A computational framework for automation of point defect calculations

International Nuclear Information System (INIS)

Goyal, Anuj; Gorai, Prashun; Peng, Haowei

2017-01-01

We have developed a complete and rigorously validated open-source Python framework to automate point defect calculations using density functional theory. Furthermore, the framework provides an effective and efficient method for defect structure generation, and creation of simple yet customizable workflows to analyze defect calculations. This package provides the capability to compute widely-accepted correction schemes to overcome finite-size effects, including (1) potential alignment, (2) image-charge correction, and (3) band filling correction to shallow defects. Using Si, ZnO and In2O3 as test examples, we demonstrate the package capabilities and validate the methodology.

Woven fabric defects detection based on texture classification algorithm

International Nuclear Information System (INIS)

Ben Salem, Y.; Nasri, S.

2011-01-01

In this paper we have compared two famous methods in texture classification to solve the problem of recognition and classification of defects occurring in a textile manufacture. We have compared local binary patterns method with co-occurrence matrix. The classifier used is the support vector machines (SVM). The system has been tested using TILDA database. The results obtained are interesting and show that LBP is a good method for the problems of recognition and classifcation defects, it gives a good running time especially for the real time applications.

How automated image analysis techniques help scientists in species identification and classification?

Science.gov (United States)

Yousef Kalafi, Elham; Town, Christopher; Kaur Dhillon, Sarinder

2017-09-04

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification increased over the last two decades. Automation of data classification is primarily focussed on images, incorporating and analysing image data has recently become easier due to developments in computational technology. Research efforts in identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, categorizing and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies.

Automated defect spatial signature analysis for semiconductor manufacturing process

Science.gov (United States)

Tobin, Jr., Kenneth W.; Gleason, Shaun S.; Karnowski, Thomas P.; Sari-Sarraf, Hamed

1999-01-01

An apparatus and method for performing automated defect spatial signature alysis on a data set representing defect coordinates and wafer processing information includes categorizing data from the data set into a plurality of high level categories, classifying the categorized data contained in each high level category into user-labeled signature events, and correlating the categorized, classified signature events to a present or incipient anomalous process condition.

Generalized classification of welds according to defect type based on raidation testing results

International Nuclear Information System (INIS)

Adamenko, A.A.; Demidko, V.G.

1980-01-01

Constructed is a generalized classification of welds according to defect type, with respect to real danger of defect, which in the first approximation is proportional to relatively decrease of the thickness, and with respect to defect potential danger which can be determined by its pointing. According to this classification the welded joints are divided into five classes according to COMECON guides. The division into classes is carried out according to two-fold numerical criterium which is applicable in case of the presence of experimental data on three defect linear sizes. The above classification is of main importance while automatic data processing of the radiation testing

Defect sizing using automated ultrasonic inspection techniques at RNL

International Nuclear Information System (INIS)

Rogerson, A.; Highmore, P.J.; Poulter, L.N.J.

1983-10-01

RNL has developed and applied automated wide-beam pulse-echo and time-of-flight techniques with synthetic aperture processing for sizing defects in clad thick-section weldments and nozzle corner regions. These techniques were amongst those used in the four test plate inspections making up the UKAEA Defect Detection Trials. In this report a critical appraisal is given of the sizing procedures adopted by RNL in these inspections. Several factors influencing sizing accuracy are discussed and results from particular defects highlighted. The time-of-flight technique with colour graphics data display is shown to be highly effective in imaging near-vertical buried defects and underclad defects of height greater than 5 mm. Early characterisation of any identified defect from its ultrasonic response under pulse-echo inspection is seen as a desirable aid to the selection of an appropriate advanced sizing technique for buried defects. (author)

Lithography-based automation in the design of program defect masks

Science.gov (United States)

Vakanas, George P.; Munir, Saghir; Tejnil, Edita; Bald, Daniel J.; Nagpal, Rajesh

2004-05-01

In this work, we are reporting on a lithography-based methodology and automation in the design of Program Defect masks (PDM"s). Leading edge technology masks have ever-shrinking primary features and more pronounced model-based secondary features such as optical proximity corrections (OPC), sub-resolution assist features (SRAF"s) and phase-shifted mask (PSM) structures. In order to define defect disposition specifications for critical layers of a technology node, experience alone in deciding worst-case scenarios for the placement of program defects is necessary but may not be sufficient. MEEF calculations initiated from layout pattern data and their integration in a PDM layout flow provide a natural approach for improvements, relevance and accuracy in the placement of programmed defects. This methodology provides closed-loop feedback between layout and hard defect disposition specifications, thereby minimizing engineering test restarts, improving quality and reducing cost of high-end masks. Apart from SEMI and industry standards, best-known methods (BKM"s) in integrated lithographically-based layout methodologies and automation specific to PDM"s are scarce. The contribution of this paper lies in the implementation of Design-For-Test (DFT) principles to a synergistic interaction of CAD Layout and Aerial Image Simulator to drive layout improvements, highlight layout-to-fracture interactions and output accurate program defect placement coordinates to be used by tools in the mask shop.

Semi-Automated Classification of Seafloor Data Collected on the Delmarva Inner Shelf

Science.gov (United States)

Sweeney, E. M.; Pendleton, E. A.; Brothers, L. L.; Mahmud, A.; Thieler, E. R.

2017-12-01

We tested automated classification methods on acoustic bathymetry and backscatter data collected by the U.S. Geological Survey (USGS) and National Oceanic and Atmospheric Administration (NOAA) on the Delmarva inner continental shelf to efficiently and objectively identify sediment texture and geomorphology. Automated classification techniques are generally less subjective and take significantly less time than manual classification methods. We used a semi-automated process combining unsupervised and supervised classification techniques to characterize seafloor based on bathymetric slope and relative backscatter intensity. Statistical comparison of our automated classification results with those of a manual classification conducted on a subset of the acoustic imagery indicates that our automated method was highly accurate (95% total accuracy and 93% Kappa). Our methods resolve sediment ridges, zones of flat seafloor and areas of high and low backscatter. We compared our classification scheme with mean grain size statistics of samples collected in the study area and found that strong correlations between backscatter intensity and sediment texture exist. High backscatter zones are associated with the presence of gravel and shells mixed with sand, and low backscatter areas are primarily clean sand or sand mixed with mud. Slope classes further elucidate textural and geomorphologic differences in the seafloor, such that steep slopes (>0.35°) with high backscatter are most often associated with the updrift side of sand ridges and bedforms, whereas low slope with high backscatter correspond to coarse lag or shell deposits. Low backscatter and high slopes are most often found on the downdrift side of ridges and bedforms, and low backscatter and low slopes identify swale areas and sand sheets. We found that poor acoustic data quality was the most significant cause of inaccurate classification results, which required additional user input to mitigate. Our method worked well

Automated Classification of Asteroids into Families at Work

Science.gov (United States)

Knežević, Zoran; Milani, Andrea; Cellino, Alberto; Novaković, Bojan; Spoto, Federica; Paolicchi, Paolo

2014-07-01

We have recently proposed a new approach to the asteroid family classification by combining the classical HCM method with an automated procedure to add newly discovered members to existing families. This approach is specifically intended to cope with ever increasing asteroid data sets, and consists of several steps to segment the problem and handle the very large amount of data in an efficient and accurate manner. We briefly present all these steps and show the results from three subsequent updates making use of only the automated step of attributing the newly numbered asteroids to the known families. We describe the changes of the individual families membership, as well as the evolution of the classification due to the newly added intersections between the families, resolved candidate family mergers, and emergence of the new candidates for the mergers. We thus demonstrate how by the new approach the asteroid family classification becomes stable in general terms (converging towards a permanent list of confirmed families), and in the same time evolving in details (to account for the newly discovered asteroids) at each update.

Comparison of an automated classification system with an empirical classification of circulation patterns over the Pannonian basin, Central Europe

Science.gov (United States)

Maheras, Panagiotis; Tolika, Konstantia; Tegoulias, Ioannis; Anagnostopoulou, Christina; Szpirosz, Klicász; Károssy, Csaba; Makra, László

2018-04-01

The aim of the study is to compare the performance of the two classification methods, based on the atmospheric circulation types over the Pannonian basin in Central Europe. Moreover, relationships including seasonal occurrences and correlation coefficients, as well as comparative diagrams of the seasonal occurrences of the circulation types of the two classification systems are presented. When comparing of the automated (objective) and empirical (subjective) classification methods, it was found that the frequency of the empirical anticyclonic (cyclonic) types is much higher (lower) than that of the automated anticyclonic (cyclonic) types both on an annual and seasonal basis. The highest and statistically significant correlations between the circulation types of the two classification systems, as well as those between the cumulated seasonal anticyclonic and cyclonic types occur in winter for both classifications, since the weather-influencing effect of the atmospheric circulation in this season is the most prevalent. Precipitation amounts in Budapest display a decreasing trend in accordance with the decrease in the occurrence of the automated cyclonic types. In contrast, the occurrence of the empirical cyclonic types displays an increasing trend. There occur types in a given classification that are usually accompanied by high ratios of certain types in the other classification.

Clever Toolbox - the Art of Automated Genre Classification

DEFF Research Database (Denmark)

2005-01-01

Automatic musical genre classification can be defined as the science of finding computer algorithms that a digitized sound clip as input and yield a musical genre as output. The goal of automated genre classification is, of course, that the musical genre should agree with the human classificasion....... This demo illustrates an approach to the problem that first extract frequency-based sound features followed by a "linear regression" classifier. The basic features are the so-called mel-frequency cepstral coefficients (MFCCs), which are extracted on a time-scale of 30 msec. From these MFCC features, auto......) is subsequently used for classification. This classifier is rather simple; current research investigates more advanced methods of classification....

“The Naming of Cats”: Automated Genre Classification

Directory of Open Access Journals (Sweden)

Yunhyong Kim

2007-07-01

Full Text Available This paper builds on the work presented at the ECDL 2006 in automated genre classification as a step toward automating metadata extraction from digital documents for ingest into digital repositories such as those run by archives, libraries and eprint services (Kim & Ross, 2006b. We have previously proposed dividing features of a document into five types (features for visual layout, language model features, stylometric features, features for semantic structure, and contextual features as an object linked to previously classified objects and other external sources and have examined visual and language model features. The current paper compares results from testing classifiers based on image and stylometric features in a binary classification to show that certain genres have strong image features which enable effective separation of documents belonging to the genre from a large pool of other documents.

Supervised learning for the automated transcription of spacer classification from spoligotype films

Directory of Open Access Journals (Sweden)

Abernethy Neil

2009-08-01

Full Text Available Abstract Background Molecular genotyping of bacteria has revolutionized the study of tuberculosis epidemiology, yet these established laboratory techniques typically require subjective and laborious interpretation by trained professionals. In the context of a Tuberculosis Case Contact study in The Gambia we used a reverse hybridization laboratory assay called spoligotype analysis. To facilitate processing of spoligotype images we have developed tools and algorithms to automate the classification and transcription of these data directly to a database while allowing for manual editing. Results Features extracted from each of the 1849 spots on a spoligo film were classified using two supervised learning algorithms. A graphical user interface allows manual editing of the classification, before export to a database. The application was tested on ten films of differing quality and the results of the best classifier were compared to expert manual classification, giving a median correct classification rate of 98.1% (inter quartile range: 97.1% to 99.2%, with an automated processing time of less than 1 minute per film. Conclusion The software implementation offers considerable time savings over manual processing whilst allowing expert editing of the automated classification. The automatic upload of the classification to a database reduces the chances of transcription errors.

Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification

Directory of Open Access Journals (Sweden)

Stefan Dech

2012-09-01

Full Text Available We present a novel and innovative automated processing environment for the derivation of land cover (LC and land use (LU information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS, as introduced by the Open Geospatial Consortium (OGC, are utilized. TWOPAC’s functionality to process geospatial raster or vector data via web resources (server, network enables TWOPAC’s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user’s perspective.

Automated classification of cell morphology by coherence-controlled holographic microscopy

Science.gov (United States)

Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

2017-08-01

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.

Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique

Science.gov (United States)

Fan, Mengbao; Wang, Qi; Cao, Binghua; Ye, Bo; Sunny, Ali Imam; Tian, Guiyun

2016-01-01

Eddy current testing is quite a popular non-contact and cost-effective method for nondestructive evaluation of product quality and structural integrity. Excitation frequency is one of the key performance factors for defect characterization. In the literature, there are many interesting papers dealing with wide spectral content and optimal frequency in terms of detection sensitivity. However, research activity on frequency optimization with respect to characterization performances is lacking. In this paper, an investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance. The influences of excitation frequency for a group of defects were revealed in terms of detection sensitivity, contrast between defect features, and classification accuracy using kernel principal component analysis (KPCA) and a support vector machine (SVM). It is observed that probe signals are the most sensitive on the whole for a group of defects when excitation frequency is set near the frequency at which maximum probe signals are retrieved for the largest defect. After the use of KPCA, the margins between the defect features are optimum from the perspective of the SVM, which adopts optimal hyperplanes for structure risk minimization. As a result, the best classification accuracy is obtained. The main contribution is that the influences of excitation frequency on defect characterization are interpreted, and experiment-based procedures are proposed to determine the optimal excitation frequency for a group of defects rather than a single defect with respect to optimal characterization performances. PMID:27164112

Automated classification of cell morphology by coherence-controlled holographic microscopy.

Science.gov (United States)

Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

2017-08-01

In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Operational experiences with automated acoustic burst classification by neural networks

International Nuclear Information System (INIS)

Olma, B.; Ding, Y.; Enders, R.

1996-01-01

Monitoring of Loose Parts Monitoring System sensors for signal bursts associated with metallic impacts of loose parts has proved as an useful methodology for on-line assessing the mechanical integrity of components in the primary circuit of nuclear power plants. With the availability of neural networks new powerful possibilities for classification and diagnosis of burst signals can be realized for acoustic monitoring with the online system RAMSES. In order to look for relevant burst signals an automated classification is needed, that means acoustic signature analysis and assessment has to be performed automatically on-line. A back propagation neural network based on five pre-calculated signal parameter values has been set up for identification of different signal types. During a three-month monitoring program of medium-operated check valves burst signals have been measured and classified separately according to their cause. The successful results of the three measurement campaigns with an automated burst type classification are presented. (author)

Advanced defect classification by smart sampling, based on sub-wavelength anisotropic scatterometry

Science.gov (United States)

van der Walle, Peter; Kramer, Esther; Ebeling, Rob; Spruit, Helma; Alkemade, Paul; Pereira, Silvania; van der Donck, Jacques; Maas, Diederik

2018-03-01

We report on advanced defect classification using TNO's RapidNano particle scanner. RapidNano was originally designed for defect detection on blank substrates. In detection-mode, the RapidNano signal from nine azimuth angles is added for sensitivity. In review-mode signals from individual angles are analyzed to derive additional defect properties. We define the Fourier coefficient parameter space that is useful to study the statistical variation in defect types on a sample. By selecting defects from each defect type for further review by SEM, information on all defects can be obtained efficiently.

Automated cell type discovery and classification through knowledge transfer

Science.gov (United States)

Lee, Hao-Chih; Kosoy, Roman; Becker, Christine E.

2017-01-01

Abstract Motivation: Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis. Results: We present a new algorithm called Automated Cell-type Discovery and Classification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data. Availability and Implementation: A Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc. Contact: brian.kidd@mssm.edu or joel.dudley@mssm.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28158442

Automated defect location and sizing by advanced ultrasonic techniques

International Nuclear Information System (INIS)

Murgatroyd, R.A.

1983-01-01

From this assessment of advanced automated defect location and sizing techniques it is concluded that, 1. Pulse-echo techniques, when used at high sensitivity, are capable of detecting all known defects in the test weldments inspected; 2. Search sensitivity has a marked influence on defect detection at both 1 and 2 MHz, and it is considered that 20% DAC is the highest amplitude threshold level which could be prudently adopted at the search stage; 3. The important through-thickness dimension of deeply buried defects in the height range 5 to 50mm can be sized to an estimated accuracy of +2mm using the Silk technique and that applying a SAFT-type algorithm to the data gives good lateral positioning of defects; 4. The 70 0 longitudinal wave twin-crystal technique has proved to be a highly effective method of detecting underclad cracks. A 70 0 shear wave, pulse-echo technique and a 0 0 longitudinal wave twin crystal method also give good detection results in the near surface region; 5. The Silk technique has been effective in sizing defects in the height range 5 to 35mm in the near-surface region

The method of diagnosis and classification of the gingival line defects of the teeth hard tissues

Directory of Open Access Journals (Sweden)

Olena Bulbuk

2017-06-01

Full Text Available For solving the problem of diagnosis and treatment of hard tissue defects the significant role belongs to the choice of tactics for dental treatment of hard tissue defects located in the gingival line of any tooth. This work aims to study the problems of diagnosis and classification of gingival line defects of the teeth hard tissues. That will contribute to the objectification of differentiated diagnostic and therapeutic approaches in the dental treatment of various clinical variants of these defects localization. The objective of the study – is to develop the anatomical-functional classification for differentiated estimation of hard tissue defects in the gingival part, as the basis for the application of differential diagnostic-therapeutic approaches to the dental treatment of hard tissue defects disposed in the gingival part of any tooth. Materials and methods of investigation: There was conducted the examination of 48 patients with hard tissue defects located in the gingival part of any tooth. To assess the magnitude of gingival line destruction the periodontal probe and X-ray examination were used. Results. The result of the performed research the classification of the gingival line defects of the hard tissues was offered using exponent power. The value of this indicator is equal to an integer number expressed in millimeters of distance from the epithelial attachment to the cavity’s bottom of defect. Conclusions. The proposed classification fills an obvious gap in academic representations about hard tissue defects located in the gingival part of any tooth. Also it offers the prospects of consensus on differentiated diagnostic-therapeutic approaches in different clinical variants of location.  This classification builds methodological “bridge of continuity” between therapeutic and prosthetic dentistry in the field of treatment of the gingival line defects of dental hard tissues.

Automated Classification of Consumer Health Information Needs in Patient Portal Messages.

Science.gov (United States)

Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Jackson, Gretchen Purcell

2015-01-01

Patients have diverse health information needs, and secure messaging through patient portals is an emerging means by which such needs are expressed and met. As patient portal adoption increases, growing volumes of secure messages may burden healthcare providers. Automated classification could expedite portal message triage and answering. We created four automated classifiers based on word content and natural language processing techniques to identify health information needs in 1000 patient-generated portal messages. Logistic regression and random forest classifiers detected single information needs well, with area under the curves of 0.804-0.914. A logistic regression classifier accurately found the set of needs within a message, with a Jaccard index of 0.859 (95% Confidence Interval: (0.847, 0.871)). Automated classification of consumer health information needs expressed in patient portal messages is feasible and may allow direct linking to relevant resources or creation of institutional resources for commonly expressed needs.

Defect detection and classification of galvanized stamping parts based on fully convolution neural network

Science.gov (United States)

Xiao, Zhitao; Leng, Yanyi; Geng, Lei; Xi, Jiangtao

2018-04-01

In this paper, a new convolution neural network method is proposed for the inspection and classification of galvanized stamping parts. Firstly, all workpieces are divided into normal and defective by image processing, and then the defective workpieces extracted from the region of interest (ROI) area are input to the trained fully convolutional networks (FCN). The network utilizes an end-to-end and pixel-to-pixel training convolution network that is currently the most advanced technology in semantic segmentation, predicts result of each pixel. Secondly, we mark the different pixel values of the workpiece, defect and background for the training image, and use the pixel value and the number of pixels to realize the recognition of the defects of the output picture. Finally, the defect area's threshold depended on the needs of the project is set to achieve the specific classification of the workpiece. The experiment results show that the proposed method can successfully achieve defect detection and classification of galvanized stamping parts under ordinary camera and illumination conditions, and its accuracy can reach 99.6%. Moreover, it overcomes the problem of complex image preprocessing and difficult feature extraction and performs better adaptability.

Automated Classification of Consumer Health Information Needs in Patient Portal Messages

Science.gov (United States)

Cronin, Robert M.; Fabbri, Daniel; Denny, Joshua C.; Jackson, Gretchen Purcell

2015-01-01

Patients have diverse health information needs, and secure messaging through patient portals is an emerging means by which such needs are expressed and met. As patient portal adoption increases, growing volumes of secure messages may burden healthcare providers. Automated classification could expedite portal message triage and answering. We created four automated classifiers based on word content and natural language processing techniques to identify health information needs in 1000 patient-generated portal messages. Logistic regression and random forest classifiers detected single information needs well, with area under the curves of 0.804–0.914. A logistic regression classifier accurately found the set of needs within a message, with a Jaccard index of 0.859 (95% Confidence Interval: (0.847, 0.871)). Automated classification of consumer health information needs expressed in patient portal messages is feasible and may allow direct linking to relevant resources or creation of institutional resources for commonly expressed needs. PMID:26958285

Defect reduction of patterned media templates and disks

Science.gov (United States)

Luo, Kang; Ha, Steven; Fretwell, John; Ramos, Rick; Ye, Zhengmao; Schmid, Gerard; LaBrake, Dwayne; Resnick, Douglas J.; Sreenivasan, S. V.

2010-05-01

Imprint lithography has been shown to be an effective technique for the replication of nano-scale features. Acceptance of imprint lithography for manufacturing will require a demonstration of defect levels commensurate with cost-effective device production. This work summarizes the results of defect inspections of hard disks patterned using Jet and Flash Imprint Lithography (J-FILTM). Inspections were performed with optical based automated inspection tools. For the hard drive market, it is important to understand the defectivity of both the template and the imprinted disk. This work presents a methodology for automated pattern inspection and defect classification for imprint-patterned media. Candela CS20 and 6120 tools from KLA-Tencor map the optical properties of the disk surface, producing highresolution grayscale images of surface reflectivity and scattered light. Defects that have been identified in this manner are further characterized according to the morphology. The imprint process was tested after optimizing both the disk cleaning and adhesion layers processes that precede imprinting. An extended imprint run was performed and both the defect types and trends are reported.

Cascade classification of endocytoscopic images of colorectal lesions for automated pathological diagnosis

Science.gov (United States)

Itoh, Hayato; Mori, Yuichi; Misawa, Masashi; Oda, Masahiro; Kudo, Shin-ei; Mori, Kensaku

2018-02-01

This paper presents a new classification method for endocytoscopic images. Endocytoscopy is a new endoscope that enables us to perform conventional endoscopic observation and ultramagnified observation of cell level. This ultramagnified views (endocytoscopic images) make possible to perform pathological diagnosis only on endo-scopic views of polyps during colonoscopy. However, endocytoscopic image diagnosis requires higher experiences for physicians. An automated pathological diagnosis system is required to prevent the overlooking of neoplastic lesions in endocytoscopy. For this purpose, we propose a new automated endocytoscopic image classification method that classifies neoplastic and non-neoplastic endocytoscopic images. This method consists of two classification steps. At the first step, we classify an input image by support vector machine. We forward the image to the second step if the confidence of the first classification is low. At the second step, we classify the forwarded image by convolutional neural network. We reject the input image if the confidence of the second classification is also low. We experimentally evaluate the classification performance of the proposed method. In this experiment, we use about 16,000 and 4,000 colorectal endocytoscopic images as training and test data, respectively. The results show that the proposed method achieves high sensitivity 93.4% with small rejection rate 9.3% even for difficult test data.

Automating the expert consensus paradigm for robust lung tissue classification

Science.gov (United States)

Rajagopalan, Srinivasan; Karwoski, Ronald A.; Raghunath, Sushravya; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Clinicians confirm the efficacy of dynamic multidisciplinary interactions in diagnosing Lung disease/wellness from CT scans. However, routine clinical practice cannot readily accomodate such interactions. Current schemes for automating lung tissue classification are based on a single elusive disease differentiating metric; this undermines their reliability in routine diagnosis. We propose a computational workflow that uses a collection (#: 15) of probability density functions (pdf)-based similarity metrics to automatically cluster pattern-specific (#patterns: 5) volumes of interest (#VOI: 976) extracted from the lung CT scans of 14 patients. The resultant clusters are refined for intra-partition compactness and subsequently aggregated into a super cluster using a cluster ensemble technique. The super clusters were validated against the consensus agreement of four clinical experts. The aggregations correlated strongly with expert consensus. By effectively mimicking the expertise of physicians, the proposed workflow could make automation of lung tissue classification a clinical reality.

Visual detection of defects in solder joints

Science.gov (United States)

Blaignan, V. B.; Bourbakis, Nikolaos G.; Moghaddamzadeh, Ali; Yfantis, Evangelos A.

1995-03-01

The automatic, real-time visual acquisition and inspection of VLSI boards requires the use of machine vision and artificial intelligence methodologies in a new `frame' for the achievement of better results regarding efficiency, products quality and automated service. In this paper the visual detection and classification of different types of defects on solder joints in PC boards is presented by combining several image processing methods, such as smoothing, segmentation, edge detection, contour extraction and shape analysis. The results of this paper are based on simulated solder defects and a real one.

Automated Stellar Classification for Large Surveys with EKF and RBF Neural Networks

Institute of Scientific and Technical Information of China (English)

Ling Bai; Ping Guo; Zhan-Yi Hu

2005-01-01

An automated classification technique for large size stellar surveys is proposed. It uses the extended Kalman filter as a feature selector and pre-classifier of the data, and the radial basis function neural networks for the classification.Experiments with real data have shown that the correct classification rate can reach as high as 93%, which is quite satisfactory. When different system models are selected for the extended Kalman filter, the classification results are relatively stable. It is shown that for this particular case the result using extended Kalman filter is better than using principal component analysis.

Estimated accuracy of classification of defects detected in welded joints by radiographic tests

International Nuclear Information System (INIS)

Siqueira, M.H.S.; De Silva, R.R.; De Souza, M.P.V.; Rebello, J.M.A.; Caloba, L.P.; Mery, D.

2004-01-01

This work is a study to estimate the accuracy of classification of the main classes of weld defects detected by radiography test, such as: undercut, lack of penetration, porosity, slag inclusion, crack or lack of fusion. To carry out this work non-linear pattern classifiers were developed, using neural networks, and the largest number of radiographic patterns as possible was used as well as statistical inference techniques of random selection of samples with and without repositioning (bootstrap) in order to estimate the accuracy of the classification. The results pointed to an estimated accuracy of around 80% for the classes of defects analyzed. (author)

Estimated accuracy of classification of defects detected in welded joints by radiographic tests

Energy Technology Data Exchange (ETDEWEB)

Siqueira, M.H.S.; De Silva, R.R.; De Souza, M.P.V.; Rebello, J.M.A. [Federal Univ. of Rio de Janeiro, Dept., of Metallurgical and Materials Engineering, Rio de Janeiro (Brazil); Caloba, L.P. [Federal Univ. of Rio de Janeiro, Dept., of Electrical Engineering, Rio de Janeiro (Brazil); Mery, D. [Pontificia Unversidad Catolica de Chile, Escuela de Ingenieria - DCC, Dept. de Ciencia de la Computacion, Casilla, Santiago (Chile)

2004-07-01

This work is a study to estimate the accuracy of classification of the main classes of weld defects detected by radiography test, such as: undercut, lack of penetration, porosity, slag inclusion, crack or lack of fusion. To carry out this work non-linear pattern classifiers were developed, using neural networks, and the largest number of radiographic patterns as possible was used as well as statistical inference techniques of random selection of samples with and without repositioning (bootstrap) in order to estimate the accuracy of the classification. The results pointed to an estimated accuracy of around 80% for the classes of defects analyzed. (author)

Prototype semantic infrastructure for automated small molecule classification and annotation in lipidomics.

Science.gov (United States)

Chepelev, Leonid L; Riazanov, Alexandre; Kouznetsov, Alexandre; Low, Hong Sang; Dumontier, Michel; Baker, Christopher J O

2011-07-26

The development of high-throughput experimentation has led to astronomical growth in biologically relevant lipids and lipid derivatives identified, screened, and deposited in numerous online databases. Unfortunately, efforts to annotate, classify, and analyze these chemical entities have largely remained in the hands of human curators using manual or semi-automated protocols, leaving many novel entities unclassified. Since chemical function is often closely linked to structure, accurate structure-based classification and annotation of chemical entities is imperative to understanding their functionality. As part of an exploratory study, we have investigated the utility of semantic web technologies in automated chemical classification and annotation of lipids. Our prototype framework consists of two components: an ontology and a set of federated web services that operate upon it. The formal lipid ontology we use here extends a part of the LiPrO ontology and draws on the lipid hierarchy in the LIPID MAPS database, as well as literature-derived knowledge. The federated semantic web services that operate upon this ontology are deployed within the Semantic Annotation, Discovery, and Integration (SADI) framework. Structure-based lipid classification is enacted by two core services. Firstly, a structural annotation service detects and enumerates relevant functional groups for a specified chemical structure. A second service reasons over lipid ontology class descriptions using the attributes obtained from the annotation service and identifies the appropriate lipid classification. We extend the utility of these core services by combining them with additional SADI services that retrieve associations between lipids and proteins and identify publications related to specified lipid types. We analyze the performance of SADI-enabled eicosanoid classification relative to the LIPID MAPS classification and reflect on the contribution of our integrative methodology in the context of

Prototype semantic infrastructure for automated small molecule classification and annotation in lipidomics

Directory of Open Access Journals (Sweden)

Dumontier Michel

2011-07-01

Full Text Available Abstract Background The development of high-throughput experimentation has led to astronomical growth in biologically relevant lipids and lipid derivatives identified, screened, and deposited in numerous online databases. Unfortunately, efforts to annotate, classify, and analyze these chemical entities have largely remained in the hands of human curators using manual or semi-automated protocols, leaving many novel entities unclassified. Since chemical function is often closely linked to structure, accurate structure-based classification and annotation of chemical entities is imperative to understanding their functionality. Results As part of an exploratory study, we have investigated the utility of semantic web technologies in automated chemical classification and annotation of lipids. Our prototype framework consists of two components: an ontology and a set of federated web services that operate upon it. The formal lipid ontology we use here extends a part of the LiPrO ontology and draws on the lipid hierarchy in the LIPID MAPS database, as well as literature-derived knowledge. The federated semantic web services that operate upon this ontology are deployed within the Semantic Annotation, Discovery, and Integration (SADI framework. Structure-based lipid classification is enacted by two core services. Firstly, a structural annotation service detects and enumerates relevant functional groups for a specified chemical structure. A second service reasons over lipid ontology class descriptions using the attributes obtained from the annotation service and identifies the appropriate lipid classification. We extend the utility of these core services by combining them with additional SADI services that retrieve associations between lipids and proteins and identify publications related to specified lipid types. We analyze the performance of SADI-enabled eicosanoid classification relative to the LIPID MAPS classification and reflect on the contribution of

Phenotype classification of zebrafish embryos by supervised learning.

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Nathalie Jeanray

Full Text Available Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

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Tan, Shing Chiang; Watada, Junzo; Ibrahim, Zuwairie; Khalid, Marzuki

2015-05-01

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

Automatically high accurate and efficient photomask defects management solution for advanced lithography manufacture

Science.gov (United States)

Zhu, Jun; Chen, Lijun; Ma, Lantao; Li, Dejian; Jiang, Wei; Pan, Lihong; Shen, Huiting; Jia, Hongmin; Hsiang, Chingyun; Cheng, Guojie; Ling, Li; Chen, Shijie; Wang, Jun; Liao, Wenkui; Zhang, Gary

2014-04-01

Defect review is a time consuming job. Human error makes result inconsistent. The defects located on don't care area would not hurt the yield and no need to review them such as defects on dark area. However, critical area defects can impact yield dramatically and need more attention to review them such as defects on clear area. With decrease in integrated circuit dimensions, mask defects are always thousands detected during inspection even more. Traditional manual or simple classification approaches are unable to meet efficient and accuracy requirement. This paper focuses on automatic defect management and classification solution using image output of Lasertec inspection equipment and Anchor pattern centric image process technology. The number of mask defect found during an inspection is always in the range of thousands or even more. This system can handle large number defects with quick and accurate defect classification result. Our experiment includes Die to Die and Single Die modes. The classification accuracy can reach 87.4% and 93.3%. No critical or printable defects are missing in our test cases. The missing classification defects are 0.25% and 0.24% in Die to Die mode and Single Die mode. This kind of missing rate is encouraging and acceptable to apply on production line. The result can be output and reloaded back to inspection machine to have further review. This step helps users to validate some unsure defects with clear and magnification images when captured images can't provide enough information to make judgment. This system effectively reduces expensive inline defect review time. As a fully inline automated defect management solution, the system could be compatible with current inspection approach and integrated with optical simulation even scoring function and guide wafer level defect inspection.

Towards more reliable automated multi-dose dispensing: retrospective follow-up study on medication dose errors and product defects.

Science.gov (United States)

Palttala, Iida; Heinämäki, Jyrki; Honkanen, Outi; Suominen, Risto; Antikainen, Osmo; Hirvonen, Jouni; Yliruusi, Jouko

2013-03-01

To date, little is known on applicability of different types of pharmaceutical dosage forms in an automated high-speed multi-dose dispensing process. The purpose of the present study was to identify and further investigate various process-induced and/or product-related limitations associated with multi-dose dispensing process. The rates of product defects and dose dispensing errors in automated multi-dose dispensing were retrospectively investigated during a 6-months follow-up period. The study was based on the analysis of process data of totally nine automated high-speed multi-dose dispensing systems. Special attention was paid to the dependence of multi-dose dispensing errors/product defects and pharmaceutical tablet properties (such as shape, dimensions, weight, scored lines, coatings, etc.) to profile the most suitable forms of tablets for automated dose dispensing systems. The relationship between the risk of errors in dose dispensing and tablet characteristics were visualized by creating a principal component analysis (PCA) model for the outcome of dispensed tablets. The two most common process-induced failures identified in the multi-dose dispensing are predisposal of tablet defects and unexpected product transitions in the medication cassette (dose dispensing error). The tablet defects are product-dependent failures, while the tablet transitions are dependent on automated multi-dose dispensing systems used. The occurrence of tablet defects is approximately twice as common as tablet transitions. Optimal tablet preparation for the high-speed multi-dose dispensing would be a round-shaped, relatively small/middle-sized, film-coated tablet without any scored line. Commercial tablet products can be profiled and classified based on their suitability to a high-speed multi-dose dispensing process.

Automated recognition system for ELM classification in JET

International Nuclear Information System (INIS)

Duro, N.; Dormido, R.; Vega, J.; Dormido-Canto, S.; Farias, G.; Sanchez, J.; Vargas, H.; Murari, A.

2009-01-01

Edge localized modes (ELMs) are instabilities occurring in the edge of H-mode plasmas. Considerable efforts are being devoted to understanding the physics behind this non-linear phenomenon. A first characterization of ELMs is usually their identification as type I or type III. An automated pattern recognition system has been developed in JET for off-line ELM recognition and classification. The empirical method presented in this paper analyzes each individual ELM instead of starting from a temporal segment containing many ELM bursts. The ELM recognition and isolation is carried out using three signals: Dα, line integrated electron density and stored diamagnetic energy. A reduced set of characteristics (such as diamagnetic energy drop, ELM period or Dα shape) has been extracted to build supervised and unsupervised learning systems for classification purposes. The former are based on support vector machines (SVM). The latter have been developed with hierarchical and K-means clustering methods. The success rate of the classification systems is about 98% for a database of almost 300 ELMs.

Classification of Atrial Septal Defect and Ventricular Septal Defect with Documented Hemodynamic Parameters via Cardiac Catheterization by Genetic Algorithms and Multi-Layered Artificial Neural Network

Directory of Open Access Journals (Sweden)

Mustafa Yıldız

2012-08-01

Full Text Available Introduction: We aimed to develop a classification method to discriminate ventricular septal defect and atrial septal defect by using severalhemodynamic parameters.Patients and Methods: Forty three patients (30 atrial septal defect, 13 ventricular septal defect; 26 female, 17 male with documentedhemodynamic parameters via cardiac catheterization are included to study. Such parameters as blood pressure values of different areas,gender, age and Qp/Qs ratios are used for classification. Parameters, we used in classification are determined by divergence analysismethod. Those parameters are; i pulmonary artery diastolic pressure, ii Qp/Qs ratio, iii right atrium pressure, iv age, v pulmonary arterysystolic pressure, vi left ventricular sistolic pressure, vii aorta mean pressure, viii left ventricular diastolic pressure, ix aorta diastolicpressure, x aorta systolic pressure. Those parameters detected from our study population, are uploaded to multi-layered artificial neuralnetwork and the network was trained by genetic algorithm.Results: Trained cluster consists of 14 factors (7 atrial septal defect and 7 ventricular septal defect. Overall success ratio is 79.2%, andwith a proper instruction of artificial neural network this ratio increases up to 89%.Conclusion: Parameters, belonging to artificial neural network, which are needed to be detected by the investigator in classical methods,can easily be detected with the help of genetic algorithms. During the instruction of artificial neural network by genetic algorithms, boththe topology of network and factors of network can be determined. During the test stage, elements, not included in instruction cluster, areassumed as in test cluster, and as a result of this study, we observed that multi-layered artificial neural network can be instructed properly,and neural network is a successful method for aimed classification.

Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: a comparison of two classification schemes with respect to the segmentation quality

Science.gov (United States)

Krappe, Sebastian; Benz, Michaela; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2015-03-01

The morphological analysis of bone marrow smears is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually with the use of bright field microscope. This is a time consuming, partly subjective and tedious process. Furthermore, repeated examinations of a slide yield intra- and inter-observer variances. For this reason an automation of morphological bone marrow analysis is pursued. This analysis comprises several steps: image acquisition and smear detection, cell localization and segmentation, feature extraction and cell classification. The automated classification of bone marrow cells is depending on the automated cell segmentation and the choice of adequate features extracted from different parts of the cell. In this work we focus on the evaluation of support vector machines (SVMs) and random forests (RFs) for the differentiation of bone marrow cells in 16 different classes, including immature and abnormal cell classes. Data sets of different segmentation quality are used to test the two approaches. Automated solutions for the morphological analysis for bone marrow smears could use such a classifier to pre-classify bone marrow cells and thereby shortening the examination duration.

Automated otolith image classification with multiple views: an evaluation on Sciaenidae.

Science.gov (United States)

Wong, J Y; Chu, C; Chong, V C; Dhillon, S K; Loh, K H

2016-08-01

Combined multiple 2D views (proximal, anterior and ventral aspects) of the sagittal otolith are proposed here as a method to capture shape information for fish classification. Classification performance of single view compared with combined 2D views show improved classification accuracy of the latter, for nine species of Sciaenidae. The effects of shape description methods (shape indices, Procrustes analysis and elliptical Fourier analysis) on classification performance were evaluated. Procrustes analysis and elliptical Fourier analysis perform better than shape indices when single view is considered, but all perform equally well with combined views. A generic content-based image retrieval (CBIR) system that ranks dissimilarity (Procrustes distance) of otolith images was built to search query images without the need for detailed information of side (left or right), aspect (proximal or distal) and direction (positive or negative) of the otolith. Methods for the development of this automated classification system are discussed. © 2016 The Fisheries Society of the British Isles.

Developmental defects in zebrafish for classification of EGF pathway inhibitors

Energy Technology Data Exchange (ETDEWEB)

Pruvot, Benoist; Curé, Yoann; Djiotsa, Joachim; Voncken, Audrey; Muller, Marc, E-mail: m.muller@ulg.ac.be

2014-01-15

One of the major challenges when testing drug candidates targeted at a specific pathway in whole animals is the discrimination between specific effects and unwanted, off-target effects. Here we used the zebrafish to define several developmental defects caused by impairment of Egf signaling, a major pathway of interest in tumor biology. We inactivated Egf signaling by genetically blocking Egf expression or using specific inhibitors of the Egf receptor function. We show that the combined occurrence of defects in cartilage formation, disturbance of blood flow in the trunk and a decrease of myelin basic protein expression represent good indicators for impairment of Egf signaling. Finally, we present a classification of known tyrosine kinase inhibitors according to their specificity for the Egf pathway. In conclusion, we show that developmental indicators can help to discriminate between specific effects on the target pathway from off-target effects in molecularly targeted drug screening experiments in whole animal systems. - Highlights: • We analyze the functions of Egf signaling on zebrafish development. • Genetic blocking of Egf expression causes cartilage, myelin and circulatory defects. • Chemical inhibition of Egf receptor function causes similar defects. • Developmental defects can reveal the specificity of Egf pathway inhibitors.

Developmental defects in zebrafish for classification of EGF pathway inhibitors

International Nuclear Information System (INIS)

Pruvot, Benoist; Curé, Yoann; Djiotsa, Joachim; Voncken, Audrey; Muller, Marc

2014-01-01

One of the major challenges when testing drug candidates targeted at a specific pathway in whole animals is the discrimination between specific effects and unwanted, off-target effects. Here we used the zebrafish to define several developmental defects caused by impairment of Egf signaling, a major pathway of interest in tumor biology. We inactivated Egf signaling by genetically blocking Egf expression or using specific inhibitors of the Egf receptor function. We show that the combined occurrence of defects in cartilage formation, disturbance of blood flow in the trunk and a decrease of myelin basic protein expression represent good indicators for impairment of Egf signaling. Finally, we present a classification of known tyrosine kinase inhibitors according to their specificity for the Egf pathway. In conclusion, we show that developmental indicators can help to discriminate between specific effects on the target pathway from off-target effects in molecularly targeted drug screening experiments in whole animal systems. - Highlights: • We analyze the functions of Egf signaling on zebrafish development. • Genetic blocking of Egf expression causes cartilage, myelin and circulatory defects. • Chemical inhibition of Egf receptor function causes similar defects. • Developmental defects can reveal the specificity of Egf pathway inhibitors

Automated retinal vessel type classification in color fundus images

Science.gov (United States)

Yu, H.; Barriga, S.; Agurto, C.; Nemeth, S.; Bauman, W.; Soliz, P.

2013-02-01

Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods

International Nuclear Information System (INIS)

Upadhyaya, B.R.; Yan, W.

1993-11-01

The primary purpose of the current research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive examination data. Specifically, data from eddy current inspection of heat exchanger tubing were utilized to evaluate this technology. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fully-connected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture. A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of ORNL. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. A PC-based data preprocessing and display program was also developed as part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods. The integration of data compression and artificial neural networks for information processing was established as an effective technique for automation of diagnostics using nondestructive examination methods

Improving reticle defect disposition via fully automated lithography simulation

Science.gov (United States)

Mann, Raunak; Goodman, Eliot; Lao, Keith; Ha, Steven; Vacca, Anthony; Fiekowsky, Peter; Fiekowsky, Dan

2016-03-01

Most advanced wafer fabs have embraced complex pattern decoration, which creates numerous challenges during in-fab reticle qualification. These optical proximity correction (OPC) techniques create assist features that tend to be very close in size and shape to the main patterns as seen in Figure 1. A small defect on an assist feature will most likely have little or no impact on the fidelity of the wafer image, whereas the same defect on a main feature could significantly decrease device functionality. In order to properly disposition these defects, reticle inspection technicians need an efficient method that automatically separates main from assist features and predicts the resulting defect impact on the wafer image. Analysis System (ADAS) defect simulation system[1]. Up until now, using ADAS simulation was limited to engineers due to the complexity of the settings that need to be manually entered in order to create an accurate result. A single error in entering one of these values can cause erroneous results, therefore full automation is necessary. In this study, we propose a new method where all needed simulation parameters are automatically loaded into ADAS. This is accomplished in two parts. First we have created a scanner parameter database that is automatically identified from mask product and level names. Second, we automatically determine the appropriate simulation printability threshold by using a new reference image (provided by the inspection tool) that contains a known measured value of the reticle critical dimension (CD). This new method automatically loads the correct scanner conditions, sets the appropriate simulation threshold, and automatically measures the percentage of CD change caused by the defect. This streamlines qualification and reduces the number of reticles being put on hold, waiting for engineer review. We also present data showing the consistency and reliability of the new method, along with the impact on the efficiency of in

A graphical automated detection system to locate hardwood log surface defects using high-resolution three-dimensional laser scan data

Science.gov (United States)

Liya Thomas; R. Edward. Thomas

2011-01-01

We have developed an automated defect detection system and a state-of-the-art Graphic User Interface (GUI) for hardwood logs. The algorithm identifies defects at least 0.5 inch high and at least 3 inches in diameter on barked hardwood log and stem surfaces. To summarize defect features and to build a knowledge base, hundreds of defects were measured, photographed, and...

Detection of delamination defects in plate type fuel elements applying an automated C-Scan ultrasonic system

International Nuclear Information System (INIS)

Katchadjian, P.; Desimone, C.; Ziobrowski, C.; Garcia, A.

2002-01-01

For the inspection of plate type fuel elements to be used in Research Nuclear Reactors it was applied an immersion pulse-echo ultrasonic technique. For that reason an automated movement system was implemented according to the axes X, Y and Z that allows to automate the test and to show the results obtained in format of C-Scan, facilitating the immediate identification of possible defects and making repetitive the inspection. In this work problems found during the laboratory tests and factors that difficult the inspection are commented. Also the results of C-Scans over UMo fuel elements with pattern defects are shown. Finally, the main characteristics of the transducer with the one the better results were obtained are detailed. (author)

Literature classification for semi-automated updating of biological knowledgebases

DEFF Research Database (Denmark)

Olsen, Lars Rønn; Kudahl, Ulrich Johan; Winther, Ole

2013-01-01

abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature. Conclusion: We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining...... types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature. Results: We defined and applied a machine...

Leveraging Long-term Seismic Catalogs for Automated Real-time Event Classification

Science.gov (United States)

Linville, L.; Draelos, T.; Pankow, K. L.; Young, C. J.; Alvarez, S.

2017-12-01

We investigate the use of labeled event types available through reviewed seismic catalogs to produce automated event labels on new incoming data from the crustal region spanned by the cataloged events. Using events cataloged by the University of Utah Seismograph Stations between October, 2012 and June, 2017, we calculate the spectrogram for a time window that spans the duration of each event as seen on individual stations, resulting in 110k event spectrograms (50% local earthquakes examples, 50% quarry blasts examples). Using 80% of the randomized example events ( 90k), a classifier is trained to distinguish between local earthquakes and quarry blasts. We explore variations of deep learning classifiers, incorporating elements of convolutional and recurrent neural networks. Using a single-layer Long Short Term Memory recurrent neural network, we achieve 92% accuracy on the classification task on the remaining 20K test examples. Leveraging the decisions from a group of stations that detected the same event by using the median of all classifications in the group increases the model accuracy to 96%. Additional data with equivalent processing from 500 more recently cataloged events (July, 2017), achieves the same accuracy as our test data on both single-station examples and multi-station medians, suggesting that the model can maintain accurate and stable classification rates on real-time automated events local to the University of Utah Seismograph Stations, with potentially minimal levels of re-training through time.

Unsupervised Classification of Surface Defects in Wire Rod Production Obtained by Eddy Current Sensors

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Sergio Saludes-Rodil

2015-04-01

Full Text Available An unsupervised approach to classify surface defects in wire rod manufacturing is developed in this paper. The defects are extracted from an eddy current signal and classified using a clustering technique that uses the dynamic time warping distance as the dissimilarity measure. The new approach has been successfully tested using industrial data. It is shown that it outperforms other classification alternatives, such as the modified Fourier descriptors.

Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment

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Rashmi Mukherjee

2014-01-01

Full Text Available The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough scheme for chronic wound (CW evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity color space and subsequently the “S” component of HSI color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM, were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793.

Online Surface Defect Identification of Cold Rolled Strips Based on Local Binary Pattern and Extreme Learning Machine

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Yang Liu

2018-03-01

Full Text Available In the production of cold-rolled strip, the strip surface may suffer from various defects which need to be detected and identified using an online inspection system. The system is equipped with high-speed and high-resolution cameras to acquire images from the moving strip surface. Features are then extracted from the images and are used as inputs of a pre-trained classifier to identify the type of defect. New types of defect often appear in production. At this point the pre-trained classifier needs to be quickly retrained and deployed in seconds to meet the requirement of the online identification of all defects in the environment of a continuous production line. Therefore, the method for extracting the image features and the training for the classification model should be automated and fast enough, normally within seconds. This paper presents our findings in investigating the computational and classification performance of various feature extraction methods and classification models for the strip surface defect identification. The methods include Scale Invariant Feature Transform (SIFT, Speeded Up Robust Features (SURF and Local Binary Patterns (LBP. The classifiers we have assessed include Back Propagation (BP neural network, Support Vector Machine (SVM and Extreme Learning Machine (ELM. By comparing various combinations of different feature extraction and classification methods, our experiments show that the hybrid method of LBP for feature extraction and ELM for defect classification results in less training and identification time with higher classification accuracy, which satisfied online real-time identification.

Automated vegetation classification using Thematic Mapper Simulation data

Science.gov (United States)

Nedelman, K. S.; Cate, R. B.; Bizzell, R. M.

1983-01-01

The present investigation is concerned with the results of a study of Thematic Mapper Simulation (TMS) data. One of the objectives of the study was related to an evaluation of the usefulness of the Thematic Mapper's (TM) improved spatial resolution and spectral coverage. The study was undertaken as part of a preparation for the efficient incorporation of Landsat 4 data into ongoing technology development in remote sensing. The study included an application of automated Landsat vegetation classification technology to TMS data. Results of comparing TMS data to Multispectral Scanner (MSS) data were found to indicate that all field definition, crop type discrimination, and subsequent proportion estimation may be greatly increased with the availability of TM data.

ClassyFire: automated chemical classification with a comprehensive, computable taxonomy.

Science.gov (United States)

Djoumbou Feunang, Yannick; Eisner, Roman; Knox, Craig; Chepelev, Leonid; Hastings, Janna; Owen, Gareth; Fahy, Eoin; Steinbeck, Christoph; Subramanian, Shankar; Bolton, Evan; Greiner, Russell; Wishart, David S

2016-01-01

Scientists have long been driven by the desire to describe, organize, classify, and compare objects using taxonomies and/or ontologies. In contrast to biology, geology, and many other scientific disciplines, the world of chemistry still lacks a standardized chemical ontology or taxonomy. Several attempts at chemical classification have been made; but they have mostly been limited to either manual, or semi-automated proof-of-principle applications. This is regrettable as comprehensive chemical classification and description tools could not only improve our understanding of chemistry but also improve the linkage between chemistry and many other fields. For instance, the chemical classification of a compound could help predict its metabolic fate in humans, its druggability or potential hazards associated with it, among others. However, the sheer number (tens of millions of compounds) and complexity of chemical structures is such that any manual classification effort would prove to be near impossible. We have developed a comprehensive, flexible, and computable, purely structure-based chemical taxonomy (ChemOnt), along with a computer program (ClassyFire) that uses only chemical structures and structural features to automatically assign all known chemical compounds to a taxonomy consisting of >4800 different categories. This new chemical taxonomy consists of up to 11 different levels (Kingdom, SuperClass, Class, SubClass, etc.) with each of the categories defined by unambiguous, computable structural rules. Furthermore each category is named using a consensus-based nomenclature and described (in English) based on the characteristic common structural properties of the compounds it contains. The ClassyFire webserver is freely accessible at http://classyfire.wishartlab.com/. Moreover, a Ruby API version is available at https://bitbucket.org/wishartlab/classyfire_api, which provides programmatic access to the ClassyFire server and database. ClassyFire has been used to

A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns

Directory of Open Access Journals (Sweden)

Gwen A. Frishkoff

2007-01-01

Full Text Available This paper describes a framework for automated classification and labeling of patterns in electroencephalographic (EEG and magnetoencephalographic (MEG data. We describe recent progress on four goals: 1 specification of rules and concepts that capture expert knowledge of event-related potentials (ERP patterns in visual word recognition; 2 implementation of rules in an automated data processing and labeling stream; 3 data mining techniques that lead to refinement of rules; and 4 iterative steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven, methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and can lead to development of tools for pattern classification and labeling that are robust and conceptually transparent to researchers. The present application focuses on patterns in averaged EEG (ERP data. We also describe efforts to extend our methods to represent patterns in MEG data, as well as EM patterns in source (anatomical space. The broader aim of this work is to design an ontology-based system to support cross-laboratory, cross-paradigm, and cross-modal integration of brain functional data. Tools developed for this project are implemented in MATLAB and are freely available on request.

Automated authorship attribution using advanced signal classification techniques.

Directory of Open Access Journals (Sweden)

Maryam Ebrahimpour

Full Text Available In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis (MDA and the other based on a Support Vector Machine (SVM. The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess of 90%. We further test our methods on the Federalist Papers, which have a partly disputed authorship and a fair degree of scholarly consensus. And finally, we apply our methodology to the question of the authorship of the Letter to the Hebrews by comparing it against a number of original Greek texts of known authorship. These tests identify where some of the limitations lie, motivating a number of open questions for future work. An open source implementation of our methodology is freely available for use at https://github.com/matthewberryman/author-detection.

Use of self-organizing maps for classification of defects in the tubes from the steam generator of nuclear power plants

International Nuclear Information System (INIS)

Mesquita, Roberto Navarro de

2002-01-01

This thesis obtains a new classification method for different steam generator tube defects in nuclear power plants using Eddy Current Test signals. The method uses self-organizing maps to compare different signal characteristics efficiency to identify and classify these defects. A multiple inference system is proposed which composes the different extracted characteristic trained maps classification to infer the final defect type. The feature extraction methods used are the Wavelet zero-crossings representation, the linear predictive coding (LPC), and other basic signal representations on time like module and phase. Many characteristic vectors are obtained with combinations of these extracted characteristics. These vectors are tested to classify the defects and the best ones are applied to the multiple inference system. A systematic study of pre-processing, calibration and analysis methods for the steam generator tube defect signals in nuclear power plants is done. The method efficiency is demonstrated and characteristic maps with the main prototypes are obtained for each steam generator tube defect type. (author)

Classification of Automated Search Traffic

Science.gov (United States)

Buehrer, Greg; Stokes, Jack W.; Chellapilla, Kumar; Platt, John C.

As web search providers seek to improve both relevance and response times, they are challenged by the ever-increasing tax of automated search query traffic. Third party systems interact with search engines for a variety of reasons, such as monitoring a web site’s rank, augmenting online games, or possibly to maliciously alter click-through rates. In this paper, we investigate automated traffic (sometimes referred to as bot traffic) in the query stream of a large search engine provider. We define automated traffic as any search query not generated by a human in real time. We first provide examples of different categories of query logs generated by automated means. We then develop many different features that distinguish between queries generated by people searching for information, and those generated by automated processes. We categorize these features into two classes, either an interpretation of the physical model of human interactions, or as behavioral patterns of automated interactions. Using the these detection features, we next classify the query stream using multiple binary classifiers. In addition, a multiclass classifier is then developed to identify subclasses of both normal and automated traffic. An active learning algorithm is used to suggest which user sessions to label to improve the accuracy of the multiclass classifier, while also seeking to discover new classes of automated traffic. Performance analysis are then provided. Finally, the multiclass classifier is used to predict the subclass distribution for the search query stream.

A Generic Deep-Learning-Based Approach for Automated Surface Inspection.

Science.gov (United States)

Ren, Ruoxu; Hung, Terence; Tan, Kay Chen

2018-03-01

Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%-25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%-19.00% in three defect types and improves accuracies by 2.29%-9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data.

Methods for sorting out the defects according to size in automated ultrasonic testing of large-diameter thin-walled tubes

International Nuclear Information System (INIS)

Golovkin, A.M.; Matveev, A.S

1977-01-01

Two methods have been considered of identifying defects according to their size in the course of an automated ultrasonic testing, namely, according to the echo-signal amplitude, and according to the conventional depth of a defect. The peculiar features of the second method are analyzed, and its equivalence to the first one is proved. For the purpose of identifying defects according to their conventional width, a technique is suggested of standartizing flaw detectors according to the control reflectors of two sizes

DEEP LEARNING AND IMAGE PROCESSING FOR AUTOMATED CRACK DETECTION AND DEFECT MEASUREMENT IN UNDERGROUND STRUCTURES

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F. Panella

2018-05-01

Full Text Available This work presents the combination of Deep-Learning (DL and image processing to produce an automated cracks recognition and defect measurement tool for civil structures. The authors focus on tunnel civil structures and survey and have developed an end to end tool for asset management of underground structures. In order to maintain the serviceability of tunnels, regular inspection is needed to assess their structural status. The traditional method of carrying out the survey is the visual inspection: simple, but slow and relatively expensive and the quality of the output depends on the ability and experience of the engineer as well as on the total workload (stress and tiredness may influence the ability to observe and record information. As a result of these issues, in the last decade there is the desire to automate the monitoring using new methods of inspection. The present paper has the goal of combining DL with traditional image processing to create a tool able to detect, locate and measure the structural defect.

Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer.

Science.gov (United States)

Rogiers, Bart; Mallants, Dirk; Batelaan, Okke; Gedeon, Matej; Huysmans, Marijke; Dassargues, Alain

2017-01-01

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.

Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer.

Directory of Open Access Journals (Sweden)

Bart Rogiers

Full Text Available Cone penetration testing (CPT is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.

Experimental Study of the Effect of Internal Defects on Stress Waves during Automated Fiber Placement

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Zhenyu Han

2018-04-01

Full Text Available The detection technique of component defects is currently only realized to detect offline defects and online surface defects during automated fiber placement (AFP. The characteristics of stress waves can be effectively applied to identify and detect internal defects in material structure. However, the correlation mechanism between stress waves and internal defects remains unclear during the AFP process. This paper proposes a novel experimental method to test stress waves, where continuous loading induced by process itself is used as an excitation source without other external excitation. Twenty-seven groups of thermosetting prepreg laminates under different processing parameters are manufactured to obtain different void content. In order to quantitatively estimate the void content in the prepreg structure, the relation model between the void content and ultrasonic attenuation coefficient is revealed using an A-scan ultrasonic flaw detector and photographic methods by optical microscope. Furthermore, the high-frequency noises of stress waves are removed using Haar wavelet transform. The peaks, the Manhattan distance and mean stress during the laying process are analyzed and evaluated. Partial conclusions in this paper could provide theoretical support for online real-time detection of internal defects based on stress wave characteristics.

An Automated Defect Prediction Framework using Genetic Algorithms: A Validation of Empirical Studies

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Juan Murillo-Morera

2016-05-01

Full Text Available Today, it is common for software projects to collect measurement data through development processes. With these data, defect prediction software can try to estimate the defect proneness of a software module, with the objective of assisting and guiding software practitioners. With timely and accurate defect predictions, practitioners can focus their limited testing resources on higher risk areas. This paper reports the results of three empirical studies that uses an automated genetic defect prediction framework. This framework generates and compares different learning schemes (preprocessing + attribute selection + learning algorithms and selects the best one using a genetic algorithm, with the objective to estimate the defect proneness of a software module. The first empirical study is a performance comparison of our framework with the most important framework of the literature. The second empirical study is a performance and runtime comparison between our framework and an exhaustive framework. The third empirical study is a sensitivity analysis. The last empirical study, is our main contribution in this paper. Performance of the software development defect prediction models (using AUC, Area Under the Curve was validated using NASA-MDP and PROMISE data sets. Seventeen data sets from NASA-MDP (13 and PROMISE (4 projects were analyzed running a NxM-fold cross-validation. A genetic algorithm was used to select the components of the learning schemes automatically, and to assess and report the results. Our results reported similar performance between frameworks. Our framework reported better runtime than exhaustive framework. Finally, we reported the best configuration according to sensitivity analysis.

Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation

CSIR Research Space (South Africa)

Ngwangwa, HM

2010-04-01

Full Text Available -1 Journal of Terramechanics Volume 47, Issue 2, April 2010, Pages 97-111 Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation H.M. Ngwangwaa, P.S. Heynsa, , , F...

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

Science.gov (United States)

Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

Detection and classification of defects in ultrasonic NDE signals using time-frequency representations

Science.gov (United States)

Qidwai, Uvais; Costa, Antonio H.; Chen, C. H.

2000-05-01

The ultrasonic wave, generated by a piezoelectric transducer coupled to the test specimen, propagates through the material and part of its energy is reflected when it encounters an non-homogeneity or discontinuity in its path, while the remainder is reflected by the back surface of the test specimen. Defect echo signals are masked by the characteristics of the measuring instruments, the propagation paths taken by the ultrasonic wave, and are corrupted by additive noise. This leads to difficulties in comparing and analyzing signals, particularly in automated defect identification systems employing different transducers. Further, the multi-component nature of material defects can add to the complexity of the defect identification criteria. With many one-dimensional (1-D) approaches, the multi-component defects can not be detected. Another drawback is that these techniques are not very robust for sharp ultrasonic peaks especially in a very hazardous environment. This paper proposes a technique based on the time-frequency representations (TFRs) of the real defect signals corresponding to artificially produced defects of various geometries in metals. Cohen's class (quadratic) TFRs with Gaussian kernels are then used to represent the signals in the time-frequency (TF) plane. Once the TFR is obtained, various image processing morphological techniques are applied to the TFR (e.g. region of interest masking, edge detection, and profile separation). Based on the results of these operations, a binary image is produced which, in turn, leads to a novel set of features. Using these new features, defects have not only been detected but also classified as flat-cut, angular-cut, and circular-drills. Moreover, with some modifications of the threshold levels of the TFR kernel design, our technique can be used in relatively hostile environments with SNRs as low as 0 dB. Another important characteristic of our approach is the detection of multiple defects. This consists of detection of

Perforator chimerism for the reconstruction of complex defects: A new chimeric free flap classification system.

Science.gov (United States)

Kim, Jeong Tae; Kim, Youn Hwan; Ghanem, Ali M

2015-11-01

Complex defects present structural and functional challenges to reconstructive surgeons. When compared to multiple free flaps or staged reconstruction, the use of chimeric flaps to reconstruct such defects have many advantages such as reduced number of operative procedures and donor site morbidity as well as preservation of recipient vessels. With increased popularity of perforator flaps, chimeric flaps' harvest and design has benefited from 'perforator concept' towards more versatile and better reconstruction solutions. This article discusses perforator based chimeric flaps and presents a practice based classification system that incorporates the perforator flap concept into "Perforator Chimerism". The authors analyzed a variety of chimeric patterns used in 31 consecutive cases to present illustrative case series and their new classification system. Accordingly, chimeric flaps are classified into four types. Type I: Classical Chimerism, Type II: Anastomotic Chimerism, Type III: Perforator Chimerism and Type IV Mixed Chimerism. Types I on specific source vessel anatomy whilst Type II requires microvascular anastomosis to create the chimeric reconstructive solution. Type III chimeric flaps utilizes the perforator concept to raise two components of tissues without microvascular anastomosis between them. Type IV chimeric flaps are mixed type flaps comprising any combination of Types I to III. Incorporation of the perforator concept in planning and designing chimeric flaps has allowed safe, effective and aesthetically superior reconstruction of complex defects. The new classification system aids reconstructive surgeons and trainees to understand chimeric flaps design, facilitating effective incorporation of this important reconstructive technique into the armamentarium of the reconstruction toolbox. Copyright © 2015 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Aerial image measurement technique for automated reticle defect disposition (ARDD) in wafer fabs

Science.gov (United States)

Zibold, Axel M.; Schmid, Rainer M.; Stegemann, B.; Scheruebl, Thomas; Harnisch, Wolfgang; Kobiyama, Yuji

2004-08-01

The Aerial Image Measurement System (AIMS)* for 193 nm lithography emulation has been brought into operation successfully worldwide. A second generation system comprising 193 nm AIMS capability, mini-environment and SMIF, the AIMS fab 193 plus is currently introduced into the market. By adjustment of numerical aperture (NA), illumination type and partial illumination coherence to match the conditions in 193 nm steppers or scanners, it can emulate the exposure tool for any type of reticles like binary, OPC and PSM down to the 65 nm node. The system allows a rapid prediction of wafer printability of defects or defect repairs, and critical features, like dense patterns or contacts on the masks without the need to perform expensive image qualification consisting of test wafer exposures followed by SEM measurements. Therefore, AIMS is a mask quality verification standard for high-end photo masks and established in mask shops worldwide. The progress on the AIMS technology described in this paper will highlight that besides mask shops there will be a very beneficial use of the AIMS in the wafer fab and we propose an Automated Reticle Defect Disposition (ARDD) process. With smaller nodes, where design rules are 65 nm or less, it is expected that smaller defects on reticles will occur in increasing numbers in the wafer fab. These smaller mask defects will matter more and more and become a serious yield limiting factor. With increasing mask prices and increasing number of defects and severability on reticles it will become cost beneficial to perform defect disposition on the reticles in wafer production. Currently ongoing studies demonstrate AIMS benefits for wafer fab applications. An outlook will be given for extension of 193 nm aerial imaging down to the 45 nm node based on emulation of immersion scanners.

Automated detection and classification of cryptographic algorithms in binary programs through machine learning

OpenAIRE

Hosfelt, Diane Duros

2015-01-01

Threats from the internet, particularly malicious software (i.e., malware) often use cryptographic algorithms to disguise their actions and even to take control of a victim's system (as in the case of ransomware). Malware and other threats proliferate too quickly for the time-consuming traditional methods of binary analysis to be effective. By automating detection and classification of cryptographic algorithms, we can speed program analysis and more efficiently combat malware. This thesis wil...

AUTOMATED UNSUPERVISED CLASSIFICATION OF THE SLOAN DIGITAL SKY SURVEY STELLAR SPECTRA USING k-MEANS CLUSTERING

Energy Technology Data Exchange (ETDEWEB)

Sanchez Almeida, J.; Allende Prieto, C., E-mail: jos@iac.es, E-mail: callende@iac.es [Instituto de Astrofisica de Canarias, E-38205 La Laguna, Tenerife (Spain)

2013-01-20

Large spectroscopic surveys require automated methods of analysis. This paper explores the use of k-means clustering as a tool for automated unsupervised classification of massive stellar spectral catalogs. The classification criteria are defined by the data and the algorithm, with no prior physical framework. We work with a representative set of stellar spectra associated with the Sloan Digital Sky Survey (SDSS) SEGUE and SEGUE-2 programs, which consists of 173,390 spectra from 3800 to 9200 A sampled on 3849 wavelengths. We classify the original spectra as well as the spectra with the continuum removed. The second set only contains spectral lines, and it is less dependent on uncertainties of the flux calibration. The classification of the spectra with continuum renders 16 major classes. Roughly speaking, stars are split according to their colors, with enough finesse to distinguish dwarfs from giants of the same effective temperature, but with difficulties to separate stars with different metallicities. There are classes corresponding to particular MK types, intrinsically blue stars, dust-reddened, stellar systems, and also classes collecting faulty spectra. Overall, there is no one-to-one correspondence between the classes we derive and the MK types. The classification of spectra without continuum renders 13 classes, the color separation is not so sharp, but it distinguishes stars of the same effective temperature and different metallicities. Some classes thus obtained present a fairly small range of physical parameters (200 K in effective temperature, 0.25 dex in surface gravity, and 0.35 dex in metallicity), so that the classification can be used to estimate the main physical parameters of some stars at a minimum computational cost. We also analyze the outliers of the classification. Most of them turn out to be failures of the reduction pipeline, but there are also high redshift QSOs, multiple stellar systems, dust-reddened stars, galaxies, and, finally, odd

Quality Control in Automated Manufacturing Processes – Combined Features for Image Processing

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B. Kuhlenkötter

2006-01-01

Full Text Available In production processes the use of image processing systems is widespread. Hardware solutions and cameras respectively are available for nearly every application. One important challenge of image processing systems is the development and selection of appropriate algorithms and software solutions in order to realise ambitious quality control for production processes. This article characterises the development of innovative software by combining features for an automatic defect classification on product surfaces. The artificial intelligent method Support Vector Machine (SVM is used to execute the classification task according to the combined features. This software is one crucial element for the automation of a manually operated production process. 

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

Science.gov (United States)

Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

2010-02-01

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

Automated classification of mouse pup isolation syllables: from cluster analysis to an Excel based ‘mouse pup syllable classification calculator’

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Jasmine eGrimsley

2013-01-01

Full Text Available Mouse pups vocalize at high rates when they are cold or isolated from the nest. The proportions of each syllable type produced carry information about disease state and are being used as behavioral markers for the internal state of animals. Manual classifications of these vocalizations identified ten syllable types based on their spectro-temporal features. However, manual classification of mouse syllables is time consuming and vulnerable to experimenter bias. This study uses an automated cluster analysis to identify acoustically distinct syllable types produced by CBA/CaJ mouse pups, and then compares the results to prior manual classification methods. The cluster analysis identified two syllable types, based on their frequency bands, that have continuous frequency-time structure, and two syllable types featuring abrupt frequency transitions. Although cluster analysis computed fewer syllable types than manual classification, the clusters represented well the probability distributions of the acoustic features within syllables. These probability distributions indicate that some of the manually classified syllable types are not statistically distinct. The characteristics of the four classified clusters were used to generate a Microsoft Excel-based mouse syllable classifier that rapidly categorizes syllables, with over a 90% match, into the syllable types determined by cluster analysis.

An automated classification system for the differentiation of obstructive lung diseases based on the textural analysis of HRCT images

International Nuclear Information System (INIS)

Park, Seong Hoon; Seo, Joon Beom; Kim, Nam Kug; Lee, Young Kyung; Kim, Song Soo; Chae, Eun Jin; Lee, June Goo

2007-01-01

To develop an automated classification system for the differentiation of obstructive lung diseases based on the textural analysis of HRCT images, and to evaluate the accuracy and usefulness of the system. For textural analysis, histogram features, gradient features, run length encoding, and a co-occurrence matrix were employed. A Bayesian classifier was used for automated classification. The images (image number n = 256) were selected from the HRCT images obtained from 17 healthy subjects (n = 67), 26 patients with bronchiolitis obliterans (n = 70), 28 patients with mild centrilobular emphysema (n = 65), and 21 patients with panlobular emphysema or severe centrilobular emphysema (n = 63). An five-fold cross-validation method was used to assess the performance of the system. Class-specific sensitivities were analyzed and the overall accuracy of the system was assessed with kappa statistics. The sensitivity of the system for each class was as follows: normal lung 84.9%, bronchiolitis obliterans 83.8%, mild centrilobular emphysema 77.0%, and panlobular emphysema or severe centrilobular emphysema 95.8%. The overall performance for differentiating each disease and the normal lung was satisfactory with a kappa value of 0.779. An automated classification system for the differentiation between obstructive lung diseases based on the textural analysis of HRCT images was developed. The proposed system discriminates well between the various obstructive lung diseases and the normal lung

Robust automated classification of first-motion polarities for focal mechanism determination with machine learning

Science.gov (United States)

Ross, Z. E.; Meier, M. A.; Hauksson, E.

2017-12-01

Accurate first-motion polarities are essential for determining earthquake focal mechanisms, but are difficult to measure automatically because of picking errors and signal to noise issues. Here we develop an algorithm for reliable automated classification of first-motion polarities using machine learning algorithms. A classifier is designed to identify whether the first-motion polarity is up, down, or undefined by examining the waveform data directly. We first improve the accuracy of automatic P-wave onset picks by maximizing a weighted signal/noise ratio for a suite of candidate picks around the automatic pick. We then use the waveform amplitudes before and after the optimized pick as features for the classification. We demonstrate the method's potential by training and testing the classifier on tens of thousands of hand-made first-motion picks by the Southern California Seismic Network. The classifier assigned the same polarity as chosen by an analyst in more than 94% of the records. We show that the method is generalizable to a variety of learning algorithms, including neural networks and random forest classifiers. The method is suitable for automated processing of large seismic waveform datasets, and can potentially be used in real-time applications, e.g. for improving the source characterizations of earthquake early warning algorithms.

Particle Swarm Optimization approach to defect detection in armour ceramics.

Science.gov (United States)

Kesharaju, Manasa; Nagarajah, Romesh

2017-03-01

In this research, various extracted features were used in the development of an automated ultrasonic sensor based inspection system that enables defect classification in each ceramic component prior to despatch to the field. Classification is an important task and large number of irrelevant, redundant features commonly introduced to a dataset reduces the classifiers performance. Feature selection aims to reduce the dimensionality of the dataset while improving the performance of a classification system. In the context of a multi-criteria optimization problem (i.e. to minimize classification error rate and reduce number of features) such as one discussed in this research, the literature suggests that evolutionary algorithms offer good results. Besides, it is noted that Particle Swarm Optimization (PSO) has not been explored especially in the field of classification of high frequency ultrasonic signals. Hence, a binary coded Particle Swarm Optimization (BPSO) technique is investigated in the implementation of feature subset selection and to optimize the classification error rate. In the proposed method, the population data is used as input to an Artificial Neural Network (ANN) based classification system to obtain the error rate, as ANN serves as an evaluator of PSO fitness function. Copyright © 2016. Published by Elsevier B.V.

Automated classification of self-grooming in mice using open-source software.

Science.gov (United States)

van den Boom, Bastijn J G; Pavlidi, Pavlina; Wolf, Casper J H; Mooij, Adriana H; Willuhn, Ingo

2017-09-01

Manual analysis of behavior is labor intensive and subject to inter-rater variability. Although considerable progress in automation of analysis has been made, complex behavior such as grooming still lacks satisfactory automated quantification. We trained a freely available, automated classifier, Janelia Automatic Animal Behavior Annotator (JAABA), to quantify self-grooming duration and number of bouts based on video recordings of SAPAP3 knockout mice (a mouse line that self-grooms excessively) and wild-type animals. We compared the JAABA classifier with human expert observers to test its ability to measure self-grooming in three scenarios: mice in an open field, mice on an elevated plus-maze, and tethered mice in an open field. In each scenario, the classifier identified both grooming and non-grooming with great accuracy and correlated highly with results obtained by human observers. Consistently, the JAABA classifier confirmed previous reports of excessive grooming in SAPAP3 knockout mice. Thus far, manual analysis was regarded as the only valid quantification method for self-grooming. We demonstrate that the JAABA classifier is a valid and reliable scoring tool, more cost-efficient than manual scoring, easy to use, requires minimal effort, provides high throughput, and prevents inter-rater variability. We introduce the JAABA classifier as an efficient analysis tool for the assessment of rodent self-grooming with expert quality. In our "how-to" instructions, we provide all information necessary to implement behavioral classification with JAABA. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of Threshold Saccadic Vector Optokinetic Perimetry (SVOP) and Standard Automated Perimetry (SAP) in Glaucoma. Part II: Patterns of Visual Field Loss and Acceptability.

Science.gov (United States)

McTrusty, Alice D; Cameron, Lorraine A; Perperidis, Antonios; Brash, Harry M; Tatham, Andrew J; Agarwal, Pankaj K; Murray, Ian C; Fleck, Brian W; Minns, Robert A

2017-09-01

We compared patterns of visual field loss detected by standard automated perimetry (SAP) to saccadic vector optokinetic perimetry (SVOP) and examined patient perceptions of each test. A cross-sectional study was done of 58 healthy subjects and 103 with glaucoma who were tested using SAP and two versions of SVOP (v1 and v2). Visual fields from both devices were categorized by masked graders as: 0, normal; 1, paracentral defect; 2, nasal step; 3, arcuate defect; 4, altitudinal; 5, biarcuate; and 6, end-stage field loss. SVOP and SAP classifications were cross-tabulated. Subjects completed a questionnaire on their opinions of each test. We analyzed 142 (v1) and 111 (v2) SVOP and SAP test pairs. SVOP v2 had a sensitivity of 97.7% and specificity of 77.9% for identifying normal versus abnormal visual fields. SAP and SVOP v2 classifications showed complete agreement in 54% of glaucoma patients, with a further 23% disagreeing by one category. On repeat testing, 86% of SVOP v2 classifications agreed with the previous test, compared to 91% of SAP classifications; 71% of subjects preferred SVOP compared to 20% who preferred SAP. Eye-tracking perimetry can be used to obtain threshold visual field sensitivity values in patients with glaucoma and produce maps of visual field defects, with patterns exhibiting close agreement to SAP. Patients preferred eye-tracking perimetry compared to SAP. This first report of threshold eye tracking perimetry shows good agreement with conventional automated perimetry and provides a benchmark for future iterations.

Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.

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Fernanda C Dórea

Full Text Available BACKGROUND: Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes--syndromic surveillance--using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users. METHODS: This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory. RESULTS: High performance (F1-macro = 0.9995 was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F(1-micro score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F(1-macro, due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F(1-micro score of 0.923 (falling to 0.311 when classes are given equal weight. A Naïve Bayes classifier learned all classes and achieved high performance (F(1-micro= 0.994 and F(1-macro = .955, however the classification process is not transparent to the domain experts. CONCLUSION: The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish

Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections.

Science.gov (United States)

Kuepper, Claus; Kallenbach-Thieltges, Angela; Juette, Hendrik; Tannapfel, Andrea; Großerueschkamp, Frederik; Gerwert, Klaus

2018-05-16

A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

Feature Subset Selection and Instance Filtering for Cross-project Defect Prediction - Classification and Ranking

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Faimison Porto

2016-12-01

Full Text Available The defect prediction models can be a good tool on organizing the project's test resources. The models can be constructed with two main goals: 1 to classify the software parts - defective or not; or 2 to rank the most defective parts in a decreasing order. However, not all companies maintain an appropriate set of historical defect data. In this case, a company can build an appropriate dataset from known external projects - called Cross-project Defect Prediction (CPDP. The CPDP models, however, present low prediction performances due to the heterogeneity of data. Recently, Instance Filtering methods were proposed in order to reduce this heterogeneity by selecting the most similar instances from the training dataset. Originally, the similarity is calculated based on all the available dataset features (or independent variables. We propose that using only the most relevant features on the similarity calculation can result in more accurate filtered datasets and better prediction performances. In this study we extend our previous work. We analyse both prediction goals - Classification and Ranking. We present an empirical evaluation of 41 different methods by associating Instance Filtering methods with Feature Selection methods. We used 36 versions of 11 open source projects on experiments. The results show similar evidences for both prediction goals. First, the defect prediction performance of CPDP models can be improved by associating Feature Selection and Instance Filtering. Second, no evaluated method presented general better performances. Indeed, the most appropriate method can vary according to the characteristics of the project being predicted.

Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives

International Nuclear Information System (INIS)

Wang Xingwei; Zheng Bin; Wood, Marc; Li Shibo; Chen Wei; Liu Hong

2005-01-01

Automated detection and classification of banded chromosomes may help clinicians diagnose cancers and other genetic disorders at an early stage more efficiently and accurately. However, developing such an automated system (including both a high-speed microscopic image scanning device and related computer-assisted schemes) is quite a challenging and difficult task. Since the 1980s, great research efforts have been made to develop fast and more reliable methods to assist clinical technicians in performing this important and time-consuming task. A number of computer-assisted methods including classical statistical methods, artificial neural networks and knowledge-based fuzzy logic systems, have been applied and tested. Based on the initial test using limited datasets, encouraging results in algorithm and system development have been demonstrated. Despite the significant research effort and progress made over the last two decades, computer-assisted chromosome detection and classification systems have not been routinely accepted and used in clinical laboratories. Further research and development is needed

Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives

Energy Technology Data Exchange (ETDEWEB)

Wang Xingwei [Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, OK (United States); Zheng Bin [Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA (United States); Wood, Marc [Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, OK (United States); Li Shibo [Department of Pediatrics, University of Oklahoma Medical Center, Oklahoma City, OK (United States); Chen Wei [Department of Physics and Engineering, University of Central Oklahoma, Edmond, OK (United States); Liu Hong [Center for Bioengineering and School of Electrical and Computer Engineering, University of Oklahoma, OK (United States)

2005-08-07

Automated detection and classification of banded chromosomes may help clinicians diagnose cancers and other genetic disorders at an early stage more efficiently and accurately. However, developing such an automated system (including both a high-speed microscopic image scanning device and related computer-assisted schemes) is quite a challenging and difficult task. Since the 1980s, great research efforts have been made to develop fast and more reliable methods to assist clinical technicians in performing this important and time-consuming task. A number of computer-assisted methods including classical statistical methods, artificial neural networks and knowledge-based fuzzy logic systems, have been applied and tested. Based on the initial test using limited datasets, encouraging results in algorithm and system development have been demonstrated. Despite the significant research effort and progress made over the last two decades, computer-assisted chromosome detection and classification systems have not been routinely accepted and used in clinical laboratories. Further research and development is needed.

Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA).

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Borràs, Eva; Ferré, Joan; Boqué, Ricard; Mestres, Montserrat; Aceña, Laura; Calvo, Angels; Busto, Olga

2016-07-15

Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV-visible spectrophotometry (UV-vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV-vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils. Copyright © 2016 Elsevier Ltd. All rights reserved.

Automated Detection of Connective Tissue by Tissue Counter Analysis and Classification and Regression Trees

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Josef Smolle

2001-01-01

Full Text Available Objective: To evaluate the feasibility of the CART (Classification and Regression Tree procedure for the recognition of microscopic structures in tissue counter analysis. Methods: Digital microscopic images of H&E stained slides of normal human skin and of primary malignant melanoma were overlayed with regularly distributed square measuring masks (elements and grey value, texture and colour features within each mask were recorded. In the learning set, elements were interactively labeled as representing either connective tissue of the reticular dermis, other tissue components or background. Subsequently, CART models were based on these data sets. Results: Implementation of the CART classification rules into the image analysis program showed that in an independent test set 94.1% of elements classified as connective tissue of the reticular dermis were correctly labeled. Automated measurements of the total amount of tissue and of the amount of connective tissue within a slide showed high reproducibility (r=0.97 and r=0.94, respectively; p < 0.001. Conclusions: CART procedure in tissue counter analysis yields simple and reproducible classification rules for tissue elements.

Automated artery-venous classification of retinal blood vessels based on structural mapping method

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Joshi, Vinayak S.; Garvin, Mona K.; Reinhardt, Joseph M.; Abramoff, Michael D.

2012-03-01

Retinal blood vessels show morphologic modifications in response to various retinopathies. However, the specific responses exhibited by arteries and veins may provide a precise diagnostic information, i.e., a diabetic retinopathy may be detected more accurately with the venous dilatation instead of average vessel dilatation. In order to analyze the vessel type specific morphologic modifications, the classification of a vessel network into arteries and veins is required. We previously described a method for identification and separation of retinal vessel trees; i.e. structural mapping. Therefore, we propose the artery-venous classification based on structural mapping and identification of color properties prominent to the vessel types. The mean and standard deviation of each of green channel intensity and hue channel intensity are analyzed in a region of interest around each centerline pixel of a vessel. Using the vector of color properties extracted from each centerline pixel, it is classified into one of the two clusters (artery and vein), obtained by the fuzzy-C-means clustering. According to the proportion of clustered centerline pixels in a particular vessel, and utilizing the artery-venous crossing property of retinal vessels, each vessel is assigned a label of an artery or a vein. The classification results are compared with the manually annotated ground truth (gold standard). We applied the proposed method to a dataset of 15 retinal color fundus images resulting in an accuracy of 88.28% correctly classified vessel pixels. The automated classification results match well with the gold standard suggesting its potential in artery-venous classification and the respective morphology analysis.

An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data

OpenAIRE

Thenkabail, Prasad S.; Wu, Zhuoting

2012-01-01

The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan u...

Interpreting complex data by methods of recognition and classification in an automated system of aerogeophysical material processing

Energy Technology Data Exchange (ETDEWEB)

Koval' , L.A.; Dolgov, S.V.; Liokumovich, G.B.; Ovcharenko, A.V.; Priyezzhev, I.I.

1984-01-01

The system of automated processing of aerogeophysical data, ASOM-AGS/YeS, is equipped with complex interpretation of multichannel measurements. Algorithms of factor analysis, automatic classification and apparatus of a priori specified (selected) decisive rules are used. The areas of effect of these procedures can be initially limited to the specified geological information. The possibilities of the method are demonstrated by the results of automated processing of the aerogram-spectrometric measurements in the region of the known copper-porphyr manifestation in Kazakhstan. This ore deposit was clearly noted after processing by the method of main components by complex aureole of independent factors U (severe increase), Th (noticeable increase), K (decrease).

An automated cirrus classification

Science.gov (United States)

Gryspeerdt, Edward; Quaas, Johannes; Goren, Tom; Klocke, Daniel; Brueck, Matthias

2018-05-01

Cirrus clouds play an important role in determining the radiation budget of the earth, but many of their properties remain uncertain, particularly their response to aerosol variations and to warming. Part of the reason for this uncertainty is the dependence of cirrus cloud properties on the cloud formation mechanism, which itself is strongly dependent on the local meteorological conditions. In this work, a classification system (Identification and Classification of Cirrus or IC-CIR) is introduced to identify cirrus clouds by the cloud formation mechanism. Using reanalysis and satellite data, cirrus clouds are separated into four main types: orographic, frontal, convective and synoptic. Through a comparison to convection-permitting model simulations and back-trajectory-based analysis, it is shown that these observation-based regimes can provide extra information on the cloud-scale updraughts and the frequency of occurrence of liquid-origin ice, with the convective regime having higher updraughts and a greater occurrence of liquid-origin ice compared to the synoptic regimes. Despite having different cloud formation mechanisms, the radiative properties of the regimes are not distinct, indicating that retrieved cloud properties alone are insufficient to completely describe them. This classification is designed to be easily implemented in GCMs, helping improve future model-observation comparisons and leading to improved parametrisations of cirrus cloud processes.

Creation of Defects Catalogue for Nonconforming Product Identification in the Foundry Organization

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Andrea Sütőová

2013-12-01

Full Text Available The paper deals with system of casting defects classification problematics and creation of defects catalogue in the foundry organization. There is described the value of correct defects classification and identification in the literature review and also some tools for defects classification are mentioned. Existing defects classifications and catalogues are often unusable for particular production processes and casting technology. Many foundries therefore create their own defects catalogues. The sample of created catalogue, which classifies and describes defects occuring in the aluminium foundry organization and its benefits are presented in the paper. The created catalogue primarily serves as a visual support for production operators and quality control processes.

A FULLY AUTOMATED PIPELINE FOR CLASSIFICATION TASKS WITH AN APPLICATION TO REMOTE SENSING

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K. Suzuki

2016-06-01

Full Text Available Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed ‘shallow’ machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view, utilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any special knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a whole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with optimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms for the sake of generally fast and fine optimization, which enables us not only to optimize (hyperparameters but also to determine appropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained untouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets, common datasets in computer vision field for character recognition and object recognition problems respectively, our automated learning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset, small amount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems.

Modified classification and single-stage microsurgical repair of posttraumatic infected massive bone defects in lower extremities.

Science.gov (United States)

Yang, Yun-fa; Xu, Zhong-he; Zhang, Guang-ming; Wang, Jian-wei; Hu, Si-wang; Hou, Zhi-qi; Xu, Da-chuan

2013-11-01

Posttraumatic infected massive bone defects in lower extremities are difficult to repair because they frequently exhibit massive bone and/or soft tissue defects, serious bone infection, and excessive scar proliferation. This study aimed to determine whether these defects could be classified and repaired at a single stage. A total of 51 cases of posttraumatic infected massive bone defect in lower extremity were included in this study. They were classified into four types on the basis of the conditions of the bone defects, soft tissue defects, and injured limb length, including Type A (without soft tissue defects), Type B (with soft tissue defects of 10 × 20 cm or less), Type C (with soft tissue defects of 10 × 20 cm or more), and Type D (with the limb shortening of 3 cm or more). Four types of single-stage microsurgical repair protocols were planned accordingly and implemented respectively. These protocols included the following: Protocol A, where vascularized fibular graft was implemented for Type A; Protocol B, where vascularized fibular osteoseptocutaneous graft was implemented for Type B; Protocol C, where vascularized fibular graft and anterior lateral thigh flap were used for Type C; and Protocol D, where limb lengthening and Protocols A, B, or C were used for Type D. There were 12, 33, 4, and 2 cases of Types A, B, C, and D, respectively, according to this classification. During the surgery, three cases of planned Protocol B had to be shifted into Protocol C; however, all microsurgical repairs were completed. With reference to Johner-Wruhs evaluation method, the total percentage of excellent and good results was 82.35% after 6 to 41 months of follow-up. It was concluded that posttraumatic massive bone defects could be accurately classified into four types on the basis of the conditions of bone defects, soft tissue coverage, and injured limb length, and successfully repaired with the single-stage repair protocols after thorough debridement. Thieme Medical

Effective automated feature construction and selection for classification of biological sequences.

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Uday Kamath

Full Text Available Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection of regulatory regions, splice sites, exons, hypersensitive sites, and more. These problems naturally lend themselves to formulation as classification problems in machine learning. When classification is based on features extracted from the sequences under investigation, success is critically dependent on the chosen set of features.We present an algorithmic framework (EFFECT for automated detection of functional signals in biological sequences. We focus here on classification problems involving DNA sequences which state-of-the-art work in machine learning shows to be challenging and involve complex combinations of local and distal features. EFFECT uses a two-stage process to first construct a set of candidate sequence-based features and then select a most effective subset for the classification task at hand. Both stages make heavy use of evolutionary algorithms to efficiently guide the search towards informative features capable of discriminating between sequences that contain a particular functional signal and those that do not.To demonstrate its generality, EFFECT is applied to three separate problems of importance in DNA research: the recognition of hypersensitive sites, splice sites, and ALU sites. Comparisons with state-of-the-art algorithms show that the framework is both general and powerful. In addition, a detailed analysis of the constructed features shows that they contain valuable biological information about DNA architecture, allowing biologists and other researchers to directly inspect the features and potentially use the insights obtained to assist wet-laboratory studies on retainment or modification

Text mining in the classification of digital documents

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Marcial Contreras Barrera

2016-11-01

Full Text Available Objective: Develop an automated classifier for the classification of bibliographic material by means of the text mining. Methodology: The text mining is used for the development of the classifier, based on a method of type supervised, conformed by two phases; learning and recognition, in the learning phase, the classifier learns patterns across the analysis of bibliographical records, of the classification Z, belonging to library science, information sciences and information resources, recovered from the database LIBRUNAM, in this phase is obtained the classifier capable of recognizing different subclasses (LC. In the recognition phase the classifier is validated and evaluates across classification tests, for this end bibliographical records of the classification Z are taken randomly, classified by a cataloguer and processed by the automated classifier, in order to obtain the precision of the automated classifier. Results: The application of the text mining achieved the development of the automated classifier, through the method classifying documents supervised type. The precision of the classifier was calculated doing the comparison among the assigned topics manually and automated obtaining 75.70% of precision. Conclusions: The application of text mining facilitated the creation of automated classifier, allowing to obtain useful technology for the classification of bibliographical material with the aim of improving and speed up the process of organizing digital documents.

Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.

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Mei Zhan

2015-04-01

Full Text Available Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM. These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a

Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.

Science.gov (United States)

Zhan, Mei; Crane, Matthew M; Entchev, Eugeni V; Caballero, Antonio; Fernandes de Abreu, Diana Andrea; Ch'ng, QueeLim; Lu, Hang

2015-04-01

Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM). These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a guide, we envision

Adaptive Automation Design and Implementation

Science.gov (United States)

2015-09-17

with an automated system to a real-world adaptive au- tomation system implementation. There have been plenty of adaptive automation 17 Adaptive...of systems without increasing manpower requirements by allocating routine tasks to automated aids, improving safety through the use of au- tomated ...between intermediate levels of au- tomation , explicitly defining which human task a given level automates. Each model aids the creation and classification

Little string origin of surface defects

Energy Technology Data Exchange (ETDEWEB)

Haouzi, Nathan; Schmid, Christian [Center for Theoretical Physics, University of California, Berkeley,LeConte Hall, Berkeley (United States)

2017-05-16

We derive a large class of codimension-two defects of 4d N=4 Super Yang-Mills (SYM) theory from the (2,0) little string. The origin of the little string is type IIB theory compactified on an ADE singularity. The defects are D-branes wrapping the 2-cycles of the singularity. We use this construction to make contact with the description of SYM defects due to Gukov and Witten https://arxiv.org/abs/hep-th/0612073. Furthermore, we provide a geometric perspective on the nilpotent orbit classification of codimension-two defects, and the connection to ADE-type Toda CFT. The only data needed to specify the defects is a set of weights of the algebra obeying certain constraints, which we give explicitly. We highlight the differences between the defect classification in the little string theory and its (2,0) CFT limit.

A simple and robust method for automated photometric classification of supernovae using neural networks

Science.gov (United States)

Karpenka, N. V.; Feroz, F.; Hobson, M. P.

2013-02-01

A method is presented for automated photometric classification of supernovae (SNe) as Type Ia or non-Ia. A two-step approach is adopted in which (i) the SN light curve flux measurements in each observing filter are fitted separately to an analytical parametrized function that is sufficiently flexible to accommodate virtually all types of SNe and (ii) the fitted function parameters and their associated uncertainties, along with the number of flux measurements, the maximum-likelihood value of the fit and Bayesian evidence for the model, are used as the input feature vector to a classification neural network that outputs the probability that the SN under consideration is of Type Ia. The method is trained and tested using data released following the Supernova Photometric Classification Challenge (SNPCC), consisting of light curves for 20 895 SNe in total. We consider several random divisions of the data into training and testing sets: for instance, for our sample D_1 (D_4), a total of 10 (40) per cent of the data are involved in training the algorithm and the remainder used for blind testing of the resulting classifier; we make no selection cuts. Assigning a canonical threshold probability of pth = 0.5 on the network output to class an SN as Type Ia, for the sample D_1 (D_4) we obtain a completeness of 0.78 (0.82), purity of 0.77 (0.82) and SNPCC figure of merit of 0.41 (0.50). Including the SN host-galaxy redshift and its uncertainty as additional inputs to the classification network results in a modest 5-10 per cent increase in these values. We find that the quality of the classification does not vary significantly with SN redshift. Moreover, our probabilistic classification method allows one to calculate the expected completeness, purity and figure of merit (or other measures of classification quality) as a function of the threshold probability pth, without knowing the true classes of the SNe in the testing sample, as is the case in the classification of real SNe

Automated classification and quantitative analysis of arterial and venous vessels in fundus images

Science.gov (United States)

Alam, Minhaj; Son, Taeyoon; Toslak, Devrim; Lim, Jennifer I.; Yao, Xincheng

2018-02-01

It is known that retinopathies may affect arteries and veins differently. Therefore, reliable differentiation of arteries and veins is essential for computer-aided analysis of fundus images. The purpose of this study is to validate one automated method for robust classification of arteries and veins (A-V) in digital fundus images. We combine optical density ratio (ODR) analysis and blood vessel tracking algorithm to classify arteries and veins. A matched filtering method is used to enhance retinal blood vessels. Bottom hat filtering and global thresholding are used to segment the vessel and skeleton individual blood vessels. The vessel tracking algorithm is used to locate the optic disk and to identify source nodes of blood vessels in optic disk area. Each node can be identified as vein or artery using ODR information. Using the source nodes as starting point, the whole vessel trace is then tracked and classified as vein or artery using vessel curvature and angle information. 50 color fundus images from diabetic retinopathy patients were used to test the algorithm. Sensitivity, specificity, and accuracy metrics were measured to assess the validity of the proposed classification method compared to ground truths created by two independent observers. The algorithm demonstrated 97.52% accuracy in identifying blood vessels as vein or artery. A quantitative analysis upon A-V classification showed that average A-V ratio of width for NPDR subjects with hypertension decreased significantly (43.13%).

Automated Defect Recognition as a Critical Element of a Three Dimensional X-ray Computed Tomography Imaging-Based Smart Non-Destructive Testing Technique in Additive Manufacturing of Near Net-Shape Parts

Directory of Open Access Journals (Sweden)

Istvan Szabo

2017-11-01

Full Text Available In this paper, a state of the art automated defect recognition (ADR system is presented that was developed specifically for Non-Destructive Testing (NDT of powder metallurgy (PM parts using three dimensional X-ray Computed Tomography (CT imaging, towards enabling online quality assurance and enhanced integrity confidence. PM parts exhibit typical defects such as microscopic cracks, porosity, and voids, internal to components that without an effective detection system, limit the growth of industrial applications. Compared to typical testing methods (e.g., destructive such as metallography that is based on sampling, cutting, and polishing of parts, CT provides full coverage of defect detection. This paper establishes the importance and advantages of an automated NDT system for the PM industry applications with particular emphasis on image processing procedures for defect recognition. Moreover, the article describes how to establish a reference library based on real 3D X-ray CT images of net-shape parts. The paper follows the development of the ADR system from processing 2D image slices of a measured 3D X-ray image to processing the complete 3D X-ray image as a whole. The introduced technique is successfully integrated into an automated in-line quality control system highly sought by major industry sectors in Oil and Gas, Automotive, and Aerospace.

Automated Classification of Radiology Reports for Acute Lung Injury: Comparison of Keyword and Machine Learning Based Natural Language Processing Approaches.

Science.gov (United States)

Solti, Imre; Cooke, Colin R; Xia, Fei; Wurfel, Mark M

2009-11-01

This paper compares the performance of keyword and machine learning-based chest x-ray report classification for Acute Lung Injury (ALI). ALI mortality is approximately 30 percent. High mortality is, in part, a consequence of delayed manual chest x-ray classification. An automated system could reduce the time to recognize ALI and lead to reductions in mortality. For our study, 96 and 857 chest x-ray reports in two corpora were labeled by domain experts for ALI. We developed a keyword and a Maximum Entropy-based classification system. Word unigram and character n-grams provided the features for the machine learning system. The Maximum Entropy algorithm with character 6-gram achieved the highest performance (Recall=0.91, Precision=0.90 and F-measure=0.91) on the 857-report corpus. This study has shown that for the classification of ALI chest x-ray reports, the machine learning approach is superior to the keyword based system and achieves comparable results to highest performing physician annotators.

"SAFEGUARDING THE INTERESTS OF THE STATE" FROM DEFECTIVE DELINQUENT GIRLS.

Science.gov (United States)

Sohasky, Kate E

2016-01-01

The 1911 mental classification, "defective delinquent," was created as a temporary legal-medical category in order to identify a peculiar class of delinquent girls in a specific institutional setting. The defective delinquent's alleged slight mental defect, combined with her appearance of normalcy, rendered her a "dangerous" and "incurable" citizen. At the intersection of institutional history and the history of ideas, this article explores the largely overlooked role of borderline mental classifications of near-normalcy in the medicalization of intelligence and criminality during the first third of the twentieth-century United States. Borderline classifications served as mechanisms of control over women's bodies through the criminalization of their minds, and the advent of psychometric tests legitimated and facilitated the spread of this classification beyond its original and intended context. The borderline case of the defective delinquent girl demonstrates the significance of marginal mental classifications to the policing of bodies through the medicalization of intellect. © 2015 Wiley Periodicals, Inc.

An automated Pearson's correlation change classification (APC3) approach for GC/MS metabonomic data using total ion chromatograms (TICs).

Science.gov (United States)

Prakash, Bhaskaran David; Esuvaranathan, Kesavan; Ho, Paul C; Pasikanti, Kishore Kumar; Chan, Eric Chun Yong; Yap, Chun Wei

2013-05-21

A fully automated and computationally efficient Pearson's correlation change classification (APC3) approach is proposed and shown to have overall comparable performance with both an average accuracy and an average AUC of 0.89 ± 0.08 but is 3.9 to 7 times faster, easier to use and have low outlier susceptibility in contrast to other dimensional reduction and classification combinations using only the total ion chromatogram (TIC) intensities of GC/MS data. The use of only the TIC permits the possible application of APC3 to other metabonomic data such as LC/MS TICs or NMR spectra. A RapidMiner implementation is available for download at http://padel.nus.edu.sg/software/padelapc3.

Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling.

Directory of Open Access Journals (Sweden)

Daniel P Riordan

Full Text Available Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the

Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations.

Science.gov (United States)

Zhang, Kevin; Demner-Fushman, Dina

2017-07-01

To develop automated classification methods for eligibility criteria in ClinicalTrials.gov to facilitate patient-trial matching for specific populations such as persons living with HIV or pregnant women. We annotated 891 interventional cancer trials from ClinicalTrials.gov based on their eligibility for human immunodeficiency virus (HIV)-positive patients using their eligibility criteria. These annotations were used to develop classifiers based on regular expressions and machine learning (ML). After evaluating classification of cancer trials for eligibility of HIV-positive patients, we sought to evaluate the generalizability of our approach to more general diseases and conditions. We annotated the eligibility criteria for 1570 of the most recent interventional trials from ClinicalTrials.gov for HIV-positive and pregnancy eligibility, and the classifiers were retrained and reevaluated using these data. On the cancer-HIV dataset, the baseline regex model, the bag-of-words ML classifier, and the ML classifier with named entity recognition (NER) achieved macro-averaged F2 scores of 0.77, 0.87, and 0.87, respectively; the addition of NER did not result in a significant performance improvement. On the general dataset, ML + NER achieved macro-averaged F2 scores of 0.91 and 0.85 for HIV and pregnancy, respectively. The eligibility status of specific patient populations, such as persons living with HIV and pregnant women, for clinical trials is of interest to both patients and clinicians. We show that it is feasible to develop a high-performing, automated trial classification system for eligibility status that can be integrated into consumer-facing search engines as well as patient-trial matching systems. Published by Oxford University Press on behalf of the American Medical Informatics Association 2017. This work is written by US Government employees and is in the public domain in the US.

CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

International Nuclear Information System (INIS)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Brink, Henrik; Crellin-Quick, Arien; Butler, Nathaniel R.

2012-01-01

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

Energy Technology Data Exchange (ETDEWEB)

Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Brink, Henrik; Crellin-Quick, Arien [Astronomy Department, University of California, Berkeley, CA 94720-3411 (United States); Butler, Nathaniel R., E-mail: jwrichar@stat.berkeley.edu [School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287 (United States)

2012-12-15

With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

Application of elastic net and infrared spectroscopy in the discrimination between defective and non-defective roasted coffees.

Science.gov (United States)

Craig, Ana Paula; Franca, Adriana S; Oliveira, Leandro S; Irudayaraj, Joseph; Ileleji, Klein

2014-10-01

The quality of the coffee beverage is negatively affected by the presence of defective coffee beans and its evaluation still relies on highly subjective sensory panels. To tackle the problem of subjectivity, sophisticated analytical techniques have been developed and have been shown capable of discriminating defective from non-defective coffees after roasting. However, these techniques are not adequate for routine analysis, for they are laborious (sample preparation) and time consuming, and reliable, simpler and faster techniques need to be developed for such purpose. Thus, it was the aim of this study to evaluate the performance of infrared spectroscopic methods, namely FTIR and NIR, for the discrimination of roasted defective and non-defective coffees, employing a novel statistical approach. The classification models based on Elastic Net exhibited high percentage of correct classification, and the discriminant infrared spectra variables extracted provided a good interpretation of the models. The discrimination of defective and non-defective beans was associated with main chemical descriptors of coffee, such as carbohydrates, proteins/amino acids, lipids, caffeine and chlorogenic acids. Copyright © 2014 Elsevier B.V. All rights reserved.

Automated Classification of Heritage Buildings for As-Built Bim Using Machine Learning Techniques

Science.gov (United States)

Bassier, M.; Vergauwen, M.; Van Genechten, B.

2017-08-01

Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects. In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.

Automated evaluation of ultrasonic indications

International Nuclear Information System (INIS)

Hansch, M.K.T.; Stegemann, D.

1994-01-01

Future requirements of reliability and reproducibility in quality assurance demand computer evaluation of defect indications. The ultrasonic method with its large field of applications and a high potential for automation provides all preconditions for fully automated inspection. The survey proposes several desirable hardware improvements, data acquisition requirements and software configurations. (orig.) [de

A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations.

Science.gov (United States)

Alsalem, M A; Zaidan, A A; Zaidan, B B; Hashim, M; Madhloom, H T; Azeez, N D; Alsyisuf, S

2018-05-01

Acute leukaemia diagnosis is a field requiring automated solutions, tools and methods and the ability to facilitate early detection and even prediction. Many studies have focused on the automatic detection and classification of acute leukaemia and their subtypes to promote enable highly accurate diagnosis. This study aimed to review and analyse literature related to the detection and classification of acute leukaemia. The factors that were considered to improve understanding on the field's various contextual aspects in published studies and characteristics were motivation, open challenges that confronted researchers and recommendations presented to researchers to enhance this vital research area. We systematically searched all articles about the classification and detection of acute leukaemia, as well as their evaluation and benchmarking, in three main databases: ScienceDirect, Web of Science and IEEE Xplore from 2007 to 2017. These indices were considered to be sufficiently extensive to encompass our field of literature. Based on our inclusion and exclusion criteria, 89 articles were selected. Most studies (58/89) focused on the methods or algorithms of acute leukaemia classification, a number of papers (22/89) covered the developed systems for the detection or diagnosis of acute leukaemia and few papers (5/89) presented evaluation and comparative studies. The smallest portion (4/89) of articles comprised reviews and surveys. Acute leukaemia diagnosis, which is a field requiring automated solutions, tools and methods, entails the ability to facilitate early detection or even prediction. Many studies have been performed on the automatic detection and classification of acute leukaemia and their subtypes to promote accurate diagnosis. Research areas on medical-image classification vary, but they are all equally vital. We expect this systematic review to help emphasise current research opportunities and thus extend and create additional research fields. Copyright �

Using support vector machines with tract-based spatial statistics for automated classification of Tourette syndrome children

Science.gov (United States)

Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

2016-03-01

Tourette syndrome (TS) is a developmental neuropsychiatric disorder with the cardinal symptoms of motor and vocal tics which emerges in early childhood and fluctuates in severity in later years. To date, the neural basis of TS is not fully understood yet and TS has a long-term prognosis that is difficult to accurately estimate. Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy children and TS children. Here we apply Tract-Based Spatial Statistics (TBSS) method to 44 TS children and 48 age and gender matched healthy children in order to extract the diffusion values from each voxel in the white matter (WM) skeleton, and a feature selection algorithm (ReliefF) was used to select the most salient voxels for subsequent classification with support vector machine (SVM). We use a nested cross validation to yield an unbiased assessment of the classification method and prevent overestimation. The accuracy (88.04%), sensitivity (88.64%) and specificity (87.50%) were achieved in our method as peak performance of the SVM classifier was achieved using the axial diffusion (AD) metric, demonstrating the potential of a joint TBSS and SVM pipeline for fast, objective classification of healthy and TS children. These results support that our methods may be useful for the early identification of subjects with TS, and hold promise for predicting prognosis and treatment outcome for individuals with TS.

ROLE OF DATA MINING CLASSIFICATION TECHNIQUE IN SOFTWARE DEFECT PREDICTION

OpenAIRE

Dr.A.R.Pon Periyasamy; Mrs A.Misbahulhuda

2017-01-01

Software defect prediction is the process of locating defective modules in software. Software quality may be a field of study and apply that describes the fascinating attributes of software package product. The performance should be excellent with none defects. Software quality metrics are a set of software package metrics that target the standard aspects of the product, process, and project. The software package defect prediction model helps in early detection of defects and contributes to t...

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.

Science.gov (United States)

Welikala, R A; Fraz, M M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A

2015-07-01

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

Science.gov (United States)

Instrella, Ron; Chirayath, Ved

2016-01-01

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.

Directory of Open Access Journals (Sweden)

Andre F Marquand

Full Text Available Progressive supranuclear palsy (PSP, multiple system atrophy (MSA and idiopathic Parkinson's disease (IPD can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs. An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i a subcortical motor network; (ii each of its component regions and (iii the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.

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 convo...... 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....

Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images

Directory of Open Access Journals (Sweden)

Hardy Craig Hall

2016-02-01

Full Text Available While novel whole-plant phenotyping technologies have been successfully implemented into functional genomics and breeding programs, the potential of automated phenotyping with cellular resolution is largely unexploited. Laser scanning confocal microscopy has the potential to close this gap by providing spatially highly resolved images containing anatomic as well as chemical information on a subcellular basis. However, in the absence of automated methods, the assessment of the spatial patterns and abundance of fluorescent markers with subcellular resolution is still largely qualitative and time-consuming. Recent advances in image acquisition and analysis, coupled with improvements in microprocessor performance, have brought such automated methods within reach, so that information from thousands of cells per image for hundreds of images may be derived in an experimentally convenient time-frame. Here, we present a MATLAB-based analytical pipeline to 1 segment radial plant organs into individual cells, 2 classify cells into cell type categories based upon random forest classification, 3 divide each cell into sub-regions, and 4 quantify fluorescence intensity to a subcellular degree of precision for a separate fluorescence channel. In this research advance, we demonstrate the precision of this analytical process for the relatively complex tissues of Arabidopsis hypocotyls at various stages of development. High speed and robustness make our approach suitable for phenotyping of large collections of stem-like material and other tissue types.

Comparison of Size Modulation Standard Automated Perimetry and Conventional Standard Automated Perimetry with a 10-2 Test Program in Glaucoma Patients.

Science.gov (United States)

Hirasawa, Kazunori; Takahashi, Natsumi; Satou, Tsukasa; Kasahara, Masayuki; Matsumura, Kazuhiro; Shoji, Nobuyuki

2017-08-01

This prospective observational study compared the performance of size modulation standard automated perimetry with the Octopus 600 10-2 test program, with stimulus size modulation during testing, based on stimulus intensity and conventional standard automated perimetry, with that of the Humphrey 10-2 test program in glaucoma patients. Eighty-seven eyes of 87 glaucoma patients underwent size modulation standard automated perimetry with Dynamic strategy and conventional standard automated perimetry using the SITA standard strategy. The main outcome measures were global indices, point-wise threshold, visual defect size and depth, reliability indices, and test duration; these were compared between size modulation standard automated perimetry and conventional standard automated perimetry. Global indices and point-wise threshold values between size modulation standard automated perimetry and conventional standard automated perimetry were moderately to strongly correlated (p 33.40, p modulation standard automated perimetry than with conventional standard automated perimetry, but the visual-field defect size was smaller (p modulation-standard automated perimetry than on conventional standard automated perimetry. The reliability indices, particularly the false-negative response, of size modulation standard automated perimetry were worse than those of conventional standard automated perimetry (p modulation standard automated perimetry than with conventional standard automated perimetry (p = 0.02). Global indices and the point-wise threshold value of the two testing modalities correlated well. However, the potential of a large stimulus presented at an area with a decreased sensitivity with size modulation standard automated perimetry could underestimate the actual threshold in the 10-2 test protocol, as compared with conventional standard automated perimetry.

HVM die yield improvement as a function of DRSEM ADC

Science.gov (United States)

Maheshwary, Sonu; Haas, Terry; McGarvey, Steve

2010-03-01

Given the current manufacturing technology roadmap and the competitiveness of the global semiconductor manufacturing environment in conjunction with the semiconductor manufacturing market dynamics, the market place continues to demand a reduced die manufacturing cost. This continuous pressure on lowering die cost in turn drives an aggressive yield learning curve, a key component of which is defect reduction of manufacturing induced anomalies. In order to meet and even exceed line and die yield targets there is a need to revamp defect classification strategies and place a greater emphasize on increasing the accuracy and purity of the Defect Review Scanning Electron Microscope (DRSEM) Automated Defect Classification (ADC) results while placing less emphasis on the ADC results of patterned/un-patterned wafer inspection systems. The increased emphasis on DRSEM ADC results allows for a high degree of automation and consistency in the classification data and eliminates variance induced by the manufacturing staff. This paper examines the use of SEM based Auto Defect Classification in a high volume manufacturing environment as a key driver in the reduction of defect limited yields.

Developing and Integrating Advanced Movement Features Improves Automated Classification of Ciliate Species.

Science.gov (United States)

Soleymani, Ali; Pennekamp, Frank; Petchey, Owen L; Weibel, Robert

2015-01-01

Recent advances in tracking technologies such as GPS or video tracking systems describe the movement paths of individuals in unprecedented details and are increasingly used in different fields, including ecology. However, extracting information from raw movement data requires advanced analysis techniques, for instance to infer behaviors expressed during a certain period of the recorded trajectory, or gender or species identity in case data is obtained from remote tracking. In this paper, we address how different movement features affect the ability to automatically classify the species identity, using a dataset of unicellular microbes (i.e., ciliates). Previously, morphological attributes and simple movement metrics, such as speed, were used for classifying ciliate species. Here, we demonstrate that adding advanced movement features, in particular such based on discrete wavelet transform, to morphological features can improve classification. These results may have practical applications in automated monitoring of waste water facilities as well as environmental monitoring of aquatic systems.

Pattern recognition and classification an introduction

CERN Document Server

Dougherty, Geoff

2012-01-01

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer visi

Toward Intelligent Software Defect Detection

Science.gov (United States)

Benson, Markland J.

2011-01-01

Source code level software defect detection has gone from state of the art to a software engineering best practice. Automated code analysis tools streamline many of the aspects of formal code inspections but have the drawback of being difficult to construct and either prone to false positives or severely limited in the set of defects that can be detected. Machine learning technology provides the promise of learning software defects by example, easing construction of detectors and broadening the range of defects that can be found. Pinpointing software defects with the same level of granularity as prominent source code analysis tools distinguishes this research from past efforts, which focused on analyzing software engineering metrics data with granularity limited to that of a particular function rather than a line of code.

Population-based evaluation of a suggested anatomic and clinical classification of congenital heart defects based on the International Paediatric and Congenital Cardiac Code

Directory of Open Access Journals (Sweden)

Goffinet François

2011-10-01

Full Text Available Abstract Background Classification of the overall spectrum of congenital heart defects (CHD has always been challenging, in part because of the diversity of the cardiac phenotypes, but also because of the oft-complex associations. The purpose of our study was to establish a comprehensive and easy-to-use classification of CHD for clinical and epidemiological studies based on the long list of the International Paediatric and Congenital Cardiac Code (IPCCC. Methods We coded each individual malformation using six-digit codes from the long list of IPCCC. We then regrouped all lesions into 10 categories and 23 subcategories according to a multi-dimensional approach encompassing anatomic, diagnostic and therapeutic criteria. This anatomic and clinical classification of congenital heart disease (ACC-CHD was then applied to data acquired from a population-based cohort of patients with CHD in France, made up of 2867 cases (82% live births, 1.8% stillbirths and 16.2% pregnancy terminations. Results The majority of cases (79.5% could be identified with a single IPCCC code. The category "Heterotaxy, including isomerism and mirror-imagery" was the only one that typically required more than one code for identification of cases. The two largest categories were "ventricular septal defects" (52% and "anomalies of the outflow tracts and arterial valves" (20% of cases. Conclusion Our proposed classification is not new, but rather a regrouping of the known spectrum of CHD into a manageable number of categories based on anatomic and clinical criteria. The classification is designed to use the code numbers of the long list of IPCCC but can accommodate ICD-10 codes. Its exhaustiveness, simplicity, and anatomic basis make it useful for clinical and epidemiologic studies, including those aimed at assessment of risk factors and outcomes.

Development of automated system based on neural network algorithm for detecting defects on molds installed on casting machines

Science.gov (United States)

Bazhin, V. Yu; Danilov, I. V.; Petrov, P. A.

2018-05-01

During the casting of light alloys and ligatures based on aluminum and magnesium, problems of the qualitative distribution of the metal and its crystallization in the mold arise. To monitor the defects of molds on the casting conveyor, a camera with a resolution of 780 x 580 pixels and a shooting rate of 75 frames per second was selected. Images of molds from casting machines were used as input data for neural network algorithm. On the preparation of a digital database and its analytical evaluation stage, the architecture of the convolutional neural network was chosen for the algorithm. The information flow from the local controller is transferred to the OPC server and then to the SCADA system of foundry. After the training, accuracy of neural network defect recognition was about 95.1% on a validation split. After the training, weight coefficients of the neural network were used on testing split and algorithm had identical accuracy with validation images. The proposed technical solutions make it possible to increase the efficiency of the automated process control system in the foundry by expanding the digital database.

MSCT follow-up in malignant lymphoma. Comparison of manual linear measurements with semi-automated lymph node analysis for therapy response classification

International Nuclear Information System (INIS)

Wessling, J.; Puesken, M.; Kohlhase, N.; Persigehl, T.; Mesters, R.; Heindel, W.; Buerke, B.; Koch, R.

2012-01-01

Purpose: Assignment of semi-automated lymph node analysis compared to manual measurements for therapy response classification of malignant lymphoma in MSCT. Materials and Methods: MSCT scans of 63 malignant lymphoma patients before and after 2 cycles of chemotherapy (307 target lymph nodes) were evaluated. The long axis diameter (LAD), short axis diameter (SAD) and bi-dimensional WHO were determined manually and semi-automatically. The time for manual and semi-automatic segmentation was evaluated. The ref. standard response was defined as the mean relative change across all manual and semi-automatic measurements (mean manual/semi-automatic LAD, SAD, semi-automatic volume). Statistical analysis encompassed t-test and McNemar's test for clustered data. Results: Response classification per lymph node revealed semi-automated volumetry and bi-dimensional WHO to be significantly more accurate than manual linear metric measurements. Response classification per patient based on RECIST revealed more patients to be correctly classified by semi-automatic measurements, e.g. 96.0 %/92.9 % (WHO bi-dimensional/volume) compared to 85.7/84.1 % for manual LAD and SAD, respectively (mean reduction in misclassified patients of 9.95 %). Considering the use of correction tools, the time expenditure for lymph node segmentation (29.7 ± 17.4 sec) was the same as with the manual approach (29.1 ± 14.5 sec). Conclusion: Semi-automatically derived 'lymph node volume' and 'bi-dimensional WHO' significantly reduce the number of misclassified patients in the CT follow-up of malignant lymphoma by at least 10 %. However, lymph node volumetry does not outperform bi-dimensional WHO. (orig.)

MSCT follow-up in malignant lymphoma. Comparison of manual linear measurements with semi-automated lymph node analysis for therapy response classification

Energy Technology Data Exchange (ETDEWEB)

Wessling, J.; Puesken, M.; Kohlhase, N.; Persigehl, T.; Mesters, R.; Heindel, W.; Buerke, B. [Muenster Univ. (Germany). Dept. of Clinical Radiology; Koch, R. [Muenster Univ. (Germany). Inst. of Biostatistics and Clinical Research

2012-09-15

Purpose: Assignment of semi-automated lymph node analysis compared to manual measurements for therapy response classification of malignant lymphoma in MSCT. Materials and Methods: MSCT scans of 63 malignant lymphoma patients before and after 2 cycles of chemotherapy (307 target lymph nodes) were evaluated. The long axis diameter (LAD), short axis diameter (SAD) and bi-dimensional WHO were determined manually and semi-automatically. The time for manual and semi-automatic segmentation was evaluated. The ref. standard response was defined as the mean relative change across all manual and semi-automatic measurements (mean manual/semi-automatic LAD, SAD, semi-automatic volume). Statistical analysis encompassed t-test and McNemar's test for clustered data. Results: Response classification per lymph node revealed semi-automated volumetry and bi-dimensional WHO to be significantly more accurate than manual linear metric measurements. Response classification per patient based on RECIST revealed more patients to be correctly classified by semi-automatic measurements, e.g. 96.0 %/92.9 % (WHO bi-dimensional/volume) compared to 85.7/84.1 % for manual LAD and SAD, respectively (mean reduction in misclassified patients of 9.95 %). Considering the use of correction tools, the time expenditure for lymph node segmentation (29.7 {+-} 17.4 sec) was the same as with the manual approach (29.1 {+-} 14.5 sec). Conclusion: Semi-automatically derived 'lymph node volume' and 'bi-dimensional WHO' significantly reduce the number of misclassified patients in the CT follow-up of malignant lymphoma by at least 10 %. However, lymph node volumetry does not outperform bi-dimensional WHO. (orig.)

Automated Surface Classification of SRF Cavities for the Investigation of the Influence of Surface Properties onto the Operational Performance

Energy Technology Data Exchange (ETDEWEB)

Wenskat, Marc

2015-07-15

Superconducting niobium radio-frequency cavities are fundamental for the European XFEL and the International Linear Collider. To use the operational advantages of superconducting cavities, the inner surface has to fulfill quite demanding requirements. The surface roughness and cleanliness improved over the last decades and with them, the achieved maximal accelerating field. Still, limitations of the maximal achieved accelerating field are observed, which are not explained by localized geometrical defects or impurities. The scope of this thesis is a better understanding of these limitations in defect free cavities based on global, rather than local, surface properties. For this goal, more than 30 cavities underwent subsequent surface treatments, cold RF tests and optical inspections within the ILC-HiGrade research program and the XFEL cavity production. An algorithm was developed which allows an automated surface characterization based on an optical inspection robot. This algorithm delivers a set of optical surface properties, which describes the inner cavity surface. These optical surface properties deliver a framework for a quality assurance of the fabrication procedures. Furthermore, they shows promising results for a better understanding of the observed limitations in defect free cavities.

Automated Surface Classification of SRF Cavities for the Investigation of the Influence of Surface Properties onto the Operational Performance

International Nuclear Information System (INIS)

Wenskat, Marc

2015-07-01

Superconducting niobium radio-frequency cavities are fundamental for the European XFEL and the International Linear Collider. To use the operational advantages of superconducting cavities, the inner surface has to fulfill quite demanding requirements. The surface roughness and cleanliness improved over the last decades and with them, the achieved maximal accelerating field. Still, limitations of the maximal achieved accelerating field are observed, which are not explained by localized geometrical defects or impurities. The scope of this thesis is a better understanding of these limitations in defect free cavities based on global, rather than local, surface properties. For this goal, more than 30 cavities underwent subsequent surface treatments, cold RF tests and optical inspections within the ILC-HiGrade research program and the XFEL cavity production. An algorithm was developed which allows an automated surface characterization based on an optical inspection robot. This algorithm delivers a set of optical surface properties, which describes the inner cavity surface. These optical surface properties deliver a framework for a quality assurance of the fabrication procedures. Furthermore, they shows promising results for a better understanding of the observed limitations in defect free cavities.

Use of self-organizing maps for classification of defects in the tubes from the steam generator of nuclear power plants; Classificacao de defeitos em tubos de gerador de vapor de plantas nucleares utilizando mapas auto-organizaveis

Energy Technology Data Exchange (ETDEWEB)

Mesquita, Roberto Navarro de

2002-07-01

This thesis obtains a new classification method for different steam generator tube defects in nuclear power plants using Eddy Current Test signals. The method uses self-organizing maps to compare different signal characteristics efficiency to identify and classify these defects. A multiple inference system is proposed which composes the different extracted characteristic trained maps classification to infer the final defect type. The feature extraction methods used are the Wavelet zero-crossings representation, the linear predictive coding (LPC), and other basic signal representations on time like module and phase. Many characteristic vectors are obtained with combinations of these extracted characteristics. These vectors are tested to classify the defects and the best ones are applied to the multiple inference system. A systematic study of pre-processing, calibration and analysis methods for the steam generator tube defect signals in nuclear power plants is done. The method efficiency is demonstrated and characteristic maps with the main prototypes are obtained for each steam generator tube defect type. (author)

Two datasets of defect reports labeled by a crowd of annotators of unknown reliability

Directory of Open Access Journals (Sweden)

Jerónimo Hernández-González

2018-06-01

Full Text Available Classifying software defects according to any defined taxonomy is not straightforward. In order to be used for automatizing the classification of software defects, two sets of defect reports were collected from public issue tracking systems from two different real domains. Due to the lack of a domain expert, the collected defects were categorized by a set of annotators of unknown reliability according to their impact from IBM's orthogonal defect classification taxonomy. Both datasets are prepared to solve the defect classification problem by means of techniques of the learning from crowds paradigm (Hernández-González et al. [1].Two versions of both datasets are publicly shared. In the first version, the raw data is given: the text description of defects together with the category assigned by each annotator. In the second version, the text of each defect has been transformed to a descriptive vector using text-mining techniques.

Future Control and Automation : Proceedings of the 2nd International Conference on Future Control and Automation

CERN Document Server

2012-01-01

This volume Future Control and Automation- Volume 2 includes best papers from 2012 2nd International Conference on Future Control and Automation (ICFCA 2012) held on July 1-2, 2012, Changsha, China. Future control and automation is the use of control systems and information technologies to reduce the need for human work in the production of goods and services. This volume can be divided into six sessions on the basis of the classification of manuscripts considered, which is listed as follows: Mathematical Modeling, Analysis and Computation, Control Engineering, Reliable Networks Design, Vehicular Communications and Networking, Automation and Mechatronics.

Cost Accounting in the Automated Manufacturing Environment

Science.gov (United States)

1988-06-01

1 NAVAL POSTGRADUATE SCHOOL M terey, California 0 DTIC II ELECTE R AD%$° NO 0,19880 -- THESIS COST ACCOUNTING IN THE AUTOMATED MANUFACTURING...PROJECT TASK WORK UNIT ELEMENT NO. NO NO ACCESSION NO 11. TITLE (Include Security Classification) E COST ACCOUNTING IN THE AUTOMATED MANUFACTURING...GROUP ’" Cost Accounting ; Product Costing ; Automated Manufacturing; CAD/CAM- CIM 19 ABSTRACT (Continue on reverse if necessary and identify by blo

Intelligent Computer Vision System for Automated Classification

International Nuclear Information System (INIS)

Jordanov, Ivan; Georgieva, Antoniya

2010-01-01

In this paper we investigate an Intelligent Computer Vision System applied for recognition and classification of commercially available cork tiles. The system is capable of acquiring and processing gray images using several feature generation and analysis techniques. Its functionality includes image acquisition, feature extraction and preprocessing, and feature classification with neural networks (NN). We also discuss system test and validation results from the recognition and classification tasks. The system investigation also includes statistical feature processing (features number and dimensionality reduction techniques) and classifier design (NN architecture, target coding, learning complexity and performance, and training with our own metaheuristic optimization method). The NNs trained with our genetic low-discrepancy search method (GLPτS) for global optimisation demonstrated very good generalisation abilities. In our view, the reported testing success rate of up to 95% is due to several factors: combination of feature generation techniques; application of Analysis of Variance (ANOVA) and Principal Component Analysis (PCA), which appeared to be very efficient for preprocessing the data; and use of suitable NN design and learning method.

Multi-Agent Information Classification Using Dynamic Acquaintance Lists.

Science.gov (United States)

Mukhopadhyay, Snehasis; Peng, Shengquan; Raje, Rajeev; Palakal, Mathew; Mostafa, Javed

2003-01-01

Discussion of automated information services focuses on information classification and collaborative agents, i.e. intelligent computer programs. Highlights include multi-agent systems; distributed artificial intelligence; thesauri; document representation and classification; agent modeling; acquaintances, or remote agents discovered through…

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach.

Science.gov (United States)

Murat, Miraemiliana; Chang, Siow-Wee; Abu, Arpah; Yap, Hwa Jen; Yong, Kien-Thai

2017-01-01

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

Directory of Open Access Journals (Sweden)

Miraemiliana Murat

2017-09-01

Full Text Available Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM, Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD, Histogram of Oriented Gradients (HOG, Hu invariant moments (Hu and Zernike moments (ZM. Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN, random forest (RF, support vector machine (SVM, k-nearest neighbour (k-NN, linear discriminant analysis (LDA and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM. In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS and Pearson’s coefficient correlation (PCC. The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia

AUTOMATED CLASSIFICATION AND SEGREGATION OF BRAIN MRI IMAGES INTO IMAGES CAPTURED WITH RESPECT TO VENTRICULAR REGION AND EYE-BALL REGION

Directory of Open Access Journals (Sweden)

C. Arunkumar

2014-05-01

Full Text Available Magnetic Resonance Imaging (MRI images of the brain are used for detection of various brain diseases including tumor. In such cases, classification of MRI images captured with respect to ventricular and eye ball regions helps in automated location and classification of such diseases. The methods employed in the paper can segregate the given MRI images of brain into images of brain captured with respect to ventricular region and images of brain captured with respect to eye ball region. First, the given MRI image of brain is segmented using Particle Swarm Optimization (PSO algorithm, which is an optimized algorithm for MRI image segmentation. The algorithm proposed in the paper is then applied on the segmented image. The algorithm detects whether the image consist of a ventricular region or an eye ball region and classifies it accordingly.

Automated Classification of Seedlings Using Computer Vision

DEFF Research Database (Denmark)

Dyrmann, Mads; Christiansen, Peter

The objective of this project is to investigate the possibilities of recognizing plant species at multiple growth stages based on RGB images. Plants and leaves are initially segmented from a database through a partly automated procedure providing samples of 2438 plants and 4767 leaves distributed...

Quantitative Estimation for the Effectiveness of Automation

International Nuclear Information System (INIS)

Lee, Seung Min; Seong, Poong Hyun

2012-01-01

In advanced MCR, various automation systems are applied to enhance the human performance and reduce the human errors in industrial fields. It is expected that automation provides greater efficiency, lower workload, and fewer human errors. However, these promises are not always fulfilled. As the new types of events related to application of the imperfect and complex automation are occurred, it is required to analyze the effects of automation system for the performance of human operators. Therefore, we suggest the quantitative estimation method to analyze the effectiveness of the automation systems according to Level of Automation (LOA) classification, which has been developed over 30 years. The estimation of the effectiveness of automation will be achieved by calculating the failure probability of human performance related to the cognitive activities

Future Control and Automation : Proceedings of the 2nd International Conference on Future Control and Automation

CERN Document Server

2012-01-01

This volume Future Control and Automation- Volume 1 includes best papers selected from 2012 2nd International Conference on Future Control and Automation (ICFCA 2012) held on July 1-2, 2012, Changsha, China. Future control and automation is the use of control systems and information technologies to reduce the need for human work in the production of goods and services. This volume can be divided into five sessions on the basis of the classification of manuscripts considered, which is listed as follows: Identification and Control, Navigation, Guidance and Sensor, Simulation Technology, Future Telecommunications and Control

ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer

Science.gov (United States)

Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

2016-05-01

At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts

Entropy-based automated classification of independent components separated from fMCG

International Nuclear Information System (INIS)

Comani, S; Srinivasan, V; Alleva, G; Romani, G L

2007-01-01

Fetal magnetocardiography (fMCG) is a noninvasive technique suitable for the prenatal diagnosis of the fetal heart function. Reliable fetal cardiac signals can be reconstructed from multi-channel fMCG recordings by means of independent component analysis (ICA). However, the identification of the separated components is usually accomplished by visual inspection. This paper discusses a novel automated system based on entropy estimators, namely approximate entropy (ApEn) and sample entropy (SampEn), for the classification of independent components (ICs). The system was validated on 40 fMCG datasets of normal fetuses with the gestational age ranging from 22 to 37 weeks. Both ApEn and SampEn were able to measure the stability and predictability of the physiological signals separated with ICA, and the entropy values of the three categories were significantly different at p <0.01. The system performances were compared with those of a method based on the analysis of the time and frequency content of the components. The outcomes of this study showed a superior performance of the entropy-based system, in particular for early gestation, with an overall ICs detection rate of 98.75% and 97.92% for ApEn and SampEn respectively, as against a value of 94.50% obtained with the time-frequency-based system. (note)

An Automated Algorithm to Screen Massive Training Samples for a Global Impervious Surface Classification

Science.gov (United States)

Tan, Bin; Brown de Colstoun, Eric; Wolfe, Robert E.; Tilton, James C.; Huang, Chengquan; Smith, Sarah E.

2012-01-01

An algorithm is developed to automatically screen the outliers from massive training samples for Global Land Survey - Imperviousness Mapping Project (GLS-IMP). GLS-IMP is to produce a global 30 m spatial resolution impervious cover data set for years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. This unprecedented high resolution impervious cover data set is not only significant to the urbanization studies but also desired by the global carbon, hydrology, and energy balance researches. A supervised classification method, regression tree, is applied in this project. A set of accurate training samples is the key to the supervised classifications. Here we developed the global scale training samples from 1 m or so resolution fine resolution satellite data (Quickbird and Worldview2), and then aggregate the fine resolution impervious cover map to 30 m resolution. In order to improve the classification accuracy, the training samples should be screened before used to train the regression tree. It is impossible to manually screen 30 m resolution training samples collected globally. For example, in Europe only, there are 174 training sites. The size of the sites ranges from 4.5 km by 4.5 km to 8.1 km by 3.6 km. The amount training samples are over six millions. Therefore, we develop this automated statistic based algorithm to screen the training samples in two levels: site and scene level. At the site level, all the training samples are divided to 10 groups according to the percentage of the impervious surface within a sample pixel. The samples following in each 10% forms one group. For each group, both univariate and multivariate outliers are detected and removed. Then the screen process escalates to the scene level. A similar screen process but with a looser threshold is applied on the scene level considering the possible variance due to the site difference. We do not perform the screen process across the scenes because the scenes might vary due to

Research on Defects Inspection of Solder Balls Based on Eddy Current Pulsed Thermography

Directory of Open Access Journals (Sweden)

Xiuyun Zhou

2015-10-01

Full Text Available In order to solve tiny defect detection for solder balls in high-density flip-chip, this paper proposed feasibility study on the effect of detectability as well as classification based on eddy current pulsed thermography (ECPT. Specifically, numerical analysis of 3D finite element inductive heat model is generated to investigate disturbance on the temperature field for different kind of defects such as cracks, voids, etc. The temperature variation between defective and non-defective solder balls is monitored for defects identification and classification. Finally, experimental study is carried on the diameter 1mm tiny solder balls by using ECPT and verify the efficacy of the technique.

Pipeline defect prediction using long range ultrasonic testing and intelligent processing

International Nuclear Information System (INIS)

Dino Isa; Rajprasad Rajkumar

2009-01-01

This paper deals with efforts to improve nondestructive testing (NDT) techniques by using artificial intelligence in detecting and predicting pipeline defects such as cracks and wall thinning. The main emphasis here will be on the prediction of corrosion type defects rather than just detection after the fact. Long range ultrasonic testing will be employed, where a ring of piezoelectric transducers are used to generate torsional guided waves. Various defects such as cracks as well as corrosion under insulation (CUI) will be simulated on a test pipe. The machine learning algorithm known as the Support Vector Machine (SVM) will be used to predict and classify transducer signals using regression and large margin classification. Regression results show that the SVM is able to accurately predict future defects based on trends of previous defect. The classification performance was also exceptional showing a facility to detect defects at different depths as well as for distinguishing closely spaced defects. (author)

Defining defect specifications to optimize photomask production and requalification

Science.gov (United States)

Fiekowsky, Peter

2006-10-01

Reducing defect repairs and accelerating defect analysis is becoming more important as the total cost of defect repairs on advanced masks increases. Photomask defect specs based on printability, as measured on AIMS microscopes has been used for years, but the fundamental defect spec is still the defect size, as measured on the photomask, requiring the repair of many unprintable defects. ADAS, the Automated Defect Analysis System from AVI is now available in most advanced mask shops. It makes the use of pure printability specs, or "Optimal Defect Specs" practical. This software uses advanced algorithms to eliminate false defects caused by approximations in the inspection algorithm, classify each defect, simulate each defect and disposition each defect based on its printability and location. This paper defines "optimal defect specs", explains why they are now practical and economic, gives a method of determining them and provides accuracy data.

Deep SOMs for automated feature extraction and classification from big data streaming

Science.gov (United States)

Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

2017-03-01

In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

Simple Fully Automated Group Classification on Brain fMRI

International Nuclear Information System (INIS)

Honorio, J.; Goldstein, R.; Samaras, D.; Tomasi, D.; Goldstein, R.Z.

2010-01-01

We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

Simple Fully Automated Group Classification on Brain fMRI

Energy Technology Data Exchange (ETDEWEB)

Honorio, J.; Goldstein, R.; Honorio, J.; Samaras, D.; Tomasi, D.; Goldstein, R.Z.

2010-04-14

We propose a simple, well grounded classification technique which is suited for group classification on brain fMRI data sets that have high dimensionality, small number of subjects, high noise level, high subject variability, imperfect registration and capture subtle cognitive effects. We propose threshold-split region as a new feature selection method and majority voteas the classification technique. Our method does not require a predefined set of regions of interest. We use average acros ssessions, only one feature perexperimental condition, feature independence assumption, and simple classifiers. The seeming counter-intuitive approach of using a simple design is supported by signal processing and statistical theory. Experimental results in two block design data sets that capture brain function under distinct monetary rewards for cocaine addicted and control subjects, show that our method exhibits increased generalization accuracy compared to commonly used feature selection and classification techniques.

An Ultrasonic Pattern Recognition Approach to Welding Defect Classification

International Nuclear Information System (INIS)

Song, Sung Jin

1995-01-01

Classification of flaws in weldments from their ultrasonic scattering signals is very important in quantitative nondestructive evaluation. This problem is ideally suited to a modern ultrasonic pattern recognition technique. Here brief discussion on systematic approach to this methodology is presented including ultrasonic feature extraction, feature selection and classification. A stronger emphasis is placed on probabilistic neural networks as efficient classifiers for many practical classification problems. In an example probabilistic neural networks are applied to classify flaws in weldments into 3 classes such as cracks, porosity and slag inclusions. Probabilistic nets are shown to be able to exhibit high performance of other classifiers without any training time overhead. In addition, forward selection scheme for sensitive features is addressed to enhance network performance

Automated reticle inspection data analysis for wafer fabs

Science.gov (United States)

Summers, Derek; Chen, Gong; Reese, Bryan; Hutchinson, Trent; Liesching, Marcus; Ying, Hai; Dover, Russell

2009-04-01

To minimize potential wafer yield loss due to mask defects, most wafer fabs implement some form of reticle inspection system to monitor photomask quality in high-volume wafer manufacturing environments. Traditionally, experienced operators review reticle defects found by an inspection tool and then manually classify each defect as 'pass, warn, or fail' based on its size and location. However, in the event reticle defects are suspected of causing repeating wafer defects on a completed wafer, potential defects on all associated reticles must be manually searched on a layer-by-layer basis in an effort to identify the reticle responsible for the wafer yield loss. This 'problem reticle' search process is a very tedious and time-consuming task and may cause extended manufacturing line-down situations. Often times, Process Engineers and other team members need to manually investigate several reticle inspection reports to determine if yield loss can be tied to a specific layer. Because of the very nature of this detailed work, calculation errors may occur resulting in an incorrect root cause analysis effort. These delays waste valuable resources that could be spent working on other more productive activities. This paper examines an automated software solution for converting KLA-Tencor reticle inspection defect maps into a format compatible with KLA-Tencor's Klarity Defect(R) data analysis database. The objective is to use the graphical charting capabilities of Klarity Defect to reveal a clearer understanding of defect trends for individual reticle layers or entire mask sets. Automated analysis features include reticle defect count trend analysis and potentially stacking reticle defect maps for signature analysis against wafer inspection defect data. Other possible benefits include optimizing reticle inspection sample plans in an effort to support "lean manufacturing" initiatives for wafer fabs.

Micro-bridge defects: characterization and root cause analysis

Science.gov (United States)

Santoro, Gaetano; Van den Heuvel, Dieter; Braggin, Jennifer; Rosslee, Craig; Leray, Philippe J.; Cheng, Shaunee; Jehoul, Christiane; Schreutelkamp, Robert; Hillel, Noam

2010-03-01

Defect review of advanced lithography processes is becoming more and more challenging as feature sizes decrease. Previous studies using a defect review SEM on immersion lithography generated wafers have resulted in a defect classification scheme which, among others, includes a category for micro-bridges. Micro-bridges are small connections between two adjacent lines in photo-resist and are considered device killing defects. Micro-bridge rates also tend to increase as feature sizes decrease, making them even more important for the next technology nodes. Especially because micro-bridge defects can originate from different root causes, the need to further refine and split up the classification of this type of defect into sub groups may become a necessity. This paper focuses on finding the correlation of the different types of micro-bridge defects to a particular root cause based on a full characterization and root cause analysis of this class of defects, by using advanced SEM review capabilities like high quality imaging in very low FOV, Multi Perspective SEM Imaging (MPSI), tilted column and rotated stage (Tilt&Rotation) imaging and Focused Ion Beam (FIB) cross sectioning. Immersion lithography material has been mainly used to generate the set of data presented in this work even though, in the last part of the results, some EUV lithography data will be presented as part of the continuing effort to extend the micro-bridge defect characterization to the EUV technology on 40 nm technology node and beyond.

Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data

Directory of Open Access Journals (Sweden)

Raphael Falque

2017-10-01

Full Text Available Remote-Field Eddy-Current (RFEC technology is often used as a Non-Destructive Evaluation (NDE method to prevent water pipe failures. By analyzing the RFEC data, it is possible to quantify the corrosion present in pipes. Quantifying the corrosion involves detecting defects and extracting their depth and shape. For large sections of pipelines, this can be extremely time-consuming if performed manually. Automated approaches are therefore well motivated. In this article, we propose an automated framework to locate and segment defects in individual pipe segments, starting from raw RFEC measurements taken over large pipelines. The framework relies on a novel feature to robustly detect these defects and a segmentation algorithm applied to the deconvolved RFEC signal. The framework is evaluated using both simulated and real datasets, demonstrating its ability to efficiently segment the shape of corrosion defects.

Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas

Science.gov (United States)

Petrov, Anton I.; Zirbel, Craig L.; Leontis, Neocles B.

2013-01-01

The analysis of atomic-resolution RNA three-dimensional (3D) structures reveals that many internal and hairpin loops are modular, recurrent, and structured by conserved non-Watson–Crick base pairs. Structurally similar loops define RNA 3D motifs that are conserved in homologous RNA molecules, but can also occur at nonhomologous sites in diverse RNAs, and which often vary in sequence. To further our understanding of RNA motif structure and sequence variability and to provide a useful resource for structure modeling and prediction, we present a new method for automated classification of internal and hairpin loop RNA 3D motifs and a new online database called the RNA 3D Motif Atlas. To classify the motif instances, a representative set of internal and hairpin loops is automatically extracted from a nonredundant list of RNA-containing PDB files. Their structures are compared geometrically, all-against-all, using the FR3D program suite. The loops are clustered into motif groups, taking into account geometric similarity and structural annotations and making allowance for a variable number of bulged bases. The automated procedure that we have implemented identifies all hairpin and internal loop motifs previously described in the literature. All motif instances and motif groups are assigned unique and stable identifiers and are made available in the RNA 3D Motif Atlas (http://rna.bgsu.edu/motifs), which is automatically updated every four weeks. The RNA 3D Motif Atlas provides an interactive user interface for exploring motif diversity and tools for programmatic data access. PMID:23970545

Improving Student Question Classification

Science.gov (United States)

Heiner, Cecily; Zachary, Joseph L.

2009-01-01

Students in introductory programming classes often articulate their questions and information needs incompletely. Consequently, the automatic classification of student questions to provide automated tutorial responses is a challenging problem. This paper analyzes 411 questions from an introductory Java programming course by reducing the natural…

Automated classification of Permanent Scatterers time-series based on statistical characterization tests

Science.gov (United States)

Berti, Matteo; Corsini, Alessandro; Franceschini, Silvia; Iannacone, Jean Pascal

2013-04-01

time series are typically affected by a significant noise to signal ratio. The results of the analysis show that even with such a rough-quality dataset, our automated classification procedure can greatly improve radar interpretation of mass movements. In general, uncorrelated PS (type 0) are concentrated in flat areas such as fluvial terraces and valley bottoms, and along stable watershed divides; linear PS (type 1) are mainly located on slopes (both inside or outside mapped landslides) or near the edge of scarps or steep slopes; non-linear PS (types 2 to 5) typically fall inside landslide deposits or in the surrounding areas. The spatial distribution of classified PS allows to detect deformation phenomena not visible by considering the average velocity alone, and provide important information on the temporal evolution of the phenomena such as acceleration, deceleration, seasonal fluctuations, abrupt or continuous changes of the displacement rate. Based on these encouraging results we integrated all the classification algorithms into a Graphical User Interface (called PSTime) which is freely available as a standalone application.

An objective method to optimize the MR sequence set for plaque classification in carotid vessel wall images using automated image segmentation.

Directory of Open Access Journals (Sweden)

Ronald van 't Klooster

Full Text Available A typical MR imaging protocol to study the status of atherosclerosis in the carotid artery consists of the application of multiple MR sequences. Since scanner time is limited, a balance has to be reached between the duration of the applied MR protocol and the quantity and quality of the resulting images which are needed to assess the disease. In this study an objective method to optimize the MR sequence set for classification of soft plaque in vessel wall images of the carotid artery using automated image segmentation was developed. The automated method employs statistical pattern recognition techniques and was developed based on an extensive set of MR contrast weightings and corresponding manual segmentations of the vessel wall and soft plaque components, which were validated by histological sections. Evaluation of the results from nine contrast weightings showed the tradeoff between scan duration and automated image segmentation performance. For our dataset the best segmentation performance was achieved by selecting five contrast weightings. Similar performance was achieved with a set of three contrast weightings, which resulted in a reduction of scan time by more than 60%. The presented approach can help others to optimize MR imaging protocols by investigating the tradeoff between scan duration and automated image segmentation performance possibly leading to shorter scanning times and better image interpretation. This approach can potentially also be applied to other research fields focusing on different diseases and anatomical regions.

Automated classification of self-grooming in mice using open-source software

NARCIS (Netherlands)

Van den Boom, B.; Pavlidi, Pavlina; Wolf, Casper M H; Mooij, Hanne A H; Willuhn, Ingo

BACKGROUND: Manual analysis of behavior is labor intensive and subject to inter-rater variability. Although considerable progress in automation of analysis has been made, complex behavior such as grooming still lacks satisfactory automated quantification. NEW METHOD: We trained a freely available,

Automated classification of self-grooming in mice using open-source software

NARCIS (Netherlands)

van den Boom, Bastijn J. G.; Pavlidi, Pavlina; Wolf, Casper M. H.; Mooij, Hanne A. H.; Willuhn, Ingo

2017-01-01

Background: Manual analysis of behavior is labor intensive and subject to inter-rater variability. Although considerable progress in automation of analysis has been made, complex behavior such as grooming still lacks satisfactory automated quantification. New method: We trained a freely available,

Automated Resource Classifier for agglomerative functional ...

Indian Academy of Sciences (India)

2007-06-16

Jun 16, 2007 ... Automated resource; functional classification; integrative biology ... which is an open source software meeting the user requirements of flexibility. ... entries into any of the 7 basic non-overlapping functional classes: Cell wall, ...

Defects in Amorphous Semiconductors: The Case of Amorphous Indium Gallium Zinc Oxide

Science.gov (United States)

de Jamblinne de Meux, A.; Pourtois, G.; Genoe, J.; Heremans, P.

2018-05-01

Based on a rational classification of defects in amorphous materials, we propose a simplified model to describe intrinsic defects and hydrogen impurities in amorphous indium gallium zinc oxide (a -IGZO). The proposed approach consists of organizing defects into two categories: point defects, generating structural anomalies such as metal—metal or oxygen—oxygen bonds, and defects emerging from changes in the material stoichiometry, such as vacancies and interstitial atoms. Based on first-principles simulations, it is argued that the defects originating from the second group always act as perfect donors or perfect acceptors. This classification simplifies and rationalizes the nature of defects in amorphous phases. In a -IGZO, the most important point defects are metal—metal bonds (or small metal clusters) and peroxides (O - O single bonds). Electrons are captured by metal—metal bonds and released by the formation of peroxides. The presence of hydrogen can lead to two additional types of defects: metal-hydrogen defects, acting as acceptors, and oxygen-hydrogen defects, acting as donors. The impact of these defects is linked to different instabilities observed in a -IGZO. Specifically, the diffusion of hydrogen and oxygen is connected to positive- and negative-bias stresses, while negative-bias illumination stress originates from the formation of peroxides.

Automated image processing method for the diagnosis and classification of malaria on thin blood smears.

Science.gov (United States)

Ross, Nicholas E; Pritchard, Charles J; Rubin, David M; Dusé, Adriano G

2006-05-01

Malaria is a serious global health problem, and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to automate the diagnosis of malaria on thin blood smears is developed. The image classification system is designed to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria. Images are acquired using a charge-coupled device camera connected to a light microscope. Morphological and novel threshold selection techniques are used to identify erythrocytes (red blood cells) and possible parasites present on microscopic slides. Image features based on colour, texture and the geometry of the cells and parasites are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. A two-stage tree classifier using backpropogation feedforward neural networks distinguishes between true and false positives, and then diagnoses the species (Plasmodium falciparum, P. vivax, P. ovale or P. malariae) of the infection. Malaria samples obtained from the Department of Clinical Microbiology and Infectious Diseases at the University of the Witwatersrand Medical School are used for training and testing of the system. Infected erythrocytes are positively identified with a sensitivity of 85% and a positive predictive value (PPV) of 81%, which makes the method highly sensitive at diagnosing a complete sample provided many views are analysed. Species were correctly determined for 11 out of 15 samples.

Extracting and identifying concrete structural defects in GPR images

Science.gov (United States)

Ye, Qiling; Jiao, Liangbao; Liu, Chuanxin; Cao, Xuehong; Huston, Dryver; Xia, Tian

2018-03-01

Traditionally most GPR data interpretations are performed manually. With the advancement of computing technologies, how to automate GPR data interpretation to achieve high efficiency and accuracy has become an active research subject. In this paper, analytical characterizations of major defects in concrete structures, including delamination, air void and moisture in GPR images, are performed. In the study, the image features of different defects are compared. Algorithms are developed for defect feature extraction and identification. For validations, both simulation results and field test data are utilized.

Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks.

Science.gov (United States)

Joshi, Vinayak S; Reinhardt, Joseph M; Garvin, Mona K; Abramoff, Michael D

2014-01-01

The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each vessel segment are utilized to find the optimal graph of vessel segments. The separated vessel trees are labeled as primary vessel or branches. We utilize the separated vessel trees for arterial-venous (AV) classification, based on the color properties of the vessels in each tree graph. We applied our approach to a dataset of 50 fundus images from 50 subjects. The proposed method resulted in an accuracy of 91.44% correctly classified vessel pixels as either artery or vein. The accuracy of correctly classified major vessel segments was 96.42%.

Feasibility of Genetic Algorithm for Textile Defect Classification Using Neural Network

OpenAIRE

Habib, Md. Tarek; Faisal, Rahat Hossain; Rokonuzzaman, M.

2012-01-01

The global market for textile industry is highly competitive nowadays. Quality control in production process in textile industry has been a key factor for retaining existence in such competitive market. Automated textile inspection systems are very useful in this respect, because manual inspection is time consuming and not accurate enough. Hence, automated textile inspection systems have been drawing plenty of attention of the researchers of different countries in order to replace...

Research of the application of the new communication technologies for distribution automation

Science.gov (United States)

Zhong, Guoxin; Wang, Hao

2018-03-01

Communication network is a key factor of distribution automation. In recent years, new communication technologies for distribution automation have a rapid development in China. This paper introduces the traditional communication technologies of distribution automation and analyse the defects of these traditional technologies. Then this paper gives a detailed analysis on some new communication technologies for distribution automation including wired communication and wireless communication and then gives an application suggestion of these new technologies.

MERGING AIRBORNE LIDAR DATA AND SATELLITE SAR DATA FOR BUILDING CLASSIFICATION

Directory of Open Access Journals (Sweden)

T. Yamamoto

2015-05-01

Full Text Available A frequent map revision is required in GIS applications, such as disaster prevention and urban planning. In general, airborne photogrammetry and LIDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, attribute data acquisition and classification depend on manual editing works including ground surveys. In general, airborne photogrammetry and LiDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, these approaches classify geometrical attributes. Moreover, ground survey and manual editing works are finally required in attribute data classification. On the other hand, although geometrical data extraction is difficult, SAR data have a possibility to automate the attribute data acquisition and classification. The SAR data represent microwave reflections on various surfaces of ground and buildings. There are many researches related to monitoring activities of disaster, vegetation, and urban. Moreover, we have an opportunity to acquire higher resolution data in urban areas with new sensors, such as ALOS2 PALSAR2. Therefore, in this study, we focus on an integration of airborne LIDAR data and satellite SAR data for building extraction and classification.

Behavioral state classification in epileptic brain using intracranial electrophysiology

Science.gov (United States)

Kremen, Vaclav; Duque, Juliano J.; Brinkmann, Benjamin H.; Berry, Brent M.; Kucewicz, Michal T.; Khadjevand, Fatemeh; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Worrell, Gregory A.

2017-04-01

Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age 34+/- 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

Radiological assessment of breast density by visual classification (BI-RADS) compared to automated volumetric digital software (Quantra): implications for clinical practice.

Science.gov (United States)

Regini, Elisa; Mariscotti, Giovanna; Durando, Manuela; Ghione, Gianluca; Luparia, Andrea; Campanino, Pier Paolo; Bianchi, Caterina Chiara; Bergamasco, Laura; Fonio, Paolo; Gandini, Giovanni

2014-10-01

This study was done to assess breast density on digital mammography and digital breast tomosynthesis according to the visual Breast Imaging Reporting and Data System (BI-RADS) classification, to compare visual assessment with Quantra software for automated density measurement, and to establish the role of the software in clinical practice. We analysed 200 digital mammograms performed in 2D and 3D modality, 100 of which positive for breast cancer and 100 negative. Radiological density was assessed with the BI-RADS classification; a Quantra density cut-off value was sought on the 2D images only to discriminate between BI-RADS categories 1-2 and BI-RADS 3-4. Breast density was correlated with age, use of hormone therapy, and increased risk of disease. The agreement between the 2D and 3D assessments of BI-RADS density was high (K 0.96). A cut-off value of 21% is that which allows us to best discriminate between BI-RADS categories 1-2 and 3-4. Breast density was negatively correlated to age (r = -0.44) and positively to use of hormone therapy (p = 0.0004). Quantra density was higher in breasts with cancer than in healthy breasts. There is no clear difference between the visual assessments of density on 2D and 3D images. Use of the automated system requires the adoption of a cut-off value (set at 21%) to effectively discriminate BI-RADS 1-2 and 3-4, and could be useful in clinical practice.

An automated approach to mapping corn from Landsat imagery

Science.gov (United States)

Maxwell, S.K.; Nuckols, J.R.; Ward, M.H.; Hoffer, R.M.

2004-01-01

Most land cover maps generated from Landsat imagery involve classification of a wide variety of land cover types, whereas some studies may only need spatial information on a single cover type. For example, we required a map of corn in order to estimate exposure to agricultural chemicals for an environmental epidemiology study. Traditional classification techniques, which require the collection and processing of costly ground reference data, were not feasible for our application because of the large number of images to be analyzed. We present a new method that has the potential to automate the classification of corn from Landsat satellite imagery, resulting in a more timely product for applications covering large geographical regions. Our approach uses readily available agricultural areal estimates to enable automation of the classification process resulting in a map identifying land cover as ‘highly likely corn,’ ‘likely corn’ or ‘unlikely corn.’ To demonstrate the feasibility of this approach, we produced a map consisting of the three corn likelihood classes using a Landsat image in south central Nebraska. Overall classification accuracy of the map was 92.2% when compared to ground reference data.

SEM AutoAnalysis: enhancing photomask and NIL defect disposition and review

Science.gov (United States)

Schulz, Kristian; Egodage, Kokila; Tabbone, Gilles; Ehrlich, Christian; Garetto, Anthony

2017-06-01

For defect disposition and repair verification regarding printability, AIMS™ is the state of the art measurement tool in industry. With its unique capability of capturing aerial images of photomasks it is the one method that comes closest to emulating the printing behaviour of a scanner. However for nanoimprint lithography (NIL) templates aerial images cannot be applied to evaluate the success of a repair process. Hence, for NIL defect dispositioning scanning, electron microscopy (SEM) imaging is the method of choice. In addition, it has been a standard imaging method for further root cause analysis of defects and defect review on optical photomasks which enables 2D or even 3D mask profiling at high resolutions. In recent years a trend observed in mask shops has been the automation of processes that traditionally were driven by operators. This of course has brought many advantages one of which is freeing cost intensive labour from conducting repetitive and tedious work. Furthermore, it reduces variability in processes due to different operator skill and experience levels which at the end contributes to eliminating the human factor. Taking these factors into consideration, one of the software based solutions available under the FAVOR® brand to support customer needs is the aerial image evaluation software, AIMS™ AutoAnalysis (AAA). It provides fully automated analysis of AIMS™ images and runs in parallel to measurements. This is enabled by its direct connection and communication with the AIMS™tools. As one of many positive outcomes, generating automated result reports is facilitated, standardizing the mask manufacturing workflow. Today, AAA has been successfully introduced into production at multiple customers and is supporting the workflow as described above. These trends indeed have triggered the demand for similar automation with respect to SEM measurements leading to the development of SEM AutoAnalysis (SAA). It aims towards a fully automated SEM image

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

Science.gov (United States)

Ramasubbu, Rajamannar; Brown, Matthew R G; Cortese, Filmeno; Gaxiola, Ismael; Goodyear, Bradley; Greenshaw, Andrew J; Dursun, Serdar M; Greiner, Russell

2016-01-01

Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD affected the accuracies of machine learned MDD-vs-Control diagnostic classifiers. Forty-five medication-free patients with DSM-IV defined MDD and 19 healthy controls participated in the study. Based on depression severity as determined by the Hamilton Rating Scale for Depression (HRSD), MDD patients were sorted into three groups: mild to moderate depression (HRSD 14-19), severe depression (HRSD 20-23), and very severe depression (HRSD ≥ 24). We collected functional magnetic resonance imaging (fMRI) data during both resting-state and an emotional-face matching task. Patients in each of the three severity groups were compared against controls in separate analyses, using either the resting-state or task-based fMRI data. We use each of these six datasets with linear support vector machine (SVM) binary classifiers for identifying individuals as patients or controls. The resting-state fMRI data showed statistically significant classification accuracy only for the very severe depression group (accuracy 66%, p = 0.012 corrected), while mild to moderate (accuracy 58%, p = 1.0 corrected) and severe depression (accuracy 52%, p = 1.0 corrected) were only at chance. With task-based fMRI data, the automated classifier performed at chance in all three severity groups. Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.

Evaluation of Advanced Signal Processing Techniques to Improve Detection and Identification of Embedded Defects

Energy Technology Data Exchange (ETDEWEB)

Clayton, Dwight A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Santos-Villalobos, Hector J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Baba, Justin S. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2016-09-01

, or an improvement in contrast over conventional SAFT reconstructed images. This report documents our efforts in four fronts: 1) Comparative study between traditional SAFT and FBD SAFT for concrete specimen with and without Alkali-Silica Reaction (ASR) damage, 2) improvement of our Model-Based Iterative Reconstruction (MBIR) for thick reinforced concrete [5], 3) development of a universal framework for sharing, reconstruction, and visualization of ultrasound NDE datasets, and 4) application of machine learning techniques for automated detection of ASR inside concrete. Our comparative study between FBD and traditional SAFT reconstruction images shows a clear difference between images of ASR and non-ASR specimens. In particular, the left first harmonic shows an increased contrast and sensitivity to ASR damage. For MBIR, we show the superiority of model-based techniques over delay and sum techniques such as SAFT. Improvements include elimination of artifacts caused by direct arrival signals, and increased contrast and Signal to Noise Ratio. For the universal framework, we document a format for data storage based on the HDF5 file format, and also propose a modular Graphic User Interface (GUI) for easy customization of data conversion, reconstruction, and visualization routines. Finally, two techniques for ASR automated detection are presented. The first technique is based on an analysis of the frequency content using Hilbert Transform Indicator (HTI) and the second technique employees Artificial Neural Network (ANN) techniques for training and classification of ultrasound data as ASR or non-ASR damaged classes. The ANN technique shows great potential with classification accuracy above 95%. These approaches are extensible to the detection of additional reinforced, thick concrete defects and damage.

Genome-Wide Comparative Gene Family Classification

Science.gov (United States)

Frech, Christian; Chen, Nansheng

2010-01-01

Correct classification of genes into gene families is important for understanding gene function and evolution. Although gene families of many species have been resolved both computationally and experimentally with high accuracy, gene family classification in most newly sequenced genomes has not been done with the same high standard. This project has been designed to develop a strategy to effectively and accurately classify gene families across genomes. We first examine and compare the performance of computer programs developed for automated gene family classification. We demonstrate that some programs, including the hierarchical average-linkage clustering algorithm MC-UPGMA and the popular Markov clustering algorithm TRIBE-MCL, can reconstruct manual curation of gene families accurately. However, their performance is highly sensitive to parameter setting, i.e. different gene families require different program parameters for correct resolution. To circumvent the problem of parameterization, we have developed a comparative strategy for gene family classification. This strategy takes advantage of existing curated gene families of reference species to find suitable parameters for classifying genes in related genomes. To demonstrate the effectiveness of this novel strategy, we use TRIBE-MCL to classify chemosensory and ABC transporter gene families in C. elegans and its four sister species. We conclude that fully automated programs can establish biologically accurate gene families if parameterized accordingly. Comparative gene family classification finds optimal parameters automatically, thus allowing rapid insights into gene families of newly sequenced species. PMID:20976221

The classification of phocomelia.

Science.gov (United States)

Tytherleigh-Strong, G; Hooper, G

2003-06-01

We studied 24 patients with 44 phocomelic upper limbs. Only 11 limbs could be grouped in the classification system of Frantz and O' Rahilly. The non-classifiable limbs were further studied and their characteristics identified. It is confirmed that phocomelia is not an intercalary defect.

USING SAS ENTERPRISE GUIDE SOFTWARE IN CLASSIFICATION

OpenAIRE

Ana Maria Mihaela IORDACHE

2011-01-01

Data mining, also known as "discovery knowledge in large databases "is a modern and powerful information technology and communications tool that can be used to extract useful information but still unknown. This automates the process of discovery some relations and mixtures from the raw data and founded results could be incorporated into an automated decision support. This paper aims to present and perform the classification of European Union countries based on the social indicators calculated...

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

Directory of Open Access Journals (Sweden)

Philippe Burlina

Full Text Available To evaluate the use of ultrasound coupled with machine learning (ML and deep learning (DL techniques for automated or semi-automated classification of myositis.Eighty subjects comprised of 19 with inclusion body myositis (IBM, 14 with polymyositis (PM, 14 with dermatomyositis (DM, and 33 normal (N subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally were acquired. We considered three problems of classification including (A normal vs. affected (DM, PM, IBM; (B normal vs. IBM patients; and (C IBM vs. other types of myositis (DM or PM. We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification.The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A, 86.6% ± 2.4% for (B and 74.8% ± 3.9% for (C, while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A, 84.3% ± 2.3% for (B and 68.9% ± 2.5% for (C.This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

Science.gov (United States)

Burlina, Philippe; Billings, Seth; Joshi, Neil; Albayda, Jemima

2017-01-01

To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.

Comparison of subjective and fully automated methods for measuring mammographic density.

Science.gov (United States)

Moshina, Nataliia; Roman, Marta; Sebuødegård, Sofie; Waade, Gunvor G; Ursin, Giske; Hofvind, Solveig

2018-02-01

Background Breast radiologists of the Norwegian Breast Cancer Screening Program subjectively classified mammographic density using a three-point scale between 1996 and 2012 and changed into the fourth edition of the BI-RADS classification since 2013. In 2015, an automated volumetric breast density assessment software was installed at two screening units. Purpose To compare volumetric breast density measurements from the automated method with two subjective methods: the three-point scale and the BI-RADS density classification. Material and Methods Information on subjective and automated density assessment was obtained from screening examinations of 3635 women recalled for further assessment due to positive screening mammography between 2007 and 2015. The score of the three-point scale (I = fatty; II = medium dense; III = dense) was available for 2310 women. The BI-RADS density score was provided for 1325 women. Mean volumetric breast density was estimated for each category of the subjective classifications. The automated software assigned volumetric breast density to four categories. The agreement between BI-RADS and volumetric breast density categories was assessed using weighted kappa (k w ). Results Mean volumetric breast density was 4.5%, 7.5%, and 13.4% for categories I, II, and III of the three-point scale, respectively, and 4.4%, 7.5%, 9.9%, and 13.9% for the BI-RADS density categories, respectively ( P for trend density categories was k w  = 0.5 (95% CI = 0.47-0.53; P density increased with increasing density category of the subjective classifications. The agreement between BI-RADS and volumetric breast density categories was moderate.

Classification of defects in honeycomb composite structure of helicopter rotor blades

International Nuclear Information System (INIS)

Balasko, M.; Svab, E.; Molnar, Gy.; Veres, I.

2005-01-01

The use of non-destructive testing methods to qualify the state of rotor blades with respect to their expected flight hours, with the aim to extend their lifetime without any risk of breakdown, is an important financial demand. In order to detect the possible defects in the composite structure of Mi-8 and Mi-24 type helicopter rotor blades used by the Hungarian Army, we have performed combined neutron- and X-ray radiography measurements at the Budapest Research Reactor. Several types of defects were detected, analysed and typified. Among the most frequent and important defects observed were cavities, holes and or cracks in the sealing elements on the interface of the honeycomb structure and the section boarders. Inhomogeneities of the resin materials (resin-rich or starved areas) at the core-honeycomb surfaces proved to be an other important point. Defects were detected at the adhesive filling, and water percolation was visualized at the sealing interfaces of the honeycomb sections. Corrosion effects, and metal inclusions have also been detected

Classification of defects in honeycomb composite structure of helicopter rotor blades

Science.gov (United States)

Balaskó, M.; Sváb, E.; Molnár, Gy.; Veres, I.

2005-04-01

The use of non-destructive testing methods to qualify the state of rotor blades with respect to their expected flight hours, with the aim to extend their lifetime without any risk of breakdown, is an important financial demand. In order to detect the possible defects in the composite structure of Mi-8 and Mi-24 type helicopter rotor blades used by the Hungarian Army, we have performed combined neutron- and X-ray radiography measurements at the Budapest Research Reactor. Several types of defects were detected, analysed and typified. Among the most frequent and important defects observed were cavities, holes and/or cracks in the sealing elements on the interface of the honeycomb structure and the section boarders. Inhomogeneities of the resin materials (resin-rich or starved areas) at the core-honeycomb surfaces proved to be an other important point. Defects were detected at the adhesive filling, and water percolation was visualized at the sealing interfaces of the honeycomb sections. Corrosion effects, and metal inclusions have also been detected.

Comparison of Manual Mapping and Automated Object-Based Image Analysis of Non-Submerged Aquatic Vegetation from Very-High-Resolution UAS Images

Directory of Open Access Journals (Sweden)

Eva Husson

2016-09-01

Full Text Available Aquatic vegetation has important ecological and regulatory functions and should be monitored in order to detect ecosystem changes. Field data collection is often costly and time-consuming; remote sensing with unmanned aircraft systems (UASs provides aerial images with sub-decimetre resolution and offers a potential data source for vegetation mapping. In a manual mapping approach, UAS true-colour images with 5-cm-resolution pixels allowed for the identification of non-submerged aquatic vegetation at the species level. However, manual mapping is labour-intensive, and while automated classification methods are available, they have rarely been evaluated for aquatic vegetation, particularly at the scale of individual vegetation stands. We evaluated classification accuracy and time-efficiency for mapping non-submerged aquatic vegetation at three levels of detail at five test sites (100 m × 100 m differing in vegetation complexity. We used object-based image analysis and tested two classification methods (threshold classification and Random Forest using eCognition®. The automated classification results were compared to results from manual mapping. Using threshold classification, overall accuracy at the five test sites ranged from 93% to 99% for the water-versus-vegetation level and from 62% to 90% for the growth-form level. Using Random Forest classification, overall accuracy ranged from 56% to 94% for the growth-form level and from 52% to 75% for the dominant-taxon level. Overall classification accuracy decreased with increasing vegetation complexity. In test sites with more complex vegetation, automated classification was more time-efficient than manual mapping. This study demonstrated that automated classification of non-submerged aquatic vegetation from true-colour UAS images was feasible, indicating good potential for operative mapping of aquatic vegetation. When choosing the preferred mapping method (manual versus automated the desired level of

Machine Learning of Human Pluripotent Stem Cell-Derived Engineered Cardiac Tissue Contractility for Automated Drug Classification

Directory of Open Access Journals (Sweden)

Eugene K. Lee

2017-11-01

Full Text Available Accurately predicting cardioactive effects of new molecular entities for therapeutics remains a daunting challenge. Immense research effort has been focused toward creating new screening platforms that utilize human pluripotent stem cell (hPSC-derived cardiomyocytes and three-dimensional engineered cardiac tissue constructs to better recapitulate human heart function and drug responses. As these new platforms become increasingly sophisticated and high throughput, the drug screens result in larger multidimensional datasets. Improved automated analysis methods must therefore be developed in parallel to fully comprehend the cellular response across a multidimensional parameter space. Here, we describe the use of machine learning to comprehensively analyze 17 functional parameters derived from force readouts of hPSC-derived ventricular cardiac tissue strips (hvCTS electrically paced at a range of frequencies and exposed to a library of compounds. A generated metric is effective for then determining the cardioactivity of a given drug. Furthermore, we demonstrate a classification model that can automatically predict the mechanistic action of an unknown cardioactive drug.

Astrophysical Information from Objective Prism Digitized Images: Classification with an Artificial Neural Network

Directory of Open Access Journals (Sweden)

Bratsolis Emmanuel

2005-01-01

Full Text Available Stellar spectral classification is not only a tool for labeling individual stars but is also useful in studies of stellar population synthesis. Extracting the physical quantities from the digitized spectral plates involves three main stages: detection, extraction, and classification of spectra. Low-dispersion objective prism images have been used and automated methods have been developed. The detection and extraction problems have been presented in previous works. In this paper, we present a classification method based on an artificial neural network (ANN. We make a brief presentation of the entire automated system and we compare the new classification method with the previously used method of maximum correlation coefficient (MCC. Digitized photographic material has been used here. The method can also be used on CCD spectral images.

Lifecycle, Iteration, and Process Automation with SMS Gateway

Directory of Open Access Journals (Sweden)

Fenny Fenny

2015-12-01

Full Text Available Producing a better quality software system requires an understanding of the indicators of the software quality through defect detection, and automated testing. This paper aims to elevate the design and automated testing process in an engine water pump of a drinking water plant. This paper proposes how software developers can improve the maintainability and reliability of automated testing system and report the abnormal state when an error occurs on the machine. The method in this paper uses literature to explain best practices and case studies of a drinking water plant. Furthermore, this paper is expected to be able to provide insights into the efforts to better handle errors and perform automated testing and monitoring on a machine.

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Science.gov (United States)

Wu, Zhuoting; Thenkabail, Prasad S.; Mueller, Rick; Zakzeski, Audra; Melton, Forrest; Johnson, Lee; Rosevelt, Carolyn; Dwyer, John; Jones, Jeanine; Verdin, James P.

2014-01-01

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer's accuracy of 93% and a user's accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification.

Science.gov (United States)

Hu, Zhihong; Medioni, Gerard G; Hernandez, Matthias; Sadda, Srinivas R

2015-01-01

Geographic atrophy (GA) is a manifestation of the advanced or late stage of age-related macular degeneration (AMD). AMD is the leading cause of blindness in people over the age of 65 in the western world. The purpose of this study is to develop a fully automated supervised pixel classification approach for segmenting GA, including uni- and multifocal patches in fundus autofluorescene (FAF) images. The image features include region-wise intensity measures, gray-level co-occurrence matrix measures, and Gaussian filter banks. A [Formula: see text]-nearest-neighbor pixel classifier is applied to obtain a GA probability map, representing the likelihood that the image pixel belongs to GA. Sixteen randomly chosen FAF images were obtained from 16 subjects with GA. The algorithm-defined GA regions are compared with manual delineation performed by a certified image reading center grader. Eight-fold cross-validation is applied to evaluate the algorithm performance. The mean overlap ratio (OR), area correlation (Pearson's [Formula: see text]), accuracy (ACC), true positive rate (TPR), specificity (SPC), positive predictive value (PPV), and false discovery rate (FDR) between the algorithm- and manually defined GA regions are [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively.

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

Science.gov (United States)

Wu, Zhuoting; Thenkabail, Prasad S.; Mueller, Rick; Zakzeski, Audra; Melton, Forrest; Johnson, Lee; Rosevelt, Carolyn; Dwyer, John; Jones, Jeanine; Verdin, James P.

2014-01-01

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

A one-versus-all class binarization strategy for bearing diagnostics of concurrent defects.

Science.gov (United States)

Ng, Selina S Y; Tse, Peter W; Tsui, Kwok L

2014-01-13

In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets.

A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects

Directory of Open Access Journals (Sweden)

Selina S. Y. Ng

2014-01-01

Full Text Available In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets.

21 CFR 864.9175 - Automated blood grouping and antibody test system.

Science.gov (United States)

2010-04-01

...) Identification. An automated blood grouping and antibody test system is a device used to group erythrocytes (red blood cells) and to detect antibodies to blood group antigens. (b) Classification. Class II (performance... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Automated blood grouping and antibody test system...

Automated vehicle for railway track fault detection

Science.gov (United States)

Bhushan, M.; Sujay, S.; Tushar, B.; Chitra, P.

2017-11-01

For the safety reasons, railroad tracks need to be inspected on a regular basis for detecting physical defects or design non compliances. Such track defects and non compliances, if not detected in a certain interval of time, may eventually lead to severe consequences such as train derailments. Inspection must happen twice weekly by a human inspector to maintain safety standards as there are hundreds and thousands of miles of railroad track. But in such type of manual inspection, there are many drawbacks that may result in the poor inspection of the track, due to which accidents may cause in future. So to avoid such errors and severe accidents, this automated system is designed.Such a concept would surely introduce automation in the field of inspection process of railway track and can help to avoid mishaps and severe accidents due to faults in the track.

Improving the Computational Performance of Ontology-Based Classification Using Graph Databases

Directory of Open Access Journals (Sweden)

Thomas J. Lampoltshammer

2015-07-01

Full Text Available The increasing availability of very high-resolution remote sensing imagery (i.e., from satellites, airborne laser scanning, or aerial photography represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, (semi- automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities—so-called individuals—is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Protégé environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed—up to 80,000 times faster than the Protégé-based approach.

Objective automated quantification of fluorescence signal in histological sections of rat lens.

Science.gov (United States)

Talebizadeh, Nooshin; Hagström, Nanna Zhou; Yu, Zhaohua; Kronschläger, Martin; Söderberg, Per; Wählby, Carolina

2017-08-01

Visual quantification and classification of fluorescent signals is the gold standard in microscopy. The purpose of this study was to develop an automated method to delineate cells and to quantify expression of fluorescent signal of biomarkers in each nucleus and cytoplasm of lens epithelial cells in a histological section. A region of interest representing the lens epithelium was manually demarcated in each input image. Thereafter, individual cell nuclei within the region of interest were automatically delineated based on watershed segmentation and thresholding with an algorithm developed in Matlab™. Fluorescence signal was quantified within nuclei, cytoplasms and juxtaposed backgrounds. The classification of cells as labelled or not labelled was based on comparison of the fluorescence signal within cells with local background. The classification rule was thereafter optimized as compared with visual classification of a limited dataset. The performance of the automated classification was evaluated by asking 11 independent blinded observers to classify all cells (n = 395) in one lens image. Time consumed by the automatic algorithm and visual classification of cells was recorded. On an average, 77% of the cells were correctly classified as compared with the majority vote of the visual observers. The average agreement among visual observers was 83%. However, variation among visual observers was high, and agreement between two visual observers was as low as 71% in the worst case. Automated classification was on average 10 times faster than visual scoring. The presented method enables objective and fast detection of lens epithelial cells and quantification of expression of fluorescent signal with an accuracy comparable with the variability among visual observers. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

BENCHMARKING MACHINE LEARNING TECHNIQUES FOR SOFTWARE DEFECT DETECTION

OpenAIRE

Saiqa Aleem; Luiz Fernando Capretz; Faheem Ahmed

2015-01-01

Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data...

Automation for Primary Processing of Hardwoods

Science.gov (United States)

Daniel L. Schmoldt

1992-01-01

Hardwood sawmills critically need to incorporate automation and computer technology into their operations. Social constraints, forest biology constraints, forest product market changes, and financial necessity are forcing primary processors to boost their productivity and efficiency to higher levels. The locations, extent, and types of defects found in logs and on...

Classification of multiple sclerosis lesions using adaptive dictionary learning.

Science.gov (United States)

Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

2015-12-01

This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

A semi-automated method for bone age assessment using cervical vertebral maturation.

Science.gov (United States)

Baptista, Roberto S; Quaglio, Camila L; Mourad, Laila M E H; Hummel, Anderson D; Caetano, Cesar Augusto C; Ortolani, Cristina Lúcia F; Pisa, Ivan T

2012-07-01

To propose a semi-automated method for pattern classification to predict individuals' stage of growth based on morphologic characteristics that are described in the modified cervical vertebral maturation (CVM) method of Baccetti et al. A total of 188 lateral cephalograms were collected, digitized, evaluated manually, and grouped into cervical stages by two expert examiners. Landmarks were located on each image and measured. Three pattern classifiers based on the Naïve Bayes algorithm were built and assessed using a software program. The classifier with the greatest accuracy according to the weighted kappa test was considered best. The classifier showed a weighted kappa coefficient of 0.861 ± 0.020. If an adjacent estimated pre-stage or poststage value was taken to be acceptable, the classifier would show a weighted kappa coefficient of 0.992 ± 0.019. Results from this study show that the proposed semi-automated pattern classification method can help orthodontists identify the stage of CVM. However, additional studies are needed before this semi-automated classification method for CVM assessment can be implemented in clinical practice.

Risk assessment for pipelines with active defects based on artificial intelligence methods

Energy Technology Data Exchange (ETDEWEB)

Anghel, Calin I. [Department of Chemical Engineering, Faculty of Chemistry and Chemical Engineering, University ' Babes-Bolyai' , Cluj-Napoca (Romania)], E-mail: canghel@chem.ubbcluj.ro

2009-07-15

The paper provides another insight into the pipeline risk assessment for in-service pressure piping containing defects. Beside of the traditional analytical approximation methods or sampling-based methods safety index and failure probability of pressure piping containing defects will be obtained based on a novel type of support vector machine developed in a minimax manner. The safety index or failure probability is carried out based on a binary classification approach. The procedure named classification reliability procedure, involving a link between artificial intelligence and reliability methods was developed as a user-friendly computer program in MATLAB language. To reveal the capacity of the proposed procedure two comparative numerical examples replicating a previous related work and predicting the failure probabilities of pressured pipeline with defects were presented.

Risk assessment for pipelines with active defects based on artificial intelligence methods

International Nuclear Information System (INIS)

Anghel, Calin I.

2009-01-01

The paper provides another insight into the pipeline risk assessment for in-service pressure piping containing defects. Beside of the traditional analytical approximation methods or sampling-based methods safety index and failure probability of pressure piping containing defects will be obtained based on a novel type of support vector machine developed in a minimax manner. The safety index or failure probability is carried out based on a binary classification approach. The procedure named classification reliability procedure, involving a link between artificial intelligence and reliability methods was developed as a user-friendly computer program in MATLAB language. To reveal the capacity of the proposed procedure two comparative numerical examples replicating a previous related work and predicting the failure probabilities of pressured pipeline with defects were presented.

Vision-Based Perception and Classification of Mosquitoes Using Support Vector Machine

Directory of Open Access Journals (Sweden)

Masataka Fuchida

2017-01-01

Full Text Available The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-based methods for mapping potential mosquito inhabitants and locations that are prone to mosquito-borne diseases, but these methods generally do not account for species-wise identification of mosquitoes in closed-perimeter regions. Traditional methods for mosquito classification involve highly manual processes requiring tedious sample collection and supervised laboratory analysis. In this research work, we present the design and experimental validation of an automated vision-based mosquito classification module that can deploy in closed-perimeter mosquito inhabitants. The module is capable of identifying mosquitoes from other bugs such as bees and flies by extracting the morphological features, followed by support vector machine-based classification. In addition, this paper presents the results of three variants of support vector machine classifier in the context of mosquito classification problem. This vision-based approach to the mosquito classification problem presents an efficient alternative to the conventional methods for mosquito surveillance, mapping and sample image collection. Experimental results involving classification between mosquitoes and a predefined set of other bugs using multiple classification strategies demonstrate the efficacy and validity of the proposed approach with a maximum recall of 98%.

Automated reliability assessment for spectroscopic redshift measurements

Science.gov (United States)

Jamal, S.; Le Brun, V.; Le Fèvre, O.; Vibert, D.; Schmitt, A.; Surace, C.; Copin, Y.; Garilli, B.; Moresco, M.; Pozzetti, L.

2018-03-01

Context. Future large-scale surveys, such as the ESA Euclid mission, will produce a large set of galaxy redshifts (≥106) that will require fully automated data-processing pipelines to analyze the data, extract crucial information and ensure that all requirements are met. A fundamental element in these pipelines is to associate to each galaxy redshift measurement a quality, or reliability, estimate. Aim. In this work, we introduce a new approach to automate the spectroscopic redshift reliability assessment based on machine learning (ML) and characteristics of the redshift probability density function. Methods: We propose to rephrase the spectroscopic redshift estimation into a Bayesian framework, in order to incorporate all sources of information and uncertainties related to the redshift estimation process and produce a redshift posterior probability density function (PDF). To automate the assessment of a reliability flag, we exploit key features in the redshift posterior PDF and machine learning algorithms. Results: As a working example, public data from the VIMOS VLT Deep Survey is exploited to present and test this new methodology. We first tried to reproduce the existing reliability flags using supervised classification in order to describe different types of redshift PDFs, but due to the subjective definition of these flags (classification accuracy 58%), we soon opted for a new homogeneous partitioning of the data into distinct clusters via unsupervised classification. After assessing the accuracy of the new clusters via resubstitution and test predictions (classification accuracy 98%), we projected unlabeled data from preliminary mock simulations for the Euclid space mission into this mapping to predict their redshift reliability labels. Conclusions: Through the development of a methodology in which a system can build its own experience to assess the quality of a parameter, we are able to set a preliminary basis of an automated reliability assessment for

Developing a database management system to support birth defects surveillance in Florida.

Science.gov (United States)

Salemi, Jason L; Hauser, Kimberlea W; Tanner, Jean Paul; Sampat, Diana; Correia, Jane A; Watkins, Sharon M; Kirby, Russell S

2010-01-01

The value of any public health surveillance program is derived from the ways in which data are managed and used to improve the public's health. Although birth defects surveillance programs vary in their case volume, budgets, staff, and objectives, the capacity to operate efficiently and maximize resources remains critical to long-term survival. The development of a fully-integrated relational database management system (DBMS) can enrich a surveillance program's data and improve efficiency. To build upon the Florida Birth Defects Registry--a statewide registry relying solely on linkage of administrative datasets and unconfirmed diagnosis codes-the Florida Department of Health provided funding to the University of South Florida to develop and pilot an enhanced surveillance system in targeted areas with a more comprehensive approach to case identification and diagnosis confirmation. To manage operational and administrative complexities, a DBMS was developed, capable of managing transmission of project data from multiple sources, tracking abstractor time during record reviews, offering tools for defect coding and case classification, and providing reports to DBMS users. Since its inception, the DBMS has been used as part of our surveillance projects to guide the receipt of over 200 case lists and review of 12,924 fetuses and infants (with associated maternal records) suspected of having selected birth defects in over 90 birthing and transfer facilities in Florida. The DBMS has provided both anticipated and unexpected benefits. Automation of the processes for managing incoming case lists has reduced clerical workload considerably, while improving accuracy of working lists for field abstraction. Data quality has improved through more effective use of internal edits and comparisons with values for other data elements, while simultaneously increasing abstractor efficiency in completion of case abstraction. We anticipate continual enhancement to the DBMS in the future

Development of an intelligent ultrasonic welding defect classification software

International Nuclear Information System (INIS)

Song, Sung Jin; Kim, Hak Joon; Jeong, Hee Don

1997-01-01

Ultrasonic pattern recognition is the most effective approach to the problem of discriminating types of flaws in weldments based on ultrasonic flaw signals. In spite of significant progress in the research on this methodology, it has not been widely used in many practical ultrasonic inspections of weldments in industry. Hence, for the convenient application of this approach in many practical situations, we develop an intelligent ultrasonic signature classification software which can discriminate types of flaws in weldments based on their ultrasonic signals using various tools in artificial intelligence such as neural networks. This software shows the excellent performance in an experimental problem where flaws in weldments are classified into two categories of cracks and non-cracks. This performance demonstrates the high possibility of this software as a practical tool for ultrasonic flaw classification in weldments.

Using historical wafermap data for automated yield analysis

International Nuclear Information System (INIS)

Tobin, K.W.; Karnowski, T.P.; Gleason, S.S.; Jensen, D.; Lakhani, F.

1999-01-01

To be productive and profitable in a modern semiconductor fabrication environment, large amounts of manufacturing data must be collected, analyzed, and maintained. This includes data collected from in- and off-line wafer inspection systems and from the process equipment itself. This data is increasingly being used to design new processes, control and maintain tools, and to provide the information needed for rapid yield learning and prediction. Because of increasing device complexity, the amount of data being generated is outstripping the yield engineer close-quote s ability to effectively monitor and correct unexpected trends and excursions. The 1997 SIA National Technology Roadmap for Semiconductors highlights a need to address these issues through open-quotes automated data reduction algorithms to source defects from multiple data sources and to reduce defect sourcing time.close quotes SEMATECH and the Oak Ridge National Laboratory have been developing new strategies and technologies for providing the yield engineer with higher levels of assisted data reduction for the purpose of automated yield analysis. In this article, we will discuss the current state of the art and trends in yield management automation. copyright 1999 American Vacuum Society

Defect inspection in hot slab surface: multi-source CCD imaging based fuzzy-rough sets method

Science.gov (United States)

Zhao, Liming; Zhang, Yi; Xu, Xiaodong; Xiao, Hong; Huang, Chao

2016-09-01

To provide an accurate surface defects inspection method and make the automation of robust image region of interests(ROI) delineation strategy a reality in production line, a multi-source CCD imaging based fuzzy-rough sets method is proposed for hot slab surface quality assessment. The applicability of the presented method and the devised system are mainly tied to the surface quality inspection for strip, billet and slab surface etcetera. In this work we take into account the complementary advantages in two common machine vision (MV) systems(line array CCD traditional scanning imaging (LS-imaging) and area array CCD laser three-dimensional (3D) scanning imaging (AL-imaging)), and through establishing the model of fuzzy-rough sets in the detection system the seeds for relative fuzzy connectedness(RFC) delineation for ROI can placed adaptively, which introduces the upper and lower approximation sets for RIO definition, and by which the boundary region can be delineated by RFC region competitive classification mechanism. For the first time, a Multi-source CCD imaging based fuzzy-rough sets strategy is attempted for CC-slab surface defects inspection that allows an automatic way of AI algorithms and powerful ROI delineation strategies to be applied to the MV inspection field.

A coverage and slicing dependencies analysis for seeking software security defects.

Science.gov (United States)

He, Hui; Zhang, Dongyan; Liu, Min; Zhang, Weizhe; Gao, Dongmin

2014-01-01

Software security defects have a serious impact on the software quality and reliability. It is a major hidden danger for the operation of a system that a software system has some security flaws. When the scale of the software increases, its vulnerability has becoming much more difficult to find out. Once these vulnerabilities are exploited, it may lead to great loss. In this situation, the concept of Software Assurance is carried out by some experts. And the automated fault localization technique is a part of the research of Software Assurance. Currently, automated fault localization method includes coverage based fault localization (CBFL) and program slicing. Both of the methods have their own location advantages and defects. In this paper, we have put forward a new method, named Reverse Data Dependence Analysis Model, which integrates the two methods by analyzing the program structure. On this basis, we finally proposed a new automated fault localization method. This method not only is automation lossless but also changes the basic location unit into single sentence, which makes the location effect more accurate. Through several experiments, we proved that our method is more effective. Furthermore, we analyzed the effectiveness among these existing methods and different faults.

Automated supervised classification of variable stars. I. Methodology

NARCIS (Netherlands)

Debosscher, J.; Sarro, L.M.; Aerts, C.C.; Cuypers, J.; Vandenbussche, B.; Garrido, R.; Solano, E.

2007-01-01

Context: The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the classes in terms of physical parameters is also

Improving settlement type classification of aerial images

CSIR Research Space (South Africa)

Mdakane, L

2014-10-01

Full Text Available , an automated method can be used to help identify human settlements in a fixed, repeatable and timely manner. The main contribution of this work is to improve generalisation on settlement type classification of aerial imagery. Images acquired at different dates...

Automatic appraisal of defects in irradiated pins by eddy current testing

International Nuclear Information System (INIS)

Marsol, R.; Cornu, B.

1986-10-01

Eddy current testing is very efficient to inspect the sheaths of spent fuel elements. Automation of the process is developed to replace visual examination of recorded eddy current signals. The method is applied to austenitic steel fuel cans for fast neutron reactors to detect cracks, voids, inclusions... The different types of defects and experimental processes are recalled then automatic detection and the method for defect qualification are presented [fr

Digital detection system of surface defects for large aperture optical elements

International Nuclear Information System (INIS)

Fan Yong; Chen Niannian; Gao Lingling; Jia Yuan; Wang Junbo; Cheng Xiaofeng

2009-01-01

Based on the light defect images against the dark background in a scattering imaging system, a digital detection system of surface defects for large aperture optical elements has been presented. In the system, the image is segmented by a multi-area self-adaptive threshold segmentation method, then a pixel labeling method based on replacing arrays is adopted to extract defect features quickly, and at last the defects are classified through back-propagation neural networks. Experiment results show that the system can achieve real-time detection and classification. (authors)

A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification

Science.gov (United States)

Cao, Jianfang; Chen, Lichao; Wang, Min; Shi, Hao; Tian, Yun

2016-01-01

Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm. Finally, we establish an automated classification model by building a Hadoop cluster. We use the Pascal VOC2007 and Caltech256 datasets to train and test the classification model. The results are superior to those obtained using traditional Adaboost-BP neural network or parallel BP neural network approaches. Our approach increased the average classification accuracy rate by approximately 14.5% and 26.0% compared to the traditional Adaboost-BP neural network and parallel BP neural network, respectively. Furthermore, the proposed approach requires less computation time and scales very well as evaluated by speedup, sizeup and scaleup. The proposed approach may provide a foundation for automated large-scale image classification and demonstrates practical value. PMID:27905520

Microsurgical reconstruction of large nerve defects using autologous nerve grafts.

Science.gov (United States)

Daoutis, N K; Gerostathopoulos, N E; Efstathopoulos, D G; Misitizis, D P; Bouchlis, G N; Anagnostou, S K

1994-01-01

Between 1986 and 1993, 643 patients with peripheral nerve trauma were treated in our clinic. Primary neurorraphy was performed in 431 of these patients and nerve grafting in 212 patients. We present the functional results after nerve grafting in 93 patients with large nerve defects who were followed for more than 2 years. Evaluation of function was based on the Medical Research Council (MRC) classification for motor and sensory recovery. Factors affecting functional outcome, such as age of the patient, denervation time, length of the defect, and level of the injury were noted. Good results according to the MRC classification were obtained in the majority of cases, although function remained less than that of the uninjured side.

Use of information-retrieval languages in automated retrieval of experimental data from long-term storage

Science.gov (United States)

Khovanskiy, Y. D.; Kremneva, N. I.

1975-01-01

Problems and methods are discussed of automating information retrieval operations in a data bank used for long term storage and retrieval of data from scientific experiments. Existing information retrieval languages are analyzed along with those being developed. The results of studies discussing the application of the descriptive 'Kristall' language used in the 'ASIOR' automated information retrieval system are presented. The development and use of a specialized language of the classification-descriptive type, using universal decimal classification indices as the main descriptors, is described.

Rapid Classification of Ordinary Chondrites Using Raman Spectroscopy

Science.gov (United States)

Fries, M.; Welzenbach, L.

2014-01-01

Classification of ordinary chondrites is typically done through measurements of the composition of olivine and pyroxenes. Historically, this measurement has usually been performed via electron microprobe, oil immersion or other methods which can be costly through lost sample material during thin section preparation. Raman microscopy can perform the same measurements but considerably faster and with much less sample preparation allowing for faster classification. Raman spectroscopy can facilitate more rapid classification of large amounts of chondrites such as those retrieved from North Africa and potentially Antarctica, are present in large collections, or are submitted to a curation facility by the public. With development, this approach may provide a completely automated classification method of all chondrite types.

Software for roof defects recognition on aerial photographs

Science.gov (United States)

Yudin, D.; Naumov, A.; Dolzhenko, A.; Patrakova, E.

2018-05-01

The article presents information on software for roof defects recognition on aerial photographs, made with air drones. An areal image segmentation mechanism is described. It allows detecting roof defects – unsmoothness that causes water stagnation after rain. It is shown that HSV-transformation approach allows quick detection of stagnation areas, their size and perimeters, but is sensitive to shadows and changes of the roofing-types. Deep Fully Convolutional Network software solution eliminates this drawback. The tested data set consists of the roofing photos with defects and binary masks for them. FCN approach gave acceptable results of image segmentation in Dice metric average value. This software can be used in inspection automation of roof conditions in the production sector and housing and utilities infrastructure.

Automated evaluation of ultrasonic indications. State of the art -development trends. Pt. 1

International Nuclear Information System (INIS)

Hansch, M.K.T.; Stegemann, D.

1994-01-01

Future requirements of reliability and reproducibility in quality assurance demand computer evaluation of defect indications. The ultrasonic method with its large field of applications and a high potential for automation provides all preconditions for fully automated inspection. The survey proposes several desirable hardware improvements, data acquisition requirements and software configurations. (orig.) [de

Semi-automated landform classification for hazard mapping of soil liquefaction by earthquake

Science.gov (United States)

Nakano, Takayuki

2018-05-01

Soil liquefaction damages were caused by huge earthquake in Japan, and the similar damages are concerned in near future huge earthquake. On the other hand, a preparation of soil liquefaction risk map (soil liquefaction hazard map) is impeded by the difficulty of evaluation of soil liquefaction risk. Generally, relative soil liquefaction risk should be able to be evaluated from landform classification data by using experimental rule based on the relationship between extent of soil liquefaction damage and landform classification items associated with past earthquake. Therefore, I rearranged the relationship between landform classification items and soil liquefaction risk intelligibly in order to enable the evaluation of soil liquefaction risk based on landform classification data appropriately and efficiently. And I developed a new method of generating landform classification data of 50-m grid size from existing landform classification data of 250-m grid size by using digital elevation model (DEM) data and multi-band satellite image data in order to evaluate soil liquefaction risk in detail spatially. It is expected that the products of this study contribute to efficient producing of soil liquefaction hazard map by local government.

Classification of COROT Exoplanet Light Curves

NARCIS (Netherlands)

Debosscher, J.; Aerts, C.C.; Vandenbussche, B.

2006-01-01

We present methodology to achieve the automated variability classification of stars based on photometric time series. Our work is done in the framework of the COROT space mission to be launched in 2006, but will also be applicable to data of the future Gaia satellite. We developed routines that are

Integrating human and machine intelligence in galaxy morphology classification tasks

Science.gov (United States)

Beck, Melanie R.; Scarlata, Claudia; Fortson, Lucy F.; Lintott, Chris J.; Simmons, B. D.; Galloway, Melanie A.; Willett, Kyle W.; Dickinson, Hugh; Masters, Karen L.; Marshall, Philip J.; Wright, Darryl

2018-06-01

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold classifying 226 124 galaxies in 92 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

Enhanced defect of interest [DOI] monitoring by utilizing sensitive inspection and ADRTrue SEM review

Science.gov (United States)

Kirsch, Remo; Zeiske, Ulrich; Shabtay, Saar; Beyer, Mirko; Yerushalmi, Liran; Goshen, Oren

2011-03-01

As semiconductor process design rules continue to shrink, the ability of optical inspection tools to separate between true defects and nuisance becomes more and more difficult. Therefore, monitoring Defect of Interest (DOI) become a real challenge (Figure 1). This phenomenon occurs due to the lower signal received from real defects while noise levels remain almost the same, resulting in inspection high nuisance rate, which jeopardizes the ability to provide a meaningful, true defect Pareto. A non-representative defect Pareto creates a real challenge to a reliable process monitoring (Figure 4). Traditionally, inspection tool recipes were optimized to keep data load at a manageable level and provide defect maps with ~10% nuisance rate, but as defects of interest get smaller with design rule shrinkage, this requirement results in a painful compromise in detection sensitivity. The inspection is usually followed by defect review and classification using scanning electron microscope (SEM), the classification done manually and it is performed on a small sample of the inspection defect map due to time and manual resources limitations. Sample is usually 50~60 randomly selected locations, review is performed manually most of the times, and manual classification is performed for all the reviewed locations. In the approach described in this paper, the inspection tool recipe is optimized for sensitivity rather than low nuisance rate (i.e. detect all DOI with compromising on a higher nuisance rate). Inspection results with high nuisance rate introduce new challenges for SEM review methodology & tools. This paper describe a new approach which enhances process monitoring quality and the results of collaborative work of the Process Diagnostic & Control Business Unit of Applied Materials® and GLOBALFOUNDRIES® utilizing Applied Materials ADRTrueTM & SEMVisionTM capabilities. The study shows that the new approach reveals new defect types in the Pareto, and improves the ability to

Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification.

Science.gov (United States)

Song, Yang; Li, Qing; Huang, Heng; Feng, Dagan; Chen, Mei; Cai, Weidong

2017-08-01

Microscopy image classification is important in various biomedical applications, such as cancer subtype identification, and protein localization for high content screening. To achieve automated and effective microscopy image classification, the representative and discriminative capability of image feature descriptors is essential. To this end, in this paper, we propose a new feature representation algorithm to facilitate automated microscopy image classification. In particular, we incorporate Fisher vector (FV) encoding with multiple types of local features that are handcrafted or learned, and we design a separation-guided dimension reduction method to reduce the descriptor dimension while increasing its discriminative capability. Our method is evaluated on four publicly available microscopy image data sets of different imaging types and applications, including the UCSB breast cancer data set, MICCAI 2015 CBTC challenge data set, and IICBU malignant lymphoma, and RNAi data sets. Our experimental results demonstrate the advantage of the proposed low-dimensional FV representation, showing consistent performance improvement over the existing state of the art and the commonly used dimension reduction techniques.

An automated approach to the design of decision tree classifiers

Science.gov (United States)

Argentiero, P.; Chin, R.; Beaudet, P.

1982-01-01

An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

A neural network approach for radiographic image classification in NDT

International Nuclear Information System (INIS)

Lavayssiere, B.

1993-05-01

Radiography is used by EDF for pipe inspection in nuclear power plants in order to detect defects. The radiographs obtained are then digitized in a well-defined protocol. The aim of EDF consists of developing a non destructive testing system for recognizing defects. In this note, we describe the recognition procedure of areas with defects. We first present the digitization protocol, specifies the poor quality of images under study and propose a procedure to enhance defects. We then examine the problem raised by the choice of good features for classification. After having proved that statistical or standard textural features such as homogeneity, entropy or contrast are not relevant, we develop a geometrical-statistical approach based on the cooperation between signal correlations study and regional extrema analysis. The principle consists of analysing and comparing for areas with defects and without any defect, the evolution of conditional probabilities matrices for increasing neighbourhood sizes, the shape of variograms and the location of regional minima. We demonstrate that anisotropy and surface of series of 'comet tails' associated with probability matrices, variograms slope and statistical indices, regional extrema location, are features able to discriminate areas with defects from areas without any. The classification is then realized by a neural network, which structure, properties and learning mechanisms are detailed. Finally we discuss the results. (author). 5 figs., 21 refs

Categorizing Children: Automated Text Classification of CHILDES files

NARCIS (Netherlands)

Opsomer, Rob; Knoth, Peter; Wiering, Marco; van Polen, Freek; Trapman, Jantine

2008-01-01

In this paper we present the application of machine learning text classification methods to two tasks: categorization of children’s speech in the CHILDES Database according to gender and age. Both tasks are binary. For age, we distinguish two age groups between the age of 1.9 and 3.0 years old. The

Automatic parquet block sorting using real-time spectral classification

Science.gov (United States)

Astrom, Anders; Astrand, Erik; Johansson, Magnus

1999-03-01

This paper presents a real-time spectral classification system based on the PGP spectrograph and a smart image sensor. The PGP is a spectrograph which extracts the spectral information from a scene and projects the information on an image sensor, which is a method often referred to as Imaging Spectroscopy. The classification is based on linear models and categorizes a number of pixels along a line. Previous systems adopting this method have used standard sensors, which often resulted in poor performance. The new system, however, is based on a patented near-sensor classification method, which exploits analogue features on the smart image sensor. The method reduces the enormous amount of data to be processed at an early stage, thus making true real-time spectral classification possible. The system has been evaluated on hardwood parquet boards showing very good results. The color defects considered in the experiments were blue stain, white sapwood, yellow decay and red decay. In addition to these four defect classes, a reference class was used to indicate correct surface color. The system calculates a statistical measure for each parquet block, giving the pixel defect percentage. The patented method makes it possible to run at very high speeds with a high spectral discrimination ability. Using a powerful illuminator, the system can run with a line frequency exceeding 2000 line/s. This opens up the possibility to maintain high production speed and still measure with good resolution.

Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus-plasma separation and cell classification using a hierarchical tree model of hematopoesis

Science.gov (United States)

Krappe, Sebastian; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2016-03-01

The morphological differentiation of bone marrow is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually under the use of bright field microscopy. This is a time-consuming, subjective, tedious and error-prone process. Furthermore, repeated examinations of a slide may yield intra- and inter-observer variances. For that reason a computer assisted diagnosis system for bone marrow differentiation is pursued. In this work we focus (a) on a new method for the separation of nucleus and plasma parts and (b) on a knowledge-based hierarchical tree classifier for the differentiation of bone marrow cells in 16 different classes. Classification trees are easily interpretable and understandable and provide a classification together with an explanation. Using classification trees, expert knowledge (i.e. knowledge about similar classes and cell lines in the tree model of hematopoiesis) is integrated in the structure of the tree. The proposed segmentation method is evaluated with more than 10,000 manually segmented cells. For the evaluation of the proposed hierarchical classifier more than 140,000 automatically segmented bone marrow cells are used. Future automated solutions for the morphological analysis of bone marrow smears could potentially apply such an approach for the pre-classification of bone marrow cells and thereby shortening the examination time.

Real-time portable system for fabric defect detection using an ARM processor

Science.gov (United States)

Fernandez-Gallego, J. A.; Yañez-Puentes, J. P.; Ortiz-Jaramillo, B.; Alvarez, J.; Orjuela-Vargas, S. A.; Philips, W.

2012-06-01

Modern textile industry seeks to produce textiles as little defective as possible since the presence of defects can decrease the final price of products from 45% to 65%. Automated visual inspection (AVI) systems, based on image analysis, have become an important alternative for replacing traditional inspections methods that involve human tasks. An AVI system gives the advantage of repeatability when implemented within defined constrains, offering more objective and reliable results for particular tasks than human inspection. Costs of automated inspection systems development can be reduced using modular solutions with embedded systems, in which an important advantage is the low energy consumption. Among the possibilities for developing embedded systems, the ARM processor has been explored for acquisition, monitoring and simple signal processing tasks. In a recent approach we have explored the use of the ARM processor for defects detection by implementing the wavelet transform. However, the computation speed of the preprocessing was not yet sufficient for real time applications. In this approach we significantly improve the preprocessing speed of the algorithm, by optimizing matrix operations, such that it is adequate for a real time application. The system was tested for defect detection using different defect types. The paper is focused in giving a detailed description of the basis of the algorithm implementation, such that other algorithms may use of the ARM operations for fast implementations.

Establishment and application of medication error classification standards in nursing care based on the International Classification of Patient Safety

Directory of Open Access Journals (Sweden)

Xiao-Ping Zhu

2014-09-01

Conclusion: Application of this classification system will help nursing administrators to accurately detect system- and process-related defects leading to medication errors, and enable the factors to be targeted to improve the level of patient safety management.

Automated visual inspection of textile

DEFF Research Database (Denmark)

Jensen, Rune Fisker; Carstensen, Jens Michael

1997-01-01

A method for automated inspection of two types of textile is presented. The goal of the inspection is to determine defects in the textile. A prototype is constructed for simulating the textile production line. At the prototype the images of the textile are acquired by a high speed line scan camera...... the protype to a production line system we only need to gain a speed factor of 4....

Formation Energies of Native Point Defects in Strained-Layer Superlattices (Postprint)

Science.gov (United States)

2017-06-05

potential; bulk materials; total energy calculations; entropy; strained- layer superlattice (SLS) 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...AFRL-RX-WP-JA-2017-0217 FORMATION ENERGIES OF NATIVE POINT DEFECTS IN STRAINED- LAYER SUPERLATTICES (POSTPRINT) Zhi-Gang Yu...2016 Interim 11 September 2013 – 5 November 2016 4. TITLE AND SUBTITLE FORMATION ENERGIES OF NATIVE POINT DEFECTS IN STRAINED- LAYER SUPERLATTICES

Process defects and in situ monitoring methods in metal powder bed fusion: a review

International Nuclear Information System (INIS)

Grasso, Marco; Colosimo, Bianca Maria

2017-01-01

Despite continuous technological enhancements of metal Additive Manufacturing (AM) systems, the lack of process repeatability and stability still represents a barrier for the industrial breakthrough. The most relevant metal AM applications currently involve industrial sectors (e.g. aerospace and bio-medical) where defects avoidance is fundamental. Because of this, there is the need to develop novel in situ monitoring tools able to keep under control the stability of the process on a layer-by-layer basis, and to detect the onset of defects as soon as possible. On the one hand, AM systems must be equipped with in situ sensing devices able to measure relevant quantities during the process, a.k.a. process signatures. On the other hand, in-process data analytics and statistical monitoring techniques are required to detect and localize the defects in an automated way. This paper reviews the literature and the commercial tools for in situ monitoring of powder bed fusion (PBF) processes. It explores the different categories of defects and their main causes, the most relevant process signatures and the in situ sensing approaches proposed so far. Particular attention is devoted to the development of automated defect detection rules and the study of process control strategies, which represent two critical fields for the development of future smart PBF systems. (paper)

Process defects and in situ monitoring methods in metal powder bed fusion: a review

Science.gov (United States)

Grasso, Marco; Colosimo, Bianca Maria

2017-04-01

Despite continuous technological enhancements of metal Additive Manufacturing (AM) systems, the lack of process repeatability and stability still represents a barrier for the industrial breakthrough. The most relevant metal AM applications currently involve industrial sectors (e.g. aerospace and bio-medical) where defects avoidance is fundamental. Because of this, there is the need to develop novel in situ monitoring tools able to keep under control the stability of the process on a layer-by-layer basis, and to detect the onset of defects as soon as possible. On the one hand, AM systems must be equipped with in situ sensing devices able to measure relevant quantities during the process, a.k.a. process signatures. On the other hand, in-process data analytics and statistical monitoring techniques are required to detect and localize the defects in an automated way. This paper reviews the literature and the commercial tools for in situ monitoring of powder bed fusion (PBF) processes. It explores the different categories of defects and their main causes, the most relevant process signatures and the in situ sensing approaches proposed so far. Particular attention is devoted to the development of automated defect detection rules and the study of process control strategies, which represent two critical fields for the development of future smart PBF systems.

Grasping devices and methods in automated production processes

DEFF Research Database (Denmark)

Fantoni, Gualtiero; Santochi, Marco; Dini, Gino

2014-01-01

assembly to disassembly, from aerospace to food industry, from textile to logistics) are discussed. Finally, the most recent research is reviewed in order to introduce the new trends in grasping. They provide an outlook on the future of both grippers and robotic hands in automated production processes. (C......In automated production processes grasping devices and methods play a crucial role in the handling of many parts, components and products. This keynote paper starts with a classification of grasping phases, describes how different principles are adopted at different scales in different applications...

EEG BASED COGNITIVE WORKLOAD CLASSIFICATION DURING NASA MATB-II MULTITASKING

Directory of Open Access Journals (Sweden)

Sushil Chandra

2015-06-01

Full Text Available The objective of this experiment was to determine the best possible input EEG feature for classification of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification.

Precise design-based defect characterization and root cause analysis

Science.gov (United States)

Xie, Qian; Venkatachalam, Panneerselvam; Lee, Julie; Chen, Zhijin; Zafar, Khurram

2017-03-01

that human operators will typically miss), to obtain the exact defect location on design, to compare all defective patterns thus detected against a library of known patterns, and to classify all defective patterns as either new or known. By applying the computer to these tasks, we automate the entire process from defective pattern identification to pattern classification with high precision, and we perform this operation en masse during R & D, ramp, and volume production. By adopting the methodology, whenever a specific weak pattern is identified, we are able to run a series of characterization operations to ultimately arrive at the root cause. These characterization operations can include (a) searching all pre-existing Review SEM images for the presence of the specific weak pattern to determine whether there is any spatial (within die or within wafer) or temporal (within any particular date range, before or after a mask revision, etc.) correlation and (b) understanding the failure rate of the specific weak pattern to prioritize the urgency of the problem, (c) comparing the weak pattern against an OPC (Optical Procimity Correction) Verification report or a PWQ (Process Window Qualification)/FEM (Focus Exposure Matrix) result to assess the likelihood of it being a litho-sensitive pattern, etc. After resolving the specific weak pattern, we will categorize it as known pattern, and the engineer will move forward with discovering new weak patterns.

Classification by a neural network approach applied to non destructive testing

International Nuclear Information System (INIS)

Lefevre, M.; Preteux, F.; Lavayssiere, B.

1995-01-01

Radiography is used by EDF for pipe inspection in nuclear power plants in order to detect defects. The radiographs obtained are then digitized in a well-defined protocol. The aim of EDF consists of developing a non destructive testing system for recognizing defects. In this paper, we describe the recognition procedure of areas with defects. We first present the digitization protocol, specifies the poor quality of images under study and propose a procedure to enhance defects. We then examine the problem raised by the choice of good features for classification. After having proved that statistical or standard textural features such as homogeneity, entropy or contrast are not relevant, we develop a geometrical-statistical approach based on the cooperation between signal correlations study and regional extrema analysis. The principle consists of analysing and comparing for areas with defects and without any defect, the evolution of conditional probabilities matrices for increasing neighborhood sizes, the shape of variograms and the location of regional minima. We demonstrate that anisotropy and surface of series of 'comet tails' associated with probability matrices, variograms slope and statistical indices, regional extrema location, are features able to discriminate areas with defects from areas without any. The classification is then realized by a neural network, which structure, properties and learning mechanisms are detailed. Finally we discuss the results. (authors). 21 refs., 5 figs

Automated mapping of building facades by machine learning

DEFF Research Database (Denmark)

Höhle, Joachim

2014-01-01

Facades of buildings contain various types of objects which have to be recorded for information systems. The article describes a solution for this task focussing on automated classification by means of machine learning techniques. Stereo pairs of oblique images are used to derive 3D point clouds...

Classification of Partial Discharge Measured under Different Levels of Noise Contamination.

Directory of Open Access Journals (Sweden)

Wong Jee Keen Raymond

Full Text Available Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS and Support Vector Machine (SVM. It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.

Exploring repetitive DNA landscapes using REPCLASS, a tool that automates the classification of transposable elements in eukaryotic genomes.

Science.gov (United States)

Feschotte, Cédric; Keswani, Umeshkumar; Ranganathan, Nirmal; Guibotsy, Marcel L; Levine, David

2009-07-23

Eukaryotic genomes contain large amount of repetitive DNA, most of which is derived from transposable elements (TEs). Progress has been made to develop computational tools for ab initio identification of repeat families, but there is an urgent need to develop tools to automate the annotation of TEs in genome sequences. Here we introduce REPCLASS, a tool that automates the classification of TE sequences. Using control repeat libraries, we show that the program can classify accurately virtually any known TE types. Combining REPCLASS to ab initio repeat finding in the genomes of Caenorhabditis elegans and Drosophila melanogaster allowed us to recover the contrasting TE landscape characteristic of these species. Unexpectedly, REPCLASS also uncovered several novel TE families in both genomes, augmenting the TE repertoire of these model species. When applied to the genomes of distant Caenorhabditis and Drosophila species, the approach revealed a remarkable conservation of TE composition profile within each genus, despite substantial interspecific covariations in genome size and in the number of TEs and TE families. Lastly, we applied REPCLASS to analyze 10 fungal genomes from a wide taxonomic range, most of which have not been analyzed for TE content previously. The results showed that TE diversity varies widely across the fungi "kingdom" and appears to positively correlate with genome size, in particular for DNA transposons. Together, these data validate REPCLASS as a powerful tool to explore the repetitive DNA landscapes of eukaryotes and to shed light onto the evolutionary forces shaping TE diversity and genome architecture.

Specific classification of financial analysis of enterprise activity

Directory of Open Access Journals (Sweden)

Synkevych Nadiia I.

2014-01-01

Full Text Available Despite the fact that one can find a big variety of classifications of types of financial analysis of enterprise activity, which differ with their approach to classification and a number of classification features and their content, in modern scientific literature, their complex comparison and analysis of existing classification have not been done. This explains urgency of this study. The article studies classification of types of financial analysis of scientists and presents own approach to this problem. By the results of analysis the article improves and builds up a specific classification of financial analysis of enterprise activity and offers classification by the following features: objects, subjects, goals of study, automation level, time period of the analytical base, scope of study, organisation system, classification features of the subject, spatial belonging, sufficiency, information sources, periodicity, criterial base, method of data selection for analysis and time direction. All types of financial analysis significantly differ with their inherent properties and parameters depending on the goals of financial analysis. The developed specific classification provides subjects of financial analysis of enterprise activity with a possibility to identify a specific type of financial analysis, which would correctly meet the set goals.

An ant colony optimization based feature selection for web page classification.

Science.gov (United States)

Saraç, Esra; Özel, Selma Ayşe

2014-01-01

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

Two-Stage Classification Approach for Human Detection in Camera Video in Bulk Ports

Directory of Open Access Journals (Sweden)

Mi Chao

2015-09-01

Full Text Available With the development of automation in ports, the video surveillance systems with automated human detection begun to be applied in open-air handling operation areas for safety and security. The accuracy of traditional human detection based on the video camera is not high enough to meet the requirements of operation surveillance. One of the key reasons is that Histograms of Oriented Gradients (HOG features of the human body will show great different between front & back standing (F&B and side standing (Side human body. Therefore, the final training for classifier will only gain a few useful specific features which have contribution to classification and are insufficient to support effective classification, while using the HOG features directly extracted by the samples from different human postures. This paper proposes a two-stage classification method to improve the accuracy of human detection. In the first stage, during preprocessing classification, images is mainly divided into possible F&B human body and not F&B human body, and then they were put into the second-stage classification among side human and non-human recognition. The experimental results in Tianjin port show that the two-stage classifier can improve the classification accuracy of human detection obviously.

Histogram deconvolution - An aid to automated classifiers

Science.gov (United States)

Lorre, J. J.

1983-01-01

It is shown that N-dimensional histograms are convolved by the addition of noise in the picture domain. Three methods are described which provide the ability to deconvolve such noise-affected histograms. The purpose of the deconvolution is to provide automated classifiers with a higher quality N-dimensional histogram from which to obtain classification statistics.

Evolution of a Benthic Imaging System From a Towed Camera to an Automated Habitat Characterization System

Science.gov (United States)

2008-09-01

automated processing of images for color correction, segmentation of foreground targets from sediment and classification of targets to taxonomic category...element in the development of HabCam as a tool for habitat characterization is the automated processing of images for color correction, segmentation of

Sensory classification of table olives using an electronic tongue: Analysis of aqueous pastes and brines.

Science.gov (United States)

Marx, Ítala; Rodrigues, Nuno; Dias, Luís G; Veloso, Ana C A; Pereira, José A; Drunkler, Deisy A; Peres, António M

2017-01-01

Table olives are highly appreciated and consumed worldwide. Different aspects are used for trade category classification being the sensory assessment of negative defects present in the olives and brines one of the most important. The trade category quality classification must follow the International Olive Council directives, requiring the organoleptic assessment of defects by a trained sensory panel. However, the training process is a hard, complex and sometimes subjective task, being the low number of samples that can be evaluated per day a major drawback considering the real needs of the olive industry. In this context, the development of electronic tongues as taste sensors for defects' sensory evaluation is of utmost relevance. So, an electronic tongue was used for table olives classification according to the presence and intensity of negative defects. Linear discrimination models were established based on sub-sets of sensor signals selected by a simulated annealing algorithm. The predictive potential of the novel approach was first demonstrated for standard solutions of chemical compounds that mimic butyric, putrid and zapateria defects (≥93% for cross-validation procedures). Then its applicability was verified; using reference table olives/brine solutions samples identified with a single intense negative attribute, namely butyric, musty, putrid, zapateria or winey-vinegary defects (≥93% cross-validation procedures). Finally, the E-tongue coupled with the same chemometric approach was applied to classify table olive samples according to the trade commercial categories (extra, 1 st choice, 2 nd choice and unsuitable for consumption) and an additional quality category (extra free of defects), established based on sensory analysis data. Despite the heterogeneity of the samples studied and number of different sensory defects perceived, the predictive linear discriminant model established showed sensitivities greater than 86%. So, the overall performance

Deep convolutional neural networks for detection of rail surface defects

NARCIS (Netherlands)

Faghih Roohi, S.; Hajizadeh, S.; Nunez Vicencio, Alfredo; Babuska, R.; De Schutter, B.H.K.; Estevez, Pablo A.; Angelov, Plamen P.; Del Moral Hernandez, Emilio

2016-01-01

In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and

Integrating Human and Machine Intelligence in Galaxy Morphology Classification Tasks

Science.gov (United States)

Beck, Melanie Renee

The large flood of data flowing from observatories presents significant challenges to astronomy and cosmology--challenges that will only be magnified by projects currently under development. Growth in both volume and velocity of astrophysics data is accelerating: whereas the Sloan Digital Sky Survey (SDSS) has produced 60 terabytes of data in the last decade, the upcoming Large Synoptic Survey Telescope (LSST) plans to register 30 terabytes per night starting in the year 2020. Additionally, the Euclid Mission will acquire imaging for 5 x 107 resolvable galaxies. The field of galaxy evolution faces a particularly challenging future as complete understanding often cannot be reached without analysis of detailed morphological galaxy features. Historically, morphological analysis has relied on visual classification by astronomers, accessing the human brains capacity for advanced pattern recognition. However, this accurate but inefficient method falters when confronted with many thousands (or millions) of images. In the SDSS era, efforts to automate morphological classifications of galaxies (e.g., Conselice et al., 2000; Lotz et al., 2004) are reasonably successful and can distinguish between elliptical and disk-dominated galaxies with accuracies of 80%. While this is statistically very useful, a key problem with these methods is that they often cannot say which 80% of their samples are accurate. Furthermore, when confronted with the more complex task of identifying key substructure within galaxies, automated classification algorithms begin to fail. The Galaxy Zoo project uses a highly innovative approach to solving the scalability problem of visual classification. Displaying images of SDSS galaxies to volunteers via a simple and engaging web interface, www.galaxyzoo.org asks people to classify images by eye. Within the first year hundreds of thousands of members of the general public had classified each of the 1 million SDSS galaxies an average of 40 times. Galaxy Zoo

Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch.

Science.gov (United States)

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Abdul Halim, Zaini; Ibrahim, Haidi; Syed Ali, Syed Salim

2012-10-22

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.

Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch

Directory of Open Access Journals (Sweden)

Haidi Ibrahim

2012-10-01

Full Text Available Ripeness classification of oil palm fresh fruit bunches (FFBs during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.

Towards Automatic Classification of Wikipedia Content

Science.gov (United States)

Szymański, Julian

Wikipedia - the Free Encyclopedia encounters the problem of proper classification of new articles everyday. The process of assignment of articles to categories is performed manually and it is a time consuming task. It requires knowledge about Wikipedia structure, which is beyond typical editor competence, which leads to human-caused mistakes - omitting or wrong assignments of articles to categories. The article presents application of SVM classifier for automatic classification of documents from The Free Encyclopedia. The classifier application has been tested while using two text representations: inter-documents connections (hyperlinks) and word content. The results of the performed experiments evaluated on hand crafted data show that the Wikipedia classification process can be partially automated. The proposed approach can be used for building a decision support system which suggests editors the best categories that fit new content entered to Wikipedia.

Defect creation in solids by a decay of electronic excitations

International Nuclear Information System (INIS)

Klinger, M.I.; Lushchik, Ch.B.; Mashovets, T.V.; Kholodar', G.A.; Shejnkman, M.K.; Ehlango, M.A.; Kievskij Gosudarstvennyj Univ.; AN Ukrainskoj SSR, Kiev. Inst. Poluprovodnikov)

1985-01-01

A new type of radiationless transitions in nonmetallic solids accompanied by neither the extraction of a heat nor the luminescence, but by a large (in comparison with the interatomic distance) displacements of a small number of atoms is discussed. A classification is given of the instabilities (electrostatic, electron-vibrational, structural) leading to a creation of the defects in crystalline and glassy solids. The processes of the defect creation, due to both the decay of self-trapped excitions in ionic crystals and the multiple ionization of atoms near the pre-existing charged centres in semiconductor are described. The mechanisms of the complex defects reconstruction in semiconductors by nonequilibrium charge carriers and by an electron-hole recombination are discussed. The role of charge carriers in a thermal defect generation is considered. A mechanism of the peculiar defect creation in glassy semiconductors is discussed

Beyond crosswalks: reliability of exposure assessment following automated coding of free-text job descriptions for occupational epidemiology.

Science.gov (United States)

Burstyn, Igor; Slutsky, Anton; Lee, Derrick G; Singer, Alison B; An, Yuan; Michael, Yvonne L

2014-05-01

Epidemiologists typically collect narrative descriptions of occupational histories because these are less prone than self-reported exposures to recall bias of exposure to a specific hazard. However, the task of coding these narratives can be daunting and prohibitively time-consuming in some settings. The aim of this manuscript is to evaluate the performance of a computer algorithm to translate the narrative description of occupational codes into standard classification of jobs (2010 Standard Occupational Classification) in an epidemiological context. The fundamental question we address is whether exposure assignment resulting from manual (presumed gold standard) coding of the narratives is materially different from that arising from the application of automated coding. We pursued our work through three motivating examples: assessment of physical demands in Women's Health Initiative observational study, evaluation of predictors of exposure to coal tar pitch volatiles in the US Occupational Safety and Health Administration's (OSHA) Integrated Management Information System, and assessment of exposure to agents known to cause occupational asthma in a pregnancy cohort. In these diverse settings, we demonstrate that automated coding of occupations results in assignment of exposures that are in reasonable agreement with results that can be obtained through manual coding. The correlation between physical demand scores based on manual and automated job classification schemes was reasonable (r = 0.5). The agreement between predictive probability of exceeding the OSHA's permissible exposure level for polycyclic aromatic hydrocarbons, using coal tar pitch volatiles as a surrogate, based on manual and automated coding of jobs was modest (Kendall rank correlation = 0.29). In the case of binary assignment of exposure to asthmagens, we observed that fair to excellent agreement in classifications can be reached, depending on presence of ambiguity in assigned job classification (κ

Automated detection and categorization of genital injuries using digital colposcopy

DEFF Research Database (Denmark)

Fernandes, Kelwin; Cardoso, Jaime S.; Astrup, Birgitte Schmidt

2017-01-01

handcrafted features and deep learning techniques in the automated processing of colposcopic images for genital injury detection. Positive results where achieved by both paradigms in segmentation and classification subtasks, being traditional and deep models the best strategy for each subtask type...

Studying the potential impact of automated document classification on scheduling a systematic review update

Science.gov (United States)

2012-01-01

Background Systematic Reviews (SRs) are an essential part of evidence-based medicine, providing support for clinical practice and policy on a wide range of medical topics. However, producing SRs is resource-intensive, and progress in the research they review leads to SRs becoming outdated, requiring updates. Although the question of how and when to update SRs has been studied, the best method for determining when to update is still unclear, necessitating further research. Methods In this work we study the potential impact of a machine learning-based automated system for providing alerts when new publications become available within an SR topic. Some of these new publications are especially important, as they report findings that are more likely to initiate a review update. To this end, we have designed a classification algorithm to identify articles that are likely to be included in an SR update, along with an annotation scheme designed to identify the most important publications in a topic area. Using an SR database containing over 70,000 articles, we annotated articles from 9 topics that had received an update during the study period. The algorithm was then evaluated in terms of the overall correct and incorrect alert rate for publications meeting the topic inclusion criteria, as well as in terms of its ability to identify important, update-motivating publications in a topic area. Results Our initial approach, based on our previous work in topic-specific SR publication classification, identifies over 70% of the most important new publications, while maintaining a low overall alert rate. Conclusions We performed an initial analysis of the opportunities and challenges in aiding the SR update planning process with an informatics-based machine learning approach. Alerts could be a useful tool in the planning, scheduling, and allocation of resources for SR updates, providing an improvement in timeliness and coverage for the large number of medical topics needing SRs

Oil defect detection of electrowetting display

Science.gov (United States)

Chiang, Hou-Chi; Tsai, Yu-Hsiang; Yan, Yung-Jhe; Huang, Ting-Wei; Mang, Ou-Yang

2015-08-01

In recent years, transparent display is an emerging topic in display technologies. Apply in many fields just like mobile device, shopping or advertising window, and etc. Electrowetting Display (EWD) is one kind of potential transparent display technology advantages of high transmittance, fast response time, high contrast and rich color with pigment based oil system. In mass production process of Electrowetting Display, oil defects should be found by Automated Optical Inspection (AOI) detection system. It is useful in determination of panel defects for quality control. According to the research of our group, we proposed a mechanism of AOI detection system detecting the different kinds of oil defects. This mechanism can detect different kinds of oil defect caused by oil overflow or material deteriorated after oil coating or driving. We had experiment our mechanism with a 6-inch Electrowetting Display panel from ITRI, using an Epson V750 scanner with 1200 dpi resolution. Two AOI algorithms were developed, which were high speed method and high precision method. In high precision method, oil jumping or non-recovered can be detected successfully. This mechanism of AOI detection system can be used to evaluate the oil uniformity in EWD panel process. In the future, our AOI detection system can be used in quality control of panel manufacturing for mass production.

Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography.

Science.gov (United States)

Jeong, Jeong-Won; Shin, Dae C; Do, Synho; Marmarelis, Vasilis Z

2006-08-01

This paper presents a novel segmentation methodology for automated classification and differentiation of soft tissues using multiband data obtained with the newly developed system of high-resolution ultrasonic transmission tomography (HUTT) for imaging biological organs. This methodology extends and combines two existing approaches: the L-level set active contour (AC) segmentation approach and the agglomerative hierarchical kappa-means approach for unsupervised clustering (UC). To prevent the trapping of the current iterative minimization AC algorithm in a local minimum, we introduce a multiresolution approach that applies the level set functions at successively increasing resolutions of the image data. The resulting AC clusters are subsequently rearranged by the UC algorithm that seeks the optimal set of clusters yielding the minimum within-cluster distances in the feature space. The presented results from Monte Carlo simulations and experimental animal-tissue data demonstrate that the proposed methodology outperforms other existing methods without depending on heuristic parameters and provides a reliable means for soft tissue differentiation in HUTT images.

Inspection of surface defects for cladding tube with laser

International Nuclear Information System (INIS)

Senoo, Shigeo; Igarashi, Miyuki; Satoh, Masakazu; Miura, Makoto

1978-01-01

This paper presents the results of experiment on mechanizing the visual inspection of surface defects of cladding tubes and improving the reliability of surface defect inspection. Laser spot inspection method was adopted for this purpose. Since laser speckle pattern includes many informations about surface aspects, the method can be utilized as an effective means for detection or classification of the surface defects. Laser beam is focussed on cladding tube surfaces, and the reflected laser beam forms typical stellar speckle patterns on a screen. Sample cladding tubes are driven in longitudinal direction, and a photo-detector is placed at a position where secondary reflection will fall on the detector. Reflected laser beam from defect-free surfaces shows uniform distribution on the detector. When the incident focussed laser beam is directed to defects, the intensity of the reflected light is reduced. In the second method, laser beam is scanned by a rotating cube mirror. As the results of experiment, the typical patterns caused by defects were observed. It is clear that reflection patterns change with the kinds of defects. The sensitivity of defect detection decreases with the increase in laser beam diameter. Surface defect detection by intensity change was also tested. (Kato, T.)

Automation of the Analysis and Classification of the Line Material

Directory of Open Access Journals (Sweden)

A. A. Machuev

2011-03-01

Full Text Available The work is devoted to the automation of the process of the analysis and verification of various formats of data presentation for what the special software is developed. Working out and testing the special software were made on an example of files with the typical expansions which features of structure are known in advance.

Automated quality characterization of 3D printed bone scaffolds

Directory of Open Access Journals (Sweden)

Tzu-Liang Bill Tseng

2014-07-01

Full Text Available Optimization of design is an important step in obtaining tissue engineering scaffolds with appropriate shapes and inner microstructures. Different shapes and sizes of scaffolds are modeled using UGS NX 6.0 software with variable pore sizes. The quality issue we are concerned is the scaffold porosity, which is mainly caused by the fabrication inaccuracies. Bone scaffolds are usually characterized using a scanning electron microscope, but this study presents a new automated inspection and classification technique. Due to many numbers and size variations for the pores, the manual inspection of the fabricated scaffolds tends to be error-prone and costly. Manual inspection also raises the chance of contamination. Thus, non-contact, precise inspection is preferred. In this study, the critical dimensions are automatically measured by the vision camera. The measured data are analyzed to classify the quality characteristics. The automated inspection and classification techniques developed in this study are expected to improve the quality of the fabricated scaffolds and reduce the overall cost of manufacturing.

Quantitative Evaluation of Defect Based on Ultrasonic Guided Wave and CHMM

Directory of Open Access Journals (Sweden)

Chen Le

2016-01-01

Full Text Available The axial length of pipe defects is not linear with the reflection coefficient, which is difficult to identify the axial length of the defect by the reflection coefficient method. Continuous Hidden Markov Model (CHMM is proposed to accurately classify the axial length of defects, achieving the objective of preliminary quantitative evaluation. Firstly, wavelet packet decomposition method is used to extract the characteristic information of the guided wave signal, and Kernel Sliced Inverse Regression (KSIR method is used to reduce the dimension of feature set. Then, a variety of CHMM models are trained for classification. Finally, the trained models are used to identify the artificial corrosion defects on the outer surface of the pipe. The results show that the CHMM model has better robustness and can accurately identify the axial defects.

Real-time ultrasonic weld evaluation system

Science.gov (United States)

Katragadda, Gopichand; Nair, Satish; Liu, Harry; Brown, Lawrence M.

1996-11-01

Ultrasonic testing techniques are currently used as an alternative to radiography for detecting, classifying,and sizing weld defects, and for evaluating weld quality. Typically, ultrasonic weld inspections are performed manually, which require significant operator expertise and time. Thus, in recent years, the emphasis is to develop automated methods to aid or replace operators in critical weld inspections where inspection time, reliability, and operator safety are major issues. During this period, significant advances wee made in the areas of weld defect classification and sizing. Very few of these methods, however have found their way into the market, largely due to the lack of an integrated approach enabling real-time implementation. Also, not much research effort was directed in improving weld acceptance criteria. This paper presents an integrated system utilizing state-of-the-art techniques for a complete automation of the weld inspection procedure. The modules discussed include transducer tracking, classification, sizing, and weld acceptance criteria. Transducer tracking was studied by experimentally evaluating sonic and optical position tracking techniques. Details for this evaluation are presented. Classification is obtained using a multi-layer perceptron. Results from different feature extraction schemes, including a new method based on a combination of time and frequency-domain signal representations are given. Algorithms developed to automate defect registration and sizing are discussed. A fuzzy-logic acceptance criteria for weld acceptance is presented describing how this scheme provides improved robustness compared to the traditional flow-diagram standards.

Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning

KAUST Repository

Aljaafari, Nura

2018-01-01

. This method is time-consuming and requires a high level of experience. The recent advances in AI have helped to solve and automate several difficult tasks which motivated us to develop a classification tool for ichthyoplankton. We show that using machine

Automated ultrasonic shop inspection of reactor pressure vessel forgings

International Nuclear Information System (INIS)

Farley, J.M.; Dikstra, B.J.; Hanstock, D.J.; Pople, C.H.

1986-01-01

Automated ultrasonic shop inspection utilizing a computer-controlled system is being applied to each of the forgings for the reactor pressure vessel of the proposed Sizewell B PWR power station. Procedures which utilize a combination of high sensitivity shear wave pulse echo, 0 degrees and 70 degrees angled longitudinal waves, tandem and through-thickness arrays have been developed to provide comprehensive coverage and an overall reliability of inspection comparable to the best achieved in UKAEA defect detection trials and in PISC II. This paper describes the ultrasonic techniques, the automated system (its design, commissioning and testing), validation and the progress of the inspections

Classification of methods for measuring current-voltage characteristics of semiconductor devices

Directory of Open Access Journals (Sweden)

Iermolenko Ia. O.

2014-06-01

Full Text Available It is shown that computer systems for measuring current-voltage characteristics are very important for semiconductor devices production. The main criteria of efficiency of such systems are defined. It is shown that efficiency of such systems significantly depends on the methods for measuring current-voltage characteristics of semiconductor devices. The aim of this work is to analyze existing methods for measuring current-voltage characteristics of semiconductor devices and to create the classification of these methods in order to specify the most effective solutions in terms of defined criteria. To achieve this aim, the most common classifications of methods for measuring current-voltage characteristics of semiconductor devices and their main disadvantages are considered. Automated and manual, continuous, pulse, mixed, isothermal and isodynamic methods for measuring current-voltage characteristics are analyzed. As a result of the analysis and generalization of existing methods the next classification criteria are defined: the level of automation, the form of measurement signals, the condition of semiconductor device during the measurements, and the use of mathematical processing of the measurement results. With the use of these criteria the classification scheme of methods for measuring current-voltage characteristics of semiconductor devices is composed and the most effective methods are specified.

New York State Thruway Authority automatic vehicle classification (AVC) : research report.

Science.gov (United States)

2008-03-31

In December 2007, the N.Y.S. Thruway Authority (Thruway) concluded a Federal : funded research effort to study technology and develop a design for retrofitting : devices required in implementing a fully automated vehicle classification system i...

Support Vector Machine and Parametric Wavelet-Based Texture Classification of Stem Cell Images

National Research Council Canada - National Science Library

Jeffreys, Christopher

2004-01-01

.... Since colony texture is a major discriminating feature in determining quality, we introduce a non-invasive, semi-automated texture-based stem cell colony classification methodology to aid researchers...

Acute leukemia classification by ensemble particle swarm model selection.

Science.gov (United States)

Escalante, Hugo Jair; Montes-y-Gómez, Manuel; González, Jesús A; Gómez-Gil, Pilar; Altamirano, Leopoldo; Reyes, Carlos A; Reta, Carolina; Rosales, Alejandro

2012-07-01

Acute leukemia is a malignant disease that affects a large proportion of the world population. Different types and subtypes of acute leukemia require different treatments. In order to assign the correct treatment, a physician must identify the leukemia type or subtype. Advanced and precise methods are available for identifying leukemia types, but they are very expensive and not available in most hospitals in developing countries. Thus, alternative methods have been proposed. An option explored in this paper is based on the morphological properties of bone marrow images, where features are extracted from medical images and standard machine learning techniques are used to build leukemia type classifiers. This paper studies the use of ensemble particle swarm model selection (EPSMS), which is an automated tool for the selection of classification models, in the context of acute leukemia classification. EPSMS is the application of particle swarm optimization to the exploration of the search space of ensembles that can be formed by heterogeneous classification models in a machine learning toolbox. EPSMS does not require prior domain knowledge and it is able to select highly accurate classification models without user intervention. Furthermore, specific models can be used for different classification tasks. We report experimental results for acute leukemia classification with real data and show that EPSMS outperformed the best results obtained using manually designed classifiers with the same data. The highest performance using EPSMS was of 97.68% for two-type classification problems and of 94.21% for more than two types problems. To the best of our knowledge, these are the best results reported for this data set. Compared with previous studies, these improvements were consistent among different type/subtype classification tasks, different features extracted from images, and different feature extraction regions. The performance improvements were statistically significant

Algorithm of Defect Segmentation for AFP Based on Prepregs

Directory of Open Access Journals (Sweden)

CAI Zhiqiang

2017-04-01

Full Text Available In order to ensure the performance of the automated fiber placement forming parts, according to the homogeneity of the image of the prepreg surface along the fiber direction, a defect segmentation algorithm which was the combination of gray compensation and substraction algorithm based on image processing technology was proposed. The gray compensation matrix of image was used to compensate the gray image, and the maximum error point of the image matrix was eliminated according to the characteristics that the gray error obeys the normal distribution. The standard image was established, using the allowed deviation coefficient K as a criterion for substraction segmentation. Experiments show that the algorithm has good effect, fast speed in segmenting two kinds of typical laying defect of bubbles or foreign objects, and provides a good theoretical basis to realize automatic laying defect online monitoring.

Eisenmenger ventricular septal defect in a Humboldt penguin (Spheniscus humboldti).

Science.gov (United States)

Laughlin, D S; Ialeggio, D M; Trupkiewicz, J G; Sleeper, M M

2016-09-01

The Eisenmenger ventricular septal defect is an uncommon type of ventricular septal defect characterised in humans by a traditionally perimembranous ventricular septal defect, anterior deviation (cranioventral deviation in small animal patients) of the muscular outlet septum causing malalignment relative to the remainder of the muscular septum, and overriding of the aortic valve. This anomaly is reported infrequently in human patients and was identified in a 45-day-old Humboldt Penguin, Spheniscus humboldti, with signs of poor growth and a cardiac murmur. This case report describes the findings in this penguin and summarises the anatomy and classification of this cardiac anomaly. To the authors' knowledge this is the first report of an Eisenmenger ventricular septal defect in a veterinary patient. Copyright © 2016 Elsevier B.V. All rights reserved.

Automated Classification of Phonological Errors in Aphasic Language

Science.gov (United States)

Ahuja, Sanjeev B.; Reggia, James A.; Berndt, Rita S.

1984-01-01

Using heuristically-guided state space search, a prototype program has been developed to simulate and classify phonemic errors occurring in the speech of neurologically-impaired patients. Simulations are based on an interchangeable rule/operator set of elementary errors which represent a theory of phonemic processing faults. This work introduces and evaluates a novel approach to error simulation and classification, it provides a prototype simulation tool for neurolinguistic research, and it forms the initial phase of a larger research effort involving computer modelling of neurolinguistic processes.

Suggestions on performance of finite element limit analysis for eliminating the necessity of stress classifications in design and defect assessment

Energy Technology Data Exchange (ETDEWEB)

Fujioka, T. [Central Research Institute of Electric Power Industry, Tokyo (Japan)

2001-07-01

In structural design of a nuclear power component, stress classification from elastic stress analysis resultants is often used. Alternatively, to improve accuracy, finite element limit analysis may be performed. This paper examines some issues relating to the use of limit analysis; specifically, the treatment of multiple applied loads and the definition of the limit load from analysis using hardening plasticity laws. These are addressed both by detailed analysis for a simple geometry and by using the reference stress approach to estimate the inelastic displacement. The proposals are also applicable to a defect assessment of a cracked component, and treatment of distributed loads. It is shown that multiple or distributed loads should be treated as if they were applied proportionally irrespective of the actual nature of loads, and that the limit load from analysis with general plasticity laws may be estimated using a newly suggested reduced elastic slope method. (author)

Suggestions on performance of finite element limit analysis for eliminating the necessity of stress classifications in design and defect assessment

International Nuclear Information System (INIS)

Fujioka, T.

2001-01-01

In structural design of a nuclear power component, stress classification from elastic stress analysis resultants is often used. Alternatively, to improve accuracy, finite element limit analysis may be performed. This paper examines some issues relating to the use of limit analysis; specifically, the treatment of multiple applied loads and the definition of the limit load from analysis using hardening plasticity laws. These are addressed both by detailed analysis for a simple geometry and by using the reference stress approach to estimate the inelastic displacement. The proposals are also applicable to a defect assessment of a cracked component, and treatment of distributed loads. It is shown that multiple or distributed loads should be treated as if they were applied proportionally irrespective of the actual nature of loads, and that the limit load from analysis with general plasticity laws may be estimated using a newly suggested reduced elastic slope method. (author)

A study on limb reduction defects in six European regions

NARCIS (Netherlands)

Stoll, C; Calzolari, E; Cornel, M; GarciaMinaur, S; Garne, E; Nevin, N

1996-01-01

Limb reduction defects (LRD) gained especial attention after the thalidomide tragedy in 1962, LRD are common congenital malformations which present as obvious congenital anomalies recognized at birth, Therefore it might be assumed that they are well documented, However classification of LRDs is

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

Science.gov (United States)

Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

Automated Classification and Analysis of Non-metallic Inclusion Data Sets

Science.gov (United States)

Abdulsalam, Mohammad; Zhang, Tongsheng; Tan, Jia; Webler, Bryan A.

2018-05-01

The aim of this study is to utilize principal component analysis (PCA), clustering methods, and correlation analysis to condense and examine large, multivariate data sets produced from automated analysis of non-metallic inclusions. Non-metallic inclusions play a major role in defining the properties of steel and their examination has been greatly aided by automated analysis in scanning electron microscopes equipped with energy dispersive X-ray spectroscopy. The methods were applied to analyze inclusions on two sets of samples: two laboratory-scale samples and four industrial samples from a near-finished 4140 alloy steel components with varying machinability. The laboratory samples had well-defined inclusions chemistries, composed of MgO-Al2O3-CaO, spinel (MgO-Al2O3), and calcium aluminate inclusions. The industrial samples contained MnS inclusions as well as (Ca,Mn)S + calcium aluminate oxide inclusions. PCA could be used to reduce inclusion chemistry variables to a 2D plot, which revealed inclusion chemistry groupings in the samples. Clustering methods were used to automatically classify inclusion chemistry measurements into groups, i.e., no user-defined rules were required.

Defect window analysis by using SEM-contour based shape quantifying method for sub-20nm node production

Science.gov (United States)

Hibino, Daisuke; Hsu, Mingyi; Shindo, Hiroyuki; Izawa, Masayuki; Enomoto, Yuji; Lin, J. F.; Hu, J. R.

2013-04-01

The impact on yield loss due to systematic defect which remains after Optical Proximity Correction (OPC) modeling has increased, and achieving an acceptable yield has become more difficult in the leading technology beyond 20 nm node production. Furthermore Process-Window has become narrow because of the complexity of IC design and less process margin. In the past, the systematic defects have been inspected by human-eyes. However the judgment by human-eyes is sometime unstable and not accurate. Moreover an enormous amount of time and labor will have to be expended on the one-by-one judgment for several thousands of hot-spot defects. In order to overcome these difficulties and improve the yield and manufacturability, the automated system, which can quantify the shape difference with high accuracy and speed, is needed. Inspection points could be increased for getting higher yield, if the automated system achieves our goal. Defect Window Analysis (DWA) system by using high-precision-contour extraction from SEM image on real silicon and quantifying method which can calculate the difference between defect pattern and non-defect pattern automatically, which was developed by Hitachi High-Technologies, has been applied to the defect judgment instead of the judgment by human-eyes. The DWA result which describes process behavior might be feedback to design or OPC or mask. This new methodology and evaluation results will be presented in detail in this paper.

Study on on-machine defects measuring system on high power laser optical elements

Science.gov (United States)

Luo, Chi; Shi, Feng; Lin, Zhifan; Zhang, Tong; Wang, Guilin

2017-10-01

The influence of surface defects on high power laser optical elements will cause some harm to the performances of imaging system, including the energy consumption and the damage of film layer. To further increase surface defects on high power laser optical element, on-machine defects measuring system was investigated. Firstly, the selection and design are completed by the working condition analysis of the on-machine defects detection system. By designing on processing algorithms to realize the classification recognition and evaluation of surface defects. The calibration experiment of the scratch was done by using the self-made standard alignment plate. Finally, the detection and evaluation of surface defects of large diameter semi-cylindrical silicon mirror are realized. The calibration results show that the size deviation is less than 4% that meet the precision requirement of the detection of the defects. Through the detection of images the on-machine defects detection system can realize the accurate identification of surface defects.

Development of an Automated MRI-Based Diagnostic Protocol for Amyotrophic Lateral Sclerosis Using Disease-Specific Pathognomonic Features: A Quantitative Disease-State Classification Study.

Science.gov (United States)

Schuster, Christina; Hardiman, Orla; Bede, Peter

2016-01-01

Despite significant advances in quantitative neuroimaging, the diagnosis of ALS remains clinical and MRI-based biomarkers are not currently used to aid the diagnosis. The objective of this study is to develop a robust, disease-specific, multimodal classification protocol and validate its diagnostic accuracy in independent, early-stage and follow-up data sets. 147 participants (81 ALS patients and 66 healthy controls) were divided into a training sample and a validation sample. Patients in the validation sample underwent follow-up imaging longitudinally. After removing age-related variability, indices of grey and white matter integrity in ALS-specific pathognomonic brain regions were included in a cross-validated binary logistic regression model to determine the probability of individual scans indicating ALS. The following anatomical regions were assessed for diagnostic classification: average grey matter density of the left and right precentral gyrus, the average fractional anisotropy and radial diffusivity of the left and right superior corona radiata, inferior corona radiata, internal capsule, mesencephalic crus of the cerebral peduncles, pontine segment of the corticospinal tract, and the average diffusivity values of the genu, corpus and splenium of the corpus callosum. Using a 50% probability cut-off value of suffering from ALS, the model was able to discriminate ALS patients and HC with good sensitivity (80.0%) and moderate accuracy (70.0%) in the training sample and superior sensitivity (85.7%) and accuracy (78.4%) in the independent validation sample. This diagnostic classification study endeavours to advance ALS biomarker research towards pragmatic clinical applications by providing an approach of automated individual-data interpretation based on group-level observations.

Automated Interpretation of Blood Culture Gram Stains by Use of a Deep Convolutional Neural Network.

Science.gov (United States)

Smith, Kenneth P; Kang, Anthony D; Kirby, James E

2018-03-01

Microscopic interpretation of stained smears is one of the most operator-dependent and time-intensive activities in the clinical microbiology laboratory. Here, we investigated application of an automated image acquisition and convolutional neural network (CNN)-based approach for automated Gram stain classification. Using an automated microscopy platform, uncoverslipped slides were scanned with a 40× dry objective, generating images of sufficient resolution for interpretation. We collected 25,488 images from positive blood culture Gram stains prepared during routine clinical workup. These images were used to generate 100,213 crops containing Gram-positive cocci in clusters, Gram-positive cocci in chains/pairs, Gram-negative rods, or background (no cells). These categories were targeted for proof-of-concept development as they are associated with the majority of bloodstream infections. Our CNN model achieved a classification accuracy of 94.9% on a test set of image crops. Receiver operating characteristic (ROC) curve analysis indicated a robust ability to differentiate between categories with an area under the curve of >0.98 for each. After training and validation, we applied the classification algorithm to new images collected from 189 whole slides without human intervention. Sensitivity and specificity were 98.4% and 75.0% for Gram-positive cocci in chains and pairs, 93.2% and 97.2% for Gram-positive cocci in clusters, and 96.3% and 98.1% for Gram-negative rods. Taken together, our data support a proof of concept for a fully automated classification methodology for blood-culture Gram stains. Importantly, the algorithm was highly adept at identifying image crops with organisms and could be used to present prescreened, classified crops to technologists to accelerate smear review. This concept could potentially be extended to all Gram stain interpretive activities in the clinical laboratory. Copyright © 2018 American Society for Microbiology.

Neural network and wavelet average framing percentage energy for atrial fibrillation classification.

Science.gov (United States)

Daqrouq, K; Alkhateeb, A; Ajour, M N; Morfeq, A

2014-03-01

ECG signals are an important source of information in the diagnosis of atrial conduction pathology. Nevertheless, diagnosis by visual inspection is a difficult task. This work introduces a novel wavelet feature extraction method for atrial fibrillation derived from the average framing percentage energy (AFE) of terminal wavelet packet transform (WPT) sub signals. Probabilistic neural network (PNN) is used for classification. The presented method is shown to be a potentially effective discriminator in an automated diagnostic process. The ECG signals taken from the MIT-BIH database are used to classify different arrhythmias together with normal ECG. Several published methods were investigated for comparison. The best recognition rate selection was obtained for AFE. The classification performance achieved accuracy 97.92%. It was also suggested to analyze the presented system in an additive white Gaussian noise (AWGN) environment; 55.14% for 0dB and 92.53% for 5dB. It was concluded that the proposed approach of automating classification is worth pursuing with larger samples to validate and extend the present study. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Automated Discovery of Speech Act Categories in Educational Games

Science.gov (United States)

Rus, Vasile; Moldovan, Cristian; Niraula, Nobal; Graesser, Arthur C.

2012-01-01

In this paper we address the important task of automated discovery of speech act categories in dialogue-based, multi-party educational games. Speech acts are important in dialogue-based educational systems because they help infer the student speaker's intentions (the task of speech act classification) which in turn is crucial to providing adequate…

The Business Case for Automated Software Engineering

Science.gov (United States)

Menzies, Tim; Elrawas, Oussama; Hihn, Jairus M.; Feather, Martin S.; Madachy, Ray; Boehm, Barry

2007-01-01

Adoption of advanced automated SE (ASE) tools would be more favored if a business case could be made that these tools are more valuable than alternate methods. In theory, software prediction models can be used to make that case. In practice, this is complicated by the 'local tuning' problem. Normally. predictors for software effort and defects and threat use local data to tune their predictions. Such local tuning data is often unavailable. This paper shows that assessing the relative merits of different SE methods need not require precise local tunings. STAR 1 is a simulated annealer plus a Bayesian post-processor that explores the space of possible local tunings within software prediction models. STAR 1 ranks project decisions by their effects on effort and defects and threats. In experiments with NASA systems. STARI found one project where ASE were essential for minimizing effort/ defect/ threats; and another project were ASE tools were merely optional.

Diabetes classification using a redundancy reduction preprocessor

Directory of Open Access Journals (Sweden)

Áurea Celeste Ribeiro

Full Text Available Introduction Diabetes patients can benefit significantly from early diagnosis. Thus, accurate automated screening is becoming increasingly important due to the wide spread of that disease. Previous studies in automated screening have found a maximum accuracy of 92.6%. Methods This work proposes a classification methodology based on efficient coding of the input data, which is carried out by decreasing input data redundancy using well-known ICA algorithms, such as FastICA, JADE and INFOMAX. The classifier used in the task to discriminate diabetics from non-diaibetics is the one class support vector machine. Classification tests were performed using noninvasive and invasive indicators. Results The results suggest that redundancy reduction increases one-class support vector machine performance when discriminating between diabetics and nondiabetics up to an accuracy of 98.47% while using all indicators. By using only noninvasive indicators, an accuracy of 98.28% was obtained. Conclusion The ICA feature extraction improves the performance of the classifier in the data set because it reduces the statistical dependence of the collected data, which increases the ability of the classifier to find accurate class boundaries.

IRIS COLOUR CLASSIFICATION SCALES--THEN AND NOW.

Science.gov (United States)

Grigore, Mariana; Avram, Alina

2015-01-01

Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual's eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale.

KEPLER ECLIPSING BINARY STARS. III. CLASSIFICATION OF KEPLER ECLIPSING BINARY LIGHT CURVES WITH LOCALLY LINEAR EMBEDDING

International Nuclear Information System (INIS)

Matijevič, Gal; Prša, Andrej; Orosz, Jerome A.; Welsh, William F.; Bloemen, Steven; Barclay, Thomas

2012-01-01

We present an automated classification of 2165 Kepler eclipsing binary (EB) light curves that accompanied the second Kepler data release. The light curves are classified using locally linear embedding, a general nonlinear dimensionality reduction tool, into morphology types (detached, semi-detached, overcontact, ellipsoidal). The method, related to a more widely used principal component analysis, produces a lower-dimensional representation of the input data while preserving local geometry and, consequently, the similarity between neighboring data points. We use this property to reduce the dimensionality in a series of steps to a one-dimensional manifold and classify light curves with a single parameter that is a measure of 'detachedness' of the system. This fully automated classification correlates well with the manual determination of morphology from the data release, and also efficiently highlights any misclassified objects. Once a lower-dimensional projection space is defined, the classification of additional light curves runs in a negligible time and the method can therefore be used as a fully automated classifier in pipeline structures. The classifier forms a tier of the Kepler EB pipeline that pre-processes light curves for the artificial intelligence based parameter estimator.

Angiographic differentiation of type of ventricular septal defects

International Nuclear Information System (INIS)

Cheon, Mal Soon; Park, Hee Young; Kim, Yang Sook

1989-01-01

Defects of the ventricular septum are the commonest type of congenital cardiac malformations. A classification with axial angiography of the subtypes of ventricular septal defects is proposed on the study of 126 patients with defects of the ventricular septum. The results were as follows: 1. The incidence of the ventricular septal defects was 39.6% of congenital heart malformation. 2. The sex distribution of cases were 70 males and 56 females, the age ranged from 13 months to 26 years. 3. Angiographic features seen by axial angiography were as follows: a. Perimembranous defects as seen on long axial view of left ventriculogram were in continuity wity aortic valve. The relation of the defect to the tricuspid valve allows distinction of the extension of the preimembranous defect toward inlet, trabecular, or infundibular zones. This relation was determined angiographically, using the course of the contrast medium from the left ventricle through the ventricular septal defect, opacifying the right ventricle. In inlet excavation, the shunted blood opacified the recess between septal leaflet of tricuspid valve and interventricular septum in early phase, in infundibular excavation, opacified the recess between anterior leaflet of tricuspid valve and anterior free wall of right ventricle and in trabecular excavation, the shunted blood traversed anterior portion of tricuspid valve ring, opacified trabecular portion of right ventricle. b. Muscular defects were separated from the semilunar and atrioventricular valves. c, Subarterial defects were related to both semilunar valves, and they were best demonstrated on the elongated right anterior oblique view of the left ventriculogram. d. Total infundibular defects were profiled in right anterior oblique 30 and long axial view, subaortic in location in both views

Discriminant Analysis of Defective and Non-Defective Field Pea (Pisum sativum L.) into Broad Market Grades Based on Digital Image Features.

Science.gov (United States)

McDonald, Linda S; Panozzo, Joseph F; Salisbury, Phillip A; Ford, Rebecca

2016-01-01

Field peas (Pisum sativum L.) are generally traded based on seed appearance, which subjectively defines broad market-grades. In this study, we developed an objective Linear Discriminant Analysis (LDA) model to classify market grades of field peas based on seed colour, shape and size traits extracted from digital images. Seeds were imaged in a high-throughput system consisting of a camera and laser positioned over a conveyor belt. Six colour intensity digital images were captured (under 405, 470, 530, 590, 660 and 850nm light) for each seed, and surface height was measured at each pixel by laser. Colour, shape and size traits were compiled across all seed in each sample to determine the median trait values. Defective and non-defective seed samples were used to calibrate and validate the model. Colour components were sufficient to correctly classify all non-defective seed samples into correct market grades. Defective samples required a combination of colour, shape and size traits to achieve 87% and 77% accuracy in market grade classification of calibration and validation sample-sets respectively. Following these results, we used the same colour, shape and size traits to develop an LDA model which correctly classified over 97% of all validation samples as defective or non-defective.

Cutaneous vascular anomalies associated with neural tube defects: nomenclature and pathology revisited.

Science.gov (United States)

Maugans, Todd; Sheridan, Rachel M; Adams, Denise; Gupta, Anita

2011-07-01

Lumbosacral cutaneous vascular anomalies associated with neural tube defects are frequently described in the literature as "hemangiomas." The classification system for pediatric vascular anomalies developed by the International Society for the Study of Vascular Anomalies provides a framework to accurately diagnose these lesions. To apply this classification to vascular cutaneous anomalies overlying myelodysplasias. A retrospective analysis of patients with neural tube defects and lumbosacral cutaneous vascular lesions was performed. All eligible patients had detailed histopathologic analysis of skin and spinal cord/placode lesions. Clinical and radiologic features were analyzed. Conventional histology and GLUT-1 immunostaining were performed to differentiate infantile capillary hemangiomas from capillary vascular malformations. Ten cases with cutaneous lesions associated with neural tube defects were reviewed. Five lesions were diagnosed as infantile capillary hemangiomas based upon histology and positive GLUT-1 endothelial reactivity. These lesions had a strong association with dermal sinus tracts. No reoperations were required for residual intraspinal vascular lesions, and overlying cutaneous vascular anomalies involuted with time. The remaining 5 lesions were diagnosed as capillary malformations. These occurred with both open and closed neural tube defects, did not involute, and demonstrated enlargement and darkening due to vascular congestion. The International Society for the Study of Vascular Anomalies scheme should be used to describe the cutaneous vascular lesions associated with neural tube defects: infantile capillary hemangiomas and capillary malformations. We advocate that these lesions be described as "vascular anomalies" or "stains" pending accurate diagnosis by clinical, histological, and immunohistochemical evaluations.

Detection, identification and classification of defects using ANN and a robotic manipulator of 2 G.L. (Kohonen and MLP algorithms)

International Nuclear Information System (INIS)

Barrera, G.; Fabian, M. A.; Ugalde, C. A.

2002-01-01

The ultrasonic inspection technique had a sustained growth since the 80's It has several advantages, compared with the contact technique. A flexible and low cost solution is presented based on virtual instrumentation for the servomechanism (manipulator) control of the ultrasound inspection transducer in the immersion technique. The developed system uses a personal computer (PC). a Windows Operating System. Virtual Instrumentation Software. DAQ cards and a GPIB card. As a solution to detection, classification and evaluation of defects an Artificial Neuronal Networks technique proposed. It consists of characterization and interpretation of acoustic signals (echoes) acquired by the immersion ultrasonic inspection technique. Two neuronal networks are proposed: Kohonen and Multilayer Perceptron (MLP). With this techniques non-linear complex processes can be modeled with great precision. The 2-degree of freedom manipulator control, the data acquisition and the net training have been carried out in a virtual instrument environment using LabVIEV and Data Engine. (Author) 14 refs

HEp-2 Cell Classification Using Shape Index Histograms With Donut-Shaped Spatial Pooling

DEFF Research Database (Denmark)

Larsen, Anders Boesen Lindbo; Vestergaard, Jacob Schack; Larsen, Rasmus

2014-01-01

We present a new method for automatic classification of indirect immunoflourescence images of HEp-2 cells into different staining pattern classes. Our method is based on a new texture measure called shape index histograms that captures second-order image structure at multiple scales. Moreover, we...... datasets. Our results show that shape index histograms are superior to other popular texture descriptors for HEp-2 cell classification. Moreover, when comparing to other automated systems for HEp-2 cell classification we show that shape index histograms are very competitive; especially considering...

Current Strategies in Reconstruction of Maxillectomy Defects

Science.gov (United States)

Andrades, Patricio; Militsakh, Oleg; Hanasono, Matthew M.; Rieger, Jana; Rosenthal, Eben L.

2014-01-01

Objective To outline a contemporary review of defect classification and reconstructive options. Design Review article. Setting Tertiary care referral centers. Results Although prosthetic rehabilitation remains the standard of care in many institutions, the discomfort of wearing, removing, and cleaning a prosthesis; the inability to retain a prosthesis in large defects; and the frequent need for readjustments often limit the value of this cost-effective and successful method of restoring speech and mastication. However, flap reconstruction offers an option for many, although there is no agreement as to which techniques should be used for optimal reconstruction. Flap reconstruction also involves a longer recovery time with increased risk of surgical complications, has higher costs associated with the procedure, and requires access to a highly experienced surgeon. Conclusion The surgeon and reconstructive team must make individualized decisions based on the extent of the maxillectomy defect (eg, the resection of the infraorbital rim, the extent of palate excision, skin compromise) and the need for radiation therapy. PMID:21844415

Automatic detection of NIL defects using microscopy and image processing

KAUST Repository

Pietroy, David

2013-12-01

Nanoimprint Lithography (NIL) is a promising technology for low cost and large scale nanostructure fabrication. This technique is based on a contact molding-demolding process, that can produce number of defects such as incomplete filling, negative patterns, sticking. In this paper, microscopic imaging combined to a specific processing algorithm is used to detect numerically defects in printed patterns. Results obtained for 1D and 2D imprinted gratings with different microscopic image magnifications are presented. Results are independent on the device which captures the image (optical, confocal or electron microscope). The use of numerical images allows the possibility to automate the detection and to compute a statistical analysis of defects. This method provides a fast analysis of printed gratings and could be used to monitor the production of such structures. © 2013 Elsevier B.V. All rights reserved.

Generating Clustered Journal Maps : An Automated System for Hierarchical Classification

NARCIS (Netherlands)

Leydesdorff, L.; Bornmann, L.; Wagner, C.S.

2017-01-01

Journal maps and classifications for 11,359 journals listed in the combined Journal Citation Reports 2015 of the Science and Social Sciences Citation Indexes are provided at https://leydesdorff.github.io/journals/ and http://www.leydesdorff.net/jcr15. A routine using VOSviewer for integrating the

Automatic liver volume segmentation and fibrosis classification

Science.gov (United States)

Bal, Evgeny; Klang, Eyal; Amitai, Michal; Greenspan, Hayit

2018-02-01

In this work, we present an automatic method for liver segmentation and fibrosis classification in liver computed-tomography (CT) portal phase scans. The input is a full abdomen CT scan with an unknown number of slices, and the output is a liver volume segmentation mask and a fibrosis grade. A multi-stage analysis scheme is applied to each scan, including: volume segmentation, texture features extraction and SVM based classification. Data contains portal phase CT examinations from 80 patients, taken with different scanners. Each examination has a matching Fibroscan grade. The dataset was subdivided into two groups: first group contains healthy cases and mild fibrosis, second group contains moderate fibrosis, severe fibrosis and cirrhosis. Using our automated algorithm, we achieved an average dice index of 0.93 ± 0.05 for segmentation and a sensitivity of 0.92 and specificity of 0.81for classification. To the best of our knowledge, this is a first end to end automatic framework for liver fibrosis classification; an approach that, once validated, can have a great potential value in the clinic.

An ordinal classification approach for CTG categorization.

Science.gov (United States)

Georgoulas, George; Karvelis, Petros; Gavrilis, Dimitris; Stylios, Chrysostomos D; Nikolakopoulos, George

2017-07-01

Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.

Application of artificial neural networks to evaluate weld defects of nuclear components

International Nuclear Information System (INIS)

Amin, E.S.

2007-01-01

Artificial neural networks (ANNs) are computational representations based on the biological neural architecture of the brain. ANNs have been successfully applied to a wide range of engineering and scientific applications, such as signal, image processing and data analysis. Although Radiographic testing is widely used for welding defects, it is unsuccessful in identifying some welding defects because of the nature of image formation and quality. Neoteric algorithms have been used for the purpose of weld defects identifications in radiographic images to replace the expert knowledge. The application of artificial neural networks in noise detection of radiographic films is used. Radial Basis (RB) and learning vector quantization (LVQ) were applied. The method shows good performance in weld defects recognition and classification problems.

Malware Classification Based on the Behavior Analysis and Back Propagation Neural Network

Directory of Open Access Journals (Sweden)

Pan Zhi-Peng

2016-01-01

Full Text Available With the development of the Internet, malwares have also been expanded on the network systems rapidly. In order to deal with the diversity and amount of the variants, a number of automated behavior analysis tools have emerged as the time requires. Yet these tools produce detailed behavior reports of the malwares, it still needs to specify its category and judge its criticality manually. In this paper, we propose an automated malware classification approach based on the behavior analysis. We firstly perform dynamic analyses to obtain the detailed behavior profiles of the malwares, which are then used to abstract the main features of the malwares and serve as the inputs of the Back Propagation (BP Neural Network model.The experimental results demonstrate that our classification technique is able to classify the malware variants effectively and detect malware accurately.

Recent advances in automated ultrasonic inspection of Magnox power stations

International Nuclear Information System (INIS)

Wooldridge, A.B.

2006-01-01

Magnox Electric operates a number of Magnox nuclear power stations, some of which have presented difficult inspection challenges. This paper will describe recent advances in automated ultrasonic techniques which have enabled additional, fully effective and qualified inspections to be introduced. The examples chosen involve phased array inspection of fillet welds and the introduction of 3D data display and analysis using NDT Workbench. Ultrasonic phased arrays have been optimised for inspection of fillet welded structures where physical access for the operator is very awkward and the surfaces available for probe scanning are very restricted. In addition to controlling the beam direction and focus depth, the systems have also optimised the depth of field by varying the number of phased array elements fired at a given time. These phased array inspections are substantially superior in quality, reliability and speed compared with that achievable by conventional manual inspection. Analysis of automated ultrasonic data can be very time-consuming if defects are complex. NDT Workbench significantly improves data analysis for complex geometries and defects primarily because of the 3D data displays of multiple beams and automated logging of measurements. This system has been used in 2005 for data analysis of complex defects where sizing accuracy was particularly important. Both these inspection procedures have been formally qualified using the ENIQ (European Network for Inspection Qualification) Methodology. Such qualification was achieved more easily because of the rigorous in house training programmes established in each case and because the Technical Justifications referred to evidence from previous related qualifications whenever appropriate. The timely achievement of inspection qualification demonstrates that the new systems have reached sufficient maturity to be used with confidence for high quality inspections. (orig.)

Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

Science.gov (United States)

Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

2017-06-01

During the last 20 years, airborne laser scanning (ALS), often combined with passive multispectral information from aerial images, has shown its high feasibility for automated mapping processes. The main benefits have been achieved in the mapping of elevated objects such as buildings and trees. Recently, the first multispectral airborne laser scanners have been launched, and active multispectral information is for the first time available for 3D ALS point clouds from a single sensor. This article discusses the potential of this new technology in map updating, especially in automated object-based land cover classification and change detection in a suburban area. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from an object-based random forests analysis suggest that the multispectral ALS data are very useful for land cover classification, considering both elevated classes and ground-level classes. The overall accuracy of the land cover classification results with six classes was 96% compared with validation points. The classes under study included building, tree, asphalt, gravel, rocky area and low vegetation. Compared to classification of single-channel data, the main improvements were achieved for ground-level classes. According to feature importance analyses, multispectral intensity features based on several channels were more useful than those based on one channel. Automatic change detection for buildings and roads was also demonstrated by utilising the new multispectral ALS data in combination with old map vectors. In change detection of buildings, an old digital surface model (DSM) based on single-channel ALS data was also used. Overall, our analyses suggest that the new data have high potential for further increasing the automation level in mapping. Unlike passive aerial imaging commonly used in mapping, the multispectral ALS technology is independent of external illumination conditions, and there are

Distribution of female genital tract anomalies in two classifications.

Science.gov (United States)

Heinonen, Pentti K

2016-11-01

This study assessed the distribution of Müllerian duct anomalies in two verified classifications of female genital tract malformations, and the presence of associated renal defects. 621 women with confirmed female genital tract anomalies were retrospectively grouped under the European (ESHRE/ESGE) and the American (AFS) classification. The diagnosis of uterine malformation was based on findings in hysterosalpingography, two-dimensional ultrasonography, endoscopies, laparotomy, cesarean section and magnetic resonance imaging in 97.3% of cases. Renal status was determined in 378 patients, including 5 with normal uterus and vagina. The European classification covered all 621 women studied. Uterine anomalies without cervical or vaginal anomaly were found in 302 (48.6%) patients. Uterine anomaly was associated with vaginal anomaly in 45.2%, and vaginal anomaly alone was found in 26 (4.2%) cases. Septate uterus was the most common (49.1%) of all genital tract anomalies, followed by bicorporeal uteri (18.2%). The American classification covered 590 (95%) out of the 621 women with genital tract anomalies. The American system did not take into account vaginal anomalies in 170 (34.7%) and cervical anomalies in 174 (35.5%) out of 490 cases with uterine malformations. Renal abnormalities were found in 71 (18.8%) out of 378 women, unilateral renal agenesis being the most common defect (12.2%), also found in 4 women without Müllerian duct anomaly. The European classification sufficiently covered uterine and vaginal abnormalities. The distribution of the main uterine anomalies was equal in both classifications. The American system missed cervical and vaginal anomalies associated with uterine anomalies. Evaluation of renal system is recommended for all patients with genital tract anomalies. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Fourier descriptor classification of differential eddy current probe impedance plane trajectories

International Nuclear Information System (INIS)

Lord, W.; Satish, S.R.

1984-01-01

This chapter describes the use of a parametric model for representing the two-dimensional eddy current impedance plane trajectory. The main advantage of this approach is the ability to reconstruct the trajectory from the model coefficients. Fourier descriptors are used to facilitate defect classification. The Fourier descriptors are obtained by expanding the complex contour function in a Fourier series. Functions of Fourier coefficients which are invariant under transformation of the trajectory are derived and incorporated into a feature vector. Defect classification is obtained by using the K-Means algorithm to cluster the feature vectors. It is demonstrated that the Fourier descriptor approach represents a powerful tool which have several advantages over nonparametric approaches including its insensitivity to drift in the eddy current instrument as well as variations in the probe speed

Demonstration of automated robotic workcell for hazardous waste characterization

International Nuclear Information System (INIS)

Holliday, M.; Dougan, A.; Gavel, D.; Gustaveson, D.; Johnson, R.; Kettering, B.; Wilhelmsen, K.

1993-02-01

An automated robotic workcell to classify hazardous waste stream items with previously unknown characteristics has been designed, tested and demonstrated The object attributes being quantified are radiation signature, metal content, and object orientation and volume. The multi sensor information is used to make segregation decisions plus do automatic grasping of objects. The work-cell control program uses an off-line programming system by Cimetrix Inc. as a server to do both simulation control as well as actual hardware control of the workcell. This paper will discuss the overall workcell layout, sensor specifications, workcell supervisory control, 2D vision based automated grasp planning and object classification algorithms

Description of data sources on defects of the Ukrainian NPP equipment

International Nuclear Information System (INIS)

Pecheritsa, A.V.; Inyushev, V.V.

1999-01-01

In the article materials a description of the basic sources of the information, which can be applied under a collection of the operational data on the defects of the Ukrainian NPP equipment, has been presented. The classification of mentioned sources is performed. The advantages and disadvantages of every of the specified sources are described. An finding regarding to a volume of the information about the defects of Safety Significant Systems equipment, which can be obtained under analysis of the different types of the sources, are presented

Dimensional Representation and Gradient Boosting for Seismic Event Classification

Science.gov (United States)

Semmelmayer, F. C.; Kappedal, R. D.; Magana-Zook, S. A.

2017-12-01

In this research, we conducted experiments of representational structures on 5009 seismic signals with the intent of finding a method to classify signals as either an explosion or an earthquake in an automated fashion. We also applied a gradient boosted classifier. While perfect classification was not attained (approximately 88% was our best model), some cases demonstrate that many events can be filtered out as very high probability being explosions or earthquakes, diminishing subject-matter experts'(SME) workload for first stage analysis. It is our hope that these methods can be refined, further increasing the classification probability.

Evaluation of cell count and classification capabilities in body fluids using a fully automated Sysmex XN equipped with high-sensitive Analysis (hsA) mode and DI-60 hematology analyzer system.

Science.gov (United States)

Takemura, Hiroyuki; Ai, Tomohiko; Kimura, Konobu; Nagasaka, Kaori; Takahashi, Toshihiro; Tsuchiya, Koji; Yang, Haeun; Konishi, Aya; Uchihashi, Kinya; Horii, Takashi; Tabe, Yoko; Ohsaka, Akimichi

2018-01-01

The XN series automated hematology analyzer has been equipped with a body fluid (BF) mode to count and differentiate leukocytes in BF samples including cerebrospinal fluid (CSF). However, its diagnostic accuracy is not reliable for CSF samples with low cell concentration at the border between normal and pathologic level. To overcome this limitation, a new flow cytometry-based technology, termed "high sensitive analysis (hsA) mode," has been developed. In addition, the XN series analyzer has been equipped with the automated digital cell imaging analyzer DI-60 to classify cell morphology including normal leukocytes differential and abnormal malignant cells detection. Using various BF samples, we evaluated the performance of the XN-hsA mode and DI-60 compared to manual microscopic examination. The reproducibility of the XN-hsA mode showed good results in samples with low cell densities (coefficient of variation; % CV: 7.8% for 6 cells/μL). The linearity of the XN-hsA mode was established up to 938 cells/μL. The cell number obtained using the XN-hsA mode correlated highly with the corresponding microscopic examination. Good correlation was also observed between the DI-60 analyses and manual microscopic classification for all leukocyte types, except monocytes. In conclusion, the combined use of cell counting with the XN-hsA mode and automated morphological analyses using the DI-60 mode is potentially useful for the automated analysis of BF cells.

Development of a system for monitoring and diagnosis of steam generator tubes using artificial intelligence techniques on Eddy Current Test signals

International Nuclear Information System (INIS)

Mesquita, Roberto Navarro de; Ting, Daniel Kao Sun; Lopez, Luis A. Negro M.; Upadhyaya, Belle R.

2002-01-01

New classification and feature extraction methods for steam generator tube defects are being developed by IPEN/CNEN-SP in cooperation with UTK to improve a monitoring and diagnosis system for classification and characterization of steam generator tube defects using Eddy Current Testing (ECT) signals. The first methodology being developed uses a set of feature extraction methods applied to different tube defect type ECT signals and each obtained feature vector is projected into a bi-dimensional map obtained by a Self-Organizing Map neural network. This methodology allows an optimal feature extraction method selection for the defect type classification. Other approach is being developed using tubes with different manufactured defect types which are tested using MIZ-17ET equipment with 4 sets of probes (two different diameter). A fuzzy inference system will be used to build a knowledge base for these defects. These methodology and algorithms will be integrated into an automated diagnosis system being developed with UTK, which is designed to read both on-line acquired data, as well as stored data files. These commercial software tools are the ones usually utilized in nuclear power plants. (author)

Development of a system for monitoring and diagnosis of steam generator tubes using artificial intelligence techniques on Eddy Current Test signals

Energy Technology Data Exchange (ETDEWEB)

Mesquita, Roberto Navarro de [Instituto de Pesquisas Energeticas e Nucleares (IPEN), Sao Paulo, SP (Brazil). Centro de Monitoracao e Diagnostico]|[Sao Paulo Univ., SP (Brazil); Ting, Daniel Kao Sun [Instituto de Pesquisas Energeticas e Nucleares (IPEN), Sao Paulo, SP (Brazil). Centro de Monitoracao e Diagnostico; Cabral, Eduardo Lobo C. [Sao Paulo Univ., SP (Brazil); Lopez, Luis A. Negro M. [Faculdade de Engenharia Industrial, Sao Bernardo do Campo, SP (Brazil); Upadhyaya, Belle R. [Tennessee Univ., Knoxville, TN (United States)

2002-07-01

New classification and feature extraction methods for steam generator tube defects are being developed by IPEN/CNEN-SP in cooperation with UTK to improve a monitoring and diagnosis system for classification and characterization of steam generator tube defects using Eddy Current Testing (ECT) signals. The first methodology being developed uses a set of feature extraction methods applied to different tube defect type ECT signals and each obtained feature vector is projected into a bi-dimensional map obtained by a Self-Organizing Map neural network. This methodology allows an optimal feature extraction method selection for the defect type classification. Other approach is being developed using tubes with different manufactured defect types which are tested using MIZ-17ET equipment with 4 sets of probes (two different diameter). A fuzzy inference system will be used to build a knowledge base for these defects. These methodology and algorithms will be integrated into an automated diagnosis system being developed with UTK, which is designed to read both on-line acquired data, as well as stored data files. These commercial software tools are the ones usually utilized in nuclear power plants. (author)

Automated classification of immunostaining patterns in breast tissue from the human protein atlas.

Science.gov (United States)

Swamidoss, Issac Niwas; Kårsnäs, Andreas; Uhlmann, Virginie; Ponnusamy, Palanisamy; Kampf, Caroline; Simonsson, Martin; Wählby, Carolina; Strand, Robin

2013-01-01

The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many

Automated classification of immunostaining patterns in breast tissue from the human protein Atlas

Directory of Open Access Journals (Sweden)

Issac Niwas Swamidoss

2013-01-01

Full Text Available Background: The Human Protein Atlas (HPA is an effort to map the location of all human proteins (http://www.proteinatlas.org/. It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples. Materials and Methods: The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM features, complex wavelet co-occurrence matrix (CWCM features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM and linear discriminant analysis (LDA classifier. Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue. Results: We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert. Conclusions: Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

Energy Technology Data Exchange (ETDEWEB)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K. [Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom); McEwen, Jason D., E-mail: dr.michelle.lochner@gmail.com [Mullard Space Science Laboratory, University College London, Surrey RH5 6NT (United Kingdom)

2016-08-01

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 Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered 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 that fit 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 (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

International Nuclear Information System (INIS)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.; McEwen, Jason D.

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 Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered 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 that fit 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 (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

Simulation based mask defect repair verification and disposition

Science.gov (United States)

Guo, Eric; Zhao, Shirley; Zhang, Skin; Qian, Sandy; Cheng, Guojie; Vikram, Abhishek; Li, Ling; Chen, Ye; Hsiang, Chingyun; Zhang, Gary; Su, Bo

2009-10-01

As the industry moves towards sub-65nm technology nodes, the mask inspection, with increased sensitivity and shrinking critical defect size, catches more and more nuisance and false defects. Increased defect counts pose great challenges in the post inspection defect classification and disposition: which defect is real defect, and among the real defects, which defect should be repaired and how to verify the post-repair defects. In this paper, we address the challenges in mask defect verification and disposition, in particular, in post repair defect verification by an efficient methodology, using SEM mask defect images, and optical inspection mask defects images (only for verification of phase and transmission related defects). We will demonstrate the flow using programmed mask defects in sub-65nm technology node design. In total 20 types of defects were designed including defects found in typical real circuit environments with 30 different sizes designed for each type. The SEM image was taken for each programmed defect after the test mask was made. Selected defects were repaired and SEM images from the test mask were taken again. Wafers were printed with the test mask before and after repair as defect printability references. A software tool SMDD-Simulation based Mask Defect Disposition-has been used in this study. The software is used to extract edges from the mask SEM images and convert them into polygons to save in GDSII format. Then, the converted polygons from the SEM images were filled with the correct tone to form mask patterns and were merged back into the original GDSII design file. This merge is for the purpose of contour simulation-since normally the SEM images cover only small area (~1 μm) and accurate simulation requires including larger area of optical proximity effect. With lithography process model, the resist contour of area of interest (AOI-the area surrounding a mask defect) can be simulated. If such complicated model is not available, a simple

Eddy Current Signature Classification of Steam Generator Tube Defects Using A Learning Vector Quantization Neural Network

International Nuclear Information System (INIS)

Garcia, Gabe V.

2005-01-01

A major cause of failure in nuclear steam generators is degradation of their tubes. Although seven primary defect categories exist, one of the principal causes of tube failure is intergranular attack/stress corrosion cracking (IGA/SCC). This type of defect usually begins on the secondary side surface of the tubes and propagates both inwards and laterally. In many cases this defect is found at or near the tube support plates

Simulation based optimization on automated fibre placement process

Science.gov (United States)

Lei, Shi

2018-02-01

In this paper, a software simulation (Autodesk TruPlan & TruFiber) based method is proposed to optimize the automate fibre placement (AFP) process. Different types of manufacturability analysis are introduced to predict potential defects. Advanced fibre path generation algorithms are compared with respect to geometrically different parts. Major manufacturing data have been taken into consideration prior to the tool paths generation to achieve high success rate of manufacturing.

AUTOMATING THE DATA SECURITY PROCESS

Directory of Open Access Journals (Sweden)

Florin Ogigau-Neamtiu

2017-11-01

Full Text Available Contemporary organizations face big data security challenges in the cyber environment due to modern threats and actual business working model which relies heavily on collaboration, data sharing, tool integration, increased mobility, etc. The nowadays data classification and data obfuscation selection processes (encryption, masking or tokenization suffer because of the human implication in the process. Organizations need to shirk data security domain by classifying information based on its importance, conduct risk assessment plans and use the most cost effective data obfuscation technique. The paper proposes a new model for data protection by using automated machine decision making procedures to classify data and to select the appropriate data obfuscation technique. The proposed system uses natural language processing capabilities to analyze input data and to select the best course of action. The system has capabilities to learn from previous experiences thus improving itself and reducing the risk of wrong data classification.

Inguinal hernia recurrence: Classification and approach

Directory of Open Access Journals (Sweden)

Campanelli Giampiero

2006-01-01

Full Text Available The authors reviewed the records of 2,468 operations of groin hernia in 2,350 patients, including 277 recurrent hernias updated to January 2005. The data obtained - evaluating technique, results and complications - were used to propose a simple anatomo-clinical classification into three types which could be used to plan the surgical strategy:Type R1: first recurrence ′high,′ oblique external, reducible hernia with small (< 2 cm defect in non-obese patients, after pure tissue or mesh repairType R2: first recurrence ′low,′ direct, reducible hernia with small (< 2 cm defect in non-obese patients, after pure tissue or mesh repairType R3: all the other recurrences - including femoral recurrences; recurrent groin hernia with big defect (inguinal eventration; multirecurrent hernias; nonreducible, linked with a controlateral primitive or recurrent hernia; and situations compromised from aggravating factors (for example obesity or anyway not easily included in R1 or R2, after pure tissue or mesh repair.

Feature extraction for ultrasonic sensor based defect detection in ceramic components

Science.gov (United States)

Kesharaju, Manasa; Nagarajah, Romesh

2014-02-01

High density silicon carbide materials are commonly used as the ceramic element of hard armour inserts used in traditional body armour systems to reduce their weight, while providing improved hardness, strength and elastic response to stress. Currently, armour ceramic tiles are inspected visually offline using an X-ray technique that is time consuming and very expensive. In addition, from X-rays multiple defects are also misinterpreted as single defects. Therefore, to address these problems the ultrasonic non-destructive approach is being investigated. Ultrasound based inspection would be far more cost effective and reliable as the methodology is applicable for on-line quality control including implementation of accept/reject criteria. This paper describes a recently developed methodology to detect, locate and classify various manufacturing defects in ceramic tiles using sub band coding of ultrasonic test signals. The wavelet transform is applied to the ultrasonic signal and wavelet coefficients in the different frequency bands are extracted and used as input features to an artificial neural network (ANN) for purposes of signal classification. Two different classifiers, using artificial neural networks (supervised) and clustering (un-supervised) are supplied with features selected using Principal Component Analysis(PCA) and their classification performance compared. This investigation establishes experimentally that Principal Component Analysis(PCA) can be effectively used as a feature selection method that provides superior results for classifying various defects in the context of ultrasonic inspection in comparison with the X-ray technique.

Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.

Science.gov (United States)

Sinha, S K; Karray, F

2002-01-01

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

A neural network for noise correlation classification

Science.gov (United States)

Paitz, Patrick; Gokhberg, Alexey; Fichtner, Andreas

2018-02-01

We present an artificial neural network (ANN) for the classification of ambient seismic noise correlations into two categories, suitable and unsuitable for noise tomography. By using only a small manually classified data subset for network training, the ANN allows us to classify large data volumes with low human effort and to encode the valuable subjective experience of data analysts that cannot be captured by a deterministic algorithm. Based on a new feature extraction procedure that exploits the wavelet-like nature of seismic time-series, we efficiently reduce the dimensionality of noise correlation data, still keeping relevant features needed for automated classification. Using global- and regional-scale data sets, we show that classification errors of 20 per cent or less can be achieved when the network training is performed with as little as 3.5 per cent and 16 per cent of the data sets, respectively. Furthermore, the ANN trained on the regional data can be applied to the global data, and vice versa, without a significant increase of the classification error. An experiment where four students manually classified the data, revealed that the classification error they would assign to each other is substantially larger than the classification error of the ANN (>35 per cent). This indicates that reproducibility would be hampered more by human subjectivity than by imperfections of the ANN.

IRIS COLOUR CLASSIFICATION SCALES – THEN AND NOW

Science.gov (United States)

Grigore, Mariana; Avram, Alina

2015-01-01

Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual’s eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale. PMID:27373112

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

Science.gov (United States)

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

Automated hardwood lumber grading utilizing a multiple sensor machine vision technology

Science.gov (United States)

D. Earl Kline; Chris Surak; Philip A. Araman

2003-01-01

Over the last 10 years, scientists at the Thomas M. Brooks Forest Products Center, the Bradley Department of Electrical and Computer Engineering, and the USDA Forest Service have been working on lumber scanning systems that can accurately locate and identify defects in hardwood lumber. Current R&D efforts are targeted toward developing automated lumber grading...

Automated grading of wood-slabs. The development of a prototype system

DEFF Research Database (Denmark)

Ersbøll, Bjarne Kjær; Conradsen, Knut

1992-01-01

This paper proposes a method for automatically grading small beechwood slabs. The method involves two classification steps: the first step detects defects based on local visual texture; the second step utilizes the relative distribution of defects to perform a final grading assessment. At a major...... as much as possible. A vast range of types of defects has to be considered when the grading is done. This and the fact that wood is a 'natural' material means it is not easily described using ordinary vision systems. The proposed method assumes a 3-D feature space which depends on local texture......-based measures of 'lightness', 'speckle' and 'dark deviation'. These measures are calculated for each pixel in an image of a slab. The feature space is separated into 12 decision regions corresponding to 12 'defect types'; these 'defects' are labeled as clear wood, wavy grain, split, black knots, ingrown bark...

Perception of olive oils sensory defects using a potentiometric taste device.

Science.gov (United States)

Veloso, Ana C A; Silva, Lucas M; Rodrigues, Nuno; Rebello, Ligia P G; Dias, Luís G; Pereira, José A; Peres, António M

2018-01-01

The capability of perceiving olive oils sensory defects and intensities plays a key role on olive oils quality grade classification since olive oils can only be classified as extra-virgin if no defect can be perceived by a human trained sensory panel. Otherwise, olive oils may be classified as virgin or lampante depending on the median intensity of the defect predominantly perceived and on the physicochemical levels. However, sensory analysis is time-consuming and requires an official sensory panel, which can only evaluate a low number of samples per day. In this work, the potential use of an electronic tongue as a taste sensor device to identify the defect predominantly perceived in olive oils was evaluated. The potentiometric profiles recorded showed that intra- and inter-day signal drifts could be neglected (i.e., relative standard deviations lower than 25%), being not statistically significant the effect of the analysis day on the overall recorded E-tongue sensor fingerprints (P-value = 0.5715, for multivariate analysis of variance using Pillai's trace test), which significantly differ according to the olive oils' sensory defect (P-value = 0.0084, for multivariate analysis of variance using Pillai's trace test). Thus, a linear discriminant model based on 19 potentiometric signal sensors, selected by the simulated annealing algorithm, could be established to correctly predict the olive oil main sensory defect (fusty, rancid, wet-wood or winey-vinegary) with average sensitivity of 75 ± 3% and specificity of 73 ± 4% (repeated K-fold cross-validation variant: 4 folds×10 repeats). Similarly, a linear discriminant model, based on 24 selected sensors, correctly classified 92 ± 3% of the olive oils as virgin or lampante, being an average specificity of 93 ± 3% achieved. The overall satisfactory predictive performances strengthen the feasibility of the developed taste sensor device as a complementary methodology for olive oils' defects analysis and subsequent

INTEGRATION OF INFORMATIONAL COMPUTER TECHNOLOGIES SMK: AUTOMATION OF THE MAIN FUNCTIONS OF THE TECHNICAL CONTROL DEPARTMENT

Directory of Open Access Journals (Sweden)

S. A. Pavlenko

2010-01-01

Full Text Available It is shown that automation of some functions of control department allows to record defects, reclamations and failures of technology, to make the necessary reporting forms and quality certificates for production.

Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch

OpenAIRE

Fadilah, Norasyikin; Mohamad-Saleh, Junita; Halim, Zaini Abdul; Ibrahim, Haidi; Ali, Syed Salim Syed

2012-01-01

Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Ne...

Automated pulmonary lobar ventilation measurements using volume-matched thoracic CT and MRI

Science.gov (United States)

Guo, F.; Svenningsen, S.; Bluemke, E.; Rajchl, M.; Yuan, J.; Fenster, A.; Parraga, G.

2015-03-01

Objectives: To develop and evaluate an automated registration and segmentation pipeline for regional lobar pulmonary structure-function measurements, using volume-matched thoracic CT and MRI in order to guide therapy. Methods: Ten subjects underwent pulmonary function tests and volume-matched 1H and 3He MRI and thoracic CT during a single 2-hr visit. CT was registered to 1H MRI using an affine method that incorporated block-matching and this was followed by a deformable step using free-form deformation. The resultant deformation field was used to deform the associated CT lobe mask that was generated using commercial software. 3He-1H image registration used the same two-step registration method and 3He ventilation was segmented using hierarchical k-means clustering. Whole lung and lobar 3He ventilation and ventilation defect percent (VDP) were generated by mapping ventilation defects to CT-defined whole lung and lobe volumes. Target CT-3He registration accuracy was evaluated using region- , surface distance- and volume-based metrics. Automated whole lung and lobar VDP was compared with semi-automated and manual results using paired t-tests. Results: The proposed pipeline yielded regional spatial agreement of 88.0+/-0.9% and surface distance error of 3.9+/-0.5 mm. Automated and manual whole lung and lobar ventilation and VDP were not significantly different and they were significantly correlated (r = 0.77, p pulmonary structural-functional maps with high accuracy and robustness, providing an important tool for image-guided pulmonary interventions.

Methodology for monitoring and automated diagnosis of ball bearing using para consistent logic, wavelet transform and digital signal processing

International Nuclear Information System (INIS)

Masotti, Paulo Henrique Ferraz

2006-01-01

The monitoring and diagnosis area is presenting an impressive development in recent years with the introduction of new diagnosis techniques as well as with the use the computers in the processing of the information and of the diagnosis techniques. The contribution of the artificial intelligence in the automation of the defect diagnosis is developing continually and the growing automation in the industry meets this new techniques. In the nuclear area, the growing concern with the safety in the facilities requires more effective techniques that have been sought to increase the safety level. Some nuclear power stations have already installed in some machines, sensors that allow the verification of their operational conditions. In this way, the present work can also collaborate in this area, helping in the diagnosis of the operational condition of the machines. This work presents a new technique for characteristic extraction based on the Zero Crossing of Wavelet Transform, contributing with the development of this dynamic area. The technique of artificial intelligence was used in this work the Paraconsistent Logic of Annotation with Two values (LPA2v), contributing with the automation of the diagnosis of defects, because this logic can deal with contradictory results that the techniques of feature extraction can present. This work also concentrated on the identification of defects in its initial phase trying to use accelerometers, because they are robust sensors, of low cost and can be easily found the industry in general. The results obtained in this work were accomplished through the use of an experimental database, and it was observed that the results of diagnoses of defects shown good results for defects in their initial phase. (author)

Featureless classification of light curves

Science.gov (United States)

Kügler, S. D.; Gianniotis, N.; Polsterer, K. L.

2015-08-01

In the era of rapidly increasing amounts of time series data, classification of variable objects has become the main objective of time-domain astronomy. Classification of irregularly sampled time series is particularly difficult because the data cannot be represented naturally as a vector which can be directly fed into a classifier. In the literature, various statistical features serve as vector representations. In this work, we represent time series by a density model. The density model captures all the information available, including measurement errors. Hence, we view this model as a generalization to the static features which directly can be derived, e.g. as moments from the density. Similarity between each pair of time series is quantified by the distance between their respective models. Classification is performed on the obtained distance matrix. In the numerical experiments, we use data from the OGLE (Optical Gravitational Lensing Experiment) and ASAS (All Sky Automated Survey) surveys and demonstrate that the proposed representation performs up to par with the best currently used feature-based approaches. The density representation preserves all static information present in the observational data, in contrast to a less-complete description by features. The density representation is an upper boundary in terms of information made available to the classifier. Consequently, the predictive power of the proposed classification depends on the choice of similarity measure and classifier, only. Due to its principled nature, we advocate that this new approach of representing time series has potential in tasks beyond classification, e.g. unsupervised learning.

Panacea : Automating attack classification for anomaly-based network intrusion detection systems

NARCIS (Netherlands)

Bolzoni, D.; Etalle, S.; Hartel, P.H.; Kirda, E.; Jha, S.; Balzarotti, D.

2009-01-01

Anomaly-based intrusion detection systems are usually criticized because they lack a classification of attacks, thus security teams have to manually inspect any raised alert to classify it. We present a new approach, Panacea, to automatically and systematically classify attacks detected by an

Panacea : Automating attack classification for anomaly-based network intrusion detection systems

NARCIS (Netherlands)

Bolzoni, D.; Etalle, S.; Hartel, P.H.

2009-01-01

Anomaly-based intrusion detection systems are usually criticized because they lack a classification of attack, thus security teams have to manually inspect any raised alert to classify it. We present a new approach, Panacea, to automatically and systematically classify attacks detected by an

Automated system for crack detection using infrared thermograph

International Nuclear Information System (INIS)

Starman, Stanislav

2009-01-01

The objective of this study was the development of the automated system for crack detection on square steel bars used in the automotive industry for axle and shaft construction. The automated system for thermographic crack detection uses brief pulsed eddy currents to heat steel components under inspection. Cracks, if present, will disturb the current flow and so generate changes in the temperature profile in the crack area. These changes of temperature are visualized using an infrared camera. The image acquired by the infrared camera is evaluated through an image processing system. The advantages afforded by the system are its inspection time, its excellent flaw detection sensitivity and its ability to detect hidden, subsurface cracks. The automated system consists of four IR cameras (each side of steel bar is evaluated at a time), coil, high frequency generator and control place with computers. The system is a part of the inspection line where the subsurface and surface cracks are searched. If the crack is present, the cracked place is automatically marked. The components without cracks are then deposited apart from defective blocks. The system is fully automated and its ability is to evaluate four meter blocks within 20 seconds. This is the real reason for using this system in real industrial applications. (author)

A minimum spanning forest based classification method for dedicated breast CT images

International Nuclear Information System (INIS)

Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei

2015-01-01

Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting model used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging

Automated Inspection of Defects in Optical Fiber Connector End Face Using Novel Morphology Approaches.

Science.gov (United States)

Mei, Shuang; Wang, Yudan; Wen, Guojun; Hu, Yang

2018-05-03

Increasing deployment of optical fiber networks and the need for reliable high bandwidth make the task of inspecting optical fiber connector end faces a crucial process that must not be neglected. Traditional end face inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. More seriously, the inspection results cannot be quantified for subsequent analysis. Aiming at the characteristics of typical defects in the inspection process for optical fiber end faces, we propose a novel method, “difference of min-max ranking filtering” (DO2MR), for detection of region-based defects, e.g., dirt, oil, contamination, pits, and chips, and a special model, a “linear enhancement inspector” (LEI), for the detection of scratches. The DO2MR is a morphology method that intends to determine whether a pixel belongs to a defective region by comparing the difference of gray values of pixels in the neighborhood around the pixel. The LEI is also a morphology method that is designed to search for scratches at different orientations with a special linear detector. These two approaches can be easily integrated into optical inspection equipment for automatic quality verification. As far as we know, this is the first time that complete defect detection methods for optical fiber end faces are available in the literature. Experimental results demonstrate that the proposed DO2MR and LEI models yield good comprehensive performance with high precision and accepted recall rates, and the image-level detection accuracies reach 96.0 and 89.3%, respectively.

Automated Inspection of Defects in Optical Fiber Connector End Face Using Novel Morphology Approaches

Directory of Open Access Journals (Sweden)

Shuang Mei

2018-05-01

Full Text Available Increasing deployment of optical fiber networks and the need for reliable high bandwidth make the task of inspecting optical fiber connector end faces a crucial process that must not be neglected. Traditional end face inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. More seriously, the inspection results cannot be quantified for subsequent analysis. Aiming at the characteristics of typical defects in the inspection process for optical fiber end faces, we propose a novel method, “difference of min-max ranking filtering” (DO2MR, for detection of region-based defects, e.g., dirt, oil, contamination, pits, and chips, and a special model, a “linear enhancement inspector” (LEI, for the detection of scratches. The DO2MR is a morphology method that intends to determine whether a pixel belongs to a defective region by comparing the difference of gray values of pixels in the neighborhood around the pixel. The LEI is also a morphology method that is designed to search for scratches at different orientations with a special linear detector. These two approaches can be easily integrated into optical inspection equipment for automatic quality verification. As far as we know, this is the first time that complete defect detection methods for optical fiber end faces are available in the literature. Experimental results demonstrate that the proposed DO2MR and LEI models yield good comprehensive performance with high precision and accepted recall rates, and the image-level detection accuracies reach 96.0 and 89.3%, respectively.

Automatic classification of DMSA scans using an artificial neural network

Science.gov (United States)

Wright, J. W.; Duguid, R.; Mckiddie, F.; Staff, R. T.

2014-04-01

DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α quality assurance assistant in clinical practice.

A Comparison of Two Scoring Methods for an Automated Speech Scoring System

Science.gov (United States)

Xi, Xiaoming; Higgins, Derrick; Zechner, Klaus; Williamson, David

2012-01-01

This paper compares two alternative scoring methods--multiple regression and classification trees--for an automated speech scoring system used in a practice environment. The two methods were evaluated on two criteria: construct representation and empirical performance in predicting human scores. The empirical performance of the two scoring models…

Preoperative cardiac computed tomography for demonstration of congenital cardiac septal defect in adults

Energy Technology Data Exchange (ETDEWEB)

Eom, Hye-Joung; Yang, Dong Hyun; Kang, Joon-Won; Lim, Tae-Hwan [University of Ulsan College of Medicine, Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, Seoul (Korea, Republic of); Kim, Dae-Hee; Song, Jong-Min; Kang, Duk-Hyun; Song, Jae-Kwan [University of Ulsan College of Medicine, Department of Cardiology and Heart Institute, Cardiac Imaging Center, Asan Medical Center, Seoul (Korea, Republic of); Kim, Joon Bum; Jung, Sung-Ho; Choo, Suk Jung; Chung, Cheol Hyun; Lee, Jae Won [University of Ulsan College of Medicine, Department of Cardiothoracic surgery, Cardiac Imaging Center, Asan Medical Center, Seoul (Korea, Republic of)

2015-06-01

We aimed to evaluate the role of preoperative cardiac computed tomography (CT) for adults with congenital cardiac septal defect (CSD). Sixty-five consecutive patients who underwent preoperative CT and surgery for CSD were included. The diagnostic accuracy of CT and the concordance rate of the subtype classification of CSD were evaluated using surgical findings as the reference standard. Sixty-five patients without CSD who underwent cardiac valve surgery were used as a control group. An incremental value of CT over echocardiography was described retrospectively. Sensitivity and specificity of CT for diagnosis of CSD were 95 % and 100 %, respectively. The concordance rate of subtype classification was 91 % in CT and 92 % in echocardiography. The maximum size of the defect measured by CT correlated well with surgical measurement (r = 0.82), and the limit of agreement was -0.9 ± 7.42 mm. In comparison with echocardiography, CT was able to detect combined abnormalities in three cases, and exclusively provided correct subtype classification or clarified suspected abnormal findings found on echocardiography in seven cases. Cardiac CT can accurately demonstrates CSD in preoperative adult patients. CT may have an incremental role in preoperative planning, particularly in those with more complex anatomy. (orig.)

SVM-based Partial Discharge Pattern Classification for GIS

Science.gov (United States)

Ling, Yin; Bai, Demeng; Wang, Menglin; Gong, Xiaojin; Gu, Chao

2018-01-01

Partial discharges (PD) occur when there are localized dielectric breakdowns in small regions of gas insulated substations (GIS). It is of high importance to recognize the PD patterns, through which we can diagnose the defects caused by different sources so that predictive maintenance can be conducted to prevent from unplanned power outage. In this paper, we propose an approach to perform partial discharge pattern classification. It first recovers the PRPD matrices from the PRPD2D images; then statistical features are extracted from the recovered PRPD matrix and fed into SVM for classification. Experiments conducted on a dataset containing thousands of images demonstrates the high effectiveness of the method.

Automated Feature Design for Time Series Classification by Genetic Programming

OpenAIRE

Harvey, Dustin Yewell

2014-01-01

Time series classification (TSC) methods discover and exploit patterns in time series and other one-dimensional signals. Although many accurate, robust classifiers exist for multivariate feature sets, general approaches are needed to extend machine learning techniques to make use of signal inputs. Numerous applications of TSC can be found in structural engineering, especially in the areas of structural health monitoring and non-destructive evaluation. Additionally, the fields of process contr...

[Difficulties of the methods for studying environmental exposure and neural tube defects].

Science.gov (United States)

Borja-Aburto, V H; Bermúdez-Castro, O; Lacasaña-Navarro, M; Kuri, P; Bustamante-Montes, P; Torres-Meza, V

1999-01-01

To discuss the attitudes in the assessment of environmental exposures as risk factors associated with neural tube defects, and to present the main risk factors studied to date. Environmental exposures have been suggested to have a roll in the genesis of birth defects. However, studies conducted in human populations have found difficulties in the design and conduction to show such an association for neural tube defects (anencephaly, espina bifida and encephalocele) because of problems raised from: a) the frequency measures used to compare time trends and communities, b) the classification of heterogeneous malformations, c) the inclusion of maternal, paternal and fetal factors as an integrated process and, d) the assessment of environmental exposures. Hypothetically both maternal and paternal environmental exposures can produce damage before and after conception by direct action on the embryo and the fetus-placenta complex. Therefore, in the assessment of environmental exposures we need to take into account: a) both paternal and maternal exposures; b) the critical exposure period, three months before conception for paternal exposures and one month around the conceptional period for maternal exposures; c) quantitatively evaluate environmental exposures when possible, avoiding a dichotomous classification; d) the use of biological markers of exposure is highly recommended as well as markers of genetic susceptibility.

Deep learning for tumor classification in imaging mass spectrometry.

Science.gov (United States)

Behrmann, Jens; Etmann, Christian; Boskamp, Tobias; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter

2018-04-01

Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification. Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks. https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS. jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de. Supplementary data are available at Bioinformatics online.

An automated image processing method for classification of diabetic retinopathy stages from conjunctival microvasculature images

Science.gov (United States)

Khansari, Maziyar M.; O'Neill, William; Penn, Richard; Blair, Norman P.; Chau, Felix; Shahidi, Mahnaz

2017-03-01

The conjunctiva is a densely vascularized tissue of the eye that provides an opportunity for imaging of human microcirculation. In the current study, automated fine structure analysis of conjunctival microvasculature images was performed to discriminate stages of diabetic retinopathy (DR). The study population consisted of one group of nondiabetic control subjects (NC) and 3 groups of diabetic subjects, with no clinical DR (NDR), non-proliferative DR (NPDR), or proliferative DR (PDR). Ordinary least square regression and Fisher linear discriminant analyses were performed to automatically discriminate images between group pairs of subjects. Human observers who were masked to the grouping of subjects performed image discrimination between group pairs. Over 80% and 70% of images of subjects with clinical and non-clinical DR were correctly discriminated by the automated method, respectively. The discrimination rates of the automated method were higher than human observers. The fine structure analysis of conjunctival microvasculature images provided discrimination of DR stages and can be potentially useful for DR screening and monitoring.

Composite Wavelet Filters for Enhanced Automated Target Recognition

Science.gov (United States)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

Directory of Open Access Journals (Sweden)

Eva Husson

2017-03-01

Full Text Available Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural features from a true-colour UAS-orthoimage with 5-cm pixels. In the present study, height data from a digital surface model (DSM created from overlapping UAS-images has been incorporated together with the spectral and textural features from the UAS-orthoimage to test if classification accuracy can be improved further. We studied two levels of thematic detail: (a Growth forms including the classes of water, nymphaeid, and helophyte; and (b dominant taxa including seven vegetation classes. We hypothesized that the incorporation of height data together with spectral and textural features would increase classification accuracy as compared to using spectral and textural features alone, at both levels of thematic detail. We tested our hypothesis at five test sites (100 m × 100 m each with varying vegetation complexity and image quality using automated object-based image analysis in combination with Random Forest classification. Overall accuracy at each of the five test sites ranged from 78% to 87% at the growth-form level and from 66% to 85% at the dominant-taxon level. In comparison to using spectral and textural features alone, the inclusion of height data increased the overall accuracy significantly by 4%–21% for growth-forms and 3%–30% for dominant taxa. The biggest improvement gained by adding height data was observed at the test site with the most complex vegetation. Height data derived from UAS-images has a large potential to efficiently increase the accuracy of automated classification of non-submerged aquatic vegetation, indicating good possibilities

Using multiclass classification to automate the identification of patient safety incident reports by type and severity.

Science.gov (United States)

Wang, Ying; Coiera, Enrico; Runciman, William; Magrabi, Farah

2017-06-12

Approximately 10% of admissions to acute-care hospitals are associated with an adverse event. Analysis of incident reports helps to understand how and why incidents occur and can inform policy and practice for safer care. Unfortunately our capacity to monitor and respond to incident reports in a timely manner is limited by the sheer volumes of data collected. In this study, we aim to evaluate the feasibility of using multiclass classification to automate the identification of patient safety incidents in hospitals. Text based classifiers were applied to identify 10 incident types and 4 severity levels. Using the one-versus-one (OvsO) and one-versus-all (OvsA) ensemble strategies, we evaluated regularized logistic regression, linear support vector machine (SVM) and SVM with a radial-basis function (RBF) kernel. Classifiers were trained and tested with "balanced" datasets (n_ Type  = 2860, n_ SeverityLevel  = 1160) from a state-wide incident reporting system. Testing was also undertaken with imbalanced "stratified" datasets (n_ Type  = 6000, n_ SeverityLevel =5950) from the state-wide system and an independent hospital reporting system. Classifier performance was evaluated using a confusion matrix, as well as F-score, precision and recall. The most effective combination was a OvsO ensemble of binary SVM RBF classifiers with binary count feature extraction. For incident type, classifiers performed well on balanced and stratified datasets (F-score: 78.3, 73.9%), but were worse on independent datasets (68.5%). Reports about falls, medications, pressure injury, aggression and blood products were identified with high recall and precision. "Documentation" was the hardest type to identify. For severity level, F-score for severity assessment code (SAC) 1 (extreme risk) was 87.3 and 64% for SAC4 (low risk) on balanced data. With stratified data, high recall was achieved for SAC1 (82.8-84%) but precision was poor (6.8-11.2%). High risk incidents (SAC2) were confused

Automation model of sewerage rehabilitation planning.

Science.gov (United States)

Yang, M D; Su, T C

2006-01-01

The major steps of sewerage rehabilitation include inspection of sewerage, assessment of structural conditions, computation of structural condition grades, and determination of rehabilitation methods and materials. Conventionally, sewerage rehabilitation planning relies on experts with professional background that is tedious and time-consuming. This paper proposes an automation model of planning optimal sewerage rehabilitation strategies for the sewer system by integrating image process, clustering technology, optimization, and visualization display. Firstly, image processing techniques, such as wavelet transformation and co-occurrence features extraction, were employed to extract various characteristics of structural failures from CCTV inspection images. Secondly, a classification neural network was established to automatically interpret the structural conditions by comparing the extracted features with the typical failures in a databank. Then, to achieve optimal rehabilitation efficiency, a genetic algorithm was used to determine appropriate rehabilitation methods and substitution materials for the pipe sections with a risk of mal-function and even collapse. Finally, the result from the automation model can be visualized in a geographic information system in which essential information of the sewer system and sewerage rehabilitation plans are graphically displayed. For demonstration, the automation model of optimal sewerage rehabilitation planning was applied to a sewer system in east Taichung, Chinese Taiwan.

A Visual Galaxy Classification Interface and its Classroom Application

Science.gov (United States)

Kautsch, Stefan J.; Phung, Chau; VanHilst, Michael; Castro, Victor H

2014-06-01

Galaxy morphology is an important topic in modern astronomy to understand questions concerning the evolution and formation of galaxies and their dark matter content. In order to engage students in exploring galaxy morphology, we developed a web-based, graphical interface that allows students to visually classify galaxy images according to various morphological types. The website is designed with HTML5, JavaScript, PHP, and a MySQL database. The classification interface provides hands-on research experience and training for students and interested clients, and allows them to contribute to studies of galaxy morphology. We present the first results of a pilot study and compare the visually classified types using our interface with that from automated classification routines.

Automated visual inspection for polished stone manufacture

Science.gov (United States)

Smith, Melvyn L.; Smith, Lyndon N.

2003-05-01

Increased globalisation of the ornamental stone market has lead to increased competition and more rigorous product quality requirements. As such, there are strong motivators to introduce new, more effective, inspection technologies that will help enable stone processors to reduce costs, improve quality and improve productivity. Natural stone surfaces may contain a mixture of complex two-dimensional (2D) patterns and three-dimensional (3D) features. The challenge in terms of automated inspection is to develop systems able to reliably identify 3D topographic defects, either naturally occurring or resulting from polishing, in the presence of concomitant complex 2D stochastic colour patterns. The resulting real-time analysis of the defects may be used in adaptive process control, in order to avoid the wasteful production of defective product. An innovative approach, using structured light and based upon an adaptation of the photometric stereo method, has been pioneered and developed at UWE to isolate and characterize mixed 2D and 3D surface features. The method is able to undertake tasks considered beyond the capabilities of existing surface inspection techniques. The approach has been successfully applied to real stone samples, and a selection of experimental results is presented.

Automation of ultrasonic testing of turbine disk billets

International Nuclear Information System (INIS)

Gorodkov, V.E.; Domashevskij, B.N.; Pron', N.I.; Tkachenko, V.A.

1984-01-01

cations of ultrasonic facility for automation of testing turbine disk billets of 25Kh2PMFA and 34KhM1A steels are considered. The operating principle and the design of ''Disk-1'' facility are described, its test results are presented. It is shown that the facility increases the test efficiency five times, enables to estimate dimensions of revealed defect with regard to the quality of acoustic contact, thus facilitating the work of personnel and improving the test quality

Design and implementation of a S-parameter wafer defect scanner

International Nuclear Information System (INIS)

Naik, P.S.; Beling, C.D.; Fung, S.

2004-01-01

We describe the design and implementation of a real-time automated scanning system that gives an S-parameter image of a semiconductor wafer, thus allowing the density of vacancy type defects to be shown as a function of position on the wafer. A conventional 22 Na positron source of 0.5 mm diameter rasters across 5 x 5 cm 2 region of two times per hour in rectilinear motion. Gamma ray energies E γ are processed using a standard HP Ge spectroscopy system and a 14 bit nuclear ADC. Over a period of 1-2 days a high resolution 128 x 128 pixel image with 256 colours (scaled to the S-parameter range) can be formed as a wafer defect map. The system is reliable, interactive and user-friendly (patent pending 2003). (orig.)

A field study on root cause analysis of defects in space software

International Nuclear Information System (INIS)

Silva, Nuno; Cunha, João Carlos; Vieira, Marco

2017-01-01

Critical systems, such as space systems, are developed under strict requirements envisaging high integrity in accordance to specific standards. For such software systems, an independent assessment is put into effect (Independent Software Verification and Validation – ISVV) after the regular development lifecycle and V&V activities, aiming at finding residual faults and raising confidence in the software. However, it has been observed that there is still a significant number of defects remaining at this stage, questioning the effectiveness of the previous engineering processes. This paper presents a root cause analysis of 1070 defects found in four space software projects during ISVV, by applying an improved Orthogonal Defect Classification (ODC) taxonomy and examining the defect types, triggers and impacts, in order to identify why they reached such a later stage in the development. The paper also puts forward proposals for modifications to both the software development (to prevent defects) and the V&V activities (to better detect defects) and an assessment methodology for future works on root cause analysis. - Highlights: • Root cause analysis of space software defects by using an enhanced ODC taxonomy. • Prioritization of the root causes according to the more important defect impacts. • Identification of improvements to systems engineering and development processes. • Improvements to V&V activities as means to reduce the occurrence of defects. • Generic process to achieve the defects root causes and the corrections suggestions.

Preliminary clinical evaluation of semi-automated nailfold capillaroscopy in the assessment of patients with Raynaud's phenomenon.

Science.gov (United States)

Murray, Andrea K; Feng, Kaiyan; Moore, Tonia L; Allen, Phillip D; Taylor, Christopher J; Herrick, Ariane L

2011-08-01

  Nailfold capillaroscopy is well established in screening patients with Raynaud's phenomenon for underlying SSc-spectrum disorders, by identifying abnormal capillaries. Our aim was to compare semi-automatic feature measurement from newly developed software with manual measurements, and determine the degree to which semi-automated data allows disease group classification.   Images from 46 healthy controls, 21 patients with PRP and 49 with SSc were preprocessed, and semi-automated measurements of intercapillary distance and capillary width, tortuosity, and derangement were performed. These were compared with manual measurements. Features were used to classify images into the three subject groups.   Comparison of automatic and manual measures for distance, width, tortuosity, and derangement had correlations of r=0.583, 0.624, 0.495 (p<0.001), and 0.195 (p=0.040). For automatic measures, correlations were found between width and intercapillary distance, r=0.374, and width and tortuosity, r=0.573 (p<0.001). Significant differences between subject groups were found for all features (p<0.002). Overall, 75% of images correctly matched clinical classification using semi-automated features, compared with 71% for manual measurements.   Semi-automatic and manual measurements of distance, width, and tortuosity showed moderate (but statistically significant) correlations. Correlation for derangement was weaker. Semi-automatic measurements are faster than manual measurements. Semi-automatic parameters identify differences between groups, and are as good as manual measurements for between-group classification. © 2011 John Wiley & Sons Ltd.

An Automated Sensing System for Steel Bridge Inspection Using GMR Sensor Array and Magnetic Wheels of Climbing Robot

Directory of Open Access Journals (Sweden)

Rui Wang

2016-01-01

Full Text Available Corrosion is one of the main causes of deterioration of steel bridges. It may cause metal loss and fatigue cracks in the steel components, which would lead to the collapse of steel bridges. This paper presents an automated sensing system to detect corrosion, crack, and other kinds of defects using a GMR (Giant Magnetoresistance sensor array. Defects will change the relative permeability and electrical conductivity of the material. As a result, magnetic field density generated by ferromagnetic material and the magnetic wheels will be changed. The defects are able to be detected by using GMR sensor array to measure the changes of magnetic flux density. In this study, magnetic wheels are used not only as the adhesion device of the robot, but also as an excitation source to provide the exciting magnetic field for the sensing system. Furthermore, compared to the eddy current method and the MFL (magnetic flux leakage method, this sensing system suppresses the noise from lift-off value fluctuation by measuring the vertical component of induced magnetic field that is perpendicular to the surface of the specimen in the corrosion inspection. Simulations and experimental results validated the feasibility of the system for the automated defect inspection.

Babcock experience of automated ultrasonic non-destructive testing of PWR pressure vessels during manufacture

International Nuclear Information System (INIS)

Dikstra, B.J.; Farley, J.M.; Scruton, G.

1990-01-01

Major developments in ultrasonic techniques, equipment and systems for automated inspection have lead, over a period of about ten years, to the regular application of sophisticated computer-controlled systems during the manufacture of nuclear reactor pressure vessels. Ten years ago the use of procedures defined in a code such as ASME XI might have been considered sufficient, but it is now necessary, as was demonstrated by the results of the UKAEA defect detection trials and the PISC II trials, to apply more comprehensive arrays of probes and higher test sensitivities. The ultrasonic techniques selected are demonstrated to be adequate by modelling or test-block exercises, the automated systems applied are subject to stringent quality assurance testing, and very rigorous inspection procedures are used in conjunction with a high degree of automation to ensure reproducibility of inspection quality. The state-of-the-art in automated ultrasonic testing of pressure vessels by Babcock is described. Current developments by the company, including automated flaw recognition, integrated modelling of inspection capability, and the use of electronically scanned variable-angle probes are reviewed. Examples quoted include the automated ultrasonic inspections of the Sizewell B pressurized water reactor vessel. (author)

Classification of radiolarian images with hand-crafted and deep features

Science.gov (United States)

Keçeli, Ali Seydi; Kaya, Aydın; Keçeli, Seda Uzunçimen

2017-12-01

Radiolarians are planktonic protozoa and are important biostratigraphic and paleoenvironmental indicators for paleogeographic reconstructions. Radiolarian paleontology still remains as a low cost and the one of the most convenient way to obtain dating of deep ocean sediments. Traditional methods for identifying radiolarians are time-consuming and cannot scale to the granularity or scope necessary for large-scale studies. Automated image classification will allow making these analyses promptly. In this study, a method for automatic radiolarian image classification is proposed on Scanning Electron Microscope (SEM) images of radiolarians to ease species identification of fossilized radiolarians. The proposed method uses both hand-crafted features like invariant moments, wavelet moments, Gabor features, basic morphological features and deep features obtained from a pre-trained Convolutional Neural Network (CNN). Feature selection is applied over deep features to reduce high dimensionality. Classification outcomes are analyzed to compare hand-crafted features, deep features, and their combinations. Results show that the deep features obtained from a pre-trained CNN are more discriminative comparing to hand-crafted ones. Additionally, feature selection utilizes to the computational cost of classification algorithms and have no negative effect on classification accuracy.

Automatic classification of DMSA scans using an artificial neural network

International Nuclear Information System (INIS)

Wright, J W; Duguid, R; Mckiddie, F; Staff, R T

2014-01-01

DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice. (paper)

A Cost Effective Method for Determining the Position of Mine Haul Road Defects from the Road Edge

Directory of Open Access Journals (Sweden)

Frank Sokolic

2010-01-01

Full Text Available This paper describes a simple method for estimating haul road defect positions from alongside roads. It is a method that is cheap and easy to implement, needing only a minimal amount of field equipment and training. Observers are required to estimate the bearing and distance to a defect, and to record their location using a GPS receiver. All further processing is automated and can be done entirely within a spatially-enabled database management system such as Microsoft SQL Server or PostgreSQL.

Automated Diatom Classification (Part A: Handcrafted Feature Approaches

Directory of Open Access Journals (Sweden)

Gloria Bueno

2017-07-01

Full Text Available This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP, to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future work.

PASTEC: an automatic transposable element classification tool.

Directory of Open Access Journals (Sweden)

Claire Hoede

Full Text Available SUMMARY: The classification of transposable elements (TEs is key step towards deciphering their potential impact on the genome. However, this process is often based on manual sequence inspection by TE experts. With the wealth of genomic sequences now available, this task requires automation, making it accessible to most scientists. We propose a new tool, PASTEC, which classifies TEs by searching for structural features and similarities. This tool outperforms currently available software for TE classification. The main innovation of PASTEC is the search for HMM profiles, which is useful for inferring the classification of unknown TE on the basis of conserved functional domains of the proteins. In addition, PASTEC is the only tool providing an exhaustive spectrum of possible classifications to the order level of the Wicker hierarchical TE classification system. It can also automatically classify other repeated elements, such as SSR (Simple Sequence Repeats, rDNA or potential repeated host genes. Finally, the output of this new tool is designed to facilitate manual curation by providing to biologists with all the evidence accumulated for each TE consensus. AVAILABILITY: PASTEC is available as a REPET module or standalone software (http://urgi.versailles.inra.fr/download/repet/REPET_linux-x64-2.2.tar.gz. It requires a Unix-like system. There are two standalone versions: one of which is parallelized (requiring Sun grid Engine or Torque, and the other of which is not.

Augmenting SCA project management and automation framework

Science.gov (United States)

Iyapparaja, M.; Sharma, Bhanupriya

2017-11-01

In our daily life we need to keep the records of the things in order to manage it in more efficient and proper way. Our Company manufactures semiconductor chips and sale it to the buyer. Sometimes it manufactures the entire product and sometimes partially and sometimes it sales the intermediary product obtained during manufacturing, so for the better management of the entire process there is a need to keep the track record of all the entity involved in it. Materials and Methods: Therefore to overcome with the problem the need raised to develop the framework for the maintenance of the project and for the automation testing. Project management framework provides an architecture which supports in managing the project by marinating the records of entire requirements, the test cases that were created for testing each unit of the software, defect raised from the past years. So through this the quality of the project can be maintained. Results: Automation framework provides the architecture which supports the development and implementation of the automation test script for the software testing process. Conclusion: For implementing project management framework the product of HP that is Application Lifecycle management is used which provides central repository to maintain the project.

A History of Cluster Analysis Using the Classification Society's Bibliography Over Four Decades

Science.gov (United States)

Murtagh, Fionn; Kurtz, Michael J.

2016-04-01

The Classification Literature Automated Search Service, an annual bibliography based on citation of one or more of a set of around 80 book or journal publications, ran from 1972 to 2012. We analyze here the years 1994 to 2011. The Classification Society's Service, as it was termed, has been produced by the Classification Society. In earlier decades it was distributed as a diskette or CD with the Journal of Classification. Among our findings are the following: an enormous increase in scholarly production post approximately 2000; a very major increase in quantity, coupled with work in different disciplines, from approximately 2004; and a major shift also from cluster analysis in earlier times having mathematics and psychology as disciplines of the journals published in, and affiliations of authors, contrasted with, in more recent times, a "centre of gravity" in management and engineering.

PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators

Science.gov (United States)

Broberg, Danny; Medasani, Bharat; Zimmermann, Nils E. R.; Yu, Guodong; Canning, Andrew; Haranczyk, Maciej; Asta, Mark; Hautier, Geoffroy

2018-05-01

Point defects have a strong impact on the performance of semiconductor and insulator materials used in technological applications, spanning microelectronics to energy conversion and storage. The nature of the dominant defect types, how they vary with processing conditions, and their impact on materials properties are central aspects that determine the performance of a material in a certain application. This information is, however, difficult to access directly from experimental measurements. Consequently, computational methods, based on electronic density functional theory (DFT), have found widespread use in the calculation of point-defect properties. Here we have developed the Python Charged Defect Toolkit (PyCDT) to expedite the setup and post-processing of defect calculations with widely used DFT software. PyCDT has a user-friendly command-line interface and provides a direct interface with the Materials Project database. This allows for setting up many charged defect calculations for any material of interest, as well as post-processing and applying state-of-the-art electrostatic correction terms. Our paper serves as a documentation for PyCDT, and demonstrates its use in an application to the well-studied GaAs compound semiconductor. We anticipate that the PyCDT code will be useful as a framework for undertaking readily reproducible calculations of charged point-defect properties, and that it will provide a foundation for automated, high-throughput calculations.

Retinal Microaneurysms Detection Using Gradient Vector Analysis and Class Imbalance Classification.

Science.gov (United States)

Dai, Baisheng; Wu, Xiangqian; Bu, Wei

2016-01-01

Retinal microaneurysms (MAs) are the earliest clinically observable lesions of diabetic retinopathy. Reliable automated MAs detection is thus critical for early diagnosis of diabetic retinopathy. This paper proposes a novel method for the automated MAs detection in color fundus images based on gradient vector analysis and class imbalance classification, which is composed of two stages, i.e. candidate MAs extraction and classification. In the first stage, a candidate MAs extraction algorithm is devised by analyzing the gradient field of the image, in which a multi-scale log condition number map is computed based on the gradient vectors for vessel removal, and then the candidate MAs are localized according to the second order directional derivatives computed in different directions. Due to the complexity of fundus image, besides a small number of true MAs, there are also a large amount of non-MAs in the extracted candidates. Classifying the true MAs and the non-MAs is an extremely class imbalanced classification problem. Therefore, in the second stage, several types of features including geometry, contrast, intensity, edge, texture, region descriptors and other features are extracted from the candidate MAs and a class imbalance classifier, i.e., RUSBoost, is trained for the MAs classification. With the Retinopathy Online Challenge (ROC) criterion, the proposed method achieves an average sensitivity of 0.433 at 1/8, 1/4, 1/2, 1, 2, 4 and 8 false positives per image on the ROC database, which is comparable with the state-of-the-art approaches, and 0.321 on the DiaRetDB1 V2.1 database, which outperforms the state-of-the-art approaches.

Classification of Mobile Laser Scanning Point Clouds from Height Features

Science.gov (United States)

Zheng, M.; Lemmens, M.; van Oosterom, P.

2017-09-01

The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of point clouds is labour intensive and thus time consuming and expensive. Hence, the need for rapid and automated methods for 3D mapping of dense point clouds is growing exponentially. The last five years the research on automated 3D mapping of MLS data has tremendously intensified. In this paper, we present our work on automated classification of MLS point clouds. In the present stage of the research we exploited three features - two height components and one reflectance value, and achieved an overall accuracy of 73 %, which is really encouraging for further refining our approach.

A new classification scheme of plastic wastes based upon recycling labels

Energy Technology Data Exchange (ETDEWEB)

Özkan, Kemal, E-mail: kozkan@ogu.edu.tr [Computer Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Ergin, Semih, E-mail: sergin@ogu.edu.tr [Electrical Electronics Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Işık, Şahin, E-mail: sahini@ogu.edu.tr [Computer Engineering Dept., Eskişehir Osmangazi University, 26480 Eskişehir (Turkey); Işıklı, İdil, E-mail: idil.isikli@bilecik.edu.tr [Electrical Electronics Engineering Dept., Bilecik University, 11210 Bilecik (Turkey)

2015-01-15

Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple

A new classification scheme of plastic wastes based upon recycling labels

International Nuclear Information System (INIS)

Özkan, Kemal; Ergin, Semih; Işık, Şahin; Işıklı, İdil

2015-01-01

Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple

Interactive Classification of Construction Materials: Feedback Driven Framework for Annotation and Analysis of 3d Point Clouds

Science.gov (United States)

Hess, M. R.; Petrovic, V.; Kuester, F.

2017-08-01

Digital documentation of cultural heritage structures is increasingly more common through the application of different imaging techniques. Many works have focused on the application of laser scanning and photogrammetry techniques for the acquisition of threedimensional (3D) geometry detailing cultural heritage sites and structures. With an abundance of these 3D data assets, there must be a digital environment where these data can be visualized and analyzed. Presented here is a feedback driven visualization framework that seamlessly enables interactive exploration and manipulation of massive point cloud data. The focus of this work is on the classification of different building materials with the goal of building more accurate as-built information models of historical structures. User defined functions have been tested within the interactive point cloud visualization framework to evaluate automated and semi-automated classification of 3D point data. These functions include decisions based on observed color, laser intensity, normal vector or local surface geometry. Multiple case studies are presented here to demonstrate the flexibility and utility of the presented point cloud visualization framework to achieve classification objectives.

CLASSIFICATION OF LEARNING MANAGEMENT SYSTEMS

Directory of Open Access Journals (Sweden)

Yu. B. Popova

2016-01-01

Full Text Available Using of information technologies and, in particular, learning management systems, increases opportunities of teachers and students in reaching their goals in education. Such systems provide learning content, help organize and monitor training, collect progress statistics and take into account the individual characteristics of each user. Currently, there is a huge inventory of both paid and free systems are physically located both on college servers and in the cloud, offering different features sets of different licensing scheme and the cost. This creates the problem of choosing the best system. This problem is partly due to the lack of comprehensive classification of such systems. Analysis of more than 30 of the most common now automated learning management systems has shown that a classification of such systems should be carried out according to certain criteria, under which the same type of system can be considered. As classification features offered by the author are: cost, functionality, modularity, keeping the customer’s requirements, the integration of content, the physical location of a system, adaptability training. Considering the learning management system within these classifications and taking into account the current trends of their development, it is possible to identify the main requirements to them: functionality, reliability, ease of use, low cost, support for SCORM standard or Tin Can API, modularity and adaptability. According to the requirements at the Software Department of FITR BNTU under the guidance of the author since 2009 take place the development, the use and continuous improvement of their own learning management system.

Locating and classifying defects using an hybrid data base

Science.gov (United States)

Luna-Avilés, A.; Hernández-Gómez, L. H.; Durodola, J. F.; Urriolagoitia-Calderón, G.; Urriolagoitia-Sosa, G.; Beltrán Fernández, J. A.; Díaz Pineda, A.

2011-07-01

A computational inverse technique was used in the localization and classification of defects. Postulated voids of two different sizes (2 mm and 4 mm diameter) were introduced in PMMA bars with and without a notch. The bar dimensions are 200×20×5 mm. One half of them were plain and the other half has a notch (3 mm × 4 mm) which is close to the defect area (19 mm × 16 mm).This analysis was done with an Artificial Neural Network (ANN) and its optimization was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). A hybrid data base was developed with numerical and experimental results. Synthetic data was generated with the finite element method using SOLID95 element of ANSYS code. A parametric analysis was carried out. Only one defect in such bars was taken into account and the first five natural frequencies were calculated. 460 cases were evaluated. Half of them were plain and the other half has a notch. All the input data was classified in two groups. Each one has 230 cases and corresponds to one of the two sort of voids mentioned above. On the other hand, experimental analysis was carried on with PMMA specimens of the same size. The first two natural frequencies of 40 cases were obtained with one void. The other three frequencies were obtained numerically. 20 of these bars were plain and the others have a notch. These experimental results were introduced in the synthetic data base. 400 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. In the next stage of this work, the ANN output was optimized with ANFIS. Previous papers showed that localization and classification of defects was reduced as notches were introduced in such bars. In the case of this paper, improved results were obtained when a hybrid data base was used.

Locating and classifying defects using an hybrid data base

Energy Technology Data Exchange (ETDEWEB)

Luna-Aviles, A; Diaz Pineda, A [Tecnologico de Estudios Superiores de Coacalco. Av. 16 de Septiembre 54, Col. Cabecera Municipal. C.P. 55700 (Mexico); Hernandez-Gomez, L H; Urriolagoitia-Calderon, G; Urriolagoitia-Sosa, G [Instituto Politecnico Nacional. ESIME-SEPI. Unidad Profesional ' Adolfo Lopez Mateos' Edificio 5, 30 Piso, Colonia Lindavista. Gustavo A. Madero. 07738 Mexico D.F. (Mexico); Durodola, J F [School of Technology, Oxford Brookes University, Headington Campus, Gipsy Lane, Oxford OX3 0BP (United Kingdom); Beltran Fernandez, J A, E-mail: alelunaav@hotmail.com, E-mail: luishector56@hotmail.com, E-mail: jdurodola@brookes.ac.uk

2011-07-19

A computational inverse technique was used in the localization and classification of defects. Postulated voids of two different sizes (2 mm and 4 mm diameter) were introduced in PMMA bars with and without a notch. The bar dimensions are 200x20x5 mm. One half of them were plain and the other half has a notch (3 mm x 4 mm) which is close to the defect area (19 mm x 16 mm).This analysis was done with an Artificial Neural Network (ANN) and its optimization was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). A hybrid data base was developed with numerical and experimental results. Synthetic data was generated with the finite element method using SOLID95 element of ANSYS code. A parametric analysis was carried out. Only one defect in such bars was taken into account and the first five natural frequencies were calculated. 460 cases were evaluated. Half of them were plain and the other half has a notch. All the input data was classified in two groups. Each one has 230 cases and corresponds to one of the two sort of voids mentioned above. On the other hand, experimental analysis was carried on with PMMA specimens of the same size. The first two natural frequencies of 40 cases were obtained with one void. The other three frequencies were obtained numerically. 20 of these bars were plain and the others have a notch. These experimental results were introduced in the synthetic data base. 400 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. In the next stage of this work, the ANN output was optimized with ANFIS. Previous papers showed that localization and classification of defects was reduced as notches were introduced in such bars. In the case of this paper, improved results were obtained when a hybrid data base was used.

Locating and classifying defects using an hybrid data base

International Nuclear Information System (INIS)

Luna-Aviles, A; Diaz Pineda, A; Hernandez-Gomez, L H; Urriolagoitia-Calderon, G; Urriolagoitia-Sosa, G; Durodola, J F; Beltran Fernandez, J A

2011-01-01

A computational inverse technique was used in the localization and classification of defects. Postulated voids of two different sizes (2 mm and 4 mm diameter) were introduced in PMMA bars with and without a notch. The bar dimensions are 200x20x5 mm. One half of them were plain and the other half has a notch (3 mm x 4 mm) which is close to the defect area (19 mm x 16 mm).This analysis was done with an Artificial Neural Network (ANN) and its optimization was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). A hybrid data base was developed with numerical and experimental results. Synthetic data was generated with the finite element method using SOLID95 element of ANSYS code. A parametric analysis was carried out. Only one defect in such bars was taken into account and the first five natural frequencies were calculated. 460 cases were evaluated. Half of them were plain and the other half has a notch. All the input data was classified in two groups. Each one has 230 cases and corresponds to one of the two sort of voids mentioned above. On the other hand, experimental analysis was carried on with PMMA specimens of the same size. The first two natural frequencies of 40 cases were obtained with one void. The other three frequencies were obtained numerically. 20 of these bars were plain and the others have a notch. These experimental results were introduced in the synthetic data base. 400 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. In the next stage of this work, the ANN output was optimized with ANFIS. Previous papers showed that localization and classification of defects was reduced as notches were introduced in such bars. In the case of this paper, improved results were obtained when a hybrid data base was used.

Do NiTi instruments show defects before separation? Defects caused by torsional fatigue in hand and rotary nickel-titanium (NiTi) instruments which lead to failure during clinical use.

Science.gov (United States)

Chakka, N V Murali Krishna; Ratnakar, P; Das, Sanjib; Bagchi, Anandamy; Sudhir, Sudhir; Anumula, Lavanya

2012-11-01

Visual and microscopic evaluation of defects caused by torsional fatigue in hand and rotary nickel titanium (NiTi) instruments. Ninety-six NiTi greater taper instruments which were routinely used for root canal treatment only in anterior teeth were selected for the study. The files taken include ProTaper for hand use, ProTaper Rotary files and Endowave rotary files. After every use, the files were observed visually and microscopically (Stereomicroscope at 10×) to evaluate the defects caused by torsional fatigue. Scoring was given according to a new classification formulated which gives an indication of the severity of the defect or damage. Data was statistically analyzed using KruskallWallis and Mann-Whitney U test. Number of files showing defects were more under stereomicroscope than visual examination. But, the difference in the evaluation methods was not statistically significant. The different types of defects observed were bent instrument, straightening/stretching of twist contour and partial reverse twisting. Endowave files showed maximum number of defects followed by ProTaper for hand use and least in ProTaper Rotary. Visible defects due to torsional fatigue do occur in NiTi instruments after clinical use. Both visual and microscopic examinations were efficient in detecting defects caused due to torsional fatigue. This study emphasizes that all files should be observed for any visible defects before and after every instrumentation cycle to minimize the risk of instrument separation and failure of endodontic therapy.

Multi-decadal classification of synoptic weather types, observed trends and links to rainfall characteristics over Saudi Arabia

KAUST Repository

El Kenawy, Ahmed M.; McCabe, Matthew; Stenchikov, Georgiy L.; Raj, Jerry

2014-01-01

An automated version of the Lamb weather type classification scheme was employed to characterize daily circulation conditions in Saudi Arabia from 1960 to 2005. Daily gridded fields of sea level pressure (SLP) from both the NCEP

Automated Essay Grading using Machine Learning Algorithm

Science.gov (United States)

Ramalingam, V. V.; Pandian, A.; Chetry, Prateek; Nigam, Himanshu

2018-04-01

Essays are paramount for of assessing the academic excellence along with linking the different ideas with the ability to recall but are notably time consuming when they are assessed manually. Manual grading takes significant amount of evaluator’s time and hence it is an expensive process. Automated grading if proven effective will not only reduce the time for assessment but comparing it with human scores will also make the score realistic. The project aims to develop an automated essay assessment system by use of machine learning techniques by classifying a corpus of textual entities into small number of discrete categories, corresponding to possible grades. Linear regression technique will be utilized for training the model along with making the use of various other classifications and clustering techniques. We intend to train classifiers on the training set, make it go through the downloaded dataset, and then measure performance our dataset by comparing the obtained values with the dataset values. We have implemented our model using java.

Identifying and locating surface defects in wood: Part of an automated lumber processing system

Science.gov (United States)

Richard W. Conners; Charles W. McMillin; Kingyao Lin; Ramon E. Vasquez-Espinosa

1983-01-01

Continued increases in the cost of materials and labor make it imperative for furniture manufacturers to control costs by improved yield and increased productivity. This paper describes an Automated Lumber Processing System (ALPS) that employs computer tomography, optical scanning technology, the calculation of an optimum cutting strategy, and 1 computer-driven laser...

Automated EEG sleep staging in the term-age baby using a generative modelling approach

Science.gov (United States)

Pillay, Kirubin; Dereymaeker, Anneleen; Jansen, Katrien; Naulaers, Gunnar; Van Huffel, Sabine; De Vos, Maarten

2018-06-01

Objective. We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38-40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader quiet sleep (QS) and active sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development. However, existing methods for automated sleep classification remain focussed only on QS and AS sleep classification. Approach. EEG features were calculated from 16 EEG recordings, in 30 s epochs, and personalized feature scaling used to correct for some of the inter-recording variability, by standardizing each recording’s feature data using its mean and standard deviation. Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) were trained, with the HMM incorporating knowledge of the sleep state transition probabilities. Performance of the GMM and HMM (with and without scaling) were compared, and Cohen’s kappa agreement calculated between the estimates and clinicians’ visual labels. Main results. For four-state classification, the HMM proved superior to the GMM. With the inclusion of personalized feature scaling, mean kappa (±standard deviation) was 0.62 (±0.16) compared to the GMM value of 0.55 (±0.15). Without feature scaling, kappas for the HMM and GMM dropped to 0.56 (±0.18) and 0.51 (±0.15), respectively. Significance. This is the first study to present a successful method for the automated staging of four states in term-age sleep using multichannel EEG. Results suggested a benefit in incorporating transition information using an HMM, and correcting for inter-recording variability through personalized feature scaling. Determining the timing and quality of these states are indicative of developmental delays in both preterm and term-born babies that may

Automated attribution of remotely-sensed ecological disturbances using spatial and temporal characteristics of common disturbance classes.

Science.gov (United States)

Cooper, L. A.; Ballantyne, A.

2017-12-01

Forest disturbances are critical components of ecosystems. Knowledge of their prevalence and impacts is necessary to accurately describe forest health and ecosystem services through time. While there are currently several methods available to identify and describe forest disturbances, especially those which occur in North America, the process remains inefficient and inaccessible in many parts of the world. Here, we introduce a preliminary approach to streamline and automate both the detection and attribution of forest disturbances. We use a combination of the Breaks for Additive Season and Trend (BFAST) detection algorithm to detect disturbances in combination with supervised and unsupervised classification algorithms to attribute the detections to disturbance classes. Both spatial and temporal disturbance characteristics are derived and utilized for the goal of automating the disturbance attribution process. The resulting preliminary algorithm is applied to up-scaled (100m) Landsat data for several different ecosystems in North America, with varying success. Our results indicate that supervised classification is more reliable than unsupervised classification, but that limited training data are required for a region. Future work will improve the algorithm through refining and validating at sites within North America before applying this approach globally.

Protein classification using modified n-grams and skip-grams.

Science.gov (United States)

Islam, S M Ashiqul; Heil, Benjamin J; Kearney, Christopher Michel; Baker, Erich J

2018-05-01

Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used to classify the proteins. Lack of this knowledge risks the selection of irrelevant features, resulting in a faulty model. In this study, we introduce a supervised protein classification method with a novel means of automating the work-intensive feature generation step via a Natural Language Processing (NLP)-dependent model, using a modified combination of n-grams and skip-grams (m-NGSG). A meta-comparison of cross-validation accuracy with twelve training datasets from nine different published studies demonstrates a consistent increase in accuracy of m-NGSG when compared to contemporary classification and feature generation models. We expect this model to accelerate the classification of proteins from primary sequence data and increase the accessibility of protein characteristic prediction to a broader range of scientists. m-NGSG is freely available at Bitbucket: https://bitbucket.org/sm_islam/mngsg/src. A web server is available at watson.ecs.baylor.edu/ngsg. erich_baker@baylor.edu. Supplementary data are available at Bioinformatics online.

Morphological images analysis and chromosomic aberrations classification based on fuzzy logic

International Nuclear Information System (INIS)

Souza, Leonardo Peres

2011-01-01

This work has implemented a methodology for automation of images analysis of chromosomes of human cells irradiated at IEA-R1 nuclear reactor (located at IPEN, Sao Paulo, Brazil), and therefore subject to morphological aberrations. This methodology intends to be a tool for helping cytogeneticists on identification, characterization and classification of chromosomal metaphasic analysis. The methodology development has included the creation of a software application based on artificial intelligence techniques using Fuzzy Logic combined with image processing techniques. The developed application was named CHRIMAN and is composed of modules that contain the methodological steps which are important requirements in order to achieve an automated analysis. The first step is the standardization of the bi-dimensional digital image acquisition procedure through coupling a simple digital camera to the ocular of the conventional metaphasic analysis microscope. Second step is related to the image treatment achieved through digital filters application; storing and organization of information obtained both from image content itself, and from selected extracted features, for further use on pattern recognition algorithms. The third step consists on characterizing, counting and classification of stored digital images and extracted features information. The accuracy in the recognition of chromosome images is 93.9%. This classification is based on classical standards obtained at Buckton [1973], and enables support to geneticist on chromosomic analysis procedure, decreasing analysis time, and creating conditions to include this method on a broader evaluation system on human cell damage due to ionizing radiation exposure. (author)

Cause Analyses of Yarn Defect%纱线疵点产生原因分析

Institute of Scientific and Technical Information of China (English)

王充; 赵帆

2016-01-01

The grading and cause of yarn defect in the process of electronic yarn cleaner were discussed.The definition and classification of yarn defects were introduced.Yarns with thick place and thin places were identified according to different shapes of yarn with defects and refering the basic diameters of yarn aimed at the cleaning technology in winding.Combined the yarn defect classifing area,the frequency distribution chart of yarn defect was analyzed,the possible causes of defects in each areas were pointed out.It is considered that the problems in each processing can be effectively analyzed through the yarn cleaning data from winding electronic yarn cleaner. The quality problems can be quickly positioned and solved,the quality managing efficiency can be improved.%探讨电子清纱器工作过程中纱线疵点的分级和产生原因。介绍了纱线疵点的定义和分类，针对络筒清纱工艺，根据疵点纱的不同形状并参考细纱的基础直径，识别粗节纱和细节纱。结合纱线疵点分级区域，分析了纱线疵点频率分布图，指出了各个区域疵点可能产生的原因。认为：通过络筒电子清纱器的清纱数据，可以有效分析各个工艺环节出现的问题，帮助快速定位和解决质量问题，提高质量管理的效率。

A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

Energy Technology Data Exchange (ETDEWEB)

Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)

2012-02-01

New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.

Automated analysis of free speech predicts psychosis onset in high-risk youths

Science.gov (United States)

Bedi, Gillinder; Carrillo, Facundo; Cecchi, Guillermo A; Slezak, Diego Fernández; Sigman, Mariano; Mota, Natália B; Ribeiro, Sidarta; Javitt, Daniel C; Copelli, Mauro; Corcoran, Cheryl M

2015-01-01

Background/Objectives: Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals. AIMS: In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis. Methods: Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; five transitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed. Results: Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosis development with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantly correlated with prodromal symptoms. Conclusions: Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry. PMID:27336038

Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images.

Science.gov (United States)

Burns, Joseph E; Yao, Jianhua; Summers, Ronald M

2017-09-01

Purpose To create and validate a computer system with which to detect, localize, and classify compression fractures and measure bone density of thoracic and lumbar vertebral bodies on computed tomographic (CT) images. Materials and Methods Institutional review board approval was obtained, and informed consent was waived in this HIPAA-compliant retrospective study. A CT study set of 150 patients (mean age, 73 years; age range, 55-96 years; 92 women, 58 men) with (n = 75) and without (n = 75) compression fractures was assembled. All case patients were age and sex matched with control subjects. A total of 210 thoracic and lumbar vertebrae showed compression fractures and were electronically marked and classified by a radiologist. Prototype fully automated spinal segmentation and fracture detection software were then used to analyze the study set. System performance was evaluated with free-response receiver operating characteristic analysis. Results Sensitivity for detection or localization of compression fractures was 95.7% (201 of 210; 95% confidence interval [CI]: 87.0%, 98.9%), with a false-positive rate of 0.29 per patient. Additionally, sensitivity was 98.7% and specificity was 77.3% at case-based receiver operating characteristic curve analysis. Accuracy for classification by Genant type (anterior, middle, or posterior height loss) was 0.95 (107 of 113; 95% CI: 0.89, 0.98), with weighted κ of 0.90 (95% CI: 0.81, 0.99). Accuracy for categorization by Genant height loss grade was 0.68 (77 of 113; 95% CI: 0.59, 0.76), with a weighted κ of 0.59 (95% CI: 0.47, 0.71). The average bone attenuation for T12-L4 vertebrae was 146 HU ± 29 (standard deviation) in case patients and 173 HU ± 42 in control patients; this difference was statistically significant (P high sensitivity and with a low false-positive rate, as well as to calculate vertebral bone density, on CT images. © RSNA, 2017 Online supplemental material is available for this article.

Time course of an X-ray picture following operation for the ventricular septal defect

Energy Technology Data Exchange (ETDEWEB)

Khenynya, R.L.; Latsis, A.T.; Rubene, M.Ya.

1983-01-01

An x-ray picture was studied over time in 80 pediatric patients by hemodynamic groups in accordance with the classification adopted in the A.I. Bakulev Institute of Cardiovascular Surgery, USSR AMS. Of them in 15 patients in addition to the ventricular septal defect, stenosis of the pulmonary artery was revealed, in 11 concomitant insufficiency of the tricuspid valve. The best results after radical correction of the defect were achieved in patients with insignificant or moderate disorder of the pulmonary hemodynamics; positive results in patients with a high pulmonary hypertension were observed over time later. X-ray studies on the regression of disorders of the pulmonary hemodynamics emphasize the necessity of early diagnosis of the ventricular septal defect followed by surgical correction.

Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning

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Remmele, Steffen; Ritzerfeld, Julia; Nickel, Walter; Hesser, Jürgen

2011-03-01

RNAi-based high-throughput microscopy screens have become an important tool in biological sciences in order to decrypt mostly unknown biological functions of human genes. However, manual analysis is impossible for such screens since the amount of image data sets can often be in the hundred thousands. Reliable automated tools are thus required to analyse the fluorescence microscopy image data sets usually containing two or more reaction channels. The herein presented image analysis tool is designed to analyse an RNAi screen investigating the intracellular trafficking and targeting of acylated Src kinases. In this specific screen, a data set consists of three reaction channels and the investigated cells can appear in different phenotypes. The main issue of the image processing task is an automatic cell segmentation which has to be robust and accurate for all different phenotypes and a successive phenotype classification. The cell segmentation is done in two steps by segmenting the cell nuclei first and then using a classifier-enhanced region growing on basis of the cell nuclei to segment the cells. The classification of the cells is realized by a support vector machine which has to be trained manually using supervised learning. Furthermore, the tool is brightness invariant allowing different staining quality and it provides a quality control that copes with typical defects during preparation and acquisition. A first version of the tool has already been successfully applied for an RNAi-screen containing three hundred thousand image data sets and the SVM extended version is designed for additional screens.

Artificial intelligence and ultrasonic tests in detection of defects

International Nuclear Information System (INIS)

Barrera Cardiel, G.; Fabian Alvarez, M. a.; Velez Martinez, M.; Villasenor, L.

2001-01-01

One of the most serious problems in the quality control of welded unions is the location, identification and classification of defects. As a solution to this problem, a technique for classification, applicable to welded unions done by electric arc welding as well as by friction, is proposed; it is based on ultrasonic signals. The neuronal networks proposed are Kohonen and Multilayer Percept ron, all in a virtual instrument environment. Currently the techniques most used in this field are: radiological analysis (X-rays) and ultrasonic analysis (ultrasonic waves). The X-ray technique in addition to being dangerous requires highly specialized personnel and equipment, therefore its use is restricted. The ultrasonic technique, in spite of being one of the most used for detection of discontinuities, requires personnel with wide experience in the interpretation of ultrasonic signals, this is a time-consuming process which necessarily increases its operation cost. The classification techniques that we propose turn out to be safe, reliable, inexpensive and easy to implement for the solution of this important problem. (Author) 8 refs

Defects and defect processes in nonmetallic solids

CERN Document Server

Hayes, W

2004-01-01

This extensive survey covers defects in nonmetals, emphasizing point defects and point-defect processes. It encompasses electronic, vibrational, and optical properties of defective solids, plus dislocations and grain boundaries. 1985 edition.

Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

International Nuclear Information System (INIS)

Korfiatis, P; Costaridou, L; Kalogeropoulou, C; Petsas, T; Daoussis, D; Adonopoulos, A

2009-01-01

Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

Science.gov (United States)

Korfiatis, P.; Kalogeropoulou, C.; Daoussis, D.; Petsas, T.; Adonopoulos, A.; Costaridou, L.

2009-07-01

Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

Preliminary Full-Scale Tests of the Center for Automated Processing of Hardwoods' Auto-Image

Science.gov (United States)

Philip A. Araman; Janice K. Wiedenbeck

1995-01-01

Automated lumber grading and yield optimization using computer controlled saws will be plausible for hardwoods if and when lumber scanning systems can reliably identify all defects by type. Existing computer programs could then be used to grade the lumber, identify the best cut-up solution, and control the sawing machines. The potential value of a scanning grading...

Automated detection of neovascularization for proliferative diabetic retinopathy screening.

Science.gov (United States)

Roychowdhury, Sohini; Koozekanani, Dara D; Parhi, Keshab K

2016-08-01

Neovascularization is the primary manifestation of proliferative diabetic retinopathy (PDR) that can lead to acquired blindness. This paper presents a novel method that classifies neovascularizations in the 1-optic disc (OD) diameter region (NVD) and elsewhere (NVE) separately to achieve low false positive rates of neovascularization classification. First, the OD region and blood vessels are extracted. Next, the major blood vessel segments in the 1-OD diameter region are classified for NVD, and minor blood vessel segments elsewhere are classified for NVE. For NVD and NVE classifications, optimal region-based feature sets of 10 and 6 features, respectively, are used. The proposed method achieves classification sensitivity, specificity and accuracy for NVD and NVE of 74%, 98.2%, 87.6%, and 61%, 97.5%, 92.1%, respectively. Also, the proposed method achieves 86.4% sensitivity and 76% specificity for screening images with PDR from public and local data sets. Thus, the proposed NVD and NVE detection methods can play a key role in automated screening and prioritization of patients with diabetic retinopathy.

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

Science.gov (United States)

Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

2018-05-01

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

Directory of Open Access Journals (Sweden)

Muhammad Faisal Siddiqui

Full Text Available A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT, principal component analysis (PCA, and least squares support vector machine (LS-SVM are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%. Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities

Costs to Automate Demand Response - Taxonomy and Results from Field Studies and Programs

Energy Technology Data Exchange (ETDEWEB)

Piette, Mary A. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Schetrit, Oren [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Kiliccote, Sila [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Cheung, Iris [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Li, Becky Z [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2017-07-31

During the past decade, the technology to automate demand response (DR) in buildings and industrial facilities has advanced significantly. Automation allows rapid, repeatable, reliable operation. This study focuses on costs for DR automation in commercial buildings with some discussion on residential buildings and industrial facilities. DR automation technology relies on numerous components, including communication systems, hardware and software gateways, standards-based messaging protocols, controls and integration platforms, and measurement and telemetry systems. This report compares cost data from several DR automation programs and pilot projects, evaluates trends in the cost per unit of DR and kilowatts (kW) available from automated systems, and applies a standard naming convention and classification or taxonomy for system elements. Median costs for the 56 installed automated DR systems studied here are about $200/kW. The deviation around this median is large with costs in some cases being an order of magnitude great or less than the median. This wide range is a result of variations in system age, size of load reduction, sophistication, and type of equipment included in cost analysis. The costs to automate fast DR systems for ancillary services are not fully analyzed in this report because additional research is needed to determine the total cost to install, operate, and maintain these systems. However, recent research suggests that they could be developed at costs similar to those of existing hot-summer DR automation systems. This report considers installation and configuration costs and does include the costs of owning and operating DR automation systems. Future analysis of the latter costs should include the costs to the building or facility manager costs as well as utility or third party program manager cost.

A controlled trial of automated classification of negation from clinical notes

Directory of Open Access Journals (Sweden)

Carruth William

2005-05-01

Full Text Available Abstract Background Identification of negation in electronic health records is essential if we are to understand the computable meaning of the records: Our objective is to compare the accuracy of an automated mechanism for assignment of Negation to clinical concepts within a compositional expression with Human Assigned Negation. Also to perform a failure analysis to identify the causes of poorly identified negation (i.e. Missed Conceptual Representation, Inaccurate Conceptual Representation, Missed Negation, Inaccurate identification of Negation. Methods 41 Clinical Documents (Medical Evaluations; sometimes outside of Mayo these are referred to as History and Physical Examinations were parsed using the Mayo Vocabulary Server Parsing Engine. SNOMED-CT™ was used to provide concept coverage for the clinical concepts in the record. These records resulted in identification of Concepts and textual clues to Negation. These records were reviewed by an independent medical terminologist, and the results were tallied in a spreadsheet. Where questions on the review arose Internal Medicine Faculty were employed to make a final determination. Results SNOMED-CT was used to provide concept coverage of the 14,792 Concepts in 41 Health Records from John's Hopkins University. Of these, 1,823 Concepts were identified as negative by Human review. The sensitivity (Recall of the assignment of negation was 97.2% (p Conclusion Automated assignment of negation to concepts identified in health records based on review of the text is feasible and practical. Lexical assignment of negation is a good test of true Negativity as judged by the high sensitivity, specificity and positive likelihood ratio of the test. SNOMED-CT had overall coverage of 88.7% of the concepts being negated.

The application of an optical Fourier spectrum analyzer on detecting defects in mass-produced satellite photographs

Science.gov (United States)

Athale, R.; Lee, S. H.

1976-01-01

Various defects in mass-produced pictures transmitted to earth from a satellite are investigated. It is found that the following defects are readily detectable via Fourier spectrum analysis: (1) bit slip, (2) breakup causing loss of image, and (3) disabled track at the top of the imagery. The scratches made on the film during mass production, which are difficult to detect by visual observation, also show themselves readily in Fourier spectrum analysis. A relation is established between the number of scratches, their width and depth and the intensity of their Fourier spectra. Other defects that are found to be equally suitable for Fourier spectrum analysis or visual (image analysis) detection are synchronous loss without blurring of image, and density variation in gray scale. However, the Fourier spectrum analysis is found to be unsuitable for detection of such defects as pin holes, annotation error, synchronous loss with blurring of images, and missing image in the beginning of the work order. The design of an automated, real time system, which will reject defective films, is treated.

Training echo state networks for rotation-invariant bone marrow cell classification.

Science.gov (United States)

Kainz, Philipp; Burgsteiner, Harald; Asslaber, Martin; Ahammer, Helmut

2017-01-01

The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.

A novel scheme for the validation of an automated classification method for epileptic spikes by comparison with multiple observers.

Science.gov (United States)

Sharma, Niraj K; Pedreira, Carlos; Centeno, Maria; Chaudhary, Umair J; Wehner, Tim; França, Lucas G S; Yadee, Tinonkorn; Murta, Teresa; Leite, Marco; Vos, Sjoerd B; Ourselin, Sebastien; Diehl, Beate; Lemieux, Louis

2017-07-01

To validate the application of an automated neuronal spike classification algorithm, Wave_clus (WC), on interictal epileptiform discharges (IED) obtained from human intracranial EEG (icEEG) data. Five 10-min segments of icEEG recorded in 5 patients were used. WC and three expert EEG reviewers independently classified one hundred IED events into IED classes or non-IEDs. First, we determined whether WC-human agreement variability falls within inter-reviewer agreement variability by calculating the variation of information for each classifier pair and quantifying the overlap between all WC-reviewer and all reviewer-reviewer pairs. Second, we compared WC and EEG reviewers' spike identification and individual spike class labels visually and quantitatively. The overlap between all WC-human pairs and all human pairs was >80% for 3/5 patients and >58% for the other 2 patients demonstrating WC falling within inter-human variation. The average sensitivity of spike marking for WC was 91% and >87% for all three EEG reviewers. Finally, there was a strong visual and quantitative similarity between WC and EEG reviewers. WC performance is indistinguishable to that of EEG reviewers' suggesting it could be a valid clinical tool for the assessment of IEDs. WC can be used to provide quantitative analysis of epileptic spikes. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Automated Single Cell Data Decontamination Pipeline

Energy Technology Data Exchange (ETDEWEB)

Tennessen, Kristin [Lawrence Berkeley National Lab. (LBNL), Walnut Creek, CA (United States). Dept. of Energy Joint Genome Inst.; Pati, Amrita [Lawrence Berkeley National Lab. (LBNL), Walnut Creek, CA (United States). Dept. of Energy Joint Genome Inst.

2014-03-21

Recent technological advancements in single-cell genomics have encouraged the classification and functional assessment of microorganisms from a wide span of the biospheres phylogeny.1,2 Environmental processes of interest to the DOE, such as bioremediation and carbon cycling, can be elucidated through the genomic lens of these unculturable microbes. However, contamination can occur at various stages of the single-cell sequencing process. Contaminated data can lead to wasted time and effort on meaningless analyses, inaccurate or erroneous conclusions, and pollution of public databases. A fully automated decontamination tool is necessary to prevent these instances and increase the throughput of the single-cell sequencing process

Regional Landslide Mapping Aided by Automated Classification of SqueeSAR™ Time Series (Northern Apennines, Italy)

Science.gov (United States)

Iannacone, J.; Berti, M.; Allievi, J.; Del Conte, S.; Corsini, A.

2013-12-01

Space borne InSAR has proven to be very valuable for landslides detection. In particular, extremely slow landslides (Cruden and Varnes, 1996) can be now clearly identified, thanks to the millimetric precision reached by recent multi-interferometric algorithms. The typical approach in radar interpretation for landslides mapping is based on average annual velocity of the deformation which is calculated over the entire times series. The Hotspot and Cluster Analysis (Lu et al., 2012) and the PSI-based matrix approach (Cigna et al., 2013) are examples of landslides mapping techniques based on average annual velocities. However, slope movements can be affected by non-linear deformation trends, (i.e. reactivation of dormant landslides, deceleration due to natural or man-made slope stabilization, seasonal activity, etc). Therefore, analyzing deformation time series is crucial in order to fully characterize slope dynamics. While this is relatively simple to be carried out manually when dealing with small dataset, the time series analysis over regional scale dataset requires automated classification procedures. Berti et al. (2013) developed an automatic procedure for the analysis of InSAR time series based on a sequence of statistical tests. The analysis allows to classify the time series into six distinctive target trends (0=uncorrelated; 1=linear; 2=quadratic; 3=bilinear; 4=discontinuous without constant velocity; 5=discontinuous with change in velocity) which are likely to represent different slope processes. The analysis also provides a series of descriptive parameters which can be used to characterize the temporal changes of ground motion. All the classification algorithms were integrated into a Graphical User Interface called PSTime. We investigated an area of about 2000 km2 in the Northern Apennines of Italy by using SqueeSAR™ algorithm (Ferretti et al., 2011). Two Radarsat-1 data stack, comprising of 112 scenes in descending orbit and 124 scenes in ascending orbit

Airborne LIDAR Power Line Classification Based on Spatial Topological Structure Characteristics

Science.gov (United States)

Wang, Y.; Chen, Q.; Li, K.; Zheng, D.; Fang, J.

2017-09-01

Automatic extraction of power lines has become a topic of great importance in airborne LiDAR data processing for transmission line management. In this paper, we present a new, fully automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) neighbourhood selection, (iii) feature extraction based on spatial topology, and (iv) SVM classification. In a detailed evaluation involving seven neighbourhood definitions, 26 geometric features and two datasets, we demonstrated that the use of multi-scale neighbourhoods for individual 3D points significantly improved the power line classification. Additionally, we showed that the spatial topological features may even further improve the results while reducing data processing time.

Automatic classification of hyperactive children: comparing multiple artificial intelligence approaches.

Science.gov (United States)

Delavarian, Mona; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Dibajnia, Parvin

2011-07-12

Automatic classification of different behavioral disorders with many similarities (e.g. in symptoms) by using an automated approach will help psychiatrists to concentrate on correct disorder and its treatment as soon as possible, to avoid wasting time on diagnosis, and to increase the accuracy of diagnosis. In this study, we tried to differentiate and classify (diagnose) 306 children with many similar symptoms and different behavioral disorders such as ADHD, depression, anxiety, comorbid depression and anxiety and conduct disorder with high accuracy. Classification was based on the symptoms and their severity. With examining 16 different available classifiers, by using "Prtools", we have proposed nearest mean classifier as the most accurate classifier with 96.92% accuracy in this research. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Scanning Electron Microscope Mapping System Developed for Detecting Surface Defects in Fatigue Specimens

Science.gov (United States)

Bonacuse, Peter J.; Kantzos, Peter T.

2002-01-01

An automated two-degree-of-freedom specimen positioning stage has been developed at the NASA Glenn Research Center to map and monitor defects in fatigue specimens. This system expedites the examination of the entire gauge section of fatigue specimens so that defects can be found using scanning electron microscopy (SEM). Translation and rotation stages are driven by microprocessor-based controllers that are, in turn, interfaced to a computer running custom-designed software. This system is currently being used to find and record the location of ceramic inclusions in powder metallurgy materials. The mapped inclusions are periodically examined during interrupted fatigue experiments. The number of cycles to initiate cracks from these inclusions and the rate of growth of initiated cracks can then be quantified. This information is necessary to quantify the effect of this type of defect on the durability of powder metallurgy materials. This system was developed with support of the Ultra Safe program.

Fluid flows due to leakproofness defects of nuclear fuel cans: application to sipping in PWR

International Nuclear Information System (INIS)

Parrat, D.

1983-10-01

This thesis establishes the theoritical laws describing fluid and gas transfers through channels representative of nuclear fuel can defects. These laws, applied to craks on fuel cans (samples) and on new or spent experimental rods, allow to know their size. A computerized modelling has been developed to analyse more particularly the examination by ''quantitative permeation testing'', giving the equivalent size of a channel from the observed signal. The utilization of this process to non-tight fuel assemblies of pressurized water reactors leads to the establishment of a classification of defects according to their importance [fr

Early Validation of Automation Plant Control Software using Simulation Based on Assumption Modeling and Validation Use Cases

Directory of Open Access Journals (Sweden)

Veronika Brandstetter

2015-10-01

Full Text Available In automation plants, technical processes must be conducted in a way that products, substances, or services are produced reliably, with sufficient quality and with minimal strain on resources. A key driver in conducting these processes is the automation plant’s control software, which controls the technical plant components and thereby affects the physical, chemical, and mechanical processes that take place in automation plants. To this end, the control software of an automation plant must adhere to strict process requirements arising from the technical processes, and from the physical plant design. Currently, the validation of the control software often starts late in the engineering process in many cases – once the automation plant is almost completely constructed. However, as widely acknowledged, the later the control software of the automation plant is validated, the higher the effort for correcting revealed defects is, which can lead to serious budget overruns and project delays. In this article we propose an approach that allows the early validation of automation control software against the technical plant processes and assumptions about the physical plant design by means of simulation. We demonstrate the application of our approach on the example of an actual plant project from the automation industry and present it’s technical implementation

Real-time defect detection on highly reflective curved surfaces

Science.gov (United States)

Rosati, G.; Boschetti, G.; Biondi, A.; Rossi, A.

2009-03-01

This paper presents an automated defect detection system for coated plastic components for the automotive industry. This research activity came up as an evolution of a previous study which employed a non-flat mirror to illuminate and inspect high reflective curved surfaces. According to this method, the rays emitted from a light source are conveyed on the surface under investigation by means of a suitably curved mirror. After the reflection on the surface, the light rays are collected by a CCD camera, in which the coating defects appear as shadows of various shapes and dimensions. In this paper we present an evolution of the above-mentioned method, introducing a simplified mirror set-up in order to reduce the costs and the complexity of the defect detection system. In fact, a set of plane mirrors is employed instead of the curved one. Moreover, the inspection of multiple bend radius parts is investigated. A prototype of the machine vision system has been developed in order to test this simplified method. This device is made up of a light projector, a set of plane mirrors for light rays reflection, a conveyor belt for handling components, a CCD camera and a desktop PC which performs image acquisition and processing. Like in the previous system, the defects are identified as shadows inside a high brightness image. At the end of the paper, first experimental results are presented.

A new framework for analysing automated acoustic species-detection data: occupancy estimation and optimization of recordings post-processing

Science.gov (United States)

Chambert, Thierry A.; Waddle, J. Hardin; Miller, David A.W.; Walls, Susan; Nichols, James D.

2018-01-01

The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors.We developed a hierarchical modelling framework for estimating species occupancy from automated species-detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false positive errors and utilizes information about both occurrence and abundance of detections to improve estimation.Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders.When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post-validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post-validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated.Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species-detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programs.

Object-oriented classification of drumlins from digital elevation models

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Saha, Kakoli

Drumlins are common elements of glaciated landscapes which are easily identified by their distinct morphometric characteristics including shape, length/width ratio, elongation ratio, and uniform direction. To date, most researchers have mapped drumlins by tracing contours on maps, or through on-screen digitization directly on top of hillshaded digital elevation models (DEMs). This paper seeks to utilize the unique morphometric characteristics of drumlins and investigates automated extraction of the landforms as objects from DEMs by Definiens Developer software (V.7), using the 30 m United States Geological Survey National Elevation Dataset DEM as input. The Chautauqua drumlin field in Pennsylvania and upstate New York, USA was chosen as a study area. As the study area is huge (approximately covers 2500 sq.km. of area), small test areas were selected for initial testing of the method. Individual polygons representing the drumlins were extracted from the elevation data set by automated recognition, using Definiens' Multiresolution Segmentation tool, followed by rule-based classification. Subsequently parameters such as length, width and length-width ratio, perimeter and area were measured automatically. To test the accuracy of the method, a second base map was produced by manual on-screen digitization of drumlins from topographic maps and the same morphometric parameters were extracted from the mapped landforms using Definiens Developer. Statistical comparison showed a high agreement between the two methods confirming that object-oriented classification for extraction of drumlins can be used for mapping these landforms. The proposed method represents an attempt to solve the problem by providing a generalized rule-set for mass extraction of drumlins. To check that the automated extraction process was next applied to a larger area. Results showed that the proposed method is as successful for the bigger area as it was for the smaller test areas.

Epidemiological study of congenital heart defects in children and adolescents: analysis of 4,538 cases

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Nelson Itiro Miyague

2003-03-01

Full Text Available OBJECTIVE: To analyze the frequency and prevalence of congenital heart defects in a tertiary care center for children with heart diseases. METHODS: We carried out an epidemiological assessment of the first medical visit of 4,538 children in a pediatric hospital from January 1995 to December 1997. All patients with congenital heart defects had their diagnoses confirmed at least on echocardiography. The frequency and prevalence of the anomalies were computed according to the classification of sequential analysis. Age, weight, and sex were compared between the groups of healthy individuals and those with congenital heart defects after distribution according to the age group. RESULTS: Of all the children assessed, 2,017 (44.4% were diagnosed with congenital heart disease, 201 (4.4% with acquired heart disease, 52 (1.2% with arrhythmias, and 2,268 (50% were healthy children. Congenital heart diseases predominated in neonates and infants, corresponding to 71.5% of the cases. Weight and age were significantly lower in children with congenital heart defects. Ventricular septal defect was the most frequent acyanotic anomaly, and tetralogy of Fallot was the most frequent cyanotic anomaly. CONCLUSION: Children with congenital heart defects are mainly referred during the neonatal period and infancy with impairment in gaining weight. Ventricular septal defect is the most frequent heart defect.

Automated Diagnosis of Otitis Media: Vocabulary and Grammar

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Kuruvilla, Anupama; Hoberman, Alejandro; Kovačević, Jelena

2013-01-01

We propose a novel automated algorithm for classifying diagnostic categories of otitis media: acute otitis media, otitis media with effusion, and no effusion. Acute otitis media represents a bacterial superinfection of the middle ear fluid, while otitis media with effusion represents a sterile effusion that tends to subside spontaneously. Diagnosing children with acute otitis media is difficult, often leading to overprescription of antibiotics as they are beneficial only for children with acute otitis media. This underscores the need for an accurate and automated diagnostic algorithm. To that end, we design a feature set understood by both otoscopists and engineers based on the actual visual cues used by otoscopists; we term this the otitis media vocabulary. We also design a process to combine the vocabulary terms based on the decision process used by otoscopists; we term this the otitis media grammar. The algorithm achieves 89.9% classification accuracy, outperforming both clinicians who did not receive special training and state-of-the-art classifiers. PMID:23997759

Multi-material classification of dry recyclables from municipal solid waste based on thermal imaging.

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Gundupalli, Sathish Paulraj; Hait, Subrata; Thakur, Atul

2017-12-01

There has been a significant rise in municipal solid waste (MSW) generation in the last few decades due to rapid urbanization and industrialization. Due to the lack of source segregation practice, a need for automated segregation of recyclables from MSW exists in the developing countries. This paper reports a thermal imaging based system for classifying useful recyclables from simulated MSW sample. Experimental results have demonstrated the possibility to use thermal imaging technique for classification and a robotic system for sorting of recyclables in a single process step. The reported classification system yields an accuracy in the range of 85-96% and is comparable with the existing single-material recyclable classification techniques. We believe that the reported thermal imaging based system can emerge as a viable and inexpensive large-scale classification-cum-sorting technology in recycling plants for processing MSW in developing countries. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Graph Cut Approach to Artery/Vein Classification in Ultra-Widefield Scanning Laser Ophthalmoscopy.

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