast cancers with expression of microcalcifications combined with a spiculate mass had a high metastatic rate of axillary lymph nodes (69.4%). A high metastatic rate of axillary lymph nodes was also found in the patients with solitary worm-like microcalcifications (57.1%), solitary spiculate mass (53.7%) and solitary non-worm-like microcalcifications (44.4%). Simple worm-like microcalcifications accompanied with metastasis of 4 to 9 axillary lymph nodes occurred in 42.9% of the(6/14) cases. The patients with microcalcifications combined by a spiculate mass and with metastasis of 4 to 9 axillary lymph nodes accounted for 27.8% (10/36) of the cases,and those with metastases of 10 and over accounted for 16.7% (6/36).CONCLUSION Solitary microcalcifications occur frequently in young women and are usually associated with early breast cancer. There is a close relationship

  11. A Radar Active Jamming Sorting Based on Feature Weighted and Support Vector Machine%基于特征加权与SVM的雷达有源干扰分类技术

    Institute of Scientific and Technical Information of China (English)

    唐翥; 张兵; 李广强; 沈浩浩

    2014-01-01

    In order to improve the the active jamming sorting accuracy effectively,a sorting method based on feature weighted and support vector machine is proposed. The feature weighting concept according to the different importance degree of each signal feature parameters to signal classification in the process is introduced. Using the gray relational analysis to obtain the weight of each feature,and some weak characteristics for the huge impact on the classification results are avoided. Finally,using support vector machine classifier,the active radar interference signal is classified and identified. Simulation experiments show that this method can improve the recognition rate of the radar active interference signal type effectively.%为了有效提高雷达有源干扰分类正确率,提出一种基于特征加权与支持向量机的分类方法。针对分类过程中各信号特征参数对信号分类的重要度不同,引入特征加权的概念。利用灰色关联分析方法求取各特征权重,避免一些弱特征对分类结果产生较大影响。最后利用支持向量机分类器,对雷达有源干扰信号进行了分类识别。通过仿真实验证明,该方法可以有效提高雷达有源干扰信号类型的识别率,具有很好的通用性。

  12. Fishery text categorization method based on feature word weight and support vector machine%基于特征词权值的渔业文本分类研究

    Institute of Scientific and Technical Information of China (English)

    谷军; 何南

    2014-01-01

    Fishery text categorization is an effective way to make full use of fishery information resources. According to the structural characteristics of Chinese literatures, a fishery text categorization method based on feature word weights and support vector machine was put forward. Text vector space was constructed by using vector space model(VSM) and the feature items in every text feature vector were calculated in consideration of feature word weights. Support vector machine(SVM) was employed as the classifier and the standard documents downloaded from CNKI as the test data. The experiments were checked with precision rate and recall rate. The experimental results show that our fishery text categorization method owns satisfactory categorization performance.%渔业文本分类是充分利用渔业信息资源的有效途径。针对中文文献资料的结构特点,提出一种结合特征词权值和支持向量机(Support Vector Machine,SVM)的渔业文本分类方法,利用向量空间模型(Vector Space Model, VSM)构建文本向量空间,并结合特征词权值计算文本特征向量中的各特征项,将构建的文本向量送入 SVM 进行渔业文本分类。采用中国知网下载的标准文档进行了实验测试,并考察了准确率和召回率两个指标,实验结果表明,文章提出的渔业文本分类方法具有较好的分类效果。

  13. Extension Association Method of Machining Features for Axis Class Parts Based on Structure Similarities%基于结构相似性的轴类零件加工特征可拓关联方法

    Institute of Scientific and Technical Information of China (English)

    黄风立; 徐春光; 顾金梅; 钱苏翔

    2015-01-01

    在现有轴类零件相似性检索方法的基础上,将轴类零件的信息模型划分为结构特征层和加工工艺层,并利用可拓基元方法进行形式化描述.提出从轴类零件结构和加工工艺两方面进行相似性检索的方法:首先基于结构矩阵表达方法,对轴类零件的结构特征进行相似性检索,从实例库中检索出2~5个相似实例,然后以可拓综合关联函数进行加工特征的相似度匹配,得出与新零件最相似的零件.通过实例验证,该方法具有可行性,并且可在检索过程中动态调整系数水平,具有检索适应性强的优点.%The information model of axis class parts are di-vided into structure feature layer and machining feature layer based on the similarity retrieval method of existing axis class parts. The extensible basic elementmethod is used for the formal description. The method makes similarity retrieval from the struc-ture and machining process aspects are provided. At first, the structure features perform the similarity retrieval based on the method of structure descripted by matrix,2~5 similar cases will be searched in the instance database. Then, the best similarity part is selected by the method, which is applied in similarity match of machining feature using extension comprehensive corre-lation functions. The good feasibility of this method is verified by a simple example, and the method has an advantage that its a-daptability and derivative are strong, with the level adjustment of coefficient in retrieval process.

  14. Simple machines

    CERN Document Server

    Graybill, George

    2007-01-01

    Just how simple are simple machines? With our ready-to-use resource, they are simple to teach and easy to learn! Chocked full of information and activities, we begin with a look at force, motion and work, and examples of simple machines in daily life are given. With this background, we move on to different kinds of simple machines including: Levers, Inclined Planes, Wedges, Screws, Pulleys, and Wheels and Axles. An exploration of some compound machines follows, such as the can opener. Our resource is a real time-saver as all the reading passages, student activities are provided. Presented in s

  15. Cyclic development of igneous features and their relationship to high-temperature hydrothermal features in the Henderson porphyry molybdenum deposit, Colorado

    Science.gov (United States)

    Carten, R.B.; Geraghty, E.P.; Walker, B.M.

    1988-01-01

    The Henderson porphyry molybdenum deposit was formed by the superposition of coupled alteration and mineralization events, of varying intensity and size, that were associated with each of at least 11 intrusions. Deposition of molybdenite was accompanied by time-equivalent silicic and potassic alteration. High-temperature alteration and mineralization are spatially and temporally linked to the crystallization of compositionally zoned magma in the apex of stocks. Differences in hydrothermal features associated with each intrusion (e.g., mass of ore, orientation and type of veins, density of veins, and intensity of alteration) correlate with differences in primary igneous features (e.g., composition, texture, morphology, and size). The systematic relations between hydrothermal and magmatic features suggest that primary magma compositions, including volatile contents, largely control the geometry, volume, level of emplacement, and mechanisms of crystallization of stocks. These elements in turn govern the orientations and densities of fractures, which ultimately determine the distribution patterns of hydrothermal alteration and mineralization. -from Authors

  16. STEP-NC oriented parts setup planning based on machining feature clustering%面向STEP-NC基于加工特征规则聚类的零件装夹规划

    Institute of Scientific and Technical Information of China (English)

    欧阳华兵; 沈斌

    2012-01-01

    Aiming at the setup planning problem in parts process planning,a heuristic clustering setup planning solving method oriented to STEP-NC machining feature was proposed.Based on the analysis of STEP-NC data model and machining unit,a mathematical model for setup planning was established.The machining unit was clustered by using manufacturing priority rules of machining features,and the setup scheme set was formed.Through expert estimation and evaluating scheme,these setup scheme sets were ordered.Thus the setup planning scheme that accord with parts setup demand was generated.The concrete shape and stability of parts were considered and each unit’s locating surface as well as clamping surface were determined.Based on Solidworks 3D computer aided design platform,the generation of parts setup planning was realized.The proposed algorithm was verified by examples.%针对零件工艺规划过程中的装夹规划问题,提出一种面向STEP-NC加工特征的启发式聚类装夹规划求解方法。在分析STEP-NC数据模型和加工单元的基础上,建立了零件装夹规划的数学模型,基于加工特征制造优先级规则对加工单元进行聚类分组,形成零件的装夹方案集;随后通过专家打分和评定策略对这些装夹方案集进行排序,生成符合零件装夹要求的装夹规划方案。装夹规划考虑了零件的具体形状及其稳定性等多种约束条件,确定零件每一个加工单元的定位面和装夹面,较好地体现了零件实际加工过程中的装夹情况。基于Solid-works三维计算机辅助设计平台实现了零件装夹规划的生成,通过实例对所提算法进行了验证。

  17. Prediction of Machine Tool Condition Using Support Vector Machine

    Science.gov (United States)

    Wang, Peigong; Meng, Qingfeng; Zhao, Jian; Li, Junjie; Wang, Xiufeng

    2011-07-01

    Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

  18. 支持向量机的全局局部特征融合目标识别%Target Recognition Based on Support Vector Machine(SVM) Features Fusion

    Institute of Scientific and Technical Information of China (English)

    易晓柯

    2011-01-01

    This paper proposes a target recognition method based on support vector machine features fusion. The method uses nonlinear discrimination analysis and local retain mapping to extract the global and local features and then makes features fusion in order to extract more comprehensive samples and obtain more accurate identification results. Then the support vector machine is used for classification. Since its power to deal with nonlinear and small samples, the identification accuracy is further improved. The simulation results of three plane targets show the effectiveness.%提出一种基于支持向量机的全局局部特征融合目标识别方法,并将其运用到雷达一维距离像目标识别.该方法采用非线性辨别方法与局部保留映射方法分别提取样本的非线性全局特征与局部特征,并进行特征融合,以便提取更全面的样本特征,得到更加准确的识别结果,随后采用支持向量机进行分类识别,利用其对于非线性小样本问题的强大处理能力,进一步改善识别结果.对三种飞机目标的实测雷达一维距离像进行了仿真实验,结果表明了方法的有效性.

  19. Conformational changes in DNA-binding proteins: relationships with precomplex features and contributions to specificity and stability.

    Science.gov (United States)

    Andrabi, Munazah; Mizuguchi, Kenji; Ahmad, Shandar

    2014-05-01

    Both Proteins and DNA undergo conformational changes in order to form functional complexes and also to facilitate interactions with other molecules. These changes have direct implications for the stability and specificity of the complex, as well as the cooperativity of interactions between multiple entities. In this work, we have extensively analyzed conformational changes in DNA-binding proteins by superimposing DNA-bound and unbound pairs of protein structures in a curated database of 90 proteins. We manually examined each of these pairs, unified the authors' annotations, and summarized our observations by classifying conformational changes into six structural categories. We explored a relationship between conformational changes and functional classes, binding motifs, target specificity, biophysical features of unbound proteins, and stability of the complex. In addition, we have also investigated the degree to which the intrinsic flexibility can explain conformational changes in a subset of 52 proteins with high quality coordinate data. Our results indicate that conformational changes in DNA-binding proteins contribute significantly to both the stability of the complex and the specificity of targets recognized by them. We also conclude that most conformational changes occur in proteins interacting with specific DNA targets, even though unbound protein structures may have sufficient information to interact with DNA in a nonspecific manner. Copyright © 2013 Wiley Periodicals, Inc.

  20. Feature gene selection for Chinese hamster classification based on support vector machine%基于支持向量机的中国地鼠分类特征基因选取

    Institute of Scientific and Technical Information of China (English)

    杨俊丽; 刘田福

    2011-01-01

    针对中国地鼠基因表达谱数据维数高和样本小的特点,提出一种基于支持向量机(SVM)的分类特征基因选取方法.该方法利用改进的Fisher判别(FDR)基因特征计分准则剔除分类无关基因,提出由空间距离和功能距离组成的新距离作为相似性度量的标准进行冗余基因的剔除,采用SVM作为分类器检验特征基因的分类性能.实验结果表明,该方法有效地剔除了分类无关基因和冗余基因,选取的特征基因满足对中国地鼠正确分类的最小基因数.%Concerning the gene expression profile of Chinese hamster feature, such as high-dimension and small sample,a method of feature selection for Chinese hamster classification based on Support Vector Machine (SVM) was proposed in this paper. The method used improved FDR gene feature score criterion to remove the genes irrelevant to the classification. A new distance composed by space distance and function distance was proposed as the criterion of comparability to remove redundant genes. A SVM was used as classifier to validate the classification performance of the feature genes selected. The experimental results show that this method effectively removes the irrelevant and redundant genes, and selected the feature genes that meet the needs of least feature genes which classify accurately on Chinese hamster.

  1. Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support Vector Machine Based Approach

    Directory of Open Access Journals (Sweden)

    Khader Shameer

    2010-06-01

    Full Text Available 3-dimensional domain swapping is a mechanism where two or more protein molecules form higher order oligomers by exchanging identical or similar subunits. Recently, this phenomenon has received much attention in the context of prions and neuro-degenerative diseases, due to its role in the functional regulation, formation of higher oligomers, protein misfolding, aggregation etc. While 3-dimensional domain swap mechanism can be detected from three-dimensional structures, it remains a formidable challenge to derive common sequence or structural patterns from proteins involved in swapping. We have developed a SVM-based classifier to predict domain swapping events using a set of features derived from sequence and structural data. The SVM classifier was trained on features derived from 150 proteins reported to be involved in 3D domain swapping and 150 proteins not known to be involved in swapped conformation or related to proteins involved in swapping phenomenon. The testing was performed using 63 proteins from the positive dataset and 63 proteins from the negative dataset. We obtained 76.33% accuracy from training and 73.81% accuracy from testing. Due to high diversity in the sequence, structure and functions of proteins involved in domain swapping, availability of such an algorithm to predict swapping events from sequence and structure-derived features will be an initial step towards identification of more putative proteins that may be involved in swapping or proteins involved in deposition disease. Further, the top features emerging in our feature selection method may be analysed further to understand their roles in the mechanism of domain swapping.

  2. Electric machine

    Science.gov (United States)

    El-Refaie, Ayman Mohamed Fawzi [Niskayuna, NY; Reddy, Patel Bhageerath [Madison, WI

    2012-07-17

    An interior permanent magnet electric machine is disclosed. The interior permanent magnet electric machine comprises a rotor comprising a plurality of radially placed magnets each having a proximal end and a distal end, wherein each magnet comprises a plurality of magnetic segments and at least one magnetic segment towards the distal end comprises a high resistivity magnetic material.

  3. 人机交互界面中形状特征的视觉显著度计算%Visual Salience Calculation of Shape Feature for Human-Machine Interface

    Institute of Scientific and Technical Information of China (English)

    王宁; 余隋怀; 周宪; 肖琳臻

    2016-01-01

    形状特征是影响人机交互界面视觉工效的关键因素,为使人机交互界面能更好地适应用户的生理及心理特性、提升用户体验,需要建构一种人机交互界面中形状特征的视觉显著度计算模型。在分析形状特征对视觉显著度影响程度的基础上,针对人机交互界面中的典型形状,利用内接正方形将形状分割为多个部分,使用相关三角形对形状部分的视觉显著度进行计算,取其中最大值作为形状的视觉显著度,实现形状视觉显著度的量化分析与计算,并通过眼动追踪实验验证该方法的有效性。%Shape feature is an important element of visual ergonomics of human-machine interface. In order to improve the user experience and increase operation efficiency, a visual saliency calculation model for shape features of human-machine interface isproposed. The influence of shape features to visual saliency is analysed and several specific shapes are obtained which are used in huaman-machine interface frequently at first. The inscribed square is used to segment the shapes and some specific parts are got consequently. The triangle is related to the parts to calculate the parts’ visual saliency. The maximal value of parts’ visual saliency is taken as the visual saliency of the shape. An eye tracking experiment verifies the effectiveness of the proposed visual saliency calculation model.

  4. 基于机器视觉的作物多姿态害虫特征提取与分类方法%Feature extraction and classification method of multi-pose pests using machine vision

    Institute of Scientific and Technical Information of China (English)

    李文勇; 李明; 陈梅香; 钱建平; 孙传恒; 杜尚丰

    2014-01-01

    Pest identification and classification is time-consuming work that requires expert knowledge for integrated pest management. Automation, including machine vision combined with pattern recognition, has achieved some applications in areas such as fruit sorting, robotic harvesting, and quality detection, etc. Automatic classification and counting of pests using machine vision is still a challenge because of variable and uncertain poses of trapped pests. Therefore, using Pseudaletia separata, Conogethes punctiferalis, Helicoverpa armigera, Agrotis ypsilon with different poses as research objects, this paper presents a novel classification method for multi-pose pests based on color and texture feature groups and using a multi-class support vector machine. 320 images were taken using field samples with an original resolution of 4 288×2 848. The subimages of pests with 640×640 pixel size were obtained from original images for computational efficiency. Color features in RGB and HSV spaces, statistical texture features, and wavelet-based texture features were extracted. Six feature vector groups were constructed using those features. In order to select effective feature parameters of each group, a genetic algorithm was designed to optimize feature vectors based on 10-fold cross-validation. Finally, the one-against-one DAGMSVM (acronym as yet undefined) algorithm was applied to classify and recognize the four kinds of target pests and to find the best feature group. 80 images (60 for the training set and 20 for the testing set) were adopted for each species. Parameter numbers were calculated and analyzed after optimization, thus the best parameters were selected for each group. The training time of the SVM model and classification accuracy, which contains false negative and false positive details, were compared between pre-optimization and post-optimization. The results showed that the highest parameter optimization ratio is from the sixth feature group with a dimension

  5. Teaching adolescents: Relationships between features of instruction and student engagement in high school mathematics and science classrooms

    Science.gov (United States)

    Dibianca, Richard Paul

    In an examination of the experiences of 375 high school students enrolled in two urban comprehensive high schools, the present study is an effort to identify those elements of high school math and science and instruction that captivate students' interest. Data were gathered over the course of approximately 20 lessons for each of 17 math and science classes using the Experience Sampling Method. A descriptive analysis revealed that the classes were dominated by traditional instructional formats such as lecture, demonstration, recitation, and reviewing problems. Lessons afforded minimal opportunities for students to use technology, instruments, and equipment; to work with other students in order to complete a task; to have any choices regarding the completion of their task; and to apply the lesson topics to the "real world." Features of academic tasks such as these, which have often been proposed to correlate with higher levels of student engagement, were the independent variables for the study. Student engagement---as measured by the student indices of involvement and concentration, as well as their overall desire to be in a given classroom at a given time---was the dependent variable. The general state of engagement among the students was found to be modest. A second set of analyses examined the relationships that each of the study's proposed independent variables had to student engagement. The more an instructional format was student-paced, challenging, and interactive, the higher the levels of student engagement. Novelty also seemed to be at work; engagement levels were frequently higher in those classes that experienced a given format less often than did other classes. Second, the presence of each of the task features that was proposed to enhance student engagement in high school math and science classes did, in fact, correspond to higher levels of student engagement. Finally, the correlation between teacher and student engagement was modest. Throughout the study

  6. Expression of HIF-1α in breast cancer and precancerous lesions and the relationship to clinicopathological features

    Institute of Scientific and Technical Information of China (English)

    Yun’ai Liang; Zengxin Li; Gangping Wang

    2014-01-01

    Objective: The aim of this study was to observe the expressions and clinical significance of HIF-1α in breast cancer and precancerous lesions, and analyze the relationship between the expressions and clinicopathological features in breast cancer. Methods: We analyzed the HIF-1α expression in 128 cases of invasive ductal carcinomas, 146 precancerous lesions patients including 89 cases of ductal carcinoma in situ and 57 cases of atypical ductal hyperplasia. 53 cases of usual ductal hyperplasia breast tissues were selected as a control group. The specimens were evaluated for HIF-1α, estrogen re-ceptor (ER) & progesterone receptor (PR), epidermal growth factor receptor type 2 (HER2/neu) and Ki-67. Immunoreactivity was semi-quantitatively evaluated in at least 1000 cells examined under the microscope at 40 × magnification and recorded as the percentage of positive tumor cells over the total number of cells examined in the same area. The percentage scores were subsequently categorized. The express of HIF-1α and their relationship with multiple biological parameters including ER& PR, HER2/neu and Ki-67, the biomarkers levels of CA153, CA125 TSGF, and CEA in blood serum and nipple discharge, histological grade, region lymph node metastasis, distant metastasis and recurrence on files were also assessed. Results:Compared with usual ductal hyperplasia, the positive expression rate of HIF-1α in atypical ductal hyperplasia, ductal carci-noma in situ and invasive ductal carcinomas group was significantly increased (P 14% groups, histological grade (I + II) and grade III invasive ductal carcinomas groups, with lymph node metastasis, distant metastasis and recurrence groups (P50 years), tumor diameter (≤ 2 cm vs > 2 cm; P > 0.05). The nipple discharge and serum levels of CA153, TSGF, CA125 and CEA in invasive ductal carcinomas HIF-1α positive patients were significantly higher than those in the negative patients (P <0.05). Conclusion: In breast cancer, HIF-1α expression

  7. Application of 'Numerical Control Special Features' on Numerical Control Quenching Machine Tool%“数控特殊功能”在数控淬火机床上的应用

    Institute of Scientific and Technical Information of China (English)

    王正

    2014-01-01

    This paper describes the special features on the devel-opment of CNC machine tool CNC quenching - "interrupt macro insert"function", " while effectively manual and automatic function", and"manual positioning function", which provides a new way to accelerate the pace of production and improve production efficiency .%本文介绍了在数控淬火机床上开发数控的特殊功能-“中断宏插入”功能、“手动自动同时有效功能”,“手动定位功能”,这些功能的应用是加快生产节拍、提高生产效率的新方法。

  8. An examination of the relationship between childhood emotional abuse and borderline personality disorder features: the role of difficulties with emotion regulation.

    Science.gov (United States)

    Kuo, Janice R; Khoury, Jennifer E; Metcalfe, Rebecca; Fitzpatrick, Skye; Goodwill, Alasdair

    2015-01-01

    Childhood abuse has been consistently linked with borderline personality disorder (BPD) and recent studies suggest that some forms of childhood abuse might be uniquely related to both BPD and BPD features. In addition, difficulties with emotion regulation have been found to be associated with childhood abuse, BPD, as well as BPD features. The present study examined (1) whether frequency of childhood emotional abuse is uniquely associated with BPD feature severity when controlling for other forms of childhood abuse and (2) whether difficulties with emotion regulation accounts for the relationship between childhood emotional abuse and BPD feature severity. A sample of undergraduates (n=243) completed the Childhood Trauma Questionnaire - Short Form, Difficulties in Emotion Regulation Scale, and Borderline Symptom List-23. Multiple regression analyses and Structural Equation Modeling were conducted. Results indicated that frequency of childhood emotional abuse (and not sexual or physical abuse) was uniquely associated with BPD feature severity. In addition, while there was no direct path between childhood emotional abuse, childhood physical abuse, or childhood sexual abuse and BPD features, there was an indirect relationship between childhood emotional abuse and BPD features through difficulties with emotion regulation. These findings suggest that, of the different forms of childhood abuse, emotional abuse specifically, may have a developmental role in BPD pathology. Prevention and treatment of BPD pathology might benefit from the provision of emotion regulation strategies.

  9. The Machine within the Machine

    CERN Multimedia

    Katarina Anthony

    2014-01-01

    Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

  10. Machine Learning for Medical Imaging.

    Science.gov (United States)

    Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

    2017-01-01

    Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. (©)RSNA, 2017.

  11. The distribution and inter-relationships of radiologic features of osteoarthrosis of the hip. A survey of 4151 subjects of the Copenhagen City Heart Study: the Osteoarthrosis Substudy

    DEFF Research Database (Denmark)

    Jacobsen, Steffen; Sonne-Holm, Stig; Søballe, Kjeld

    2004-01-01

    OBJECTIVE: The aims of this study were to investigate the influence of sex, age and individual physical and occupational factors on the distribution of radiographic features of hip joint osteoarthritis (OA), and to determine the inter-relationships between the primary radiographic OA discriminato...

  12. Machine Learning

    CERN Document Server

    CERN. Geneva

    2017-01-01

    Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

  13. People and Machines: Changing Relationships?

    Science.gov (United States)

    Malinconico, S. Michael

    1983-01-01

    Discussion of the effect on the workforce of the application of electronic data processing technologies to information handling activities highlights task specialization; white collar alienation; feelings of powerlessness, meaninglessness, and normlessness in workers; the chronology of automation; and experimentation and communication. Nineteen…

  14. PENETRATION QUALITY EVALUATION IN ROBOTIZED ARC WELDING BASED ON SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    Ye Feng; Song Yonglun; Li Di; Lai Yizong

    2003-01-01

    A quality monitoring method by means of support vector machines (SVM) for robotized gas metal arc welding (GMAW) is introduced. Through the feature extraction of the welding process signal,a SVM classifier is constructed to establish the relationship between the feature of process parameters and the quality of weld penetration. Under the samples obtained from auto parts welding production line, the learning machine with a radial basis function kernel shows good performance. And this method can be feasible to identify defect online in welding production.

  15. A Study of the Subject Categorization of the MIS-related Journals in the ISI Databases Using Topical Features in the Text Content and Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Sung-Chien Lin

    2015-07-01

    Full Text Available In this study we analyzed and discussed that the MIS-related journals under the ISI subject category of IS&LS are simultaneously given with subject category Management, using methods of topic modeling, journal clustering and subject category prediction. In the experiment of journal clustering, all journals under subject category Management and other journals also having similar topical features can be gathered into a cluster, and “management” is their common and the most distinct topic. Because the journals belonged to this cluster are almost same to those in the MIS clusters generated by the previous studies, we considered it as the MIS cluster in this study. In the second experiment, we used the classification and regression tree (CART technique to predict assignment of subject category with that the journals in the original subject category Management and in the MIS cluster produced in this study as positive examples, respectively. The trees generated by the two tests both used the occurring probabilities of the topic “management” as the main classification rule. However, in the latter test, we did not only obtain a simpler classification tree but also had a result with less predicting errors. This means that if all journals in the MIS cluster could be given with subject category Management, the retrieval results can be more effective and complete.

  16. The thematic structure of passenger comfort experience and its relationship to the context features in the aircraft cabin.

    Science.gov (United States)

    Ahmadpour, Naseem; Lindgaard, Gitte; Robert, Jean-Marc; Pownall, Bernard

    2014-01-01

    This paper describes passenger comfort as an experience generated by the cabin interior features. The findings of previous studies are affirmed regarding a set of 22 context features. Passengers experience a certain level of comfort when these features impact their body and elicit subjective perceptions. New findings characterise these perceptions in the form of eight themes and outline their particular eliciting features. Comfort is depicted as a complex construct derived by passengers' perceptions beyond the psychological (i.e. peace of mind) and physical (i.e. physical well-being) aspects, and includes perceptual (e.g. proxemics) and semantic (e.g. association) aspects. The seat was shown to have a focal role in eliciting seven of those themes and impacting comfort through its diverse characteristics. In a subsequent study, a group of aircraft cabin interior designers highlighted the possibility of employing the eight themes and their eliciting features as a framework for design and evaluation of new aircraft interiors.

  17. Attention: A Machine Learning Perspective

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    2012-01-01

    We review a statistical machine learning model of top-down task driven attention based on the notion of ‘gist’. In this framework we consider the task to be represented as a classification problem with two sets of features — a gist of coarse grained global features and a larger set of low...

  18. Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

    2017-04-21

    The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

  19. On the relationship between retrospective childhood ADHD symptoms and adult BPD features: the mediating role of action-oriented personality traits.

    Science.gov (United States)

    Carlotta, Davide; Borroni, Serena; Maffei, Cesare; Fossati, Andrea

    2013-10-01

    A number of studies have reported data suggestive of a significant association between ADHD and BPD, nevertheless, the nature of this relation has not been fully understood yet. In our study, we tried to evaluate if the relationship between retrospectively assessed ADHD symptoms and adult BPD features could mediated by selected temperament/personality traits. Four hundred forty-seven in- and outpatients consecutively admitted to the Clinical Psychology and Psychotherapy Unit of the Scientific Institute H San Raffaele of Milan, Italy, were administered the Italian versions of the following instruments: Structured Clinical Interview for DSM-IV Axis II Personality Disorders, Version 2.0 (SCID-II), Wender Utah Rating Scale (WURS), Temperament and Character Inventory-Revised (TCI-R), Barratt Impulsiveness Scale-11 (BIS-11), and Aggression Questionnaire (AQ). Our mediation analyses showed that the combination of impulsivity, aggression, novelty seeking, and juvenile conduct problems completely mediate the relationship between retrospectively assessed ADHD symptoms and current BPD features.

  20. The Relationship between Syntactic Structure Analysis Features, Histological Grade and High-Risk HPV DNA in Cervical Intraepithelial Neoplasia

    Directory of Open Access Journals (Sweden)

    Arnold‐Jan Kruse

    2004-01-01

    Full Text Available Aim: To assess the correlation between syntactic structure analysis (SSA features, revised dysplasia grade and the presence of high‐risk human papillomavirus DNA in cervical intraepithelial neoplasia (CIN. Materials and methods: HPV polymerase chain reaction (PCR was assessed in 101 consecutive biopsies and consensus in CIN grade between the experts occurred in 88 cases (CIN1=16, CIN2=27, CIN3=45. SSA was performed in the diagnostic histological section of the CIN lesions in these patients and SSA features were compared with the blind review CIN grade, and presence/absence of high‐risk HPV DNA. Results: One of the SSA features (points from which the surrounding surfaces has 4 edges, PECO‐4 was significantly different between all three consensus CIN grades. Many more features revealed significant differences between CIN1 and CIN2 or between CIN2 and CIN3 cases. With stepwise discriminant analysis, the best multivariate combination of features to distinguish the different CIN grades were the Maximum MST Line Length (MML and the Area Disorder. Crude overall classification of the consensus grades with these features was 69%. The MML and the Area Disorder is also the best combination to distinguish cases with and without high‐risk HPV DNA (77% correct classifications. Conclusions: SSA features are correlated with both CIN grade and presence of high‐risk HPV DNA, but the discrimination power is not good enough to be used as a routine method for quality control of subjective grade or as a surrogate marker for high‐risk HPV DNA presence.

  1. Tree species diversity and its relationship to stand parameters and geomorphology features in the eastern Black Sea region forests of Turkey.

    Science.gov (United States)

    Ozcelik, Ramazan; Gul, Altay Ugur; Merganic, Jan; Merganicova, Katarina

    2008-05-01

    We studied the effects of stand parameters (crown closure, basal area, stand volume, age, mean stand diameter number of trees, and heterogeneity index) and geomorphology features (elevation, aspect and slope) on tree species diversity in an example of untreated natural mixed forest stands in the eastern Black Sea region of Turkey. Tree species diversity and basal area heterogeneity in forest ecosystems are quantified using the Shannon-Weaver and Simpson indices. The relationship between tree species diversity basal area heterogeneity stand parameters and geomorphology features are examined using regression analysis. Our work revealed that the relationship between tree species diversity and stand parameters is loose with a correlation coefficient between 0.02 and 0.70. The correlation of basal area heterogeneity with stand parameters fluctuated between 0.004 and 0.77 (R2). According to our results, stands with higher tree species diversity are characterised by higher mean stand diameter number of diameter classes, basal area and lower homogeneity index value. Considering the effect of geomorphology features on tree species or basal area heterogeneity we found that all investigated relationships are loose with R tree species diversity and aspect. Future work is required to verify the detected trends in behaviour of tree species diversity if it is to estimate from the usual forest stand parameters and topography characteristics.

  2. Machine testning

    DEFF Research Database (Denmark)

    De Chiffre, Leonardo

    This document is used in connection with a laboratory exercise of 3 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercise includes a series of tests carried out by the student on a conventional and a numerically controled lathe, respectively. This document...

  3. Representational Machines

    DEFF Research Database (Denmark)

    Petersson, Dag; Dahlgren, Anna; Vestberg, Nina Lager

    to the enterprises of the medium. This is the subject of Representational Machines: How photography enlists the workings of institutional technologies in search of establishing new iconic and social spaces. Together, the contributions to this edited volume span historical epochs, social environments, technological...

  4. Computer-aided diagnosis of Parkinson’s disease based on [123I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines

    Science.gov (United States)

    Oliveira, Francisco P. M.; Castelo-Branco, Miguel

    2015-04-01

    Objective. The aim of the present study was to develop a fully-automated computational solution for computer-aided diagnosis in Parkinson syndrome based on [123I]FP-CIT single photon emission computed tomography (SPECT) images. Approach. A dataset of 654 [123I]FP-CIT SPECT brain images from the Parkinson’s Progression Markers Initiative were used. Of these, 445 images were of patients with Parkinson’s disease at an early stage and the remainder formed a control group. The images were pre-processed using automated template-based registration followed by the computation of the binding potential at a voxel level. Then, the binding potential images were used for classification, based on the voxel-as-feature approach and using the support vector machines paradigm. Main results. The obtained estimated classification accuracy was 97.86%, the sensitivity was 97.75% and the specificity 98.09%. Significance. The achieved classification accuracy was very high and, in fact, higher than accuracies found in previous studies reported in the literature. In addition, results were obtained on a large dataset of early Parkinson’s disease subjects. In summation, the information provided by the developed computational solution potentially supports clinical decision-making in nuclear medicine, using important additional information beyond the commonly used uptake ratios and respective statistical comparisons. (ClinicalTrials.gov Identifier: NCT01141023)

  5. Design features of a sulphuric acid plant based on lead and zinc sintering machine off-gas%硫酸用新型耐蚀合金的研究与开发

    Institute of Scientific and Technical Information of China (English)

    刘焕安

    2001-01-01

    Design features of a 150kt/a sulphuric acid plant based on lead and zinc sintering machine off-gases are described. The plant adopted a single absorption technology including closed dilute-acid-scrubbing gas cleaning and ammonia-acid off-gas treatment. The dilute acid settling system,cooling water circulation system, installation of electrostatic precipitator, high-temperature absorption technology and acid distributor of drying and absorption section, and preheater, hot bypass and insulation of conversion section are emphasized in detail.%论述硫酸对金属腐蚀的特殊性和合金设计的基本原理。介绍高温浓硫酸用高硅不锈钢HD-1、合金球墨铸铁HD-3以及稀硫酸用高钼含氮奥氏体不锈钢HD-7、HD-11的研究开发和应用范围。

  6. The relationship between the morphological features of A1 segment of anterior cerebral artery and anterior communicating artery aneurysms

    Institute of Scientific and Technical Information of China (English)

    冯文峰

    2013-01-01

    Objective To improve the predictability of surgical clipping and guide the steam shaping of microcatheters in endovascular embolization by analyzing the association of morphological features of A1 segment of anterior cerebral artery(ACA) with formation and classification of anterior

  7. Pattern, age, and origin of structural features within the Ozark plateau and the relationship to ore deposits

    Science.gov (United States)

    Arvidson, R. E.

    1981-01-01

    Topography and gravity anomaly images for the continental United States were constructed. Evidence was found based on gravity, remote sensing data, the presence, trend, and character of fractures, and on rock type data, for a Precambrian rift through Missouri. The feature is probably the failed arm of a triple junction that existed prior to formation of the granite-rhyolite terrain of southern Missouri.

  8. Adding machine and calculating machine

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    In 1642 the French mathematician Blaise Pascal(1623-1662) invented a machine;.that could add and subtract. It had.wheels that each had: 1 to 10 marked off along its circumference. When the wheel at the right, representing units, made one complete circle, it engaged the wheel to its left, represents tens, and moved it forward one notch.

  9. Family Perspectives on Siblings' Conflict Goals in Middle Childhood: Links to Hierarchical and Affective Features of Sibling Relationships

    Science.gov (United States)

    Recchia, Holly E.; Witwit, Ma-ab

    2017-01-01

    This study examined parents' and children's descriptions of older and younger siblings' conflict goals in the late preschool and middle childhood years, and how these attributions were related to sibling relationship quality. Parents and 4- to 10-year-old children from 62 families were interviewed separately about siblings' motivations in two…

  10. Relationship Between Sea Surface Salinity from L-Band Radiometer and Optical Features in the East China Sea

    Science.gov (United States)

    2014-01-01

    Temperature and Roughness Remote Scanner (STARRS), over Mobile Bay and Mississippi Sound ’^- ’\\ have been compared with SSS estimates derived from optically...Maryland, 45 pp. (2002). [26] Sprent, P., and Dolby, G. R., "The geometric mean functional relationship," Biometrics , 36(3), 547-550(1980). Proc. of

  11. Performance of machine learning methods for classification tasks

    OpenAIRE

    B. Krithika; Dr. V. Ramalingam; Rajan, K

    2013-01-01

    In this paper, the performance of various machine learning methods on pattern classification and recognition tasks are proposed. The proposed method for evaluating performance will be based on the feature representation, feature selection and setting model parameters. The nature of the data, the methods of feature extraction and feature representation are discussed. The results of the Machine Learning algorithms on the classification task are analysed. The performance of Machine Learning meth...

  12. Genesis machines

    CERN Document Server

    Amos, Martyn

    2014-01-01

    Silicon chips are out. Today's scientists are using real, wet, squishy, living biology to build the next generation of computers. Cells, gels and DNA strands are the 'wetware' of the twenty-first century. Much smaller and more intelligent, these organic computers open up revolutionary possibilities. Tracing the history of computing and revealing a brave new world to come, Genesis Machines describes how this new technology will change the way we think not just about computers - but about life itself.

  13. A Knowledge base model for complex forging die machining

    CERN Document Server

    Mawussi, Kwamiwi; 10.1016/j.cie.2011.02.016

    2011-01-01

    Recent evolutions on forging process induce more complex shape on forging die. These evolutions, combined with High Speed Machining (HSM) process of forging die lead to important increase in time for machining preparation. In this context, an original approach for generating machining process based on machining knowledge is proposed in this paper. The core of this approach is to decompose a CAD model of complex forging die in geometric features. Technological data and topological relations are aggregated to a geometric feature in order to create machining features. Technological data, such as material, surface roughness and form tolerance are defined during forging process and dies design. These data are used to choose cutting tools and machining strategies. Topological relations define relative positions between the surfaces of the die CAD model. After machining features identification cutting tools and machining strategies currently used in HSM of forging die, are associated to them in order to generate mac...

  14. The impact of childhood traumas, depressive and anxiety symptoms on the relationship between borderline personality features and symptoms of adult attention deficit hyperactivity disorder in Turkish university students.

    Science.gov (United States)

    Dalbudak, Ercan; Evren, Cuneyt

    2015-01-01

    Previous studies reported that there is a significant association between attention deficit hyperactivity disorder (ADHD) in childhood and borderline personality disorder (BPD) in adulthood. The aim of this study is to investigate the relationship of borderline personality features (BPF) and ADHD symptoms while controlling the effect of childhood traumas, symptoms of depression and anxiety in adulthood on this relationship in Turkish university students. A total of 271 Turkish university students participated in this study. The students were assessed through the Turkish version of the Borderline Personality Inventory (BPI), the Adult ADHD Self-Report Scale (ASRS), the Childhood Trauma Questionnaire (CTQ-28), the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory (BAI). Correlation analyses have revealed that severity of BPF is related with adult ADHD symptoms, emotional, physical abuse and depression scores. Hierarchical regression analysis has indicated that depressive symptoms, emotional and physical abuse and the severity of ADHD symptoms are the predictors for severity of BPF. Findings of the present study suggests that clinicians must carefully evaluate these variables and the relationship between them to understand BPF and ADHD symptoms in university students better. Together with depressive symptoms, emotional and physical abuse may play a mediator role on this relationship. Further studies are needed to evaluate causal relationship between these variables in both clinical and non-clinical populations.

  15. Relationship between morphological feature of submarine landslides and geological condition -focus on Oshima-Oshima, Kaimon and Hawaii regions-

    Science.gov (United States)

    Kaji, T.; Yamazaki, H.; Kato, Y.

    2008-12-01

    Huge submarine landslides which generate the tsunami are found in the world. Those submarine landslides are generated by the collapse of the volcano and an unstable slope of sediments on the continental shelf. It is thought that a generation mechanism and morphological features of submarine landslides are different according to the environment (geological condition, topography, and transportation mechanism, etc) in each region. We compared submarine landslides in three different regions to clarify the relation of them. The comparison items are geological condition, morphological feature, form of submarine landslide and transportation mechanism. Oshima-Oshima is a volcanic island and tsunami was generated by collapse of volcanic edifice in 1741 eruption. Kaimon submarine landslide was generated by collapse of continental shelf slope off Kaimon volcano which has acted since 4000BP. There are many submarine landslides around Hawaii Islands. Nuuanu-Wailau submarine landslides are peculiar in those submarine landslides. Moreover, we compare some submarine landslides around Hawaii islands with Oshima-Oshima debris avalanche. Both Oshima-Oshima and Hawaii islands are volcanic islands, however the morphological features are different. As a morphological feature, Oshima-Oshima has thick sediment of 100-120m in front of collapse area and those sediment thins with distance. Nuuanu-Wailau submarine landslides have sediment including a huge blocks of 2km height at equal intervals around Hawaii islands. On the other hand, Kaimon submarine landslide has evenly thin sediment as a non volcanic type. In addition, in the case of Nuuanu-Wailau slides are smaller than Oshima-Oshima's case when we think about sediment extension to lateral side. Especially, sediment extension of Kaimon submarine landslide is small. These sediment distributions are related to the transportation mechanism. In general, sediment gravity flow is divided into 4 types (turbidity current, fluidized sediment flow

  16. Radiogenomics of glioblastoma: a pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype

    Science.gov (United States)

    Czarnek, Nicholas M.; Clark, Kal; Peters, Katherine B.; Collins, Leslie M.; Mazurowski, Maciej A.

    2016-03-01

    Genomic subtype has been shown to be an important predictor of therapy response for patients with glioblastomas. Unfortunately, obtaining the genomic subtype is an expensive process that is not typically included in the standard of care. It is therefore of interest to investigate potential surrogates of molecular subtypes that use standard diagnostic data such as magnetic resonance (MR) imaging. In this study, we analyze the relationship between tumor genomic subtypes, proposed by Verhaak et al, 2010, and novel features that capture the shape of abnormalities as seen in fluid attenuated inversion recovery (FLAIR) MR images. In our study, we used data from 54 patients with glioblastomas from four institutions provided by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). We explore five shape features calculated by computer algorithms implemented in our laboratory that assess shape both in individual slices and in rendered three-dimensional tumor volumes. The association between each feature and molecular subtype was assessed using area under the receiver operating characteristic curve analysis. We show that the two dimensional measures of edge complexity are significant discriminators between mesenchymal and classical tumors. These preliminary findings show promise for an imaging-based surrogate of molecular subtype and contribute to the understanding of the relationship between tumor biology and its radiology phenotype.

  17. Introduction: Minds, Bodies, Machines

    Directory of Open Access Journals (Sweden)

    Deirdre Coleman

    2008-10-01

    Full Text Available This issue of 19 brings together a selection of essays from an interdisciplinary conference on 'Minds, Bodies, Machines' convened last year by Birkbeck's Centre for Nineteenth-Century Studies, University of London, in partnership with the English programme, University of Melbourne and software developers Constraint Technologies International (CTI. The conference explored the relationship between minds, bodies and machines in the long nineteenth century, with a view to understanding the history of our technology-driven, post-human visions. It is in the nineteenth century that the relationship between the human and the machine under post-industrial capitalism becomes a pervasive theme. From Blake on the mills of the mind by which we are enslaved, to Carlyle's and Arnold's denunciation of the machinery of modern life, from Dickens's sooty fictional locomotive Mr Pancks, who 'snorted and sniffed and puffed and blew, like a little labouring steam-engine', and 'shot out […]cinders of principles, as if it were done by mechanical revolvency', to the alienated historical body of the late-nineteenth-century factory worker under Taylorization, whose movements and gestures were timed, regulated and rationalised to maximize efficiency; we find a cultural preoccupation with the mechanisation of the nineteenth-century human body that uncannily resonates with modern dreams and anxieties around technologies of the human.

  18. Chip breaking system for automated machine tool

    Science.gov (United States)

    Arehart, Theodore A.; Carey, Donald O.

    1987-01-01

    The invention is a rotary selectively directional valve assembly for use in an automated turret lathe for directing a stream of high pressure liquid machining coolant to the interface of a machine tool and workpiece for breaking up ribbon-shaped chips during the formation thereof so as to inhibit scratching or other marring of the machined surfaces by these ribbon-shaped chips. The valve assembly is provided by a manifold arrangement having a plurality of circumferentially spaced apart ports each coupled to a machine tool. The manifold is rotatable with the turret when the turret is positioned for alignment of a machine tool in a machining relationship with the workpiece. The manifold is connected to a non-rotational header having a single passageway therethrough which conveys the high pressure coolant to only the port in the manifold which is in registry with the tool disposed in a working relationship with the workpiece. To position the machine tools the turret is rotated and one of the tools is placed in a material-removing relationship of the workpiece. The passageway in the header and one of the ports in the manifold arrangement are then automatically aligned to supply the machining coolant to the machine tool workpiece interface for breaking up of the chips as well as cooling the tool and workpiece during the machining operation.

  19. Research of Relationship Between Interelectrode Dielectric Properties and Energy Distribution in Wire Electrical Discharge Machining%电火花线切割极问介电特性与放电能量分配关系

    Institute of Scientific and Technical Information of China (English)

    刘志东; 魏为; 陆霖琰; 徐安阳

    2012-01-01

    建立了电火花线切割极间介质电阻模型,分析了加工能量与极间介质电导率、工件厚度之间的关系,并对极间介质电阻模型进行了实验验证.研究结果表明,随着电导率的升高或者工件厚度的增加,放电期间极间“漏电流”增大,损耗在极间介质的能量增加,加工效率降低,电导率变化导致的极间电阻改变对于加工效率的影响显著.%A resistance model of (WEDM) was established. And interelectrode dielectric fluid in wire electrical discharge machining the relationship among machining energy, electrical conductivity and thickness of a workpiece was analyzed. The resistance model of interelectrode dielectric fluid was verified by experiments. The results show that with the higher electrical conductivity or the thickness of work- piece, the larger the interelectrode leakage current is during discharge, the more the energy loss in the in- terelectrode dielectric fluid, the lower the machining efficiency. Cutting efficiency is obviously influenced by the change of the resistance which is due to the change of electrical conductivity.

  20. [Relationship between the courses of clinical Features of patients with schizophrenia in adolescents and admission to psychiatric clinic].

    Science.gov (United States)

    Hattori, Isao; Miyauchi, Toshiro

    2005-01-01

    In order to improve diagnosis of schizophrenia with onset in adolescents at an early stage, we investigated in detail the clinical features of 74 patients with schizophrenia, (23 males) at adolescents psychiatric clinic. Many of the subjects had been suffering from the illness about 14 years old but had not undergone their first psychiatric examination until a few years later. A high percentage (more than 80%) of our subjects presented psychiatric symptoms such as delusional remembrance, delusional moods, delusions of persecution and hypobulia. Additionally, more than 60% of our subjects presented auditory hallucinations. In general, teenage patients with schizophrenia onset show vague symptoms such as anxiety, embarrassment and strange moods rather than obvious hallucinations. Nevertheless, it was possible to identify certain clinical features of this disorder in adolescents: many patients suffer delusional remembrance, delusional moods and delusions of persecution immediately after the onset of the illness. Gradually, problematic behaviors such as anorexia, self injury, offences against their families, voluntary vomiting, etc., develop, but patients do not always receive psychiatric examination at this stage. After socially obvious problems such as school refusal, withdrawal from social activities and lowering of school record develop over a period of time, patients may be urged to undergo psychiatric examination. Our research again underlines the difficulty of achieving diagnosis of schizophrenia at an early stage. The key to early diagnosis appears to be the accurate identification of psychiatric symptoms in the early stages of the illness at school, or at home if possible, before socially problematic behaviors arise.

  1. Simulating Turing machines on Maurer machines

    NARCIS (Netherlands)

    Bergstra, J.A.; Middelburg, C.A.

    2008-01-01

    In a previous paper, we used Maurer machines to model and analyse micro-architectures. In the current paper, we investigate the connections between Turing machines and Maurer machines with the purpose to gain an insight into computability issues relating to Maurer machines. We introduce ways to

  2. Environmentally Friendly Machining

    CERN Document Server

    Dixit, U S; Davim, J Paulo

    2012-01-01

    Environment-Friendly Machining provides an in-depth overview of environmentally-friendly machining processes, covering numerous different types of machining in order to identify which practice is the most environmentally sustainable. The book discusses three systems at length: machining with minimal cutting fluid, air-cooled machining and dry machining. Also covered is a way to conserve energy during machining processes, along with useful data and detailed descriptions for developing and utilizing the most efficient modern machining tools. Researchers and engineers looking for sustainable machining solutions will find Environment-Friendly Machining to be a useful volume.

  3. STEP based Finish Machining CAPP system

    OpenAIRE

    A Arivazhagan; Mehta, NK; Jain, PK

    2012-01-01

    This research paper presents various methodologies developed in a STEP based Computer Aided Process Planning (CAPP) system named "Finish Machining – CAPP" (FM-CAPP). It is developed to generate automatic process plans for finish machining prismatic parts. It is designed in a modular fashion consisting of three main modules, namely (i) Feature Recognition module (FRM) (ii) Machining Planning Module (MPM) and (iii) Setup Planning Module (SPM). The FRM Module analyses the geometrical and topolog...

  4. The relationship between the MRI features of mild osteoarthritis in the patellofemoral and tibiofemoral compartments of the knee

    Energy Technology Data Exchange (ETDEWEB)

    Kornaat, Peter R.; Watt, Iain; Bloem, Johan L. [Leiden University Medical Center, Department of Radiology, Leiden (Netherlands); Riyazi, Naghmeh; Kloppenburg, Margreet [Leiden University Medical Center, Department of Rheumatology, Leiden (Netherlands)

    2005-08-01

    The aim of this work was to demonstrate the relationship between osteoarthritic changes seen on magnetic resonance (MR) images of the patellofemoral (PF) or tibiofemoral (TF) compartments in patients with mild osteoarthritis (OA) of the knee. MR images of the knee were obtained in 105 sib pairs (210 patients) who had been diagnosed with OA at multiple joints. Entry criteria included that the degree of OA in the knee examined should be between a Kellgren and Lawrence score of 2 or 3. MR images were analyzed for the presence of cartilaginous lesions, bone marrow edema (BME) and meniscal tears. The relationship between findings in the medial and lateral aspects of the PF and TF compartments was examined. The number of cartilaginous defects on either side of the PF compartment correlated positively with number of cartilaginous defects in the ipsilateral TF compartment (odds ratio, OR, 55, confidence interval, CI, 7.8-382). The number of cartilaginous defects in the PF compartment correlated positively with ipsilateral meniscal tears (OR 3.7, CI 1.0-14) and ipsilateral PF BME (OR 17, CI 3.8-72). Cartilaginous defects in the TF compartment correlated positively with ipsilateral meniscal tears (OR 9.8, CI 2.5-38) and ipsilateral TF BME (OR 120, CI 6.5-2,221). Osteoarthritic defects lateralize or medialize in the PF and TF compartments of the knee in patients with mild OA. (orig.)

  5. Morphological features of the maxillary incisors roots and relationship with neighbouring anatomical structures: possible implications in endodontic surgery.

    Science.gov (United States)

    Taschieri, S; Weinstein, T; Rosano, G; Del Fabbro, M

    2012-05-01

    The purpose of this study was to investigate the relationship between the root apex of the upper incisors and neighbouring anatomical structures as well as the morphology of the root-end foramen after apicoectomy. Fifty-seven patients requiring endodontic surgical treatment for a maxillary anterior root were enrolled. A preoperative diagnostic computed tomography (CT) scan was analysed to determine: the distance between the anterior wall of the nasopalatine duct and the central (CI-ND) incisor root 4mm from the apex; and the distance between the floor of the nasal cavity and the tip of either the central (CI-NF) or the lateral (LI-NF) incisor root. After apicoectomy, root-end foramen endoscopic pictures were taken in order to characterize their morphology. Fifty-nine central and 26 lateral incisors were evaluated. The average CI-ND was 4.71 ± 1.26 (SD) mm. The average CI-NF was 10.62 ± 2.25 mm. The average LI-NF was 13.05 ± 2.43 mm. The foramen shape after apicoectomy was ovoid to circular in about 90% of cases in both central and lateral incisors. A sound knowledge of the anatomical relationships at the surgical site is essential for the clinician to perform a safe endodontic surgical procedure. Copyright © 2011 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

  6. The distribution and inter-relationships of radiologic features of osteoarthrosis of the hip. A survey of 4151 subjects of the Copenhagen City Heart Study: the Osteoarthrosis Substudy

    DEFF Research Database (Denmark)

    Jacobsen, Steffen; Sonne-Holm, Stig; Søballe, Kjeld

    2004-01-01

    OBJECTIVE: The aims of this study were to investigate the influence of sex, age and individual physical and occupational factors on the distribution of radiographic features of hip joint osteoarthritis (OA), and to determine the inter-relationships between the primary radiographic OA discriminator...... and progressive in women after the fifth decade compared to men. Applying logistic regression analyses, only age was found to be significantly associated to pathologically reduced minimum JSW (cut off value set at ....00 to 0.03). Minimum JSW logistic regression analysis. The presence...

  7. Machine Transliteration

    CERN Document Server

    Knight, K; Knight, Kevin; Graehl, Jonathan

    1997-01-01

    It is challenging to translate names and technical terms across languages with different alphabets and sound inventories. These items are commonly transliterated, i.e., replaced with approximate phonetic equivalents. For example, "computer" in English comes out as "konpyuutaa" in Japanese. Translating such items from Japanese back to English is even more challenging, and of practical interest, as transliterated items make up the bulk of text phrases not found in bilingual dictionaries. We describe and evaluate a method for performing backwards transliterations by machine. This method uses a generative model, incorporating several distinct stages in the transliteration process.

  8. Relationships among depressive, passive-aggressive, sadistic and self-defeating personality disorder features with suicidal ideation and reasons for living among older adults.

    Science.gov (United States)

    Segal, Daniel L; Gottschling, Juliana; Marty, Meghan; Meyer, William J; Coolidge, Frederick L

    2015-01-01

    Suicide among older adults is a major public health problem in the USA. In our recent study, we examined relationships between the 10 standard DSM-5 personality disorders (PDs) and suicidal ideation, and found that the PD dimensions explained a majority (55%) of the variance in suicidal ideation. To extend this line of research, the purpose of the present follow-up study was to explore relationships between the four PDs that previously were included in prior versions of the DSM (depressive, passive-aggressive, sadistic, and self-defeating) with suicidal ideation and reasons for living. Community-dwelling older adults (N = 109; age range = 60-95 years; 61% women; 88% European-American) completed anonymously the Coolidge Axis II Inventory, the Reasons for Living Inventory (RFL), and the Geriatric Suicide Ideation Scale (GSIS). Correlational analyses revealed that simple relationships between PD scales with GSIS subscales were generally stronger than with RFL subscales. Regarding GSIS subscales, all four PD scales had medium-to-large positive relationships, with the exception of sadistic PD traits, which was unrelated to the death ideation subscale. Multiple regression analyses showed that the amount of explained variance for the GSIS (48%) was higher than for the RFL (11%), and this finding was attributable to the high predictive power of depressive PD. These findings suggest that depressive PD features are strongly related to increased suicidal thinking and lowered resilience to suicide among older adults. Assessment of depressive PD features should also be especially included in the assessment of later-life suicidal risk.

  9. Studies on the relationship of pleiotrophin and MMP2 with the clinicopathological features of invasive breast carcinoma

    Directory of Open Access Journals (Sweden)

    Bo ZHANG

    2012-08-01

    Full Text Available Objective To study the correlation between the expressions of both pleitropin (PTN and matrix metalloproteinase-2 (MMP2 to the clinicopathological features of patients with breast cancer. Methods The pathological specimens were collected from 103 cases of invasive breast cancer, including 51 cases of triple negative breast cancer (TNBC, i.e. all the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 were negatively expressed and 52 cases of non-TNBC. Ten specimens of paraneoplastic tissue were also collected as controls. The expressions of PTN and MMP2 were detected with immunohistochemical method, and the correlation of PTN and MMP2 expressions to the clinicopathological features of breast cancer (age, tumor size, histopathological grading and axillary lymph node metastases was assessed. Results Among the 103 patients with breast cancer, no statistical difference was found between TNBC group and non-TNBC group in age of onset, tumor size and the axillary lymph node metastasis (P > 0.05, but significant difference was found in histopathological grading (P < 0.05. The positive rate of PTN expression was 83.5% (86/103, and of MMP2 expression was 68% (70/103, and no significant difference was found between TNBC group and non-TNBC group. The expressions of PTN and MMP2 were correlated with the age of onset, histopathological grading and axillary lymph node metastasis, but showed poor consistency in breast cancer (Kappa coefficient=0.1817, 95% CI=-0.0091-0.3726; Z=2.0212, P=0.0433. Conclusions The expression of PTN and MMP2 is correlated with the age, histopathological grading and axillary lymph node metastasis of patients with invasive breast cancer, and not correlated with TNBC. The expression of PTN and MMP2 shows poor consistency in invasive breast cancer.

  10. 基于图像多特征融合和支持向量机的气液两相流流型识别%Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    周云龙; 陈飞; 孙斌

    2008-01-01

    The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bubbly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.

  11. The relationship between the lizard eye and associated bony features: a cautionary note for interpreting fossil activity patterns.

    Science.gov (United States)

    Hall, Margaret I

    2009-06-01

    Activity pattern, the time of day when an animal is active, is associated with ecology. There are two major activity patterns: diurnal (awake during the day in a photopic environment) and nocturnal (awake at night in a scotopic environment). Lizards exhibit characteristic eye shapes associated with activity pattern, with scotopic-adapted lizard eyes optimized for visual sensitivity with large corneal diameters relative to their eye axial lengths, and photopic-adapted lizards optimized for visual acuity, with larger axial lengths of the eye relative to their corneal diameters. This study: (1) quantifies the relationship between the lizard eye and its associated bony anatomy (the orbit, sclerotic ring, and associated skull widths); (2) investigates how activity pattern is reflected in that bony anatomy; and (3) determines if it is possible to reliably interpret activity pattern for a lizard that does not have the soft tissue available for study, specifically, for a fossil. Knowledge of extinct lizards' activity patterns would be useful in making paleoecological interpretations. Here, 96 scotopic- and photopic-adapted lizard species are analyzed in a phylogenetic context. Although there is a close relationship between the lepidosaur eye and associated bony anatomy, based on these data activity pattern cannot be reliably interpreted for bony-only specimens, such as a fossil, possibly because of the limited ossification of the lepidosaur skull. Caution should be exercised when utilizing lizard bony anatomy to interpret light-level adaptation, either for a fossil lizard or as part of an extant phylogenetic bracket to interpret other extinct animals with sclerotic rings, such as dinosaurs.

  12. Machine Protection

    CERN Document Server

    Schmidt, R

    2014-01-01

    The protection of accelerator equipment is as old as accelerator technology and was for many years related to high-power equipment. Examples are the protection of powering equipment from overheating (magnets, power converters, high-current cables), of superconducting magnets from damage after a quench and of klystrons. The protection of equipment from beam accidents is more recent. It is related to the increasing beam power of high-power proton accelerators such as ISIS, SNS, ESS and the PSI cyclotron, to the emission of synchrotron light by electron–positron accelerators and FELs, and to the increase of energy stored in the beam (in particular for hadron colliders such as LHC). Designing a machine protection system requires an excellent understanding of accelerator physics and operation to anticipate possible failures that could lead to damage. Machine protection includes beam and equipment monitoring, a system to safely stop beam operation (e.g. dumping the beam or stopping the beam at low energy) and an ...

  13. Clustering Categories in Support Vector Machines

    DEFF Research Database (Denmark)

    Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero

    2017-01-01

    The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM classifier in the presence of categorical features, leading to a gain in in...

  14. RELATIONSHIPS BETWEEN ANATOMICAL FEATURES AND INTRA-RING WOOD DENSITY PROFILES IN Gmelina arborea APPLYING X-RAY DENSITOMETRY

    Directory of Open Access Journals (Sweden)

    Mario Tomazelo-Filho

    2007-12-01

    Full Text Available Four annual tree-rings (2 of juvenile wood and 2 of mature wood were sampled from fast-growth plantations ofGmelina arborea in two climatic conditions (dry and wet tropical in Costa Rica. Each annual tree-ring was divided in equal parts ina radial direction. For each part, X-ray density as well as vessel percentage, length and width fiber, cell wall thickness and lumendiameter were measured. Wood density and profile patterns of cell dimension demonstrated inconsistency between juvenile andmature wood and climatic conditions. The Pearson correlation matrix showed that intra-ring wood density was positively correlatedwith the cell wall thickness and negatively correlated with vessel percentage, fiber length, lumen diameter and width. The forwardstepwise regressions determined that: (i intra-ring wood density variation could be predicted from 76 to 96% for anatomicalvariation; (ii cell wall thickness was the most important anatomical feature to produce intra-ring wood density variation and (iii thevessel percentage, fiber length, lumen diameter and width were the second most statically significant characteristics to intra-ring wooddensity, however, with low participation of the determination coefficient of stepwise regressions.

  15. Clinical Relationships Extraction Techniques from Patient Narratives

    Directory of Open Access Journals (Sweden)

    Wafaa Tawfik Abdel-Moneim

    2013-01-01

    Full Text Available The Clinical E-Science Framework (CLEF project was used to extract important information from medical texts by building a system for the purpose of clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. The system is divided into two parts, one part concerns with the identification of relationships between clinically important entities in the text. The full parses and domain-specific grammars had been used to apply many approaches to extract the relationship. In the second part of the system, statistical machine learning (ML approaches are applied to extract relationship. A corpus of oncology narratives that hand annotated with clinical relationships can be used to train and test a system that has been designed and implemented by supervised machine learning (ML approaches. Many features can be extracted from these texts that are used to build a model by the classifier. Multiple supervised machine learning algorithms can be applied for relationship extraction. Effects of adding the features, changing the size of the corpus, and changing the type of the algorithm on relationship extraction are examined.

  16. How do disease perception, treatment features, and dermatologist–patient relationship impact on patients assuming topical treatment? An Italian survey

    Directory of Open Access Journals (Sweden)

    Burroni AG

    2015-02-01

    Full Text Available Anna Graziella Burroni,1 Mariella Fassino,2 Antonio Torti,3 Elena Visentin4 1IRCCS University Hospital San Martino, IST National Institute for Cancer Research, Genoa, Italy; 2Department of Psychology, Specialization School in Clinical Psychology, University of Turin, Turin, Italy; 3Dermatology practice, Milan, Italy; 4HTA and Scientific Support, CSD Medical Research Srl, Milan, Italy Background: Psoriasis largely affects daily activities and social interactions and has a strong impact on patients’ quality of life. Psoriatic patients have different attitudes toward their condition. Topical medications are essential for the treatment of psoriasis, but the majority of patients do not adhere to these therapies. Objective: The history of treatment success or failure seems to influence patient attitude toward topical therapy. Therefore, it is important to understand the psychological, experiential, and motivational aspects that could be critical for treatment adherence, and to describe the different attitudes toward topical treatment. Furthermore, the physician–patient relationship and the willingness to trust the dermatologist may have a substantial role in encouraging or discouraging patients’ attitudes toward topical therapy. Methods: A survey was designed to collect aspects that could be relevant to understanding different patient attitudes toward psoriasis and its treatments. A total of 495 self-administered questionnaires compiled by psoriatic patients were analyzed from 20 Italian specialized hospital centers in order to provide a nationwide picture. Results: Psoriatic patients have different perceptions and experiences in relation to their condition: half of them consider psoriasis as a disease, while the other half consider psoriasis as a disorder or a nuisance. Topical therapy is the most widely used treatment, even though it is not considered the most effective one and often perceived to be cosmetic. The main findings are: 1

  17. The Improved Relevance Voxel Machine

    DEFF Research Database (Denmark)

    Ganz, Melanie; Sabuncu, Mert; Van Leemput, Koen

    The concept of sparse Bayesian learning has received much attention in the machine learning literature as a means of achieving parsimonious representations of features used in regression and classification. It is an important family of algorithms for sparse signal recovery and compressed sensing...

  18. Relationships between production, quality of milk and udder health status of ewes during machine milking Zvislost medzi produkciou, kvalitou mlieka a zdravotnm stavom vemena bahnc pocas strojovho dojenia

    Directory of Open Access Journals (Sweden)

    Margetin MARGETN

    2013-03-01

    Full Text Available The chosen traits of production, composition and quality of milk (analysis of 255 milk samples were evaluated in ewes on the 1st to 3rd lactation (n=64 during milking period (4 control measurements CM and in the machine milking conditions. Udder health status of the ewes (UHS; n=255 was evaluated by means of 5 point linear scale subjectively at the same time. The ewes of three pure breeds (Tsigai T, Improved Valachian IV and Lacaune LC and two crossbreds (TxLC and IVxLC were involved in the experiment. The machine milked milk (milk production at CM (0.462 kg, the proportion of machine stripped milk (25.19 % and also UHS (1.65 were significantly affected by the genotype group (P<0.001. The basic components of milk, somatic cell count (SCC=452.2 thousands and somatic cell score (SCS=2.32 were not significantly affected by the genotype group. UHS of the ewes was effected by the genotype group and lactation order (P<0.001. The content of fat (7.72 %; F, lactose (4.62 %; L and protein (5.68 %; P were significantly affected by the CM (P<0.001, P<0.001 and P<0.05 respectively. The significant phenotype and residual correlations we determined between L and SCS (r = -0.372 and -0.399 respectively; P<0.001. The ewes with the worst UHS had on the basis of phenotype and residual correlations significantly lower lactose content in the milk (r = -0.206 and -0.194 respectively; P<0.001 and P<0.01 respectively. Between SCC and UHS and between SCS and UHS we found only weak phenotype and residual correlations (P<0.05 and P>0.05 respectively.

  19. Analysis of machining and machine tools

    CERN Document Server

    Liang, Steven Y

    2016-01-01

    This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

  20. [Relationship between inertial features of the upper extremity and simple reaction time in boys and girls aged 17-18].

    Science.gov (United States)

    Gutnik, B I; Pankova, N B; Karganov, M Iu; Nash, D

    2014-01-01

    The latent period of visual sensor-motor reaction depends, in part, on the sensory and integrative processes in the brain, but is also influenced by the rate of the muscle contraction. There is no clear evidence in the literature whether the rotational inertia of segments of limbs has any direct effect on the reaction time. The aim of our study was to identify this relationship. The study involved 566 right handed students aged 16-17 of both genders beginning their post puberty period. Reaction time was measured during experimental adduction of the forearm and hand, using a special rotating handle and lever connected to a computer that recorded the reaction time (+/- 1 ms). Calculations of the rotational inertia were carried out using regression models by Zatsiorsky and other authors. Each gender group was divided into three subgroups: with high, medium and low values of rotational inertia. It was found that individuals with high values of rotational inertia of forearm and wrist demonstrated significantly longer reaction times. This pattern was apparent in both gender groups. Although males illustrated greater values of rotational inertia than females they demonstrated relatively shorter reaction times. This contradiction can be explained by greater muscle power of young men. We recommend taking into account the amount of rotational inertia of the responsive segment in all kinds of research which require measurement of reaction time.

  1. Interannual water level variations in Lake Izabal, Guatemala, Centroamerica, using radar altimetry and its relationship with oceanographic features

    Science.gov (United States)

    Medina, C.; Gomez-Enri, J.; Alonso, J.; Villares, P.; Arias, M.; Catalan, M.; Labrador, I.

    2007-10-01

    It is well known that ocean-atmosphere dynamic affects the weather conditions over the continents and the ocean itself. The hydrologic cycle is driven by climatic parameters like precipitation, temperature, evaporation, winds and humidity. Hence, the river's water discharges and lake water level variations are impelled by climatic conditions also. Lake Izabal is the largest one in Guatemala; its main tributary is the Polochic River. Its level is related to the Polochic Rivers runoff and therefore to the precipitation/evaporation over its catchment area. The Lake Izabal water level fluctuations are driven by the annual cycle of rainy and dry seasons. In this study the ENVISAT RA-2 Geophysical Data Records orbits over the lake, coupled with in-situ measurements are used in order to determine and characterize the lake level fluctuations. The precipitation records over the lake's catchment area are also analyzed. In addition, some relationships of the lake level interannual variations with the climate indexes of Southern Oscillation Index SOI and the Tropical North Atlantic NATL were investigated. The main result is that the abrupt lake level rise in July 2006 is correlated to an abnormal precipitation in June 2006. Theoretically, this was forced by "La Nina" Southern Oscillation events during early 2006.

  2. Meta-analysis of the relationship between Epstein-Barr virus infection and clinicopathological features of patients with gastric carcinoma

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Epstein-Barr virus (EBV) infection has been causally associated with occurrence of many malignant neoplasms. EBV-encoded small RNAs (EBERs) have been detected from about 10% of gastric carcinoma tissue cells, suggesting that EBV infection is associated with the development of gastric carcinoma. The present study pooled the data from the papers concerning EBV-related gastric cancers and performed a meta-analysis of 22 research papers. Among these papers, a total of 5475 cases with gastric cancer were enrolled, of whom 411 cases were found EBV-positive, with the EBV-positive rate being 7.5%. Among the EBV-positive gastric cancer cases, the detection rate was 11.1% in males and 3.0% in females. Compared with EBV-negative gastric cancer, EBV-positive gastric cancer had less lymph node metastasis. Based on the histological typing, of the EBV-positive gastric cancers, the diffuse type was 8.1%, and intestinal type was 8.0%. The examined specimen types included stored paraffin blocks and fresh surgically removed specimens, their EBV positive rates were 7.9% and 6.5% respectively. In terms of geographical distribution, the detection rate of EBV-positive gastric cancer was 9.4% in America, 6.1% in Asia and 9.1% in Europe. Meta-analysis showed that EBV infection occurred only in gastric cancer tissue cells and was significantly associated with the patients’ gender, lymph node metastases, and the location where tumor tissue generated and geographical distribution (P<0.05), but was not significantly associated with the patients’ histological types of tumor and the types of specimens (P>0.05). These results suggested that EBV-positive gastric cancer has distinct clinicopathological features.

  3. The severity of Internet addiction risk and its relationship with the severity of borderline personality features, childhood traumas, dissociative experiences, depression and anxiety symptoms among Turkish university students.

    Science.gov (United States)

    Dalbudak, Ercan; Evren, Cuneyt; Aldemir, Secil; Evren, Bilge

    2014-11-30

    The aim of this study was to investigate the relationship of Internet addiction (IA) risk with the severity of borderline personality features, childhood traumas, dissociative experiences, depression and anxiety symptoms among Turkish university students. A total of 271 Turkish university students participated in this study. The students were assessed through the Internet Addiction Scale (IAS), the Borderline Personality Inventory (BPI), the Dissociative Experiences Scale (DES), the Childhood Trauma Questionnaire (CTQ-28), the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory (BAI). The rates of students were 19.9% (n=54) in the high IA risk group, 38.7% (n=105) in the mild IA risk group and 41.3% (n=112) in the group without IA risk. Correlation analyses revealed that the severity of IA risk was related with BPI, DES, emotional abuse, CTQ-28, depression and anxiety scores. Univariate covariance analysis (ANCOVA) indicated that the severity of borderline personality features, emotional abuse, depression and anxiety symptoms were the predictors of IAS score, while gender had no effect on IAS score. Among childhood trauma types, emotional abuse seems to be the main predictor of IA risk severity. Borderline personality features predicted the severity of IA risk together with emotional abuse, depression and anxiety symptoms among Turkish university students.

  4. Vehicle brand recognition based on HOG feature and support vector machine%基于 HOG 特征及支持向量机的车辆品牌识别方法

    Institute of Scientific and Technical Information of China (English)

    张小琴; 赵池航; 沙月进; 党倩; 张运胜

    2013-01-01

    In order to solve the problem of fake-licensed car and illegal car identification, a vehicle brand recognition method is proposed.It detects the car front face based on symmetric feature, ex-tracts the HOG ( histogar of driented gradient) feature of the car face and then uses support vector machine ( SVM) to classify the vehicle brand.According to the road bayonet pictures provided by the Suzhou Municipal Public Security Bureau, a car face database is built, which includes 3000 pic-tures of 15 kinds of vehicle brands such as Audi, Changan and Nissan.Based on the built database, experiments are conducted using the proposed method for vehicle brand recognition.The perform-ance of three kinds of kernel functions ( linear kernel function, polynomial kernel function and radial basis kernel function) of SVM is compared and analyzed.The overall classification accuracy of the three kernel functions are 89.27% , 89.74% and 89.89%.Theoretical analysis and experimental results show that the proposed recognition method based on HOG features and SVM is feasible, and the SVM classifier based on the radial basis function performs optimal.%为了解决套牌车与违章车的身份确认问题,提出了一种车辆品牌识别方法.该方法首先基于对称特征检测车辆前脸区域,然后提取车辆前脸区域的HOG特征,最后采用支持向量机对车辆品牌进行分类.实验根据苏州市公安局提供的道路卡口图片,构建了车脸数据库,该数据库包括奥迪、长安、日产等15种车辆品牌,共3000张图片.基于构建的车脸数据库,采用所提出的车辆品牌识别方法进行了实验,并对比分析了支持向量机( support vector machine, SVM)线性核函数、多项式核函数和径向基核函数的性能,3种核函数的整体分类精度分别为89.27%,89.74%和89.89%.理论分析和实验结果表明,所提出的基于HOG特征及支持向量机的车辆品牌识别方法是

  5. The relationship between superficial muscle activity during the cranio-cervical flexion test and clinical features in patients with chronic neck pain.

    Science.gov (United States)

    O'Leary, Shaun; Falla, Deborah; Jull, Gwendolen

    2011-10-01

    Changes in motor behavior are a known feature of chronic mechanical neck pain disorders. This study examined the strength of the association between reported levels of pain and disability from 84 individuals (63 women, 21 men) with chronic mechanical neck pain and levels of electromyographic activity recorded from superficial cervical flexor (sternocleidomastoid; SCM and anterior scalene; AS) muscles during progressive stages of the cranio-cervical flexion muscle test. A significant positive association was observed between superficial muscle activity and pain intensity (P 0.5) or perceived disability (P > 0.21). The strongest relationship between pain intensity and superficial muscle activity occurred at the final increment of the cranio-cervical flexion test (inner-range test position) for both the SCM and AS muscles (R(2) = 0.16). Although a positive and significant relationship between pain intensity and superficial muscle activity was shown, the relationship was only modest (16% explained variance), indicating that multiple factors contribute to the altered motor function observed in individuals with chronic mechanical neck pain.

  6. The relationship between dispositional mindfulness, borderline personality features, and suicidal ideation in a sample of women in residential substance use treatment.

    Science.gov (United States)

    Shorey, Ryan C; Elmquist, JoAnna; Wolford-Clevenger, Caitlin; Gawrysiak, Michael J; Anderson, Scott; Stuart, Gregory L

    2016-04-30

    Borderline personality disorder (BPD), which is characterized by unstable moods, behavior, and relationships, is also associated with heightened suicidal ideation. Prior research has demonstrated that BPD and suicidal ideation are prevalent among women in substance use treatment. Efforts to treat substance use in this population are made difficult due to the severity of BPD, and it is possible that mindfulness-based interventions specific to substance use could be an effective approach for this population. However, basic research is needed on the relationship between dispositional mindfulness, BPD, and suicidal ideation among women in treatment for substance use to support their associations, which was the purpose of the present study. Pre-existing medical records were reviewed from a residential substance use treatment center. A total of 81 female patients were included in the current study. Patients completed self-report measures of mindfulness, BPD, suicidal ideation, substance use, and impression management at treatment intake. Findings demonstrated dispositional mindfulness to be negatively associated with BPD features and suicidal ideation. With the exception of self-harm, this negative relationship was found even after controlling for age, substance use, and impression management. Future research should examine whether mindfulness-based interventions are an effective treatment for comorbid substance use and BPD.

  7. The Garden and the Machine

    DEFF Research Database (Denmark)

    Clemmensen, Thomas Juel

    2014-01-01

    The aim of this paper is to explore how the concepts of garden and machine might inform our understanding of the complex relationship between infrastructure and nature. The garden is introduced as a third nature and used to shed a critical light on the promotion of landscape ‘as’ infrastructure...

  8. THE GARDEN AND THE MACHINE

    DEFF Research Database (Denmark)

    Clemmensen, Thomas Juel

    2012-01-01

    The aim of this paper is to explore how the concepts of garden and machine might inform our understanding of the complex relationship between infrastructure and nature. The garden is introduced as a third nature and used to shed a critical light on the promotion of landscape as infrastructure...

  9. Des-γ-carboxyprothrombin (DCP and NX-DCP expressions and their relationship with clinicopathological features in hepatocellular carcinoma.

    Directory of Open Access Journals (Sweden)

    Akiko Sumi

    Full Text Available Des-γ-carboxyprothrombin (DCP has been used as a tumor marker for hepatocellular carcinoma (HCC. Recently the DCP/NX-DCP ratio, calculated by dividing DCP by NX-DCP, has been reported useful in detecting HCC. The purpose of this study is to clarify the significance of DCP and NX-DCP expression in HCC tissues.HCC and non-HCC tissue samples were obtained from 157 patients and were immunohistochemically examined for DCP and NX-DCP expression using anti-DCP antibody and anti-NX-DCP antibody. DCP and NX-DCP expression scores were calculated by multiplying staining intensity grade by percentage of stained area. Serum DCP and NX-DCP levels were determined in 89 patients. We evaluated the relationship between tumor expression, serum level, and pathomorphological findings.Intrahepatic metastasis (im was significantly more frequent in cases with high DCP expression than in cases with low DCP expression. High NX-DCP expression was associated with significantly lower histological grade, and less frequent im or portal vein invasion (vp than low NX-DCP expression. Serum DCP was correlated with DCP expression, but serum NX-DCP was not correlated with NX-DCP expression. DCP-positive (≥40 mAU/L, NX-DCP-positive (≥90 mAU/L, and DCP/NX-DCP ratio-positive (≥1.5 cases were associated with significantly larger tumor size and more frequent vp than negative cases. DCP was rarely expressed, but NX-DCP was frequently expressed in non-cancerous liver tissues. Patients with NX-DCP expression-negative tumors showed a lower survival rate than those with NX-DCP expression-positive tumors (p = 0.04, whereas the survival in serum NX-DCP-positive cases was lower than that of serum negative cases (p = 0.02.DCP and NX-DCP were produced in HCC tissues, but differed in expression level and biological properties. DCP expression, serum DCP or NX-DCP level, and DCP/NX-DCP ratio were closely related to malignant properties of HCC.

  10. Des-γ-carboxyprothrombin (DCP) and NX-DCP expressions and their relationship with clinicopathological features in hepatocellular carcinoma.

    Science.gov (United States)

    Sumi, Akiko; Akiba, Jun; Ogasawara, Sachiko; Nakayama, Masamichi; Nomura, Yoriko; Yasumoto, Makiko; Sanada, Sakiko; Nakashima, Osamu; Abe, Toshi; Yano, Hirohisa

    2015-01-01

    Des-γ-carboxyprothrombin (DCP) has been used as a tumor marker for hepatocellular carcinoma (HCC). Recently the DCP/NX-DCP ratio, calculated by dividing DCP by NX-DCP, has been reported useful in detecting HCC. The purpose of this study is to clarify the significance of DCP and NX-DCP expression in HCC tissues. HCC and non-HCC tissue samples were obtained from 157 patients and were immunohistochemically examined for DCP and NX-DCP expression using anti-DCP antibody and anti-NX-DCP antibody. DCP and NX-DCP expression scores were calculated by multiplying staining intensity grade by percentage of stained area. Serum DCP and NX-DCP levels were determined in 89 patients. We evaluated the relationship between tumor expression, serum level, and pathomorphological findings. Intrahepatic metastasis (im) was significantly more frequent in cases with high DCP expression than in cases with low DCP expression. High NX-DCP expression was associated with significantly lower histological grade, and less frequent im or portal vein invasion (vp) than low NX-DCP expression. Serum DCP was correlated with DCP expression, but serum NX-DCP was not correlated with NX-DCP expression. DCP-positive (≥40 mAU/L), NX-DCP-positive (≥90 mAU/L), and DCP/NX-DCP ratio-positive (≥1.5) cases were associated with significantly larger tumor size and more frequent vp than negative cases. DCP was rarely expressed, but NX-DCP was frequently expressed in non-cancerous liver tissues. Patients with NX-DCP expression-negative tumors showed a lower survival rate than those with NX-DCP expression-positive tumors (p = 0.04), whereas the survival in serum NX-DCP-positive cases was lower than that of serum negative cases (p = 0.02). DCP and NX-DCP were produced in HCC tissues, but differed in expression level and biological properties. DCP expression, serum DCP or NX-DCP level, and DCP/NX-DCP ratio were closely related to malignant properties of HCC.

  11. Machine Learning in Parliament Elections

    Directory of Open Access Journals (Sweden)

    Ahmad Esfandiari

    2012-09-01

    Full Text Available Parliament is considered as one of the most important pillars of the country governance. The parliamentary elections and prediction it, had been considered by scholars of from various field like political science long ago. Some important features are used to model the results of consultative parliament elections. These features are as follows: reputation and popularity, political orientation, tradesmen's support, clergymen's support, support from political wings and the type of supportive wing. Two parameters of reputation and popularity and the support of clergymen and religious scholars that have more impact in reducing of prediction error in election results, have been used as input parameters in implementation. In this study, the Iranian parliamentary elections, modeled and predicted using learnable machines of neural network and neuro-fuzzy. Neuro-fuzzy machine combines the ability of knowledge representation of fuzzy sets and the learning power of neural networks simultaneously. In predicting the social and political behavior, the neural network is first trained by two learning algorithms using the training data set and then this machine predict the result on test data. Next, the learning of neuro-fuzzy inference machine is performed. Then, be compared the results of two machines.

  12. Application of Machine Learning to Rotorcraft Health Monitoring

    Science.gov (United States)

    Cody, Tyler; Dempsey, Paula J.

    2017-01-01

    Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

  13. Automation of printing machine

    OpenAIRE

    Sušil, David

    2016-01-01

    Bachelor thesis is focused on the automation of the printing machine and comparing the two types of printing machines. The first chapter deals with the history of printing, typesettings, printing techniques and various kinds of bookbinding. The second chapter describes the difference between sheet-fed printing machines and offset printing machines, the difference between two representatives of rotary machines, technological process of the products on these machines, the description of the mac...

  14. Weighted Feature Distance

    DEFF Research Database (Denmark)

    Ortiz-Arroyo, Daniel; Yazdani, Hossein

    2017-01-01

    The accuracy of machine learning methods for clustering depends on the optimal selection of similarity functions. Conventional distance functions for the vector space might cause an algorithm to being affected by some dominant features that may skew its final results. This paper introduces a flexib...

  15. Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem.

    Science.gov (United States)

    Wingfield, Cai; Su, Li; Liu, Xunying; Zhang, Chao; Woodland, Phil; Thwaites, Andrew; Fonteneau, Elisabeth; Marslen-Wilson, William D

    2017-09-01

    There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.

  16. The relationship between personality organization as assessed by theory-driven profiles of the Dutch Short Form of the MMPI and self-reported features of personality organization.

    Science.gov (United States)

    Eurelings-Bontekoe, Elisabeth H M; Luyten, Patrick; Remijsen, Mila; Koelen, Jurrijn

    2010-11-01

    In this study, we investigated the relationships between features of personality organization (PO) as assessed by theory driven profiles of the Dutch Short Form of the MMPI (DSFM; Luteijn & Kok, 1985) and 2 self-report measures of personality pathology, that is, the Dutch Inventory of Personality Organization (Berghuis, Kamphuis, Boedijn, & Verheul, 2009) and the Dutch Schizotypy Personality Questionnaire-Revised (Vollema & Hoijtink, 2000), in a sample of 190 outpatient psychiatric patients. Results showed that the single scales of all 3 measures segregated into 2 theoretically expected and meaningful dimensions, that is, a dimension assessing severity of personality pathology and an introversion/extraversion dimension. Theory-driven combinations of single DSFM subscales as a measure of level of PO distinguished characteristics of patients at various levels of PO in theoretically predicted ways. Results also suggest that structural personality pathology may not be fully captured by self-report measures.

  17. Machine musicianship

    Science.gov (United States)

    Rowe, Robert

    2002-05-01

    The training of musicians begins by teaching basic musical concepts, a collection of knowledge commonly known as musicianship. Computer programs designed to implement musical skills (e.g., to make sense of what they hear, perform music expressively, or compose convincing pieces) can similarly benefit from access to a fundamental level of musicianship. Recent research in music cognition, artificial intelligence, and music theory has produced a repertoire of techniques that can make the behavior of computer programs more musical. Many of these were presented in a recently published book/CD-ROM entitled Machine Musicianship. For use in interactive music systems, we are interested in those which are fast enough to run in real time and that need only make reference to the material as it appears in sequence. This talk will review several applications that are able to identify the tonal center of musical material during performance. Beyond this specific task, the design of real-time algorithmic listening through the concurrent operation of several connected analyzers is examined. The presentation includes discussion of a library of C++ objects that can be combined to perform interactive listening and a demonstration of their capability.

  18. Assessment of use of specific features of subcutaneous insulin infusion systems and their relationship to metabolic control in patients with type 1 diabetes.

    Science.gov (United States)

    Quirós, Carmen; Patrascioiu, Ioana; Giménez, Marga; Vinagre, Irene; Vidal, Mercè; Jansà, Margarita; Conget, Ignacio

    2014-01-01

    Patients with type 1 diabetes (T1DM) treated with continuous subcutaneous insulin infusion (CSII) have available several specific features of these devices. The aim of this study was to evaluate the relationship between real use of them and the degree of glycemic control in patients using this therapy. Forty-four T1DM patients on CSII therapy with or without real-time continuous glucose monitoring (CGM) were included. Data from 14 consecutive days were retrospectively collected using the therapy management software CareLink Personal/Pro(®) and HbA1c measurement performed at that period. The relationship between the frequency of usie of specific features of insulin pumps (non-sensor augmented or sensor-augmented) and glycemic control was analyzed. Mean HbA1c in the group was 7.5 ± .8%. Mean daily number of boluses administered was 5.1 ± 1.8, with 75.4% of them being bolus wizards (BW). Daily number of boluses was significantly greater in patients with HbA1c 7.5% (5.3 ± 1.6 vs. 4.3 ± 1.6, P=.056). There was a trend to greater use of BW in patients with better control (82.8 ± 21.4% vs. 69.9 ± 29.1%, P=.106). HbA1c was lower in patients using CGM (n=8) as compared to those not using sensor-augmented pumps (7.6 ± .8 vs 7.1 ± .7, P=.067), but the difference was not statistically significant. More frequent use of BW appears to be associated to better metabolic control in patients with T1DM using pump therapy. In standard clinical practice, augmentation of insulin pump with CGM may be associated to improved glycemic control. Copyright © 2013 SEEN. Published by Elsevier Espana. All rights reserved.

  19. Use of Quasi-SMILES and Monte Carlo Optimization to Develop Quantitative Feature Property/Activity Relationships (QFPR/QFAR) for Nanomaterials.

    Science.gov (United States)

    Toropov, Andrey A; Rallo, Robert; Toropova, Alla P

    2015-01-01

    The CORAL software (http://www.insilico.eu/coral) has been used to develop quantitative feature-property/activity relationships (QFPRs/QFARs) for the prediction of endpoints related to different categories of nanomaterials. In contrast to previous models built up by using CORAL from a representation of the molecular structure by using simplified molecular input-line entry system (SMILES), the current QFPR/QFARs are based on an integrated representation of acting conditions (i.e., a combination of physicochemical and/or biochemical factors) of nanomaterials via the so-called quasi-SMILES notation. In contrast to traditional quantitative structure - property / activity relationships (QSPRs/QSARs), the new models are able to provide new insight on the conditions of acting of substances (e.g., chemicals and nanomaterials) independently of their molecular structure. The development and validation of the QFPR/QFAR models was carried out following the OECD principles. The statistical quality of models developed from quasi-SMILES is acceptable, with values for the determination coefficient in the range of 0.70 to 0.85 for various endpoints of environmental and human health relevance. Perspectives of the QFPR/QFAR and their interaction and overlapping with traditional QSPR/QSAR are also discussed.

  20. Electrical machines mathematical fundamentals of machine topologies

    CERN Document Server

    Gerling, Dieter

    2015-01-01

    Electrical Machines and Drives play a powerful role in industry with an ever increasing importance. This fact requires the understanding of machine and drive principles by engineers of many different disciplines. Therefore, this book is intended to give a comprehensive deduction of these principles. Special attention is given to the precise mathematical derivation of the necessary formulae to calculate machines and drives and to the discussion of simplifications (if applied) with the associated limits. The book shows how the different machine topologies can be deduced from general fundamentals, and how they are linked together. This book addresses graduate students, researchers, and developers of Electrical Machines and Drives, who are interested in getting knowledge about the principles of machine and drive operation and in detecting the mathematical and engineering specialties of the different machine and drive topologies together with their mutual links. The detailed - but nevertheless compact - mat...

  1. A Knowledge base model for complex forging die machining

    OpenAIRE

    Mawussi, Kwamiwi; Tapie, Laurent

    2011-01-01

    International audience; Recent evolutions on forging process induce more complex shape on forging die. These evolutions, combined with High Speed Machining (HSM) process of forging die lead to important increase in time for machining preparation. In this context, an original approach for generating machining process based on machining knowledge is proposed in this paper. The core of this approach is to decompose a CAD model of complex forging die in geometric features. Technological data and ...

  2. INVESTIGATION OF MAGNESIUM ALLOYS MACHINABILITY

    Directory of Open Access Journals (Sweden)

    Berat Barıs BULDUM

    2013-01-01

    Full Text Available Magnesium is the lightest structural metal. Magnesium alloys have a hexagonal lattice structure, which affects the fundamental properties of these alloys. Plastic deformation of the hexagonal lattice is more complicated than in cubic latticed metals like aluminum, copper and steel. Magnesium alloy developments have traditionally been driven by industry requirements for lightweight materials to operate under increasingly demanding conditions. Magnesium alloys have always been attractive to designers due to their low density, only two thirds that of aluminium and its alloys [1]. The element and its alloys take a big part of modern industry needs. Especially nowadays magnesium alloys are used in automotive and mechanical (trains and wagons manufacture, because of its lightness and other features. Magnesium and magnesium alloys are the easiest of all metals to machine, allowing machining operations at extremely high speed. All standard machining operations such as turning, drilling, milling, are commonly performed on magnesium parts.

  3. Laser machining of advanced materials

    CERN Document Server

    Dahotre, Narendra B

    2011-01-01

    Advanced materialsIntroductionApplicationsStructural ceramicsBiomaterials CompositesIntermetallicsMachining of advanced materials IntroductionFabrication techniquesMechanical machiningChemical Machining (CM)Electrical machiningRadiation machining Hybrid machiningLaser machiningIntroductionAbsorption of laser energy and multiple reflectionsThermal effectsLaser machining of structural ceramicsIntrodu

  4. Template-Directed Biopolymerization: Tape-Copying Turing Machines

    Science.gov (United States)

    Sharma, Ajeet K.; Chowdhury, Debashish

    2012-10-01

    DNA, RNA and proteins are among the most important macromolecules in a living cell. These molecules are polymerized by molecular machines. These natural nano-machines polymerize such macromolecules, adding one monomer at a time, using another linear polymer as the corresponding template. The machine utilizes input chemical energy to move along the template which also serves as a track for the movements of the machine. In the Alan Turing year 2012, it is worth pointing out that these machines are "tape-copying Turing machines". We review the operational mechanisms of the polymerizer machines and their collective behavior from the perspective of statistical physics, emphasizing their common features in spite of the crucial differences in their biological functions. We also draw the attention of the physics community to another class of modular machines that carry out a different type of template-directed polymerization. We hope this review will inspire new kinetic models for these modular machines.

  5. Template-directed biopolymerization: tape-copying Turing machines

    CERN Document Server

    Sharma, Ajeet K; 10.1142/S1793048012300083

    2013-01-01

    DNA, RNA and proteins are among the most important macromolecules in a living cell. These molecules are polymerized by molecular machines. These natural nano-machines polymerize such macromolecules, adding one monomer at a time, using another linear polymer as the corresponding template. The machine utilizes input chemical energy to move along the template which also serves as a track for the movements of the machine. In the Alan Turing year 2012, it is worth pointing out that these machines are "tape-copying Turing machines". We review the operational mechanisms of the polymerizer machines and their collective behavior from the perspective of statistical physics, emphasizing their common features in spite of the crucial differences in their biological functions. We also draw attention of the physics community to another class of modular machines that carry out a different type of template-directed polymerization. We hope this review will inspire new kinetic models for these modular machines.

  6. Discriminating Induced-Microearthquakes Using New Seismic Features

    Science.gov (United States)

    Mousavi, S. M.; Horton, S.

    2016-12-01

    We studied characteristics of induced-microearthquakes on the basis of the waveforms recorded on a limited number of surface receivers using machine-learning techniques. Forty features in the time, frequency, and time-frequency domains were measured on each waveform, and several techniques such as correlation-based feature selection, Artificial Neural Networks (ANNs), Logistic Regression (LR) and X-mean were used as research tools to explore the relationship between these seismic features and source parameters. The results show that spectral features have the highest correlation to source depth. Two new measurements developed as seismic features for this study, spectral centroids and 2D cross-correlations in the time-frequency domain, performed better than the common seismic measurements. These features can be used by machine learning techniques for efficient automatic classification of low energy signals recorded at one or more seismic stations. We applied the technique to 440 microearthquakes-1.7Reference: Mousavi, S.M., S.P. Horton, C. A. Langston, B. Samei, (2016) Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression, Geophys. J. Int. doi: 10.1093/gji/ggw258.

  7. The deleuzian abstract machines

    DEFF Research Database (Denmark)

    Werner Petersen, Erik

    2005-01-01

    production. In Kafka: Toward a Minor Literature, Deleuze and Guatari gave the most comprehensive explanation to the abstract machine in the work of art. Like the war-machines of Virilio, the Kafka-machine operates in three gears or speeds. Furthermore, the machine is connected to spatial diagrams...

  8. Machine drawing

    CERN Document Server

    Narayana, KL; Reddy, K Venkata

    2006-01-01

    About the Book: Written by three distinguished authors with ample academic and teaching experience, this textbook, meant for diploma and degree students of Mechanical Engineering as well as those preparing for AMIE examination, incorporates the latest standards. The new edition includes the features of assembly drawings, part drawings and computer-aided drawings to cater to the needs of students pursuing various courses. The text of the new edition has been thoroughly revised to include new concepts and practices in the subject. It should prove an ideal textbook. Contents: Introduction

  9. Analysis of Relationship Between Thymoma CT Imaging Features and Pathology%胸腺瘤CT影像学特点与病理的关系分析

    Institute of Scientific and Technical Information of China (English)

    杨志惠

    2016-01-01

    目的:探讨胸腺瘤CT表现及其与病理的关系。方法对本院2014年1月-2016年1月就诊64例胸腺瘤患者CT影像学及临床病理资料进行回顾性分析,观察胸腺瘤CT表现,并分析CT与病理分型、分期的关系。结果 CT表现:平扫显示胸腺瘤病灶均在前纵隔部位;不规则形态占79.69%;密度不均占68.75%;增强扫描显示明显强化46例;胸膜侵犯25例,肺结节、纵隔淋巴结肿大各5例。良性(A型与AB型)、恶性(B型与C型)胸腺瘤在肿块形态、边缘、密度、强化程度、局部侵犯及转移方面差异显著(P<0.05)。胸腺瘤CT分期与病理分期比较差异无统计学意义(P>0.05);CT分期I-II期中A型、AB型24例,III-IV期中B型、C型27例。结论病理A型、AB型与B型、C型胸腺瘤CT表现存在较大的差异,CT影像学表现对胸腺瘤良恶性(病理分型)及临床分期判断有重大价值,能为胸腺瘤临床诊治、预后评估提供重要依据。%Objective To explore thymoma CT imaging features and its relationship with pathology. Methods Arestrospective analysis of CT imaging and clinicopathological data was carried out in 64 patients with thymoma treated in our hospital from January 2014 to January 2016,thymoma CT features were observed, relationship between CT, pathological type and stage were analyzed. Results CT features: plain scan showed that thymoma lesions existed in mediastinum anterius part, irregular shapes accounted for 79.69%, uneven densities accounted for 68.75%, enhancement scanning showed obvious enhancement existed in 46 cases, pleural invasion existed in 25 cases, pulmonary nodule existed in 5 cases, mediastinal lymph node enlargement existed in 5 cases. There was a significant difference in tumor shape, edge, density, strengthening degree, local invasion and metastasis of benign (type A and type AB) and malignant (type B and type C) thymomas (P0.05), there were 24 cases of type A, type AB in CT stage

  10. Machine learning methods in chemoinformatics

    Science.gov (United States)

    Mitchell, John B O

    2014-01-01

    Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers. WIREs Comput Mol Sci 2014, 4:468–481. How to cite this article: WIREs Comput Mol Sci 2014, 4:468–481. doi:10.1002/wcms.1183 PMID:25285160

  11. The Relationship of Epicardial Fat Volume to Coronary Plaque, Severe Coronary Stenosis, and High-Risk Coronary Plaque Features Assessed by Coronary CT Angiography

    Science.gov (United States)

    Rajani, Ronak; Shmilovich, Haim; Nakazato, Ryo; Nakanishi, Rine; Otaki, Yuka; Cheng, Victor Y.; Hayes, Sean W.; Thomson, Louise E.J.; Friedman, John D.; Slomka, Piotr J.; Min, James K.; Berman, Daniel S.; Dey, Damini

    2013-01-01

    Background Associations of epicardial fat volume (EFV) measured on non-contrast cardiac computed tomography (NCT) include coronary plaque, myocardial ischemia and adverse cardiac events. Objectives This study aimed to define the relationship of EFV to coronary plaque type, severe coronary stenosis, and to the presence of high-risk plaque features (HRPFs). Methods We retrospectively evaluated 402 consecutive patients, with no prior history of coronary artery disease, who underwent same day non-contrast cardiac computed tomography (NCT) and coronary CT angiography (CTA). EFV was measured on NCT using validated, semi-automated, software. The coronary arteries were evaluated for coronary plaque type [calcified (CP), non-calcified (NCP) or partially-calcified (MP)] and coronary stenosis severity ≥70% using coronary CTA. For patients with NCP and PCP, 2 high risk plaque features were evaluated: Low-attenuation plaque and positive remodeling. Results There were 402 patients with a median age of 66 years (range 23–92) of whom 226 (56%) were male. The EFV was larger in patients with CP (112 ± 55 cm3 vs. 89 ± 39 cm3), PCP (110 ± 57 cm3 vs. 98 ± 45 cm3) and NCP (115 ± 44 cm3 vs. EFV 100 ± 52 cm3. In the 192 patients with PCP or NCP, on multivariable analysis, after adjusting for conventional cardiovascular risk factors, EFV was an independent predictor of ≥70% coronary artery stenosis (OR 3.0, 95% CI 1.3–6.6, p=0.008), any high risk plaque features (OR 1.7, 95% CI 0.9–3.4, p=0.04) and low attention plaque (OR 2.4, 95% CI 1.1–5.1, p=0.02), but not of positive remodeling. Conclusions Epicardial fat volume is larger in patients with CP, PCP and NCP. In patients with NCP and PCP, EFV is significantly associated with severe coronary stenosis, high risk plaque features and low attenuation plaque. PMID:23622507

  12. Mineral mining machines

    Energy Technology Data Exchange (ETDEWEB)

    Mc Gaw, B.H.

    1984-01-01

    A machine for mining minerals is patented. It is a cutter loader with a drum actuating element of the worm type equipped with a multitude of cutting teeth reinforced with tungsten carbide. A feature of the patented machine is that all of the cutting teeth and holders on the drum have the identical design. This is achieved through selecting a slant angle for the cutting teeth which is the mean between the slant angle of the conventional radial teeth and the slant angle of the advance teeth. This, in turn, is provided thanks to the corresponding slant of the holders relative to the drum and (or) the slant of the cutting part of the teeth relative to their stems. Thus, the advance teeth projecting beyond the surface of the drum on the face side and providing upper and lateral clearances have the same angle of attack as the radial teeth, that is, from 20 to 35 degrees. A series of modifications of the cutting teeth is patented. One of the designs allows the cutting tooth to occupy a varying position relative to the drum, from the conventional vertical to an inverted, axially projecting position. In the last case the tooth in the extraction process provides the upper and lateral clearances for the drum on the face side. Among the different modifications of the cutting teeth, a design is proposed which provides for the presence of a stem which is shaped like a truncated cone. This particular stem is designed for use jointly with a wedge which unfastens the teeth and is placed in a holder. The latter is completed in a transverse slot thanks to which the rear end of the stem is compressed, which simplifies replacement of a tooth. Channels are provided in the patented machine for feeding water to the worm spiral, the holders and the cutting teeth themselves in order to deal with dust.

  13. Feature Engineering for Drug Name Recognition in Biomedical Texts: Feature Conjunction and Feature Selection

    Directory of Open Access Journals (Sweden)

    Shengyu Liu

    2015-01-01

    Full Text Available Drug name recognition (DNR is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features and a large number of noisy features when singleton features are combined into conjunction features. However, singleton features that can only capture one linguistic characteristic of a word are not sufficient to describe the information for DNR when multiple characteristics should be considered. In this study, we explore feature conjunction and feature selection for DNR, which have never been reported. We intuitively select 8 types of singleton features and combine them into conjunction features in two ways. Then, Chi-square, mutual information, and information gain are used to mine effective features. Experimental results show that feature conjunction and feature selection can improve the performance of the DNR system with a moderate number of features and our DNR system significantly outperforms the best system in the DDIExtraction 2013 challenge.

  14. Feature engineering for drug name recognition in biomedical texts: feature conjunction and feature selection.

    Science.gov (United States)

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong; Fan, Xiaoming

    2015-01-01

    Drug name recognition (DNR) is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features and a large number of noisy features when singleton features are combined into conjunction features. However, singleton features that can only capture one linguistic characteristic of a word are not sufficient to describe the information for DNR when multiple characteristics should be considered. In this study, we explore feature conjunction and feature selection for DNR, which have never been reported. We intuitively select 8 types of singleton features and combine them into conjunction features in two ways. Then, Chi-square, mutual information, and information gain are used to mine effective features. Experimental results show that feature conjunction and feature selection can improve the performance of the DNR system with a moderate number of features and our DNR system significantly outperforms the best system in the DDIExtraction 2013 challenge.

  15. Managing virtual machines with Vac and Vcycle

    Science.gov (United States)

    McNab, A.; Love, P.; MacMahon, E.

    2015-12-01

    We compare the Vac and Vcycle virtual machine lifecycle managers and our experiences in providing production job execution services for ATLAS, CMS, LHCb, and the GridPP VO at sites in the UK, France and at CERN. In both the Vac and Vcycle systems, the virtual machines are created outside of the experiment's job submission and pilot framework. In the case of Vac, a daemon runs on each physical host which manages a pool of virtual machines on that host, and a peer-to-peer UDP protocol is used to achieve the desired target shares between experiments across the site. In the case of Vcycle, a daemon manages a pool of virtual machines on an Infrastructure-as-a-Service cloud system such as OpenStack, and has within itself enough information to create the types of virtual machines to achieve the desired target shares. Both systems allow unused shares for one experiment to temporarily taken up by other experiements with work to be done. The virtual machine lifecycle is managed with a minimum of information, gathered from the virtual machine creation mechanism (such as libvirt or OpenStack) and using the proposed Machine/Job Features API from WLCG. We demonstrate that the same virtual machine designs can be used to run production jobs on Vac and Vcycle/OpenStack sites for ATLAS, CMS, LHCb, and GridPP, and that these technologies allow sites to be operated in a reliable and robust way.

  16. FGFR2 point mutations in 466 endometrioid endometrial tumors: relationship with MSI, KRAS, PIK3CA, CTNNB1 mutations and clinicopathological features.

    Directory of Open Access Journals (Sweden)

    Sara A Byron

    Full Text Available Mutations in multiple oncogenes including KRAS, CTNNB1, PIK3CA and FGFR2 have been identified in endometrial cancer. The aim of this study was to provide insight into the clinicopathological features associated with patterns of mutation in these genes, a necessary step in planning targeted therapies for endometrial cancer. 466 endometrioid endometrial tumors were tested for mutations in FGFR2, KRAS, CTNNB1, and PIK3CA. The relationships between mutation status, tumor microsatellite instability (MSI and clinicopathological features including overall survival (OS and disease-free survival (DFS were evaluated using Kaplan-Meier survival analysis and Cox proportional hazard models. Mutations were identified in FGFR2 (48/466; KRAS (87/464; CTNNB1 (88/454 and PIK3CA (104/464. KRAS and FGFR2 mutations were significantly more common, and CTNNB1 mutations less common, in MSI positive tumors. KRAS and FGFR2 occurred in a near mutually exclusive pattern (p = 0.05 and, surprisingly, mutations in KRAS and CTNNB1 also occurred in a near mutually exclusive pattern (p = 0.0002. Multivariate analysis revealed that mutation in KRAS and FGFR2 showed a trend (p = 0.06 towards longer and shorter DFS, respectively. In the 386 patients with early stage disease (stage I and II, FGFR2 mutation was significantly associated with shorter DFS (HR = 3.24; 95% confidence interval, CI, 1.35-7.77; p = 0.008 and OS (HR = 2.00; 95% CI 1.09-3.65; p = 0.025 and KRAS was associated with longer DFS (HR = 0.23; 95% CI 0.05-0.97; p = 0.045. In conclusion, although KRAS and FGFR2 mutations share similar activation of the MAPK pathway, our data suggest very different roles in tumor biology. This has implications for the implementation of anti-FGFR or anti-MEK biologic therapies.

  17. The use of fluoride as a natural tracer in water and the relationship to geological features: Examples from the Animas River Watershed, San Juan Mountains, Silverton, Colorado

    Science.gov (United States)

    Bove, D.J.; Walton-Day, K.; Kimball, B.A.

    2009-01-01

    Investigations within the Silverton caldera, in southwestern Colorado, used a combination of traditional geological mapping, alteration-assemblage mapping, and aqueous geochemical sampling that showed a relationship between geological and hydrologic features that may be used to better understand the provenance and evolution of the water. Veins containing fluorite, huebnerite, and elevated molybdenum concentrations are temporally and perhaps genetically associated with the emplacement of high-silica rhyolite intrusions. Both the rhyolites and the fluorite-bearing veins produce waters containing elevated concentrations of F-, K and Be. The identification of water samples with elevated F/Cl molar ratios (> 10) has also aided in the location of water draining F-rich sources, even after these waters have been diluted substantially. These unique aqueous geochemical signatures can be used to relate water chemistry to key geological features and mineralized source areas. Two examples that illustrate this relationship are: (1) surface-water samples containing elevated F-concentrations (> 1.8 mg/l) that closely bracket the extent of several small high-silica rhyolite intrusions; and (2) water samples containing elevated concentrations of F-(> 1.8 mg/ l) that spatially relate to mines or areas that contain late-stage fluorite/huebnerite veins. In two additional cases, the existence of high F-concentrations in water can be used to: (1) infer interaction of the water with mine waste derived from systems known to contain the fluorite/huebnerite association; and (2) relate changes in water quality over time at a high elevation mine tunnel to plugging of a lower elevation mine tunnel and the subsequent rise of the water table into mineralized areas containing fluorite/huebnerite veining. Thus, the unique geochemical signature of the water produced from fluorite veins indicates the location of high-silica rhyolites, mines, and mine waste containing the veins. Existence of high F

  18. Relationship between the temporal changes in positron-emission-tomography-imaging-based textural features and pathologic response and survival in esophageal cancer patients

    Directory of Open Access Journals (Sweden)

    Stephen ShingFan Yip

    2016-03-01

    Full Text Available Purpose: Although change in SUV measures and PET-based textural features during treatment have shown promise in tumor response prediction, it is unclear which quantitative measure is the most predictive. We compared the relationship between PET-based features and pathologic response and overall survival with the SUV measures in esophageal cancer. Methods: Fifty-four esophageal cancer patients received PET/CT scans before and after chemo-radiotherapy. Of these, 45 patients underwent surgery and were classified into complete, partial, and non-responders to the preoperative chemoradiation. SUVmax and SUVmean, two co-occurrence matrix (Entropy and Homogeneity, two run-length-matrix (High-gray-run-emphasis and Short-run-high-gray-run-emphasis, and two size-zone-matrix (High-gray-zone-emphasis and Short-zone-high-gray-emphasis textures were computed. The relationship between the relative difference of each measure at different treatment time points and the pathologic response and overall survival was assessed using the area under the receiver-operating-characteristic curve (AUC and Kaplan-Meier statistics respectively. Results: All Textures, except Homogeneity, were better related to pathologic response than SUVmax and SUVmean. Entropy was found to significantly distinguish non-responders from the complete (AUC=0.79, p=1.7x10^-4 and partial (AUC=0.71, p=0.01 responders. Non-responders can also be significantly differentiated from partial and complete responders by the change in the run length and size zone matrix textures (AUC=0.71‒0.76, p≤0.02. Homogeneity, SUVmax and SUVmean failed to differentiate between any of the responders (AUC=0.50‒0.57, p≥0.46. However, none of the measures were found to significantly distinguish between complete and partial responders with AUC0.25. Conclusions: For the patients studied, temporal change in Entropy and all Run length matrix were better correlated with pathological response and survival than the SUV

  19. Hypermethylation of CpG island loci and hypomethylation of LINE-1 and Alu repeats in prostate adenocarcinoma and their relationship to clinicopathological features.

    Science.gov (United States)

    Cho, N-Y; Kim, B-H; Choi, M; Yoo, E J; Moon, K C; Cho, Y-M; Kim, D; Kang, G H

    2007-02-01

    Promoter CpG island hypermethylation is an important carcinogenic event in prostate adenocarcinoma. Regardless of tissue type, human cancers have in common both focal CpG island hypermethylation and global genomic hypomethylation. The present study evaluated CpG island loci hypermethylation and LINE-1 and Alu repeat hypomethylation in prostate adenocarcinoma, analysed the relationship between them, and correlated these findings with clinicopathological features. We examined 179 cases of prostate adenocarcinoma and 30 cases of benign prostate hypertrophy for the methylation status of 22 CpG island loci and the methylation levels of LINE-1 and Alu repeats using methylation-specific polymerase chain reaction and combined bisulphite restriction analysis, respectively. The following 16 CpG island loci were found to display cancer-related hypermethylation: RASSF1A, GSTP1, RARB, TNFRSF10C, APC, BCL2, MDR1, ASC, TIG1, RBP1, COX2, THBS1, TNFRSF10D, CD44, p16, and RUNX3. Except for the last four CpG island loci, hypermethylation of each of the remaining 12 CpG island loci displayed a close association with one or more of the prognostic parameters (ie preoperative serum prostate specific antigen level, Gleason score sum, and clinical stage). Prostate adenocarcinoma with hypermethylation of each of ASC, COX2, RARB, TNFRSF10C, MDR1, TIG1, RBP1, NEUROG1, RASSF1A, and GSTP1 showed a significantly lower methylation level of Alu or LINE-1 than prostate adenocarcinoma without hypermethylation. In addition, hypomethylation of Alu or LINE-1 was closely associated with one or more of the above prognostic parameters. These data suggest that in tumour progression a close relationship exists between CpG island hypermethylation and the hypomethylation of repetitive elements, and that CpG island hypermethylation and DNA hypomethylation contribute to cancer progression.

  20. The Predication of Relationship Between Harvesting Moisture Content and The Most Convenient Rice Moisture After Drying to Obtained The Highest Head Rice Yield When Rice is Dried in Drying Machine

    Directory of Open Access Journals (Sweden)

    P. Ulger

    2006-09-01

    Full Text Available Rice is dried after harvesting and milled before consumption. But cracklings occur on rice while rice isbeing dried with hot weather. Depending on cracking amount milling rice yield will be high or low. In orderto minimize breakage during the drying process, the relationship between harvesting grain moisture contentand storage moisture content was examined. Thus the highest head rice yield could be obtained. For thatobject , rice samples that were harvested in different harvested moisture content, were dried with hot weatherin prototype dryer which was designed for this object. Rice samples were dried to different moisture andhead rice yield was predicated after drying process , thus the moisture of dried rice which was obtainedmaximum head rice yield was found.According to the data, relationship between harvesting moisture content and the moisture of dried rice onprototypes drying machine is significant as statistical. A regression equation was obtained with data fromdrying process. With this equation, the most convenient moisture to obtained highest head rice yield afterdrying can be predicted. “The most convenient rice moisture after drying =( 4.66 + 0.59 x Harvestingmoisture content±0.54”.

  1. J1140型压铸机增压控制油路设计特点与应用%Design Features and Application of Booster Pressure Control Circuit for J1140 Type Die-casting Machine

    Institute of Scientific and Technical Information of China (English)

    张海华

    2015-01-01

    According to the requirements of J1140 type die-casting machine injection system, boost pressure control circuit was designed. The problem for the change of injection process parameters due to artificial pouring amount different was solved. So the qual-ity stability of die-casting machine parts production is improved, injection force can be adjusted in a wide range, the process-range of die-casting machine is expanded.%根据J1140型压铸机压射系统的要求, 设计增压控制油路, 解决了由于人工浇筑量的不同而造成的压铸工艺参数变化的问题, 提高了压铸机生产零件质量的稳定性, 压射力可在较大范围内进行调整, 扩大了压铸机的工艺范围.

  2. Adaptive machine and its thermodynamic costs

    Science.gov (United States)

    Allahverdyan, Armen E.; Wang, Q. A.

    2013-03-01

    We study the minimal thermodynamically consistent model for an adaptive machine that transfers particles from a higher chemical potential reservoir to a lower one. This model describes essentials of the inhomogeneous catalysis. It is supposed to function with the maximal current under uncertain chemical potentials: if they change, the machine tunes its own structure fitting it to the maximal current under new conditions. This adaptation is possible under two limitations: (i) The degree of freedom that controls the machine's structure has to have a stored energy (described via a negative temperature). The origin of this result is traced back to the Le Chatelier principle. (ii) The machine has to malfunction at a constant environment due to structural fluctuations, whose relative magnitude is controlled solely by the stored energy. We argue that several features of the adaptive machine are similar to those of living organisms (energy storage, aging).

  3. [Psychic experience of pathological machine gamblers].

    Science.gov (United States)

    Avtonomov, D A

    2011-01-01

    The author presents results of the psychopathological phenomena and subjective experience study of 38 patients with the verified diagnosis "Pathological addiction to gambling" (F63.0) without psychotic disorders. In 84,2% cases, the patients preferred slot machine gambling. The causes of such preferences were analyzed. The phenomenology of the psychic experience of the patients who are slot machine gamblers is presented. With the formation of the addiction, the gamblers began to think about slot machines as human beings (creatures), feel attachment to them, see the individuality in them, and experience slot machines as live and real partners in imaginative or even verbal dialogs. Two main "forms of contact" with slot machines were elicited and described: verbal and non-verbal. The gambler has been gradually depleted the image of himself and experiences the "loss of contact" with his own features, qualities, wishes, and intentions. The data obtained may be helpful in psychotherapeutic and rehabilitative work with such patients.

  4. Decomposition of forging die for high speed machining

    CERN Document Server

    Tapie, Laurent

    2009-01-01

    Today's forging die manufacturing process must be adapted to several evolutions in machining process generation: CAD/CAM models, CAM software solutions and High Speed Machining (HSM). In this context, the adequacy between die shape and HSM process is in the core of machining preparation and process planning approaches. This paper deals with an original approach of machining preparation integrating this adequacy in the main tasks carried out. In this approach, the design of the machining process is based on two levels of decomposition of the geometrical model of a given die with respect to HSM cutting conditions (cutting speed and feed rate) and technological constrains (tool selection, features accessibility). This decomposition assists machining assistant to generate an HSM process. The result of this decomposition is the identification of machining features.

  5. Recent advances in micro- and nano-machining technologies

    Science.gov (United States)

    Gao, Shang; Huang, Han

    2016-12-01

    Device miniaturization is an emerging advanced technology in the 21st century. The miniaturization of devices in different fields requires production of micro- and nano-scale components. The features of these components range from the sub-micron to a few hundred microns with high tolerance to many engineering materials. These fields mainly include optics, electronics, medicine, bio-technology, communications, and avionics. This paper reviewed the recent advances in micro- and nano-machining technologies, including micro-cutting, micro-electrical-discharge machining, laser micro-machining, and focused ion beam machining. The four machining technologies were also compared in terms of machining efficiency, workpiece materials being machined, minimum feature size, maximum aspect ratio, and surface finish.

  6. Machine tool structures

    CERN Document Server

    Koenigsberger, F

    1970-01-01

    Machine Tool Structures, Volume 1 deals with fundamental theories and calculation methods for machine tool structures. Experimental investigations into stiffness are discussed, along with the application of the results to the design of machine tool structures. Topics covered range from static and dynamic stiffness to chatter in metal cutting, stability in machine tools, and deformations of machine tool structures. This volume is divided into three sections and opens with a discussion on stiffness specifications and the effect of stiffness on the behavior of the machine under forced vibration c

  7. Archetypal Analysis for Machine Learning

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    2010-01-01

    Archetypal analysis (AA) proposed by Cutler and Breiman in [1] estimates the principal convex hull of a data set. As such AA favors features that constitute representative ’corners’ of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys the interpretability of clustering - ...... for K-means [2]. We demonstrate that the AA model is relevant for feature extraction and dimensional reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, text mining and collaborative filtering....

  8. Compensation strategy for machining optical freeform surfaces by the combined on- and off-machine measurement.

    Science.gov (United States)

    Zhang, Xiaodong; Zeng, Zhen; Liu, Xianlei; Fang, Fengzhou

    2015-09-21

    Freeform surface is promising to be the next generation optics, however it needs high form accuracy for excellent performance. The closed-loop of fabrication-measurement-compensation is necessary for the improvement of the form accuracy. It is difficult to do an off-machine measurement during the freeform machining because the remounting inaccuracy can result in significant form deviations. On the other side, on-machine measurement may hides the systematic errors of the machine because the measuring device is placed in situ on the machine. This study proposes a new compensation strategy based on the combination of on-machine and off-machine measurement. The freeform surface is measured in off-machine mode with nanometric accuracy, and the on-machine probe achieves accurate relative position between the workpiece and machine after remounting. The compensation cutting path is generated according to the calculated relative position and shape errors to avoid employing extra manual adjustment or highly accurate reference-feature fixture. Experimental results verified the effectiveness of the proposed method.

  9. Reverse hypothesis machine learning a practitioner's perspective

    CERN Document Server

    Kulkarni, Parag

    2017-01-01

    This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as ...

  10. A generic trust framework for large-scale open systems using machine learning

    CERN Document Server

    Liu, Xin; Datta, Anwitaman

    2011-01-01

    In many large scale distributed systems and on the web, agents need to interact with other unknown agents to carry out some tasks or transactions. The ability to reason about and assess the potential risks in carrying out such transactions is essential for providing a safe and reliable environment. A traditional approach to reason about the trustworthiness of a transaction is to determine the trustworthiness of the specific agent involved, derived from the history of its behavior. As a departure from such traditional trust models, we propose a generic, machine learning approach based trust framework where an agent uses its own previous transactions (with other agents) to build a knowledge base, and utilize this to assess the trustworthiness of a transaction based on associated features, which are capable of distinguishing successful transactions from unsuccessful ones. These features are harnessed using appropriate machine learning algorithms to extract relationships between the potential transaction and prev...

  11. Fault Diagnosis Based on Fuzzy Support Vector Machine with Parameter Tuning and Feature Selection%基于结合参数整定和特征策略的模糊支持向量机的故障诊断

    Institute of Scientific and Technical Information of China (English)

    毛勇; 夏铮; 尹征; 孙优贤; 万征

    2007-01-01

    This study describes a classification methodology based on support vector machines (SVMs),which offer superior classification perlormance for fault diagnosis in chemical process engineering.The method incorporates an efficient parameter tuning procedure (based on minimization of radius/margin bound for SVM's leave-one-out errors) into a multi-class classification strategy using a fuzzy decision factor,which is named fuzzy support vector machine (FSVM).The datasets generated from the Tennessee Eastman process (TEP) simulator were used to evaluate the classification performance.To decrease the negative influence of the auto-correlated and irrelevant variables,a key variable identification procedure using recursive feature elimination,based on the SVM is implemented.with time lags incorporated,before every classifier is trained,and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation.Performance comparisons are implemented among several kinds of multi-class decision machines,by which the effectiveness of the proposed approach is proved.

  12. Hemodialysis machine technology: a global overview.

    Science.gov (United States)

    Polaschegg, Hans-Dietrich

    2010-11-01

    The market for hemodialysis machines, the background, the current products of manufacturers and the features of hemodialysis machines are described in this article. In addition to the established companies and their products, Chinese manufacturers, and new developments for home hemodialysis, are outlined based on publications available on the internet and patent applications. Here, a critical review of the state of the art questions the medical usefulness of high-tech developments, compared with the benefits of more frequent and/or longer dialysis treatment with comparable simple machines.

  13. BADMINTON TRAINING MACHINE WITH IMPACT MECHANISM

    OpenAIRE

    B.F. Yousif; KOK SOON YEH

    2011-01-01

    In the current work, a newly machine was designed and fabricated for badminton training purpose. In the designing process, CATIA software was used to design and simulate the machine components. The design was based on direct impact method to launch the shuttle using spring as the source of the impact. Hook’s law was used theoretically to determine the initial and the maximum lengths of the springs. The main feature of the machine is that can move in two axes (up and down, left and right). For...

  14. Operating System For Numerically Controlled Milling Machine

    Science.gov (United States)

    Ray, R. B.

    1992-01-01

    OPMILL program is operating system for Kearney and Trecker milling machine providing fast easy way to program manufacture of machine parts with IBM-compatible personal computer. Gives machinist "equation plotter" feature, which plots equations that define movements and converts equations to milling-machine-controlling program moving cutter along defined path. System includes tool-manager software handling up to 25 tools and automatically adjusts to account for each tool. Developed on IBM PS/2 computer running DOS 3.3 with 1 MB of random-access memory.

  15. Operating System For Numerically Controlled Milling Machine

    Science.gov (United States)

    Ray, R. B.

    1992-01-01

    OPMILL program is operating system for Kearney and Trecker milling machine providing fast easy way to program manufacture of machine parts with IBM-compatible personal computer. Gives machinist "equation plotter" feature, which plots equations that define movements and converts equations to milling-machine-controlling program moving cutter along defined path. System includes tool-manager software handling up to 25 tools and automatically adjusts to account for each tool. Developed on IBM PS/2 computer running DOS 3.3 with 1 MB of random-access memory.

  16. Design of Demining Machines

    CERN Document Server

    Mikulic, Dinko

    2013-01-01

    In constant effort to eliminate mine danger, international mine action community has been developing safety, efficiency and cost-effectiveness of clearance methods. Demining machines have become necessary when conducting humanitarian demining where the mechanization of demining provides greater safety and productivity. Design of Demining Machines describes the development and testing of modern demining machines in humanitarian demining.   Relevant data for design of demining machines are included to explain the machinery implemented and some innovative and inspiring development solutions. Development technologies, companies and projects are discussed to provide a comprehensive estimate of the effects of various design factors and to proper selection of optimal parameters for designing the demining machines.   Covering the dynamic processes occurring in machine assemblies and their components to a broader understanding of demining machine as a whole, Design of Demining Machines is primarily tailored as a tex...

  17. Applied machining technology

    CERN Document Server

    Tschätsch, Heinz

    2010-01-01

    Machining and cutting technologies are still crucial for many manufacturing processes. This reference presents all important machining processes in a comprehensive and coherent way. It includes many examples of concrete calculations, problems and solutions.

  18. Machining with abrasives

    CERN Document Server

    Jackson, Mark J

    2011-01-01

    Abrasive machining is key to obtaining the desired geometry and surface quality in manufacturing. This book discusses the fundamentals and advances in the abrasive machining processes. It provides a complete overview of developing areas in the field.

  19. Women, Men, and Machines.

    Science.gov (United States)

    Form, William; McMillen, David Byron

    1983-01-01

    Data from the first national study of technological change show that proportionately more women than men operate machines, are more exposed to machines that have alienating effects, and suffer more from the negative effects of technological change. (Author/SSH)

  20. Brain versus Machine Control.

    Directory of Open Access Journals (Sweden)

    Jose M Carmena

    2004-12-01

    Full Text Available Dr. Octopus, the villain of the movie "Spiderman 2", is a fusion of man and machine. Neuroscientist Jose Carmena examines the facts behind this fictional account of a brain- machine interface

  1. Kinematic Analysis of a New Parallel Machine Tool: the Orthoglide

    CERN Document Server

    Wenger, Philippe

    2007-01-01

    This paper describes a new parallel kinematic architecture for machining applications: the orthoglide. This machine features three fixed parallel linear joints which are mounted orthogonally and a mobile platform which moves in the Cartesian x-y-z space with fixed orientation. The main interest of the orthoglide is that it takes benefit from the advantages of the popular PPP serial machines (regular Cartesian workspace shape and uniform performances) as well as from the parallel kinematic arrangement of the links (less inertia and better dynamic performances), which makes the orthoglide well suited to high-speed machining applications. Possible extension of the orthoglide to 5-axis machining is also investigated.

  2. A New Three-DOF Parallel Mechanism: Milling Machine Applications

    CERN Document Server

    Chablat, Damien

    2000-01-01

    This paper describes a new parallel kinematic architecture for machining applications, namely, the orthoglide. This machine features three fixed parallel linear joints which are mounted orthogonally and a mobile platform which moves in the Cartesian x-y-z space with fixed orientation. The main interest of the orthoglide is that it takes benefit from the advantages of the popular PPP serial machines (regular Cartesian workspace shape and uniform performances) as well as from the parallel kinematic arrangement of the links (less inertia and better dynamic performances), which makes the orthoglide well suited to high-speed machining applications. Possible extension of the orthoglide to 5-axis machining is also investigated.

  3. Mass screening of prostate cancer in a Chinese population:the relationship between pathological features of prostate cancer and serum prostate specific antigen

    Institute of Scientific and Technical Information of China (English)

    Hong-Wen Gao; Masaaki Kuwahara; Xue-Jian Zhao; Yu-Lin Li; Shan Wu; Yi-Shu Wang; Hai-Feng Zhang; Yu-Zhuo Pan; Ling Zhang; Hiroo Tateno; Ikuro Sato

    2005-01-01

    Aim: To investigate the pathological features of the prostate biopsy through mass screening for prostate cancer in a Chinese cohort and their association with serum prostate specific antigen (PSA). Methods: A total of 12 027 Chinese men in Changchun were screened for prostate cancer by means of the serum total prostate specific antigen (tPSA) test (oy Elisa assay). Transrectal ultrasound-guided systematic six-sextant biopsies were performed on those whose serum tPSA value was >4.0 ng/mL and those who had obstructive symptoms (despite their tPSA value) and were subject to subsequent pathological analysis with the aid of the statistic software SPSS 10.0 (SPSS. Inc., Chicago. USA). Results: Of the 12 027 cases, 158 (including 137 patients whose serum tPSA values were >4.0 ng/mL and 21 patients [serum tPSA <4.0 ng/mL] who had obstructive symptoms) undertook prostate biopsy. Of the 158 biopsies, 41 cases of prostatic carcinoma were found (25.9 %, 41/158). The moderately differentiated carcinoma and poorly differentiated carcinoma accounted for 61% and 34%, respectively. A significant linear positive correlation between the serum tPSA and the Gleason scores in the 41 cases of prostatic carcinoma (r = 0.312, P < 0.01) was established. A significant linear positive correlation between the serum tPSA value of the 41 prostatic carcinoma and the positive counts of carcinoma in sextant biopsies was established (r = 0.406, P < 0.01), indicating a significant linear relationship between serum tPSA and the size of tumor.Conclusion: This study was the first to conduct mass screening for prostate cancer by testing for serum tPSA values and the first to investigate the pathological features of prostate cancer in a cohort of Chinese men. Our results reveal that the moderately differentiated carcinoma is the most common type of prostate cancer. This study also has shown that the serum tPSA value in prostate cancer is associated with the Gleason score and the size of tumor.

  4. Decision Support System for Diabetes Mellitus through Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Tarik A. Rashid

    2016-07-01

    Full Text Available recently, the diseases of diabetes mellitus have grown into extremely feared problems that can have damaging effects on the health condition of their sufferers globally. In this regard, several machine learning models have been used to predict and classify diabetes types. Nevertheless, most of these models attempted to solve two problems; categorizing patients in terms of diabetic types and forecasting blood surge rate of patients. This paper presents an automatic decision support system for diabetes mellitus through machine learning techniques by taking into account the above problems, plus, reflecting the skills of medical specialists who believe that there is a great relationship between patient’s symptoms with some chronic diseases and the blood sugar rate. Data sets are collected from Layla Qasim Clinical Center in Kurdistan Region, then, the data is cleaned and proposed using feature selection techniques such as Sequential Forward Selection and the Correlation Coefficient, finally, the refined data is fed into machine learning models for prediction, classification, and description purposes. This system enables physicians and doctors to provide diabetes mellitus (DM patients good health treatments and recommendations.

  5. A Universal Reactive Machine

    DEFF Research Database (Denmark)

    Andersen, Henrik Reif; Mørk, Simon; Sørensen, Morten U.

    1997-01-01

    Turing showed the existence of a model universal for the set of Turing machines in the sense that given an encoding of any Turing machine asinput the universal Turing machine simulates it. We introduce the concept of universality for reactive systems and construct a CCS processuniversal...

  6. Probabilistic hazard assessment for skin sensitization potency by dose–response modeling using feature elimination instead of quantitative structure–activity relationships

    Science.gov (United States)

    McKim, James M.; Hartung, Thomas; Kleensang, Andre; Sá-Rocha, Vanessa

    2016-01-01

    Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose–response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse outcome pathways (AOP) and the development of tests reflecting these mechanisms. Simple approaches to combine skin sensitization data sets, such as weight of evidence, fail due to problems in information redundancy and high dimension-ality. The problem is further amplified when potency information (dose/response) of hazards would be estimated. Skin sensitization currently serves as the foster child for AOP and ITS development, as legislative pressures combined with a very good mechanistic understanding of contact dermatitis have led to test development and relatively large high-quality data sets. We curated such a data set and combined a recursive variable selection algorithm to evaluate the information available through in silico, in chemico and in vitro assays. Chemical similarity alone could not cluster chemicals’ potency, and in vitro models consistently ranked high in recursive feature elimination. This allows reducing the number of tests included in an ITS. Next, we analyzed with a hidden Markov model that takes advantage of an intrinsic inter-relationship among the local lymph node assay classes, i.e. the monotonous connection between local lymph node assay and dose. The dose-informed random forest/hidden Markov model was superior to the dose-naive random forest model on all data sets. Although balanced accuracy improvement may seem small, this obscures the actual improvement in misclassifications as the dose-informed hidden Markov model strongly reduced "false-negatives" (i.e. extreme sensitizers as non-sensitizer) on all data sets. PMID:26046447

  7. Meso-scale machining capabilities and issues

    Energy Technology Data Exchange (ETDEWEB)

    BENAVIDES,GILBERT L.; ADAMS,DAVID P.; YANG,PIN

    2000-05-15

    Meso-scale manufacturing processes are bridging the gap between silicon-based MEMS processes and conventional miniature machining. These processes can fabricate two and three-dimensional parts having micron size features in traditional materials such as stainless steels, rare earth magnets, ceramics, and glass. Meso-scale processes that are currently available include, focused ion beam sputtering, micro-milling, micro-turning, excimer laser ablation, femto-second laser ablation, and micro electro discharge machining. These meso-scale processes employ subtractive machining technologies (i.e., material removal), unlike LIGA, which is an additive meso-scale process. Meso-scale processes have different material capabilities and machining performance specifications. Machining performance specifications of interest include minimum feature size, feature tolerance, feature location accuracy, surface finish, and material removal rate. Sandia National Laboratories is developing meso-scale electro-mechanical components, which require meso-scale parts that move relative to one another. The meso-scale parts fabricated by subtractive meso-scale manufacturing processes have unique tribology issues because of the variety of materials and the surface conditions produced by the different meso-scale manufacturing processes.

  8. Laser-assisted machining of difficult-to-machine materials

    Energy Technology Data Exchange (ETDEWEB)

    Incropera, F.P.; Rozzi, J.C.; Pfefferkorn, F.E.; Lei, S.; Shin, Y.C.

    1999-07-01

    Laser-assisted machining (LAM) is a hybrid process for which a difficult-to-machine material, such as a ceramic or super alloy, is irradiated by a laser source prior to material removal by a cutting tool. The process has the potential to significantly increase material removal rates, as well as to improve the geometry and properties of the finished work piece. Features and limitations of theoretical and experimental procedures for determining the transient thermal response of a work piece during LAM are described, and representative results are presented for laser-assisted turning of sintered silicon nitride. Significant physical trends are revealed by the calculations, as are guidelines for the selection of appropriate operating conditions.

  9. Characterization of one-dimensional cellular automata rules through topological network features

    Science.gov (United States)

    D'Alotto, Lou; Pizzuti, Clara

    2016-10-01

    The paper investigates the relationship between the classification schemes, defined by Wolfram and Gilman, of one-dimensional cellular automata through concepts coming from network theory. An automaton is represented with a network, generated from the elementary rule defining its behavior. Characteristic features of this graph are computed and machine learning classification models are built. Such models allow to classify automaton rules and to compare Wolfram's and Gilman's classes by comparing the classes predicted by these models.

  10. Asynchronized synchronous machines

    CERN Document Server

    Botvinnik, M M

    1964-01-01

    Asynchronized Synchronous Machines focuses on the theoretical research on asynchronized synchronous (AS) machines, which are "hybrids” of synchronous and induction machines that can operate with slip. Topics covered in this book include the initial equations; vector diagram of an AS machine; regulation in cases of deviation from the law of full compensation; parameters of the excitation system; and schematic diagram of an excitation regulator. The possible applications of AS machines and its calculations in certain cases are also discussed. This publication is beneficial for students and indiv

  11. Quantum machine learning.

    Science.gov (United States)

    Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

    2017-09-13

    Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

  12. Precision machine design

    CERN Document Server

    Slocum, Alexander H

    1992-01-01

    This book is a comprehensive engineering exploration of all the aspects of precision machine design - both component and system design considerations for precision machines. It addresses both theoretical analysis and practical implementation providing many real-world design case studies as well as numerous examples of existing components and their characteristics. Fast becoming a classic, this book includes examples of analysis techniques, along with the philosophy of the solution method. It explores the physics of errors in machines and how such knowledge can be used to build an error budget for a machine, how error budgets can be used to design more accurate machines.

  13. Development of precision numerical controlled high vacuum electron beam welding machine

    CERN Document Server

    Li Shao Lin

    2002-01-01

    The structure, main technical parameters and characteristics of the precision numerical controlled high vacuum electron beam welding machine are introduced. The design principle, some features and solutions to some key technique problems of this new type machine are described

  14. Characterization of machining quality attributes based on spindle probe, coordinate measuring machine, and surface roughness data

    Directory of Open Access Journals (Sweden)

    Tzu-Liang Bill Tseng

    2014-04-01

    Full Text Available This study investigates the effects of machining parameters as they relate to the quality characteristics of machined features. Two most important quality characteristics are set as the dimensional accuracy and the surface roughness. Before any newly acquired machine tool is put to use for production, it is important to test the machine in a systematic way to find out how different parameter settings affect machining quality. The empirical verification was made by conducting a Design of Experiment (DOE with 3 levels and 3 factors on a state-of-the-art Cincinnati Hawk Arrow 750 Vertical Machining Center (VMC. Data analysis revealed that the significant factor was the Hardness of the material and the significant interaction effect was the Hardness + Feed for dimensional accuracy, while the significant factor was Speed for surface roughness. Since the equally important thing is the capability of the instruments from which the quality characteristics are being measured, a comparison was made between the VMC touch probe readings and the measurements from a Mitutoyo coordinate measuring machine (CMM on bore diameters. A machine mounted touch probe has gained a wide acceptance in recent years, as it is more suitable for the modern manufacturing environment. The data vindicated that the VMC touch probe has the capability that is suitable for the production environment. The test results can be incorporated in the process plan to help maintain the machining quality in the subsequent runs.

  15. Experimental Investigation of process parameters influence on machining Inconel 800 in the Electrical Spark Eroding Machine

    Science.gov (United States)

    Karunakaran, K.; Chandrasekaran, M.

    2016-11-01

    The Electrical Spark Eroding Machining is an entrenched sophisticated machining process for producing complex geometry with close tolerances in hard materials like super alloy which are extremely difficult-to-machine by using conventional machining processes. It is sometimes offered as a better alternative or sometimes as an only alternative for generating accurate 3D complex shapes of macro, micro and nano-features in such difficult-to-machine materials among other advanced machining processes. The accomplishment of such challenging task by use of Electrical Spark Eroding Machining or Electrical Discharge Machining (EDM) is depending upon selection of apt process parameters. This paper is about analyzing the influencing of parameter in electrical eroding machining for Inconel 800 with electrolytic copper as a tool. The experimental runs were performed with various input conditions to process Inconel 800 nickel based super alloy for analyzing the response of material removal rate, surface roughness and tool wear rate. These are the measures of performance of individual experimental value of parameters such as pulse on time, Pulse off time, peak current. Taguchi full factorial Design by using Minitab release 14 software was employed to meet the manufacture requirements of preparing process parameter selection card for Inconel 800 jobs. The individual parameter's contribution towards surface roughness was observed from 13.68% to 64.66%.

  16. Mastering Virtual Machine Manager 2008 R2

    CERN Document Server

    Michael, Michael

    2009-01-01

    One-of-a-kind guide from Microsoft insiders on Virtual Machine Manager 2008 R2!. What better way to learn VMM 2008 R2 than from the high-powered Microsoft program managers themselves? This stellar author team takes you under the hood of VMM 2008 R2, providing intermediate and advanced coverage of all features.: Walks you through Microsoft's new System Center Virtual Machine Manager 2008, a unified system for managing all virtual and physical assets; VMM 2008 not only supports Windows Server 2008 Hyper-V, but also VMware ESXas well!; Features a winning author team behind the new VMM; Describes

  17. The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from {sup 18}F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer

    Energy Technology Data Exchange (ETDEWEB)

    Gao, Xuan [Center of PET/CT, The Third Affiliated Hospital of Harbin Medical University, The Affiliated Tumor Hospital of Harbin Medical University, Harbin (China); Chu, Chunyu [HIT–INSA Sino French Research Centre for Biomedical Imaging, Harbin Institute of Technology, Harbin (China); Li, Yingci; Lu, Peiou; Wang, Wenzhi [Center of PET/CT, The Third Affiliated Hospital of Harbin Medical University, The Affiliated Tumor Hospital of Harbin Medical University, Harbin (China); Liu, Wanyu [HIT–INSA Sino French Research Centre for Biomedical Imaging, Harbin Institute of Technology, Harbin (China); Yu, Lijuan, E-mail: yulijuan2003@126.com [Center of PET/CT, The Third Affiliated Hospital of Harbin Medical University, The Affiliated Tumor Hospital of Harbin Medical University, Harbin (China)

    2015-02-15

    Highlights: • Three support vector machine classifiers were constructed from PET-CT images. • The areas under the ROC curve for SVM1, SVM2, and SVM3 were 0.689, 0.579, and 0.685, respectively. • The areas under curves for maximum short diameter and SUV{sub max} were 0.684 and 0.652, respectively. • The algorithm based on SVM was potential in the diagnosis of mediastinal lymph nodes. - Abstract: Objectives: In clinical practice, image analysis is dependent on simply visual perception and the diagnostic efficacy of this analysis pattern is limited for mediastinal lymph nodes in patients with lung cancer. In order to improve diagnostic efficacy, we developed a new computer-based algorithm and tested its diagnostic efficacy. Methods: 132 consecutive patients with lung cancer underwent {sup 18}F-FDG PET/CT examination before treatment. After all data were imported into the database of an on-line medical image analysis platform, the diagnostic efficacy of visual analysis was first evaluated without knowing pathological results, and the maximum short diameter and maximum standardized uptake value (SUV{sub max}) were measured. Then lymph nodes were segmented manually. Three classifiers based on support vector machine (SVM) were constructed from CT, PET, and combined PET-CT images, respectively. The diagnostic efficacy of SVM classifiers was obtained and evaluated. Results: According to ROC curves, the areas under curves for maximum short diameter and SUV{sub max} were 0.684 and 0.652, respectively. The areas under the ROC curve for SVM1, SVM2, and SVM3 were 0.689, 0.579, and 0.685, respectively. Conclusion: The algorithm based on SVM was potential in the diagnosis of mediastinal lymph nodes.

  18. Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data

    Directory of Open Access Journals (Sweden)

    Qingqing Lu

    2014-01-01

    Full Text Available Ground penetrating radar (GPR is a powerful tool for detecting objects buried underground. However, the interpretation of the acquired signals remains a challenging task since an experienced user is required to manage the entire operation. Particularly difficult is the classification of the material type of underground objects in noisy environment. This paper proposes a new feature extraction method. First, discrete wavelet transform (DWT transforms A-Scan data and approximation coefficients are extracted. Then, fractional Fourier transform (FRFT is used to transform approximation coefficients into fractional domain and we extract features. The features are supplied to the support vector machine (SVM classifiers to automatically identify underground objects material. Experiment results show that the proposed feature-based SVM system has good performances in classification accuracy compared to statistical and frequency domain feature-based SVM system in noisy environment and the classification accuracy of features proposed in this paper has little relationship with the SVM models.

  19. Perspex machine: VII. The universal perspex machine

    Science.gov (United States)

    Anderson, James A. D. W.

    2006-01-01

    The perspex machine arose from the unification of projective geometry with the Turing machine. It uses a total arithmetic, called transreal arithmetic, that contains real arithmetic and allows division by zero. Transreal arithmetic is redefined here. The new arithmetic has both a positive and a negative infinity which lie at the extremes of the number line, and a number nullity that lies off the number line. We prove that nullity, 0/0, is a number. Hence a number may have one of four signs: negative, zero, positive, or nullity. It is, therefore, impossible to encode the sign of a number in one bit, as floating-point arithmetic attempts to do, resulting in the difficulty of having both positive and negative zeros and NaNs. Transrational arithmetic is consistent with Cantor arithmetic. In an extension to real arithmetic, the product of zero, an infinity, or nullity with its reciprocal is nullity, not unity. This avoids the usual contradictions that follow from allowing division by zero. Transreal arithmetic has a fixed algebraic structure and does not admit options as IEEE, floating-point arithmetic does. Most significantly, nullity has a simple semantics that is related to zero. Zero means "no value" and nullity means "no information." We argue that nullity is as useful to a manufactured computer as zero is to a human computer. The perspex machine is intended to offer one solution to the mind-body problem by showing how the computable aspects of mind and, perhaps, the whole of mind relates to the geometrical aspects of body and, perhaps, the whole of body. We review some of Turing's writings and show that he held the view that his machine has spatial properties. In particular, that it has the property of being a 7D lattice of compact spaces. Thus, we read Turing as believing that his machine relates computation to geometrical bodies. We simplify the perspex machine by substituting an augmented Euclidean geometry for projective geometry. This leads to a general

  20. Potential relationship between pancreatic histological features and its diseases%胰腺组织学特点与疾病潜在关系的分析

    Institute of Scientific and Technical Information of China (English)

    韦军民; 徐新建; 王喜艳; 乔江春; 吕骅; 杨乐

    2008-01-01

    Objective To investigate the potential relationship between the pancreatic histological features and its diseases. Methods We collected 49 pancreatic samples from Beijing Hospital and the First Af-filiated Hospital of Xinjiang Medical University from April 2005 to June 2006 to determine the structure of pancreatic peripheral tissue, the structural characteristics of pancreatic lobule and duct in healthy pancreas and its abnormality. Results There were loose connective tissue nccupyiug the frontage and backside of healthy pancreas.The basic unit of pancreas was pancreatic lobule. The blood supply of pancreatic parenchyma was through the gap of pancreatic lobule from periphery. Pancreatic duct was mainly composed of simple epithelial cells and dense connective tissue but not muscular layer.There was dense connective tissue occup-ying the frontage and backside of pancreas while there was chronic pancreatitis happened. The situation presented more apparently with decreasing of distance to lesion of pancreatic tissue. A great deal of fibrous structure and lymphocyte soaked the gap of pancreatic lobule and the gap of pancreatic lobule became wider and pancreatic lobule atrophied. Conclusion Pancreas can not form envelope. Pass structure of the loose connective tissue in the blank of glandular 10bde is in accord with endocrine and exocrine function of pancreas .Difficulty of the therapy in acute pancreatitis, chronic pancreatitis or pancreatic cancer is directly related to the structural features of pancreas.%目的 研究胰腺组织学特点与其疾病的潜在关系.方法 收集卫生部北京医院与新疆医科大学第一附属医院2005年4月至2006年6月胰腺标本49例,分别从胰腺周围组织、胰腺小叶间隙和胰管的组织结构特点的角度分析胰腺组织学特点与胰腺疾病的潜在关系.结果 正常胰腺周围为疏松结缔组织,无明确被膜结构;胰腺小叶间隙是胰腺血液、神经、淋巴出入胰腺实质的通

  1. Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

    Science.gov (United States)

    Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

    2012-08-07

    Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

  2. Relationship of EMG/SMG features and muscle strength level: an exploratory study on tibialis anterior muscles during plantar-flexion among hemiplegia patients.

    Science.gov (United States)

    Li, Huihui; Zhao, Guoru; Zhou, Yongjin; Chen, Xin; Ji, Zhen; Wang, Lei

    2014-01-27

    Improvement in muscle strength is an important aim for the rehabilitation of hemiplegia patients. Presently, the rehabilitation prescription depends on the evaluation results of muscle strength, which are routinely estimated by experienced physicians and therefore not finely quantitative. Widely-used quantification methods for disability, such as Barthel Index (BI) and motor component of Functional Independent Measure (M-FIM), yet have limitations in their application, since both of them differentiated disability better in lower than higher disability, and they are subjective and recorded in wide scales. In this paper, to explore finely quantitative measures for evaluation of muscle strength level (MSL), we start with the study on quantified electromyography (EMG) and sonomyography (SMG) features of tibialis anterior (TA) muscles among hemiplegia patients. 12 hemiplegia subjects volunteered to perform several sets of plantar-flexion movements in the study, and their EMG signals and SMG signals were recorded on TA independently to avoid interference. EMG data were filtered and then the root-mean-square (RMS) was computed. SMG signals, specifically speaking, the muscle thickness of TA, were manually measured by two experienced operators using ultrasonography. Reproducibility of the SMG assessment on TA between operators was evaluated by non-parametric test (independent sample T test). Possible relationship between muscle thickness changes (TC) of TA and muscle strength level of hemiplegia patients was estimated. Mean of EMG RMS between subjects is found linearly correlated with MSL (R2 = 0.903). And mean of TA muscle TC amplitudes is also linearly correlated with MSL among dysfunctional legs (R2 = 0.949). Moreover, rectified TC amplitudes (dysfunctional leg/ healthy leg, DLHL) and rectified EMG signals (DLHL) are found in linear correlation with MSL, with R2 = 0.756 and R2 = 0.676 respectively. Meanwhile, the preliminary results demonstrate that

  3. BADMINTON TRAINING MACHINE WITH IMPACT MECHANISM

    Directory of Open Access Journals (Sweden)

    B. F. YOUSIF

    2011-02-01

    Full Text Available In the current work, a newly machine was designed and fabricated for badminton training purpose. In the designing process, CATIA software was used to design and simulate the machine components. The design was based on direct impact method to launch the shuttle using spring as the source of the impact. Hook’s law was used theoretically to determine the initial and the maximum lengths of the springs. The main feature of the machine is that can move in two axes (up and down, left and right. For the control system, infra-red sensor and touch switch were adapted in microcontroller. The final product was locally fabricated and proved that the machine can operate properly.

  4. The Fuzzy Cluster Analysis in Identification of Key Temperatures in Machine Tool

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was needed. The relationship can be deduced by virtual of FEM (Finite Element Method ), ANN (Artificial Neural Network) or MRA (Multiple Regression Analysis). MR A is on the basis of a total understanding of the temperature distribution of th e machine tool. Although the more the temperatures measu...

  5. Thermal models of electric machines with dynamic workloads

    Directory of Open Access Journals (Sweden)

    Christian Pohlandt

    2015-07-01

    Full Text Available Electric powertrains are increasingly used in off-highway machines because of easy controllability and excellent overall efficiency. The main goals are increasing the energy efficiency of the machine and the optimization of the work process. The thermal behaviour of electric machines with dynamic workloads applied to is a key design factor for electric powertrains in off-highway machines. This article introduces a methodology to model the thermal behaviour of electric machines. Using a noncausal modelling approach, an electric powertrain is analysed for dynamic workloads. Cause-effect relationships and reasons for increasing temperature are considered as well as various cooling techniques. The validation of the overall simulation model of the powertrain with measured field data workloads provides convincing results to evaluate numerous applications of electric machines in off-highway machines.

  6. Machine Learning for Silver Nanoparticle Electron Transfer Property Prediction.

    Science.gov (United States)

    Sun, Baichuan; Fernandez, Michael; Barnard, Amanda S

    2017-09-22

    Nanoparticles exhibit diverse structural and morphological features that are often inter-connected, making the correlation of structure/property relationships challenging. In this study a multi-structure/single-property relationship of silver nanoparticles is developed for the energy of Fermi level, which can be tuned to improve the transfer of electrons in a variety of applications. By combining different machine learning analytical algorithms, including k-mean, logistic regression and random forest with electronic structure simulations, we find that the degree of twinning (characterised by the fraction of hexagonal closed packed atoms) and the population of {111} facet (characterized by a surface coordination number of 9) are strongly correlated to the Fermi energy of silver nanoparticles. A concise 3 layer artificial neural network together with principal component analysis is built to predict this property, with reduced geometrical, structural and topological features, making the method ideal for efficient and accurate high-throughput screening of large-scale virtual nanoparticles libraries, and the creation of single-structure/single-property, multi-structure/single-property and single-structure/multi-property relationships in the near future.

  7. Vane Pump Casing Machining of Dumpling Machine Based on CAD/CAM

    Science.gov (United States)

    Huang, Yusen; Li, Shilong; Li, Chengcheng; Yang, Zhen

    Automatic dumpling forming machine is also called dumpling machine, which makes dumplings through mechanical motions. This paper adopts the stuffing delivery mechanism featuring the improved and specially-designed vane pump casing, which can contribute to the formation of dumplings. Its 3D modeling in Pro/E software, machining process planning, milling path optimization, simulation based on UG and compiling post program were introduced and verified. The results indicated that adoption of CAD/CAM offers firms the potential to pursue new innovative strategies.

  8. MACHINE LEARNING TECHNIQUES USED IN BIG DATA

    Directory of Open Access Journals (Sweden)

    STEFANIA LOREDANA NITA

    2016-07-01

    Full Text Available The classical tools used in data analysis are not enough in order to benefit of all advantages of big data. The amount of information is too large for a complete investigation, and the possible connections and relations between data could be missed, because it is difficult or even impossible to verify all assumption over the information. Machine learning is a great solution in order to find concealed correlations or relationships between data, because it runs at scale machine and works very well with large data sets. The more data we have, the more the machine learning algorithm is useful, because it “learns” from the existing data and applies the found rules on new entries. In this paper, we present some machine learning algorithms and techniques used in big data.

  9. GRINDING OF DOUBLE DISC GRINDING MACHINE

    Institute of Scientific and Technical Information of China (English)

    Hu Huiqing

    2005-01-01

    The grinding of two parallel sides of a component is accomplished with the accuracy and higher productivity by passing a blank through the truncated cone shape grinders, which are turned angles. The machine is designated by the name of double disc grinding machine (DDGM). Usually, it is used in the mass production. The relationship between these angles, the accuracy, productivity,allowance and parameters of the machine and technology is explained in detail by math, such as vector analysis, transformation of 3D space coordinates, etc. Therefore, in the aspects of qualitative and quantitative analyses, the grinding potential of DDGM is enormous increased and superior to conventional methods. Furthermore, the theoretical foundation of DDGM grinding design and technology is provided to improve, to expand and to create for future. The established machine design and practical experience of grinding technology will get great benefit by them.

  10. Machinability of advanced materials

    CERN Document Server

    Davim, J Paulo

    2014-01-01

    Machinability of Advanced Materials addresses the level of difficulty involved in machining a material, or multiple materials, with the appropriate tooling and cutting parameters.  A variety of factors determine a material's machinability, including tool life rate, cutting forces and power consumption, surface integrity, limiting rate of metal removal, and chip shape. These topics, among others, and multiple examples comprise this research resource for engineering students, academics, and practitioners.

  11. Support vector machines applications

    CERN Document Server

    Guo, Guodong

    2014-01-01

    Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.

  12. Machining of titanium alloys

    CERN Document Server

    2014-01-01

    This book presents a collection of examples illustrating the resent research advances in the machining of titanium alloys. These materials have excellent strength and fracture toughness as well as low density and good corrosion resistance; however, machinability is still poor due to their low thermal conductivity and high chemical reactivity with cutting tool materials. This book presents solutions to enhance machinability in titanium-based alloys and serves as a useful reference to professionals and researchers in aerospace, automotive and biomedical fields.

  13. Photometric Supernova Classification With Machine Learning

    CERN Document Server

    Lochner, Michelle; Peiris, Hiranya V; Lahav, Ofer; Winter, Max K

    2016-01-01

    Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Telescope (LSST), given that spectroscopic confirmation of type for all supernovae discovered with these surveys will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques fitting parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks and boosted decision trees. We test the pipeline on simulated multi-ba...

  14. Implementation of Java Card Virtual Machine

    Institute of Scientific and Technical Information of China (English)

    刘嵩岩; 毛志刚; 叶以正

    2000-01-01

    Java card is a new system for programming smart cards, which is based on the Java language and Virtual Machine. Java card programs (applets)run in Java Card Runtime Environment (JCRE) including the Java Card Virtual Machine (JCVM), the framework, the associated native methods and the API (Application Programming Interface). JCVM is implemented as two separate pieces:off-card VM (Virtual Machine) and on-card VM. The stack model and heap memory structure used by on-card VM and exception handling are introduced. Because there are limited resources within smart card environment, and garbage collection is not supported in JCVM, the preferred way to exception handling does not directly involve the use of throw, although the throw keyword is supported. Security is the most important feature of smart card. The Java card applet security feature is also discussed.

  15. On the Optimal Selection of Electrical Machines Fans

    Directory of Open Access Journals (Sweden)

    Mădălin Costin

    2014-09-01

    Full Text Available In this paper an analytic relationship for electrical machine fan design has been developed. In the particularly case of salient poles synchronous machine (with salient poles – for electromagnetic field excitation or surface mounded permanent magnet, this approach allowed to express the fan power as a function of machine middle axe air gap. This analytic foundation developed may leads to different optimization criteria as specific active materials or costs. Numerical simulations confirm our approach.

  16. Rotating electrical machines

    CERN Document Server

    Le Doeuff, René

    2013-01-01

    In this book a general matrix-based approach to modeling electrical machines is promulgated. The model uses instantaneous quantities for key variables and enables the user to easily take into account associations between rotating machines and static converters (such as in variable speed drives).   General equations of electromechanical energy conversion are established early in the treatment of the topic and then applied to synchronous, induction and DC machines. The primary characteristics of these machines are established for steady state behavior as well as for variable speed scenarios. I

  17. Chaotic Boltzmann machines.

    Science.gov (United States)

    Suzuki, Hideyuki; Imura, Jun-ichi; Horio, Yoshihiko; Aihara, Kazuyuki

    2013-01-01

    The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, efficient hardware implementation is expected. Moreover, the ferromagnetic phase transition of the Ising model is shown to be characterised by the largest Lyapunov exponent of the proposed system. In general, a method to relate probabilistic models to nonlinear dynamics by derandomising Gibbs sampling is presented.

  18. Tribology in machine design

    CERN Document Server

    Stolarski, Tadeusz

    1999-01-01

    ""Tribology in Machine Design is strongly recommended for machine designers, and engineers and scientists interested in tribology. It should be in the engineering library of companies producing mechanical equipment.""Applied Mechanics ReviewTribology in Machine Design explains the role of tribology in the design of machine elements. It shows how algorithms developed from the basic principles of tribology can be used in a range of practical applications within mechanical devices and systems.The computer offers today's designer the possibility of greater stringen

  19. Machine learning with R

    CERN Document Server

    Lantz, Brett

    2013-01-01

    Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or

  20. Induction machine handbook

    CERN Document Server

    Boldea, Ion

    2002-01-01

    Often called the workhorse of industry, the advent of power electronics and advances in digital control are transforming the induction motor into the racehorse of industrial motion control. Now, the classic texts on induction machines are nearly three decades old, while more recent books on electric motors lack the necessary depth and detail on induction machines.The Induction Machine Handbook fills industry's long-standing need for a comprehensive treatise embracing the many intricate facets of induction machine analysis and design. Moving gradually from simple to complex and from standard to

  1. DRIVE AND CONTROL OF VIRTUAL-AXIS NC MACHINE TOOLS

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    The structure features and driving modes of virtual-axis NC machine tools are studied.Accor- ding to different application requirements,the three-axis control method,the five-axis control method and the six-freedom control method are put forward.These results lay a foundation for the product development of the virtual-axis NC machine tools.

  2. SUPPORT VECTOR MACHINE METHOD FOR PREDICTING INVESTMENT MEASURES

    Directory of Open Access Journals (Sweden)

    Olga V. Kitova

    2016-01-01

    Full Text Available Possibilities of applying intelligent machine learning technique based on support vectors for predicting investment measures are considered in the article. The base features of support vector method over traditional econometric techniques for improving the forecast quality are described. Computer modeling results in terms of tuning support vector machine models developed with programming language Python for predicting some investment measures are shown.

  3. Bionic machines and systems

    Energy Technology Data Exchange (ETDEWEB)

    Halme, A.; Paanajaervi, J. (eds.)

    2004-07-01

    of bio-structures. Today's robotics research is directed towards solving the problems of the third generation intelligent robots. Most of them are not any more intended for working in production lines, as their second generation predecessors do, but for serving in different tasks related to natural environment or urban structures. Many of them are supposed to work in close cooperation with humans as a member of their community. One of the basic features needed is mobility, capability to go to the work, because works are not any more coming to the machine - as they do in factories - but the machines have to move. This, in turn, implies need for other primary functions, such as localization and navigation. Further, because the environment and details of the task are usually not known beforehand, the control system of the robot has to relay on perceptive information through sensors and senses in order to complete satisfactorily the task. Biological species have developed a large variety of solutions for all these primary functions. The variety in motion control methods provides also many interesting solutions, like walking, swimming and flying, which all are worth of mimicking in robotics. Learning is still in its infancy in intelligent robotics, especially regarding skilled tasks done with hands or tools. Biological research offers many interesting results on animal learning, which, while being a complex process in its own right, is still simpler than the corresponding human learning and thus easier to mimic. The report is based on the presentations given by the participants. The material has been collected from published references in literature and Web. Besides written material, also a video file archive has been collected and is available as an appendix to this report. The presentation order follows in a way bottom up hierarchy of subsystems in biological machines. Chapter 2 introduces the background of biological energy. Chapter 3 deals with motions, motion

  4. 基于特征比较和最大熵模型的统计机器翻译错误检测%Error Detection for Statistical Machine Translation Based on Feature Comparison and Maximum Entropy Model Classifier

    Institute of Scientific and Technical Information of China (English)

    杜金华; 王莎

    2013-01-01

    首先介绍3种典型的用于翻译错误检测和分类的单词后验概率特征,即基于固定位置的词后验概率、基于滑动窗的词后验概率和基于词对齐的词后验概率,分析其对错误检测性能的影响;然后,将其分别与语言学特征如词性、词及由LG句法分析器抽取的句法特征等进行组合,利用最大熵分类器预测翻译错误,并在汉英NIST数据集上进行实验验证和比较.实验结果表明,不同的单词后验概率对分类错误率的影响是显著的,并且在词后验概率基础上加入语言学特征的组合特征可以显著降低分类错误率,提高译文错误预测性能.%The authors firstly introduce three typical word posterior probabilities (WPP) for error detection and classification, which are fixed position WPP, sliding window WPP, and alignment-based WPP, and analyzes their impact on the detection performance. Then each WPP feature is combined with three linguistic features (Word, POS and LG Parsing knowledge) over the maximum entropy classifier to predict the translation errors. Experimental results on Chinese-to-English NIST datasets show that the influences of different WPP features on the classification error rate (CER) are significant, and the combination of WPP with linguistic features can significantly reduce the CER and improve the prediction capability of the classifier.

  5. The Application of Support Vector Machine (svm) Using Cielab Color Model, Color Intensity and Color Constancy as Features for Ortho Image Classification of Benthic Habitats in Hinatuan, Surigao del Sur, Philippines

    Science.gov (United States)

    Cubillas, J. E.; Japitana, M.

    2016-06-01

    This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a) Blk66L005, (b) Blk66L021, (c) Blk66L024, and (d) Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier) rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.

  6. THE APPLICATION OF SUPPORT VECTOR MACHINE (SVM USING CIELAB COLOR MODEL, COLOR INTENSITY AND COLOR CONSTANCY AS FEATURES FOR ORTHO IMAGE CLASSIFICATION OF BENTHIC HABITATS IN HINATUAN, SURIGAO DEL SUR, PHILIPPINES

    Directory of Open Access Journals (Sweden)

    J. E. Cubillas

    2016-06-01

    Full Text Available This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a Blk66L005, (b Blk66L021, (c Blk66L024, and (d Blk66L0114. SVM optimization was performed in Matlab® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier rule set in eCognition® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.

  7. Virtual machine vs Real Machine: Security Systems

    Directory of Open Access Journals (Sweden)

    Dr. C. Suresh Gnana Das

    2009-08-01

    Full Text Available This paper argues that the operating system and applications currently running on a real machine should relocate into a virtual machine. This structure enables services to be added below the operating system and to do so without trusting or modifying the operating system or applications. To demonstrate the usefulness of this structure, we describe three services that take advantage of it: secure logging, intrusion prevention and detection, and environment migration. In particular, we can provide services below the guest operating system without trusting or modifying it. We believe providing services at this layer are especially useful for enhancing security and mobility. This position paper describes the general benefits and challenges that arise from running most applications in a virtual machine, and then describes some example services and alternative ways to provide those services.

  8. Discriminative syntactic reranking for statistical machine translation

    NARCIS (Netherlands)

    Carter, S.; Monz, C.

    2010-01-01

    This paper describes a method that successfully exploits simple syntactic features for n-best translation candidate reranking using perceptrons. Our approach uses discriminative language modelling to rerank the n-best translations generated by a statistical machine translation system. The performanc

  9. Feature Selection: Algorithms and Challenges

    Institute of Scientific and Technical Information of China (English)

    Xindong Wu; Yanglan Gan; Hao Wang; Xuegang Hu

    2006-01-01

    Feature selection is an active area in data mining research and development. It consists of efforts and contributions from a wide variety of communities, including statistics, machine learning, and pattern recognition. The diversity, on one hand, equips us with many methods and tools. On the other hand, the profusion of options causes confusion. This paper reviews various feature selection methods and identifies research challenges that are at the forefront of this exciting area.

  10. Three-dimensional facial feature points matching based on K-means clustering of relative angle context distribution and support vector machine%基于相对角分布聚类和支持向量机的3维人脸特征点匹配技术的研究

    Institute of Scientific and Technical Information of China (English)

    麻宏静; 张德同; 冯筠; 耿国华

    2011-01-01

    Feature points searching or point correspondence matching is a challenge in computer vision and pattern recognition, which is very important perquisite for many 2D/3D applications such as image registration, object recognition and statistical model construction. In this paper, we propose an algorithm for facial feature points matching among 3D point cloud models. Specifically, the surface points are clustered based on relative angle context (RAC) features, and then the geometric features of the clustered points are extracted. Afterwards, supported Vector Machine based classification is employed for final accurate correspondence location. The experimental results demonstrate that our algorithm achieves better performance than RAC algorithm proposed. Within the confines of a given distance threshold, the accuracy rates of 50% feature points have even reached to 100%.%人脸特征点自动定位及对应点匹配是计算机视觉和模式识别领域一个非常热门的研究方向,应用领域包括图像配准、对象识别与跟踪、3维重建、立体匹配等.通过相对角直方图分布和K均值聚类确定脸部特征点的聚类点集,再利用几何信息提取聚类点集的特征,进而采用支持向量机分类最终从点集中分离出39个脸部特征点.实验结果表明,此混合提取方法比单纯使用RAC得到了更好的匹配准确率,在给定的距离阈值范围内,50%的特征点定位准确率达到了100%.

  11. Optimization of machining processes using pattern search algorithm

    Directory of Open Access Journals (Sweden)

    Miloš Madić

    2014-04-01

    Full Text Available Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers and practitioners. This paper introduces the use of pattern search (PS algorithm, as a deterministic direct search optimization method, for solving machining optimization problems. To analyze the applicability and performance of the PS algorithm, six case studies of machining optimization problems, both single and multi-objective, were considered. The PS algorithm was employed to determine optimal combinations of machining parameters for different machining processes such as abrasive waterjet machining, turning, turn-milling, drilling, electrical discharge machining and wire electrical discharge machining. In each case study the optimization solutions obtained by the PS algorithm were compared with the optimization solutions that had been determined by past researchers using meta-heuristic algorithms. Analysis of obtained optimization results indicates that the PS algorithm is very applicable for solving machining optimization problems showing good competitive potential against stochastic direct search methods such as meta-heuristic algorithms. Specific features and merits of the PS algorithm were also discussed.

  12. Machine Vision Automation for Ground Control Tele-Robotics Project

    Data.gov (United States)

    National Aeronautics and Space Administration — This project seeks to advance ground based tele-robotic capabilities with the development of natural feature target tracking technology with the use of machine...

  13. Performance of machine learning methods for classification tasks

    Directory of Open Access Journals (Sweden)

    B. Krithika

    2013-06-01

    Full Text Available In this paper, the performance of various machine learning methods on pattern classification and recognition tasks are proposed. The proposed method for evaluating performance will be based on the feature representation, feature selection and setting model parameters. The nature of the data, the methods of feature extraction and feature representation are discussed. The results of the Machine Learning algorithms on the classification task are analysed. The performance of Machine Learning methods on classifying Tamil word patterns, i.e., classification of noun and verbs are analysed.The software WEKA (data mining tool is used for evaluating the performance. WEKA has several machine learning algorithms like Bayes, Trees, Lazy, Rule based classifiers.

  14. Spiking neuron network Helmholtz machine.

    Science.gov (United States)

    Sountsov, Pavel; Miller, Paul

    2015-01-01

    An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

  15. Stirling machine operating experience

    Energy Technology Data Exchange (ETDEWEB)

    Ross, B. [Stirling Technology Co., Richland, WA (United States); Dudenhoefer, J.E. [Lewis Research Center, Cleveland, OH (United States)

    1994-09-01

    Numerous Stirling machines have been built and operated, but the operating experience of these machines is not well known. It is important to examine this operating experience in detail, because it largely substantiates the claim that stirling machines are capable of reliable and lengthy operating lives. The amount of data that exists is impressive, considering that many of the machines that have been built are developmental machines intended to show proof of concept, and are not expected to operate for lengthy periods of time. Some Stirling machines (typically free-piston machines) achieve long life through non-contact bearings, while other Stirling machines (typically kinematic) have achieved long operating lives through regular seal and bearing replacements. In addition to engine and system testing, life testing of critical components is also considered. The record in this paper is not complete, due to the reluctance of some organizations to release operational data and because several organizations were not contacted. The authors intend to repeat this assessment in three years, hoping for even greater participation.

  16. Perpetual Motion Machine

    Directory of Open Access Journals (Sweden)

    D. Tsaousis

    2008-01-01

    Full Text Available Ever since the first century A.D. there have been relative descriptions of known devices as well as manufactures for the creation of perpetual motion machines. Although physics has led, with two thermodynamic laws, to the opinion that a perpetual motion machine is impossible to be manufactured, inventors of every age and educational level appear to claim that they have invented something «entirely new» or they have improved somebody else’s invention, which «will function henceforth perpetually»! However the fact of the failure in manufacturing a perpetual motion machine till now, it does not mean that countless historical elements for these fictional machines become indifferent. The discussion on every version of a perpetual motion machine on the one hand gives the chance to comprehend the inventor’s of each period level of knowledge and his way of thinking, and on the other hand, to locate the points where this «perpetual motion machine» clashes with the laws of nature and that’s why it is impossible to have been manufactured or have functioned. The presentation of a new «perpetual motion machine» has excited our interest to locate its weak points. According to the designer of it the machine functions with the work produced by the buoyant force

  17. Machine Intelligence and Explication

    NARCIS (Netherlands)

    Wieringa, Roelf J.

    1987-01-01

    This report is an MA ("doctoraal") thesis submitted to the department of philosophy, university of Amsterdam. It attempts to answer the question whether machines can think by conceptual analysis. Ideally. a conceptual analysis should give plausible explications of the concepts of "machine" and "inte

  18. Microsoft Azure machine learning

    CERN Document Server

    Mund, Sumit

    2015-01-01

    The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.

  19. Reactive Turing machines

    NARCIS (Netherlands)

    Baeten, J.C.M.; Luttik, B.; Tilburg, P.J.A. van

    2013-01-01

    We propose reactive Turing machines (RTMs), extending classical Turing machines with a process-theoretical notion of interaction, and use it to define a notion of executable transition system. We show that every computable transition system with a bounded branching degree is simulated modulo diverge

  20. Machine Intelligence and Explication

    NARCIS (Netherlands)

    Wieringa, Roel

    1987-01-01

    This report is an MA ("doctoraal") thesis submitted to the department of philosophy, university of Amsterdam. It attempts to answer the question whether machines can think by conceptual analysis. Ideally. a conceptual analysis should give plausible explications of the concepts of "machine" and "inte