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Sample records for automatic vehicle classification

  1. Video Analytics Algorithm for Automatic Vehicle Classification (Intelligent Transport System

    Directory of Open Access Journals (Sweden)

    ArtaIftikhar

    2013-04-01

    Full Text Available Automated Vehicle detection and classification is an important component of intelligent transport system. Due to significant importance in various fields such as traffic accidents avoidance, toll collection, congestion avoidance, terrorist activities monitoring, security and surveillance systems, intelligent transport system has become important field of study. Various technologies have been used for detecting and classifying vehicles automatically. Automated vehicle detection is broadly divided into two types- Hardware based and software based detection. Various algorithms have been implemented to classify different vehicles from videos. In this paper an efficient and economical solution for automatic vehicle detection and classification is proposed. The proposed system first isolates the object through background subtraction followed by vehicle detection using ontology. Vehicle detection is based on low level features such as shape, size, and spatial location. Finally system classifies vehicles into one of the known classes of vehicle based on size.

  2. Comparative Analysis of Automatic Vehicle Classification Techniques: A Survey

    Directory of Open Access Journals (Sweden)

    Kanwal Yousaf

    2012-09-01

    Full Text Available Vehicle classification has emerged as a significant field of study because of its importance in variety of applications like surveillance, security system, traffic congestion avoidance and accidents prevention etc. So far numerous algorithms have been implemented for classifying vehicle. Each algorithm follows different procedures for detecting vehicles from videos. By evaluating some of the commonly used techniques we highlighted most beneficial methodology for classifying vehicles. In this paper we pointed out the working of several video based vehicle classification algorithms and compare these algorithms on the basis of different performance metrics such as classifiers, classification methodology or principles and vehicle detection ratio etc. After comparing these parameters we concluded that Hybrid Dynamic Bayesian Network (HDBN Classification algorithm is far better than the other algorithms due to its nature of estimating the simplest features of vehicles from different videos. HDBN detects vehicles by following important stages of feature extraction, selection and classification. It extracts the rear view information of vehicles rather than other information such as distance between the wheels and height of wheel etc.

  3. Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

    Science.gov (United States)

    Kachach, Redouane; Cañas, José María

    2016-05-01

    Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade-Lucas-Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

  4. Image-based Vehicle Classification System

    CERN Document Server

    Ng, Jun Yee

    2012-01-01

    Electronic toll collection (ETC) system has been a common trend used for toll collection on toll road nowadays. The implementation of electronic toll collection allows vehicles to travel at low or full speed during the toll payment, which help to avoid the traffic delay at toll road. One of the major components of an electronic toll collection is the automatic vehicle detection and classification (AVDC) system which is important to classify the vehicle so that the toll is charged according to the vehicle classes. Vision-based vehicle classification system is one type of vehicle classification system which adopt camera as the input sensing device for the system. This type of system has advantage over the rest for it is cost efficient as low cost camera is used. The implementation of vision-based vehicle classification system requires lower initial investment cost and very suitable for the toll collection trend migration in Malaysia from single ETC system to full-scale multi-lane free flow (MLFF). This project ...

  5. Autoclass: An automatic classification system

    Science.gov (United States)

    Stutz, John; Cheeseman, Peter; Hanson, Robin

    1991-01-01

    The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework, and using various mathematical and algorithmic approximations, the AutoClass System searches for the most probable classifications, automatically choosing the number of classes and complexity of class descriptions. A simpler version of AutoClass has been applied to many large real data sets, has discovered new independently-verified phenomena, and has been released as a robust software package. Recent extensions allow attributes to be selectively correlated within particular classes, and allow classes to inherit, or share, model parameters through a class hierarchy. The mathematical foundations of AutoClass are summarized.

  6. Automatic Classification of Marine Mammals with Speaker Classification Methods.

    Science.gov (United States)

    Kreimeyer, Roman; Ludwig, Stefan

    2016-01-01

    We present an automatic acoustic classifier for marine mammals based on human speaker classification methods as an element of a passive acoustic monitoring (PAM) tool. This work is part of the Protection of Marine Mammals (PoMM) project under the framework of the European Defense Agency (EDA) and joined by the Research Department for Underwater Acoustics and Geophysics (FWG), Bundeswehr Technical Centre (WTD 71) and Kiel University. The automatic classification should support sonar operators in the risk mitigation process before and during sonar exercises with a reliable automatic classification result.

  7. A Novel Vehicle Classification Using Embedded Strain Gauge Sensors

    Directory of Open Access Journals (Sweden)

    Qi Wang

    2008-11-01

    Full Text Available Abstract: This paper presents a new vehicle classification and develops a traffic monitoring detector to provide reliable vehicle classification to aid traffic management systems. The basic principle of this approach is based on measuring the dynamic strain caused by vehicles across pavement to obtain the corresponding vehicle parameters – wheelbase and number of axles – to then accurately classify the vehicle. A system prototype with five embedded strain sensors was developed to validate the accuracy and effectiveness of the classification method. According to the special arrangement of the sensors and the different time a vehicle arrived at the sensors one can estimate the vehicle’s speed accurately, corresponding to the estimated vehicle wheelbase and number of axles. Because of measurement errors and vehicle characteristics, there is a lot of overlap between vehicle wheelbase patterns. Therefore, directly setting up a fixed threshold for vehicle classification often leads to low-accuracy results. Using the machine learning pattern recognition method to deal with this problem is believed as one of the most effective tools. In this study, support vector machines (SVMs were used to integrate the classification features extracted from the strain sensors to automatically classify vehicles into five types, ranging from small vehicles to combination trucks, along the lines of the Federal Highway Administration vehicle classification guide. Test bench and field experiments will be introduced in this paper. Two support vector machines classification algorithms (one-against-all, one-against-one are used to classify single sensor data and multiple sensor combination data. Comparison of the two classification method results shows that the classification accuracy is very close using single data or multiple data. Our results indicate that using multiclass SVM-based fusion multiple sensor data significantly improves

  8. Automatic lexical classification: bridging research and practice.

    Science.gov (United States)

    Korhonen, Anna

    2010-08-13

    Natural language processing (NLP)--the automatic analysis, understanding and generation of human language by computers--is vitally dependent on accurate knowledge about words. Because words change their behaviour between text types, domains and sub-languages, a fully accurate static lexical resource (e.g. a dictionary, word classification) is unattainable. Researchers are now developing techniques that could be used to automatically acquire or update lexical resources from textual data. If successful, the automatic approach could considerably enhance the accuracy and portability of language technologies, such as machine translation, text mining and summarization. This paper reviews the recent and on-going research in automatic lexical acquisition. Focusing on lexical classification, it discusses the many challenges that still need to be met before the approach can benefit NLP on a large scale.

  9. Automatic Hierarchical Color Image Classification

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    Jing Huang

    2003-02-01

    Full Text Available Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.

  10. Towards automatic classification of all WISE sources

    CERN Document Server

    Kurcz, Agnieszka; Solarz, Aleksandra; Krupa, Magdalena; Pollo, Agnieszka; Małek, Katarzyna

    2016-01-01

    The WISE satellite has detected hundreds of millions sources over the entire sky. Classifying them reliably is however a challenging task due to degeneracies in WISE multicolour space and low levels of detection in its two longest-wavelength bandpasses. Here we aim at obtaining comprehensive and reliable star, galaxy and quasar catalogues based on automatic source classification in full-sky WISE data. This means that the final classification will employ only parameters available from WISE itself, in particular those reliably measured for a majority of sources. For the automatic classification we applied the support vector machines (SVM) algorithm, which requires a training sample with relevant classes already identified, and we chose to use the SDSS spectroscopic dataset for that purpose. By calibrating the classifier on the test data drawn from SDSS, we first established that a polynomial kernel is preferred over a radial one for this particular dataset. Next, using three classification parameters (W1 magnit...

  11. Real time automatic scene classification

    NARCIS (Netherlands)

    Israël, Menno; Broek, van den Egon L.; Putten, van der Peter; Uyl, den Marten J.; Verbrugge, R.; Taatgen, N.; Schomaker, L.

    2004-01-01

    This work has been done as part of the EU VICAR (IST) project and the EU SCOFI project (IAP). The aim of the first project was to develop a real time video indexing classification annotation and retrieval system. For our systems, we have adapted the approach of Picard and Minka [3], who categorized

  12. Automatic Control of Personal Rapid Transit Vehicles

    Science.gov (United States)

    Smith, P. D.

    1972-01-01

    The requirements for automatic longitudinal control of a string of closely packed personal vehicles are outlined. Optimal control theory is used to design feedback controllers for strings of vehicles. An important modification of the usual optimal control scheme is the inclusion of jerk in the cost functional. While the inclusion of the jerk term was considered, the effect of its inclusion was not sufficiently studied. Adding the jerk term will increase passenger comfort.

  13. Automatic Classification of Attacks on IP Telephony

    Directory of Open Access Journals (Sweden)

    Jakub Safarik

    2013-01-01

    Full Text Available This article proposes an algorithm for automatic analysis of attack data in IP telephony network with a neural network. Data for the analysis is gathered from variable monitoring application running in the network. These monitoring systems are a typical part of nowadays network. Information from them is usually used after attack. It is possible to use an automatic classification of IP telephony attacks for nearly real-time classification and counter attack or mitigation of potential attacks. The classification use proposed neural network, and the article covers design of a neural network and its practical implementation. It contains also methods for neural network learning and data gathering functions from honeypot application.

  14. Automatic modulation classification principles, algorithms and applications

    CERN Document Server

    Zhu, Zhechen

    2014-01-01

    Automatic Modulation Classification (AMC) has been a key technology in many military, security, and civilian telecommunication applications for decades. In military and security applications, modulation often serves as another level of encryption; in modern civilian applications, multiple modulation types can be employed by a signal transmitter to control the data rate and link reliability. This book offers comprehensive documentation of AMC models, algorithms and implementations for successful modulation recognition. It provides an invaluable theoretical and numerical comparison of AMC algo

  15. Automatic classification of blank substrate defects

    Science.gov (United States)

    Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

    2014-10-01

    Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask

  16. Towards automatic classification of all WISE sources

    Science.gov (United States)

    Kurcz, A.; Bilicki, M.; Solarz, A.; Krupa, M.; Pollo, A.; Małek, K.

    2016-07-01

    Context. The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. Classifying them reliably is, however, a challenging task owing to degeneracies in WISE multicolour space and low levels of detection in its two longest-wavelength bandpasses. Simple colour cuts are often not sufficient; for satisfactory levels of completeness and purity, more sophisticated classification methods are needed. Aims: Here we aim to obtain comprehensive and reliable star, galaxy, and quasar catalogues based on automatic source classification in full-sky WISE data. This means that the final classification will employ only parameters available from WISE itself, in particular those which are reliably measured for the majority of sources. Methods: For the automatic classification we applied a supervised machine learning algorithm, support vector machines (SVM). It requires a training sample with relevant classes already identified, and we chose to use the SDSS spectroscopic dataset (DR10) for that purpose. We tested the performance of two kernels used by the classifier, and determined the minimum number of sources in the training set required to achieve stable classification, as well as the minimum dimension of the parameter space. We also tested SVM classification accuracy as a function of extinction and apparent magnitude. Thus, the calibrated classifier was finally applied to all-sky WISE data, flux-limited to 16 mag (Vega) in the 3.4 μm channel. Results: By calibrating on the test data drawn from SDSS, we first established that a polynomial kernel is preferred over a radial one for this particular dataset. Next, using three classification parameters (W1 magnitude, W1-W2 colour, and a differential aperture magnitude) we obtained very good classification efficiency in all the tests. At the bright end, the completeness for stars and galaxies reaches ~95%, deteriorating to ~80% at W1 = 16 mag, while for quasars it stays at a level of

  17. Automatic vehicle counting system for traffic monitoring

    Science.gov (United States)

    Crouzil, Alain; Khoudour, Louahdi; Valiere, Paul; Truong Cong, Dung Nghy

    2016-09-01

    The article is dedicated to the presentation of a vision-based system for road vehicle counting and classification. The system is able to achieve counting with a very good accuracy even in difficult scenarios linked to occlusions and/or presence of shadows. The principle of the system is to use already installed cameras in road networks without any additional calibration procedure. We propose a robust segmentation algorithm that detects foreground pixels corresponding to moving vehicles. First, the approach models each pixel of the background with an adaptive Gaussian distribution. This model is coupled with a motion detection procedure, which allows correctly location of moving vehicles in space and time. The nature of trials carried out, including peak periods and various vehicle types, leads to an increase of occlusions between cars and between cars and trucks. A specific method for severe occlusion detection, based on the notion of solidity, has been carried out and tested. Furthermore, the method developed in this work is capable of managing shadows with high resolution. The related algorithm has been tested and compared to a classical method. Experimental results based on four large datasets show that our method can count and classify vehicles in real time with a high level of performance (>98%) under different environmental situations, thus performing better than the conventional inductive loop detectors.

  18. Automatic figure classification in bioscience literature.

    Science.gov (United States)

    Kim, Daehyun; Ramesh, Balaji Polepalli; Yu, Hong

    2011-10-01

    Millions of figures appear in biomedical articles, and it is important to develop an intelligent figure search engine to return relevant figures based on user entries. In this study we report a figure classifier that automatically classifies biomedical figures into five predefined figure types: Gel-image, Image-of-thing, Graph, Model, and Mix. The classifier explored rich image features and integrated them with text features. We performed feature selection and explored different classification models, including a rule-based figure classifier, a supervised machine-learning classifier, and a multi-model classifier, the latter of which integrated the first two classifiers. Our results show that feature selection improved figure classification and the novel image features we explored were the best among image features that we have examined. Our results also show that integrating text and image features achieved better performance than using either of them individually. The best system is a multi-model classifier which combines the rule-based hierarchical classifier and a support vector machine (SVM) based classifier, achieving a 76.7% F1-score for five-type classification. We demonstrated our system at http://figureclassification.askhermes.org/.

  19. Automatic cloud classification of whole sky images

    Directory of Open Access Journals (Sweden)

    A. Heinle

    2010-05-01

    Full Text Available The recently increasing development of whole sky imagers enables temporal and spatial high-resolution sky observations. One application already performed in most cases is the estimation of fractional sky cover. A distinction between different cloud types, however, is still in progress. Here, an automatic cloud classification algorithm is presented, based on a set of mainly statistical features describing the color as well as the texture of an image. The k-nearest-neighbour classifier is used due to its high performance in solving complex issues, simplicity of implementation and low computational complexity. Seven different sky conditions are distinguished: high thin clouds (cirrus and cirrostratus, high patched cumuliform clouds (cirrocumulus and altocumulus, stratocumulus clouds, low cumuliform clouds, thick clouds (cumulonimbus and nimbostratus, stratiform clouds and clear sky. Based on the Leave-One-Out Cross-Validation the algorithm achieves an accuracy of about 97%. In addition, a test run of random images is presented, still outperforming previous algorithms by yielding a success rate of about 75%, or up to 88% if only "serious" errors with respect to radiation impact are considered. Reasons for the decrement in accuracy are discussed, and ideas to further improve the classification results, especially in problematic cases, are investigated.

  20. Automatic Approach to Vhr Satellite Image Classification

    Science.gov (United States)

    Kupidura, P.; Osińska-Skotak, K.; Pluto-Kossakowska, J.

    2016-06-01

    In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture), which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the preliminary step

  1. AUTOMATIC APPROACH TO VHR SATELLITE IMAGE CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    P. Kupidura

    2016-06-01

    Full Text Available In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture, which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the

  2. Automatic web services classification based on rough set theory

    Institute of Scientific and Technical Information of China (English)

    陈立; 张英; 宋自林; 苗壮

    2013-01-01

    With development of web services technology, the number of existing services in the internet is growing day by day. In order to achieve automatic and accurate services classification which can be beneficial for service related tasks, a rough set theory based method for services classification was proposed. First, the services descriptions were preprocessed and represented as vectors. Elicited by the discernibility matrices based attribute reduction in rough set theory and taking into account the characteristic of decision table of services classification, a method based on continuous discernibility matrices was proposed for dimensionality reduction. And finally, services classification was processed automatically. Through the experiment, the proposed method for services classification achieves approving classification result in all five testing categories. The experiment result shows that the proposed method is accurate and could be used in practical web services classification.

  3. Vehicle classification in WAMI imagery using deep network

    Science.gov (United States)

    Yi, Meng; Yang, Fan; Blasch, Erik; Sheaff, Carolyn; Liu, Kui; Chen, Genshe; Ling, Haibin

    2016-05-01

    Humans have always had a keen interest in understanding activities and the surrounding environment for mobility, communication, and survival. Thanks to recent progress in photography and breakthroughs in aviation, we are now able to capture tens of megapixels of ground imagery, namely Wide Area Motion Imagery (WAMI), at multiple frames per second from unmanned aerial vehicles (UAVs). WAMI serves as a great source for many applications, including security, urban planning and route planning. These applications require fast and accurate image understanding which is time consuming for humans, due to the large data volume and city-scale area coverage. Therefore, automatic processing and understanding of WAMI imagery has been gaining attention in both industry and the research community. This paper focuses on an essential step in WAMI imagery analysis, namely vehicle classification. That is, deciding whether a certain image patch contains a vehicle or not. We collect a set of positive and negative sample image patches, for training and testing the detector. Positive samples are 64 × 64 image patches centered on annotated vehicles. We generate two sets of negative images. The first set is generated from positive images with some location shift. The second set of negative patches is generated from randomly sampled patches. We also discard those patches if a vehicle accidentally locates at the center. Both positive and negative samples are randomly divided into 9000 training images and 3000 testing images. We propose to train a deep convolution network for classifying these patches. The classifier is based on a pre-trained AlexNet Model in the Caffe library, with an adapted loss function for vehicle classification. The performance of our classifier is compared to several traditional image classifier methods using Support Vector Machine (SVM) and Histogram of Oriented Gradient (HOG) features. While the SVM+HOG method achieves an accuracy of 91.2%, the accuracy of our deep

  4. Super pixel density based clustering automatic image classification method

    Science.gov (United States)

    Xu, Mingxing; Zhang, Chuan; Zhang, Tianxu

    2015-12-01

    The image classification is an important means of image segmentation and data mining, how to achieve rapid automated image classification has been the focus of research. In this paper, based on the super pixel density of cluster centers algorithm for automatic image classification and identify outlier. The use of the image pixel location coordinates and gray value computing density and distance, to achieve automatic image classification and outlier extraction. Due to the increased pixel dramatically increase the computational complexity, consider the method of ultra-pixel image preprocessing, divided into a small number of super-pixel sub-blocks after the density and distance calculations, while the design of a normalized density and distance discrimination law, to achieve automatic classification and clustering center selection, whereby the image automatically classify and identify outlier. After a lot of experiments, our method does not require human intervention, can automatically categorize images computing speed than the density clustering algorithm, the image can be effectively automated classification and outlier extraction.

  5. Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

    Science.gov (United States)

    Ribeiro, Sidarta; Pereira, Danillo R.; Papa, João P.; de Albuquerque, Victor Hugo C.

    2016-01-01

    Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available. PMID:27654941

  6. Automatic phases recognition in pituitary surgeries by microscope images classification

    OpenAIRE

    Lalys, Florent; Riffaud, Laurent; Morandi, Xavier; Jannin, Pierre

    2010-01-01

    International audience; The segmentation of the surgical workflow might be helpful for providing context-sensitive user interfaces, or generating automatic report. Our approach focused on the automatic recognition of surgical phases by microscope image classification. Our workflow, including images features extraction, image database labelisation, Principal Component Analysis (PCA) transformation and 10-fold cross-validation studies was performed on a specific type of neurosurgical interventi...

  7. AUTOMATIC CLASSIFICATION OF STRUCTURAL MRI FOR DIAGNOSIS OF NEURODEGENERATIVE DISEASES

    Directory of Open Access Journals (Sweden)

    Hernández-Tamames Juan Antonio

    2010-12-01

    Full Text Available This paper presents an automatic approach which classifies structural Magnetic Resonance images into pathological or healthy controls. A classification model was trained to find the boundaries that allow to separate the study groups. The method uses the deformation values from a set of regions, automatically identified as relevant, in a process that selects the statistically significant regions of a t-test under the restriction that this significance must be spatially coherent within a neighborhood of 5 voxels. The proposed method was assessed to distinguish healthy controls from schizophrenia patients. Classification results showed accuracy between 74% and 89%, depending on the stage of the disease and number of training samples.

  8. Automatic classification of time-variable X-ray sources

    CERN Document Server

    Lo, Kitty K; Murphy, Tara; Gaensler, B M

    2014-01-01

    To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the second \\textit{XMM-Newton} serendipitous source catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10-fold cross validation accuracy of the training data is ${\\sim}$97% on a seven-class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest der...

  9. Automatic document classification of biological literature

    Directory of Open Access Journals (Sweden)

    Sternberg Paul W

    2006-08-01

    Full Text Available Abstract Background Document classification is a wide-spread problem with many applications, from organizing search engine snippets to spam filtering. We previously described Textpresso, a text-mining system for biological literature, which marks up full text according to a shallow ontology that includes terms of biological interest. This project investigates document classification in the context of biological literature, making use of the Textpresso markup of a corpus of Caenorhabditis elegans literature. Results We present a two-step text categorization algorithm to classify a corpus of C. elegans papers. Our classification method first uses a support vector machine-trained classifier, followed by a novel, phrase-based clustering algorithm. This clustering step autonomously creates cluster labels that are descriptive and understandable by humans. This clustering engine performed better on a standard test-set (Reuters 21578 compared to previously published results (F-value of 0.55 vs. 0.49, while producing cluster descriptions that appear more useful. A web interface allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept. Conclusion We have demonstrated a simple method to classify biological documents that embodies an improvement over current methods. While the classification results are currently optimized for Caenorhabditis elegans papers by human-created rules, the classification engine can be adapted to different types of documents. We have demonstrated this by presenting a web interface that allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept.

  10. Multibody simulation of vehicles equipped with an automatic transmission

    Science.gov (United States)

    Olivier, B.; Kouroussis, G.

    2016-09-01

    Nowadays automotive vehicles remain as one of the most used modes of transportation. Furthermore automatic transmissions are increasingly used to provide a better driving comfort and a potential optimization of the engine performances (by placing the gear shifts at specific engine and vehicle speeds). This paper presents an effective modeling of the vehicle using the multibody methodology (numerically computed under EasyDyn, an open source and in-house library dedicated to multibody simulations). However, the transmission part of the vehicle is described by the usual equations of motion computed using a systematic matrix approach: del Castillo's methodology for planetary gear trains. By coupling the analytic equations of the transmission and the equations computed by the multibody methodology, the performances of any vehicle can be obtained if the characteristics of each element in the vehicle are known. The multibody methodology offers the possibilities to develop the vehicle modeling from 1D-motion to 3D-motion by taking into account the rotations and implementing tire models. The modeling presented in this paper remains very efficient and provides an easy and quick vehicle simulation tool which could be used in order to calibrate the automatic transmission.

  11. Automatic Classification of Kepler Planetary Transit Candidates

    OpenAIRE

    McCauliff, Sean D.; Jenkins, Jon M.; Catanzarite, Joseph; Burke, Christopher J.; Coughlin, Jeffrey L.; Twicken, Joseph D.; Tenenbaum, Peter; Seader, Shawn; Li, Jie; Cote, Miles

    2014-01-01

    In the first three years of operation the Kepler mission found 3,697 planet candidates from a set of 18,406 transit-like features detected on over 200,000 distinct stars. Vetting candidate signals manually by inspecting light curves and other diagnostic information is a labor intensive effort. Additionally, this classification methodology does not yield any information about the quality of planet candidates; all candidates are as credible as any other candidate. The torrent of exoplanet disco...

  12. Semantic Annotation to Support Automatic Taxonomy Classification

    DEFF Research Database (Denmark)

    Kim, Sanghee; Ahmed, Saeema; Wallace, Ken

    2006-01-01

    , the annotations identify which parts of a text are more important for understanding its contents. The extraction of salient sentences is a major issue in text summarisation. Commonly used methods are based on statistical analysis, but for subject-matter type texts, linguistically motivated natural language...... processing techniques, like semantic annotations, are preferred. An experiment to test the method using 140 documents collected from industry demonstrated that classification accuracy can be improved by up to 16%....

  13. Personality in speech assessment and automatic classification

    CERN Document Server

    Polzehl, Tim

    2015-01-01

    This work combines interdisciplinary knowledge and experience from research fields of psychology, linguistics, audio-processing, machine learning, and computer science. The work systematically explores a novel research topic devoted to automated modeling of personality expression from speech. For this aim, it introduces a novel personality assessment questionnaire and presents the results of extensive labeling sessions to annotate the speech data with personality assessments. It provides estimates of the Big 5 personality traits, i.e. openness, conscientiousness, extroversion, agreeableness, and neuroticism. Based on a database built on the questionnaire, the book presents models to tell apart different personality types or classes from speech automatically.

  14. Automatic Cataract Hardness Classification Ex Vivo by Ultrasound Techniques.

    Science.gov (United States)

    Caixinha, Miguel; Santos, Mário; Santos, Jaime

    2016-04-01

    To demonstrate the feasibility of a new methodology for cataract hardness characterization and automatic classification using ultrasound techniques, different cataract degrees were induced in 210 porcine lenses. A 25-MHz ultrasound transducer was used to obtain acoustical parameters (velocity and attenuation) and backscattering signals. B-Scan and parametric Nakagami images were constructed. Ninety-seven parameters were extracted and subjected to a Principal Component Analysis. Bayes, K-Nearest-Neighbours, Fisher Linear Discriminant and Support Vector Machine (SVM) classifiers were used to automatically classify the different cataract severities. Statistically significant increases with cataract formation were found for velocity, attenuation, mean brightness intensity of the B-Scan images and mean Nakagami m parameter (p hardness characterization and automatic classification.

  15. Automatic Spectral Classification of Galaxies in the Infrared

    Science.gov (United States)

    Navarro, S. G.; Guzmán, V.; Dafonte, C.; Kemp, S. N.; Corral, L. J.

    2016-10-01

    Multi-object spectroscopy (MOS) provides us with numerous spectral data, and the projected new facilities and survey missions will increment the available spectra from stars and galaxies. In order to better understand this huge amount of data we need to develop new techniques of analysis and classification. Over the past decades it has been demonstrated that artificial neural networks are excellent tools for automatic spectral classification and identification, being robust tools and highly resistant to the presence of noise. We present here the result of the application of unsupervised neural networks: competitive neural networks (CNN) and self organized maps (SOM), to a sample of 747 galaxy spectra from the Infrared Spectrograph (IRS) of Spitzer. We obtained an automatic classification on 17 groups with the CNN, and we compare the results with those obtained with SOMs.The final goal of the project is to develop an automatic spectral classification tool for galaxies in the infrared, making use of artificial neural networks with unsupervised training and analyze the spectral characteristics of the galaxies that can give us clues to the physical processes taking place inside them.

  16. Automatic lung nodule classification with radiomics approach

    Science.gov (United States)

    Ma, Jingchen; Wang, Qian; Ren, Yacheng; Hu, Haibo; Zhao, Jun

    2016-03-01

    Lung cancer is the first killer among the cancer deaths. Malignant lung nodules have extremely high mortality while some of the benign nodules don't need any treatment .Thus, the accuracy of diagnosis between benign or malignant nodules diagnosis is necessary. Notably, although currently additional invasive biopsy or second CT scan in 3 months later may help radiologists to make judgments, easier diagnosis approaches are imminently needed. In this paper, we propose a novel CAD method to distinguish the benign and malignant lung cancer from CT images directly, which can not only improve the efficiency of rumor diagnosis but also greatly decrease the pain and risk of patients in biopsy collecting process. Briefly, according to the state-of-the-art radiomics approach, 583 features were used at the first step for measurement of nodules' intensity, shape, heterogeneity and information in multi-frequencies. Further, with Random Forest method, we distinguish the benign nodules from malignant nodules by analyzing all these features. Notably, our proposed scheme was tested on all 79 CT scans with diagnosis data available in The Cancer Imaging Archive (TCIA) which contain 127 nodules and each nodule is annotated by at least one of four radiologists participating in the project. Satisfactorily, this method achieved 82.7% accuracy in classification of malignant primary lung nodules and benign nodules. We believe it would bring much value for routine lung cancer diagnosis in CT imaging and provide improvement in decision-support with much lower cost.

  17. Automatic Mining of Numerical Classification Rules with Parliamentary Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    KIZILOLUK, S.

    2015-11-01

    Full Text Available In recent years, classification rules mining has been one of the most important data mining tasks. In this study, one of the newest social-based metaheuristic methods, Parliamentary Optimization Algorithm (POA, is firstly used for automatically mining of comprehensible and accurate classification rules within datasets which have numerical attributes. Four different numerical datasets have been selected from UCI data warehouse and classification rules of high quality have been obtained. Furthermore, the results obtained from designed POA have been compared with the results obtained from four different popular classification rules mining algorithms used in WEKA. Although POA is very new and no applications in complex data mining problems have been performed, the results seem promising. The used objective function is very flexible and many different objectives can easily be added to. The intervals of the numerical attributes in the rules have been automatically found without any a priori process, as done in other classification rules mining algorithms, which causes the modification of datasets.

  18. Automatic classification of time-variable X-ray sources

    Energy Technology Data Exchange (ETDEWEB)

    Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M. [Sydney Institute for Astronomy, School of Physics, The University of Sydney, Sydney, NSW 2006 (Australia)

    2014-05-01

    To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

  19. Automatic classification of protein structure by using Gauss integrals

    DEFF Research Database (Denmark)

    Røgen, Peter; Fain, B.

    2003-01-01

    has only one adjustable parameter. We assign 95.51% of the chains into the proper C (class), A (architecture), T (topology), and H (homologous superfamily) fold, find all new folds, and detect no false geometric positives. Using the SGM, we display a "map" of the space of folds projected onto two...... dimensions, show the relative locations of the major structural classes, and "zoom into" the space of proteins to show architecture, topology, and fold clusters. The existence of a simple measure of a protein fold computed from the chain path will have a major impact on automatic fold classification.......) of similarity of protein shapes. Under this metric, protein chains naturally separate into fold clusters. We use SGM to construct an automatic classification procedure for the CATH2.4 database. The method is very fast because it requires neither alignment of the chains nor any chain-chain comparison. It also...

  20. Desktop calibration of automatic transmission for passenger vehicle

    Institute of Scientific and Technical Information of China (English)

    FANG Chi; SHI Jian-peng; WANG Jun

    2014-01-01

    Desktop calibration of automatic transmission (AT) is a method which can reduce cost, enhance efficiency and shorten the development periods of a vehicle effectively. We primary introduced the principle and approach of desktop calibration of AT based on the condition of coupling characteristics between engine and torque converter and obtained right point exactly. It is shown to agree with experimental measurements reasonably well. It was used in different applications abroad based on AT technology and achieved a good performance of the vehicle compared with traditional AT technology which primary focuses on the drivability, performance and fuel consumption.

  1. Automatic Parallelization Tool: Classification of Program Code for Parallel Computing

    Directory of Open Access Journals (Sweden)

    Mustafa Basthikodi

    2016-04-01

    Full Text Available Performance growth of single-core processors has come to a halt in the past decade, but was re-enabled by the introduction of parallelism in processors. Multicore frameworks along with Graphical Processing Units empowered to enhance parallelism broadly. Couples of compilers are updated to developing challenges forsynchronization and threading issues. Appropriate program and algorithm classifications will have advantage to a great extent to the group of software engineers to get opportunities for effective parallelization. In present work we investigated current species for classification of algorithms, in that related work on classification is discussed along with the comparison of issues that challenges the classification. The set of algorithms are chosen which matches the structure with different issues and perform given task. We have tested these algorithms utilizing existing automatic species extraction toolsalong with Bones compiler. We have added functionalities to existing tool, providing a more detailed characterization. The contributions of our work include support for pointer arithmetic, conditional and incremental statements, user defined types, constants and mathematical functions. With this, we can retain significant data which is not captured by original speciesof algorithms. We executed new theories into the device, empowering automatic characterization of program code.

  2. Automatic Classification of Variable Stars in Catalogs with missing data

    CERN Document Server

    Pichara, Karim

    2013-01-01

    We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks, a probabilistic graphical model, that allows us to perform inference to pre- dict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incomplete data, we use an iterative algorithm that utilises sampling methods and expectation maximization to estimate the distributions and probabilistic dependencies of variables from data with missing values. To test our model we use three catalogs with missing data (SAGE, 2MASS and UBVI) and one complete catalog (MACHO). We examine how classification accuracy changes when information from missing data catalogs is included, how our method compares to traditional missing data approaches and at what computational cost. Integrating these catalogs with missing data we find that classification of variable objects improves by few percent and by 15% for quasar detection while keeping the computational co...

  3. An estimation-based automatic vehicle location system for public transport vehicles

    OpenAIRE

    Morenz, Tino; MEIER, RENE

    2008-01-01

    PUBLISHED Public transport vehicles often share a road network with other road users making their journeys susceptive to changing road conditions and especially to congestion. Travelers using such public transport increasingly depend on real-time information to plan their journeys. While such information can be provided by Automatic Vehicle Location (AVL) systems, AVLs depend heavily on large-scale deployment of designated sensory equipment, which may prevent their ...

  4. Modeling and Prototyping of Automatic Clutch System for Light Vehicles

    Science.gov (United States)

    Murali, S.; Jothi Prakash, V. M.; Vishal, S.

    2017-03-01

    Nowadays, recycling or regenerating the waste in to something useful is appreciated all around the globe. It reduces greenhouse gas emissions that contribute to global climate change. This study deals with provision of the automatic clutch mechanism in vehicles to facilitate the smooth changing of gears. This study proposed to use the exhaust gases which are normally expelled out as a waste from the turbocharger to actuate the clutch mechanism in vehicles to facilitate the smooth changing of gears. At present, clutches are operated automatically by using an air compressor in the four wheelers. In this study, a conceptual design is proposed in which the clutch is operated by the exhaust gas from the turbocharger and this will remove the usage of air compressor in the existing system. With this system, usage of air compressor is eliminated and the riders need not to operate the clutch manually. This work involved in development, analysation and validation of the conceptual design through simulation software. Then the developed conceptual design of an automatic pneumatic clutch system is tested with proto type.

  5. Evolutionary synthesis of automatic classification on astroinformatic big data

    Science.gov (United States)

    Kojecky, Lumir; Zelinka, Ivan; Saloun, Petr

    2016-06-01

    This article describes the initial experiments using a new approach to automatic identification of Be and B[e] stars spectra in large archives. With enormous amount of these data it is no longer feasible to analyze it using classical approaches. We introduce an evolutionary synthesis of the classification by means of analytic programming, one of methods of symbolic regression. By this method, we synthesize the most suitable mathematical formulas that approximate chosen samples of the stellar spectra. As a result is then selected the category whose formula has the lowest difference compared to the particular spectrum. The results show us that classification of stellar spectra by means of analytic programming is able to identify different shapes of the spectra.

  6. Sensor Architecture and Task Classification for Agricultural Vehicles and Environments

    Directory of Open Access Journals (Sweden)

    Francisco Rovira-Más

    2010-12-01

    Full Text Available The long time wish of endowing agricultural vehicles with an increasing degree of autonomy is becoming a reality thanks to two crucial facts: the broad diffusion of global positioning satellite systems and the inexorable progress of computers and electronics. Agricultural vehicles are currently the only self-propelled ground machines commonly integrating commercial automatic navigation systems. Farm equipment manufacturers and satellite-based navigation system providers, in a joint effort, have pushed this technology to unprecedented heights; yet there are many unresolved issues and an unlimited potential still to uncover. The complexity inherent to intelligent vehicles is rooted in the selection and coordination of the optimum sensors, the computer reasoning techniques to process the acquired data, and the resulting control strategies for automatic actuators. The advantageous design of the network of onboard sensors is necessary for the future deployment of advanced agricultural vehicles. This article analyzes a variety of typical environments and situations encountered in agricultural fields, and proposes a sensor architecture especially adapted to cope with them. The strategy proposed groups sensors into four specific subsystems: global localization, feedback control and vehicle pose, non-visual monitoring, and local perception. The designed architecture responds to vital vehicle tasks classified within three layers devoted to safety, operative information, and automatic actuation. The success of this architecture, implemented and tested in various agricultural vehicles over the last decade, rests on its capacity to integrate redundancy and incorporate new technologies in a practical way.

  7. Research on Fuzzy Control for Automatic Transmission of Tracked Vehicles

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A principle of fuzzy control for tracked vehicles is proposed to make its automatic transmission system be able to adapt complex running conditions, and a model of its power train is established to be used in simulation. Based on the fuzzy control method, a fuzzy shift control system composed of a basic shift strategy and a fuzzy modification module is developed to improve the dynamic characteristics and cross-country maneuverability. Simulation results show that the fuzzy shift strategy can improve the shift quality under manifold driving conditions and avoid cycled shift effectively. Therefore,the proposed fuzzy shift strategies are proved to be feasible and practicable.

  8. Improve mask inspection capacity with Automatic Defect Classification (ADC)

    Science.gov (United States)

    Wang, Crystal; Ho, Steven; Guo, Eric; Wang, Kechang; Lakkapragada, Suresh; Yu, Jiao; Hu, Peter; Tolani, Vikram; Pang, Linyong

    2013-09-01

    As optical lithography continues to extend into low-k1 regime, resolution of mask patterns continues to diminish. The adoption of RET techniques like aggressive OPC, sub-resolution assist features combined with the requirements to detect even smaller defects on masks due to increasing MEEF, poses considerable challenges for mask inspection operators and engineers. Therefore a comprehensive approach is required in handling defects post-inspections by correctly identifying and classifying the real killer defects impacting the printability on wafer, and ignoring nuisance defect and false defects caused by inspection systems. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at the SMIC mask shop for the 40nm technology node. Traditionally, each defect is manually examined and classified by the inspection operator based on a set of predefined rules and human judgment. At SMIC mask shop due to the significant total number of detected defects, manual classification is not cost-effective due to increased inspection cycle time, resulting in constrained mask inspection capacity, since the review has to be performed while the mask stays on the inspection system. Luminescent Technologies Automated Defect Classification (ADC) product offers a complete and systematic approach for defect disposition and classification offline, resulting in improved utilization of the current mask inspection capability. Based on results from implementation of ADC in SMIC mask production flow, there was around 20% improvement in the inspection capacity compared to the traditional flow. This approach of computationally reviewing defects post mask-inspection ensures no yield loss by qualifying reticles without the errors associated with operator mis-classification or human error. The ADC engine retrieves the high resolution inspection images and uses a decision-tree flow to classify a given defect. Some identification mechanisms adopted by ADC to

  9. Classification of Dynamic Vehicle Routing Systems

    DEFF Research Database (Denmark)

    Larsen, Allan; Madsen, Oli B.G.; Solomon, Marius M.

    2007-01-01

    This chapter discusses important characteristics seen within dynamic vehicle routing problems. We discuss the differences between the traditional static vehicle routing problems and its dynamic counterparts. We give an in-depth introduction to the degree of dynamism measure which can be used...

  10. Automatic comparison of striation marks and automatic classification of shoe prints

    Science.gov (United States)

    Geradts, Zeno J.; Keijzer, Jan; Keereweer, Isaac

    1995-09-01

    A database for toolmarks (named TRAX) and a database for footwear outsole designs (named REBEZO) have been developed on a PC. The databases are filled with video-images and administrative data about the toolmarks and the footwear designs. An algorithm for the automatic comparison of the digitized striation patterns has been developed for TRAX. The algorithm appears to work well for deep and complete striation marks and will be implemented in TRAX. For REBEZO some efforts have been made to the automatic classification of outsole patterns. The algorithm first segments the shoeprofile. Fourier-features are selected for the separate elements and are classified with a neural network. In future developments information on invariant moments of the shape and rotation angle will be included in the neural network.

  11. Automatic medical X-ray image classification using annotation.

    Science.gov (United States)

    Zare, Mohammad Reza; Mueen, Ahmed; Seng, Woo Chaw

    2014-02-01

    The demand for automatically classification of medical X-ray images is rising faster than ever. In this paper, an approach is presented to gain high accuracy rate for those classes of medical database with high ratio of intraclass variability and interclass similarities. The classification framework was constructed via annotation using the following three techniques: annotation by binary classification, annotation by probabilistic latent semantic analysis, and annotation using top similar images. Next, final annotation was constructed by applying ranking similarity on annotated keywords made by each technique. The final annotation keywords were then divided into three levels according to the body region, specific bone structure in body region as well as imaging direction. Different weights were given to each level of the keywords; they are then used to calculate the weightage for each category of medical images based on their ground truth annotation. The weightage computed from the generated annotation of query image was compared with the weightage of each category of medical images, and then the query image would be assigned to the category with closest weightage to the query image. The average accuracy rate reported is 87.5 %.

  12. Exploring Sound Signature for Vehicle Detection and Classification Using ANN

    Directory of Open Access Journals (Sweden)

    Jobin George

    2013-06-01

    Full Text Available This paper attempts to explore the possibility of using sound signatures for vehicle detection andclassification purposes. Sound emitted by vehicles are captured for a two lane undivided road carryingmoderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound ofhorns, random but identifiable back ground noises, continuous high energy noises on the back ground arethe different challenges encountered in the data collection. Different features were explored out of whichsmoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Mel-frequency ceptral coefficients extracted from fixed regions around the detected peaks along with themanual vehicle labels are utilised to train an Artificial Neural Network (ANN. The classifier for fourbroad classes heavy, medium, light and horns was trained. The ANN classifier developed was able topredict categories well.

  13. An Approach for Automatic Classification of Radiology Reports in Spanish.

    Science.gov (United States)

    Cotik, Viviana; Filippo, Darío; Castaño, José

    2015-01-01

    Automatic detection of relevant terms in medical reports is useful for educational purposes and for clinical research. Natural language processing (NLP) techniques can be applied in order to identify them. In this work we present an approach to classify radiology reports written in Spanish into two sets: the ones that indicate pathological findings and the ones that do not. In addition, the entities corresponding to pathological findings are identified in the reports. We use RadLex, a lexicon of English radiology terms, and NLP techniques to identify the occurrence of pathological findings. Reports are classified using a simple algorithm based on the presence of pathological findings, negation and hedge terms. The implemented algorithms were tested with a test set of 248 reports annotated by an expert, obtaining a best result of 0.72 F1 measure. The output of the classification task can be used to look for specific occurrences of pathological findings.

  14. OPTIMAL CONTROL APPLIED IN AUTOMATIC CLUTCH ENGAGEMENTS OF VEHICLES

    Institute of Scientific and Technical Information of China (English)

    Sun Chengshun; Zhang Jianwu

    2004-01-01

    Start-up working condition is the key to the research of optimal engagement of automatic clutch for AMT.In order to guarantee an ideal dynamic performance of the clutch engagement,an optimal controller is designed by considering throttle angle,engine speed,gear ratio,vehicle acceleration and road condition.The minimum value principle is also introduced to achieve an optimal dynamic performance of the nonlinear system compromised in friction plate wear and vehicle drive quality.The optimal trajectory of the clutch engagement can be described in the form of explicit and analytical expressions and characterized by the deterministic and accurate control strategy in stead of indeterministic and soft control techniques which need thousands of experiments.For validation of the controller,test work is carried out for the automated clutch engagements in a commercial car with an traditional mechanical transmission,a hydraulic actuator,a group of sensors and a portable computer system.It is shown through experiments that dynamic behaviors of the clutch engagement operated by the optimal control are more effective and efficient than those by fuzzy control.

  15. Automatic target classification of man-made objects in synthetic aperture radar images using Gabor wavelet and neural network

    Science.gov (United States)

    Vasuki, Perumal; Roomi, S. Mohamed Mansoor

    2013-01-01

    Processing of synthetic aperture radar (SAR) images has led to the development of automatic target classification approaches. These approaches help to classify individual and mass military ground vehicles. This work aims to develop an automatic target classification technique to classify military targets like truck/tank/armored car/cannon/bulldozer. The proposed method consists of three stages via preprocessing, feature extraction, and neural network (NN). The first stage removes speckle noise in a SAR image by the identified frost filter and enhances the image by histogram equalization. The second stage uses a Gabor wavelet to extract the image features. The third stage classifies the target by an NN classifier using image features. The proposed work performs better than its counterparts, like K-nearest neighbor (KNN). The proposed work performs better on databases like moving and stationary target acquisition and recognition against the earlier methods by KNN.

  16. Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

    Science.gov (United States)

    2014-03-27

    SALIENT FEATURE IDENTIFICATION AND ANALYSIS USING KERNEL-BASED CLASSIFICATION TECHNIQUES FOR SYNTHETIC APERTURE RADAR AUTOMATIC TARGET RECOGNITION...FEATURE IDENTIFICATION AND ANALYSIS USING KERNEL-BASED CLASSIFICATION TECHNIQUES FOR SYNTHETIC APERTURE RADAR AUTOMATIC TARGET RECOGNITION THESIS Presented...SALIENT FEATURE IDENTIFICATION AND ANALYSIS USING KERNEL-BASED CLASSIFICATION TECHNIQUES FOR SYNTHETIC APERTURE RADAR AUTOMATIC TARGET RECOGNITION

  17. Deep transfer learning for automatic target classification: MWIR to LWIR

    Science.gov (United States)

    Ding, Zhengming; Nasrabadi, Nasser; Fu, Yun

    2016-05-01

    Publisher's Note: This paper, originally published on 5/12/2016, was replaced with a corrected/revised version on 5/18/2016. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. When dealing with sparse or no labeled data in the target domain, transfer learning shows its appealing performance by borrowing the supervised knowledge from external domains. Recently deep structure learning has been exploited in transfer learning due to its attractive power in extracting effective knowledge through multi-layer strategy, so that deep transfer learning is promising to address the cross-domain mismatch. In general, cross-domain disparity can be resulted from the difference between source and target distributions or different modalities, e.g., Midwave IR (MWIR) and Longwave IR (LWIR). In this paper, we propose a Weighted Deep Transfer Learning framework for automatic target classification through a task-driven fashion. Specifically, deep features and classifier parameters are obtained simultaneously for optimal classification performance. In this way, the proposed deep structures can extract more effective features with the guidance of the classifier performance; on the other hand, the classifier performance is further improved since it is optimized on more discriminative features. Furthermore, we build a weighted scheme to couple source and target output by assigning pseudo labels to target data, therefore we can transfer knowledge from source (i.e., MWIR) to target (i.e., LWIR). Experimental results on real databases demonstrate the superiority of the proposed algorithm by comparing with others.

  18. Vehicle classification by pattern-matching gauge sensors

    Science.gov (United States)

    Huston, Dryver R.; Spillman, William B., Jr.; Claus, Richard O.; Arya, Vivek; Zabaronick, Noel

    1996-05-01

    This paper describes a method of using matched-pattern gage sensors that are embedded into highway pavements to classify vehicles, i.e. cars vs. trucks. The classification of vehicle type is an important technology for a variety of highway operations, e.g. traffic control, maintenance planning, weigh-in-motion, and the assignment of tolls. Vehicle classification schemes that are based on strip-crossing methods are not very robust due to the large variability of strip-crossing sequences. Visual methods still rely primarily on human identification. The method described here involves placing long gage length sensors in highway pavements. The spatial pattern of the sensor is configured so that it will match the wheel pattern of the type of vehicle that is being identified. Theoretical modeling shows that the signal received from the sensor is a cross-correlation function relating the wheel and sensor patterns in space and time. The sensor can be any one of a variety that transduce by integrating pressure along a length. The technique is demonstrated in the laboratory with PVDF and fiber optic sensors. Experimental results and computer simulations are presented as well as a discussion of the realistic possibility of using such a vehicle identification scheme under field conditions.

  19. Carrier-phase differential GPS for automatic control of land vehicles

    Science.gov (United States)

    O'Connor, Michael Lee

    Real-time centimeter-level navigation has countless potential applications in land vehicles, including precise topographic field mapping, runway snowplowing in bad weather, and land mine detection and avoidance. Perhaps the most obvious and immediate need for accurate, robust land vehicle sensing is in the guidance and control of agricultural vehicles. Accurate guidance and automatic control of farm vehicles offers many potential advantages; however, previous attempts to automate these vehicles have been unsuccessful due to sensor limitations. With the recent development of real-time carrier-phase differential GPS (CDGPS), a single inexpensive GPS receiver can measure a vehicle's position to within a few centimeters and orientation to fractions of a degree. This ability to provide accurate real-time measurements of multiple vehicle states makes CDGPS ideal for automatic control of vehicles. This work describes the theoretical and experimental work behind the first successfully demonstrated automatic control system for land vehicles based on CDGPS. An extension of pseudolite-based CDGPS initialization methods was explored for land vehicles and demonstrated experimentally. Original land vehicle dynamic models were developed and identified using this innovative sensor. After initial automatic control testing using a Yamaha Fleetmaster golf cart, a centimeter-level, fully autonomous row guidance capability was demonstrated on a John Deere 7800 farm tractor.

  20. Measuring Service Reliability Using Automatic Vehicle Location Data

    Directory of Open Access Journals (Sweden)

    Zhenliang Ma

    2014-01-01

    Full Text Available Bus service reliability has become a major concern for both operators and passengers. Buffer time measures are believed to be appropriate to approximate passengers' experienced reliability in the context of departure planning. Two issues with regard to buffer time estimation are addressed, namely, performance disaggregation and capturing passengers’ perspectives on reliability. A Gaussian mixture models based method is applied to disaggregate the performance data. Based on the mixture models distribution, a reliability buffer time (RBT measure is proposed from passengers’ perspective. A set of expected reliability buffer time measures is developed for operators by using different spatial-temporal levels combinations of RBTs. The average and the latest trip duration measures are proposed for passengers that can be used to choose a service mode and determine the departure time. Using empirical data from the automatic vehicle location system in Brisbane, Australia, the existence of mixture service states is verified and the advantage of mixture distribution model in fitting travel time profile is demonstrated. Numerical experiments validate that the proposed reliability measure is capable of quantifying service reliability consistently, while the conventional ones may provide inconsistent results. Potential applications for operators and passengers are also illustrated, including reliability improvement and trip planning.

  1. Analysis of vehicle and driving condition influences on road classification from vehicle signals

    NARCIS (Netherlands)

    Jansen, S.T.H.; Schmeitz, R.M.T.; Wouters, A.P.; Teerhuis, A.P.

    2014-01-01

    Suspension settings generally are a compromise for performance on different road roughness and stability requirements. With a priori information of the road roughness a superior performance can be achieved, and this information can be obtained from vehicle based road classification methods that use

  2. Image structural analysis in the tasks of automatic navigation of unmanned vehicles and inspection of Earth surface

    Science.gov (United States)

    Lutsiv, Vadim; Malyshev, Igor

    2013-10-01

    The automatic analysis of images of terrain is urgent for several decades. On the one hand, such analysis is a base of automatic navigation of unmanned vehicles. On the other hand, the amount of information transferred to the Earth by modern video-sensors increases, thus a preliminary classification of such data by onboard computer becomes urgent. We developed an object-independent approach to structural analysis of images. While creating the methods of image structural description, we did our best to abstract away from the partial peculiarities of scenes. Only the most general limitations were taken into account, that were derived from the laws of organization of observable environment and from the properties of image formation systems. The practical application of this theoretic approach enables reliable matching the aerospace photographs acquired from differing aspect angles, in different day-time and seasons by sensors of differing types. The aerospace photographs can be matched even with the geographic maps. The developed approach enabled solving the tasks of automatic navigation of unmanned vehicles. The signs of changes and catastrophes can be detected by means of matching and comparison of aerospace photographs acquired at different time. We present the theoretical proofs of chosen strategy of structural description and matching of images. Several examples of matching of acquired images with template pictures and maps of terrain are shown within the frameworks of navigation of unmanned vehicles or detection of signs of disasters.

  3. Towards Automatic Trunk Classification on Young Conifers

    DEFF Research Database (Denmark)

    Petri, Stig; Immerkær, John

    2009-01-01

    In the garden nursery industry providing young Nordmann firs for Christmas tree plantations, there is a rising interest in automatic classification of their products to ensure consistently high quality and reduce the cost of manual labor. This paper describes a fully automatic single-view algorithm...

  4. A Dynamic Visualization Environment For The Design And Evaluation Of Automatic Vehicle Control Systems

    OpenAIRE

    Xu, Z.

    1995-01-01

    This document presents Dynamic Visualization, a project associated with the California PATH Program. The objective of the project is to develop a software which can animate automated highways, visualize the dynamics of automatic vehicles, and help the design and evaluation of automatic vehicle systems. This report summarizes the accomplishments of the project, describes the functions of the developed software, and provides an explanation of how to use the software.

  5. Detection and Classification of Motor Vehicle Noise in a Forested Landscape

    Science.gov (United States)

    Brown, Casey L.; Reed, Sarah E.; Dietz, Matthew S.; Fristrup, Kurt M.

    2013-11-01

    Noise emanating from human activity has become a common addition to natural soundscapes and has the potential to harm wildlife and erode human enjoyment of nature. In particular, motor vehicles traveling along roads and trails produce high levels of both chronic and intermittent noise, eliciting varied responses from a wide range of animal species. Anthropogenic noise is especially conspicuous in natural areas where ambient background sound levels are low. In this article, we present an acoustic method to detect and analyze motor vehicle noise. Our approach uses inexpensive consumer products to record sound, sound analysis software to automatically detect sound events within continuous recordings and measure their acoustic properties, and statistical classification methods to categorize sound events. We describe an application of this approach to detect motor vehicle noise on paved, gravel, and natural-surface roads, and off-road vehicle trails in 36 sites distributed throughout a national forest in the Sierra Nevada, CA, USA. These low-cost, unobtrusive methods can be used by scientists and managers to detect anthropogenic noise events for many potential applications, including ecological research, transportation and recreation planning, and natural resource management.

  6. A REVIEW OF COMPUTER VISION SYSTEM FOR THE VEHICLE IDENTIFICATION AND CLASSIFICATION FROM ONLINE AND OFFLINE VIDEOS

    Directory of Open Access Journals (Sweden)

    Baljit Singh Mokha

    2015-10-01

    Full Text Available The traffic on the roads is increasing day by day. There is dire need of developing an automation system that can effectively manage and control the traffic on roads. The traffic data of multiple vehicle types on roads is also important for taking various decisions related to traffic. A video based traffic data collection system for multiple vehicle types is helpful for monitoring vehicles under homogenous and heterogeneous traffic conditions. In this paper, we have studied different methods for the identification, classification and counting vehicles from online and offline videos in India as well as other countries. The paper also discusses the various applications of video based automatic traffic control system. The various challenges faced by the researchers for developing such systems are also discussed.

  7. Vehicle Maneuver Detection with Accelerometer-Based Classification

    Directory of Open Access Journals (Sweden)

    Javier Cervantes-Villanueva

    2016-09-01

    Full Text Available In the mobile computing era, smartphones have become instrumental tools to develop innovative mobile context-aware systems. In that sense, their usage in the vehicular domain eases the development of novel and personal transportation solutions. In this frame, the present work introduces an innovative mechanism to perceive the current kinematic state of a vehicle on the basis of the accelerometer data from a smartphone mounted in the vehicle. Unlike previous proposals, the introduced architecture targets the computational limitations of such devices to carry out the detection process following an incremental approach. For its realization, we have evaluated different classification algorithms to act as agents within the architecture. Finally, our approach has been tested with a real-world dataset collected by means of the ad hoc mobile application developed.

  8. Unattended vehicle detection for automatic traffic light control

    Science.gov (United States)

    Abdel Hady, Aya Salama; Moustafa, Mohamed

    2013-12-01

    Machine vision based traffic light control depends mainly on measuring traffic statistics at cross roads. Most of the previous studies have not taken unattended vehicles into consideration when calculating either the traffic density or the traffic flow. In this paper, we propose incorporating unattended vehicles into a new metric for measuring the traffic congestion. In addition to the vehicle motion analysis, opening the driver's side door is an important indicator that this vehicle is going to be unattended. Therefore, we focus in this paper on presenting how to detect this event for stationary vehicles from a live camera or a video feed. Through a set of experiments, we have found out that a Scale Invariant Feature Transform (SIFT) feature-descriptor with a Support Vector Machines (SVM) classifier was able to successfully classify open-door vehicles from closed-door ones in 96.7% of our test dataset.

  9. Application of Machine Vision to Vehicle Automatic Collision Warning Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Jiang-feng; GAO Feng; XU Guo-yan; YAO Sheng-zhuo

    2008-01-01

    Using the new technologies such as information technology, communication technology and electronic control technology, vehicle collision warning system(CWS) can acquire road condition, adjacent vehicle march condition as well as its dynamics performance continuously, then it can forecast the oncoming potential collision and give a warning. Based on the analysis of driver's driving behavior, algorithm's warning norms are determined. Based on warning norms adopting machine vision method, the cooperation collision warning algorithm(CWA) model with multi-input and multi-output is established which is used in supporting vehicle CWS. The CWA is tested using the actual data and the result shows that this algorithm can identify and carry out warning for vehicle collision efficiently, which has important meaning for improving the vehicle travel safety.

  10. The systems of automatic weight control of vehicles in the road and rail transport in Poland

    Directory of Open Access Journals (Sweden)

    2011-09-01

    Full Text Available . Condition of roads in Poland, despite the on-going modernisation works is still unsatisfactory. One reason is the excessive wear caused by overloaded vehicles. This problem also applies to rail transport, although to a much lesser extent. One solution may be the system of automatic weight control of road and rail vehicles. The article describes the legal and organizational conditions of oversize vehicles inspection in Poland. Characterized current practices weighing road vehicles, based on measurements of static technology. The article includes the description of the existing applications of the automatic dynamic weighing technology, known as systems WIM (Weigh in Motion. Additionally, the weighing technology and construction of weighing stands in road and rail are characterized. The article ends with authors' conclusions indicating the direction and ways of improving the weighing control systems for vehicles.

  11. Derivation and Testing of Computer Algorithms for Automatic Real-Time Determination of Space Vehicle Potentials in Various Plasma Environments

    Science.gov (United States)

    1988-05-31

    COMPUTER ALGORITHMS FOR AUTOMATIC REAL-TIME DETERMINATION OF SPACE VEHICLE POTENTIALS IN VARIOUS PLASMA ENVIRONMENTS May 31, 1988 Stanley L. Spiegel...crrnaion DiviSiofl 838 12 2 DERIVATION AND TESTING OF COMPUTER ALGORITHMS FOR AUTOMATIC REAL-TIME DETERMINATION OF SPACE VEHICLE POTENTIALS IN VARIOUS...S.L., "Derivation and testing of computer algorithms for automatic real time determination of space vehicle poteuatials in various plasma

  12. Towards an automatic classification of protein structural domains based on structural similarity

    Directory of Open Access Journals (Sweden)

    Gibrat Jean-Francois

    2008-01-01

    Full Text Available Abstract Background Formal classification of a large collection of protein structures aids the understanding of evolutionary relationships among them. Classifications involving manual steps, such as SCOP and CATH, face the challenge of increasing volume of available structures. Automatic methods such as FSSP or Dali Domain Dictionary, yield divergent classifications, for reasons not yet fully investigated. One possible reason is that the pairwise similarity scores used in automatic classification do not adequately reflect the judgments made in manual classification. Another possibility is the difference between manual and automatic classification procedures. We explore the degree to which these two factors might affect the final classification. Results We use DALI, SHEBA and VAST pairwise scores on the SCOP C class domains, to investigate a variety of hierarchical clustering procedures. The constructed dendrogram is cut in a variety of ways to produce a partition, which is compared to the SCOP fold classification. Ward's method dendrograms led to partitions closest to the SCOP fold classification. Dendrogram- or tree-cutting strategies fell into four categories according to the similarity of resulting partitions to the SCOP fold partition. Two strategies which optimize similarity to SCOP, gave an average of 72% true positives rate (TPR, at a 1% false positive rate. Cutting the largest size cluster at each step gave an average of 61% TPR which was one of the best strategies not making use of prior knowledge of SCOP. Cutting the longest branch at each step produced one of the worst strategies. We also developed a method to detect irreducible differences between the best possible automatic partitions and SCOP, regardless of the cutting strategy. These differences are substantial. Visual examination of hard-to-classify proteins confirms our previous finding, that global structural similarity of domains is not the only criterion used in the SCOP

  13. Automatic classification of protein structures using physicochemical parameters.

    Science.gov (United States)

    Mohan, Abhilash; Rao, M Divya; Sunderrajan, Shruthi; Pennathur, Gautam

    2014-09-01

    Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

  14. Poster Abstract: Automatic Calibration of Device Attitude in Inertial Measurement Unit Based Traffic Probe Vehicles

    KAUST Repository

    Mousa, Mustafa

    2016-04-28

    Probe vehicles consist in mobile traffic sensor networks that evolve with the flow of vehicles, transmitting velocity and position measurements along their path, generated using GPSs. To address the urban positioning issues of GPSs, we propose to replace them with inertial measurement units onboard vehicles, to estimate vehicle location and attitude using inertial data only. While promising, this technology requires one to carefully calibrate the orientation of the device inside the vehicle to be able to process the acceleration and rate gyro data. In this article, we propose a scheme that can perform this calibration automatically by leveraging the kinematic constraints of ground vehicles, and that can be implemented on low-end computational platforms. Preliminary testing shows that the proposed scheme enables one to accurately estimate the actual accelerations and rotation rates in the vehicle coordinates. © 2016 IEEE.

  15. Search and Classification Using Multiple Autonomous Vehicles Decision-Making and Sensor Management

    CERN Document Server

    Wang, Yue

    2012-01-01

    Search and Classification Using Multiple Autonomous Vehicles provides a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles (MAV) under both deterministic and probabilistic frameworks. It serves as a first discussion of the problem of effective resource allocation using MAV with sensing limitations, i.e., for search and classification missions over large-scale domains, or when there are far more objects to be found and classified than there are autonomous vehicles available. Under such scenarios, search and classification compete for limited sensing resources. This is because search requires vehicle mobility while classification restricts the vehicles to the vicinity of any objects found. The authors develop decision-making strategies to choose between these competing tasks and vehicle-motion-control laws to achieve the proposed management scheme. Deterministic Lyapunov-based, probabilistic Bayesian-based, and risk-based decision-mak...

  16. Automatic counting and classification of bacterial colonies using hyperspectral imaging

    Science.gov (United States)

    Detection and counting of bacterial colonies on agar plates is a routine microbiology practice to get a rough estimate of the number of viable cells in a sample. There have been a variety of different automatic colony counting systems and software algorithms mainly based on color or gray-scale pictu...

  17. Automatic classification of trees from laser scanning point clouds

    NARCIS (Netherlands)

    Sirmacek, B.; Lindenbergh, R.C.

    2015-01-01

    Development of laser scanning technologies has promoted tree monitoring studies to a new level, as the laser scanning point clouds enable accurate 3D measurements in a fast and environmental friendly manner. In this paper, we introduce a probability matrix computation based algorithm for automatical

  18. Methods for automatic cloud classification from MODIS data

    Science.gov (United States)

    Astafurov, V. G.; Kuriyanovich, K. V.; Skorokhodov, A. V.

    2016-12-01

    In this paper, different texture-analysis methods are used to describe different cloud types in MODIS satellite images. A universal technique is suggested for the formation of efficient sets of textural features using the algorithm of truncated scanning of the features for different classifiers based on neural networks and cluster-analysis methods. Efficient sets of textural features are given for the considered classifiers; the cloud-image classification results are discussed. The characteristics of the classification methods used in this work are described: the probabilistic neural network, K-nearest neighbors, self-organizing Kohonen network, fuzzy C-means, and density clustering algorithm methods. It is shown that the algorithm based on a probabilistic neural network is the most efficient. It provides for the best classification reliability for 25 cloud types and allows the recognition of 11 cloud types with a probability greater than 0.7. As an example, the cloud classification results are given for the Tomsk region. The classifications were carried out using full-size satellite cloud images and different methods. The results agree with each other and agree well with the observational data from ground-based weather stations.

  19. A Wireless Framework for Lecturers' Attendance System with Automatic Vehicle Identification (AVI Technology

    Directory of Open Access Journals (Sweden)

    Emammer Khamis Shafter

    2015-10-01

    Full Text Available Automatic Vehicle Identification (AVI technology is one type of Radio Frequency Identification (RFID method which can be used to significantly improve the efficiency of lecturers' attendance system. It provides the capability of automatic data capture for attendance records using mobile device equipped in users’ vehicle. The intent of this article is to propose a framework for automatic lecturers' attendance system using AVI technology. The first objective of this work involves gathering of requirements for Automatic Lecturers' Attendance System and to represent them using UML diagrams. The second objective is to put forward a framework that will provide guidelines for developing the system. A prototype has also been created as a pilot project.

  20. Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors

    OpenAIRE

    K. COUSSEMENT; D. VAN DEN POEL

    2007-01-01

    Customer complaint management is becoming a critical key success factor in today’s business environment. This study introduces a methodology to improve complaint handling strategies through an automatic email classification system that distinguishes complaints from non-complaints. As such, complaint handling becomes less time-consuming and more successful. The classification system combines traditional text information with new information about the linguistic style of an email. The empirical...

  1. Automatic Classification of Cetacean Vocalizations Using an Aural Classifier

    Science.gov (United States)

    2013-09-30

    were inspired by research directed at discriminating the timbre of different musical instruments – a passive classification problem – which suggests...the method should be able to classify marine mammal vocalizations since these calls possess many of the acoustic attributes of music . APPROACH

  2. Building a robust vehicle detection and classification module

    Science.gov (United States)

    Grigoryev, Anton; Khanipov, Timur; Koptelov, Ivan; Bocharov, Dmitry; Postnikov, Vassily; Nikolaev, Dmitry

    2015-12-01

    The growing adoption of intelligent transportation systems (ITS) and autonomous driving requires robust real-time solutions for various event and object detection problems. Most of real-world systems still cannot rely on computer vision algorithms and employ a wide range of costly additional hardware like LIDARs. In this paper we explore engineering challenges encountered in building a highly robust visual vehicle detection and classification module that works under broad range of environmental and road conditions. The resulting technology is competitive to traditional non-visual means of traffic monitoring. The main focus of the paper is on software and hardware architecture, algorithm selection and domain-specific heuristics that help the computer vision system avoid implausible answers.

  3. Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

    Science.gov (United States)

    Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

    2016-09-01

    It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image

  4. Vehicle tracking and classification in challenging scenarios via slice sampling

    Science.gov (United States)

    Nieto, Marcos; Unzueta, Luis; Barandiaran, Javier; Cortés, Andoni; Otaegui, Oihana; Sánchez, Pedro

    2011-12-01

    This article introduces a 3D vehicle tracking system in a traffic surveillance environment devised for shadow tolling applications. It has been specially designed to operate in real time with high correct detection and classification rates. The system is capable of providing accurate and robust results in challenging road scenarios, with rain, traffic jams, casted shadows in sunny days at sunrise and sunset times, etc. A Bayesian inference method has been designed to generate estimates of multiple variable objects entering and exiting the scene. This framework allows easily mixing different nature information, gathering in a single step observation models, calibration, motion priors and interaction models. The inference of results is carried out with a novel optimization procedure that generates estimates of the maxima of the posterior distribution combining concepts from Gibbs and slice sampling. Experimental tests have shown excellent results for traffic-flow video surveillance applications that can be used to classify vehicles according to their length, width, and height. Therefore, this vision-based system can be seen as a good substitute to existing inductive loop detectors.

  5. Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

    Science.gov (United States)

    Gibbons, Steven J.; Kværna, Tormod; Harris, David B.; Dodge, Douglas A.

    2016-04-01

    Aftershock sequences following very large earthquakes present enormous challenges to near-realtime generation of seismic bulletins. The increase in analyst resources needed to relocate an inflated number of events is compounded by failures of phase association algorithms and a significant deterioration in the quality of underlying fully automatic event bulletins. Current processing pipelines were designed a generation ago and, due to computational limitations of the time, are usually limited to single passes over the raw data. With current processing capability, multiple passes over the data are feasible. Processing the raw data at each station currently generates parametric data streams which are then scanned by a phase association algorithm to form event hypotheses. We consider the scenario where a large earthquake has occurred and propose to define a region of likely aftershock activity in which events are detected and accurately located using a separate specially targeted semi-automatic process. This effort may focus on so-called pattern detectors, but here we demonstrate a more general grid search algorithm which may cover wider source regions without requiring waveform similarity. Given many well-located aftershocks within our source region, we may remove all associated phases from the original detection lists prior to a new iteration of the phase association algorithm. We provide a proof-of-concept example for the 2015 Gorkha sequence, Nepal, recorded on seismic arrays of the International Monitoring System. Even with very conservative conditions for defining event hypotheses within the aftershock source region, we can automatically remove over half of the original detections which could have been generated by Nepal earthquakes and reduce the likelihood of false associations and spurious event hypotheses. Further reductions in the number of detections in the parametric data streams are likely using correlation and subspace detectors and/or empirical matched

  6. Automatic age and gender classification using supervised appearance model

    Science.gov (United States)

    Bukar, Ali Maina; Ugail, Hassan; Connah, David

    2016-11-01

    Age and gender classification are two important problems that recently gained popularity in the research community, due to their wide range of applications. Research has shown that both age and gender information are encoded in the face shape and texture, hence the active appearance model (AAM), a statistical model that captures shape and texture variations, has been one of the most widely used feature extraction techniques for the aforementioned problems. However, AAM suffers from some drawbacks, especially when used for classification. This is primarily because principal component analysis (PCA), which is at the core of the model, works in an unsupervised manner, i.e., PCA dimensionality reduction does not take into account how the predictor variables relate to the response (class labels). Rather, it explores only the underlying structure of the predictor variables, thus, it is no surprise if PCA discards valuable parts of the data that represent discriminatory features. Toward this end, we propose a supervised appearance model (sAM) that improves on AAM by replacing PCA with partial least-squares regression. This feature extraction technique is then used for the problems of age and gender classification. Our experiments show that sAM has better predictive power than the conventional AAM.

  7. Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ

    NARCIS (Netherlands)

    Alegre, Enrique; Biehl, Michael; Petkov, Nicolai; Sanchez, Lidia

    2008-01-01

    We consider images of boar spermatozoa obtained with ail optical phase-contrast microscope. Our goal is to automatically classify single sperm cells as acrosome-intact (class 1) or acrosome-damaged (class 2). Such classification is important for the estimation of the fertilization potential of a spe

  8. Region descriptors for automatic classification of small sea targets in infrared video

    NARCIS (Netherlands)

    Mouthaan, M.M.; Broek, S.P. van den; Hendriks, E.A.; Schwering, P.B.W.

    2011-01-01

    We evaluate the performance of different key-point detectors and region descriptors when used for automatic classification of small sea targets in infrared video. In our earlier research performed on this subject as well as in other literature, many different region descriptors have been proposed. H

  9. Construction of Cubic Dynamic and User-oriented Taxonomy forAutomatic Classification of Internet Information

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Based on the requirements of the development of Internet, thenecessity of establishing cubic dynamic and user-oriented taxonomy for automatic classification is presented. Then the basic algorithm to construct such taxonomy is discussed. The view is up to date in current world.

  10. Automatic classification and speaker identification of African elephant (Loxodonta africana) vocalizations

    Science.gov (United States)

    Clemins, Patrick J.; Johnson, Michael T.; Leong, Kirsten M.; Savage, Anne

    2005-02-01

    A hidden Markov model (HMM) system is presented for automatically classifying African elephant vocalizations. The development of the system is motivated by successful models from human speech analysis and recognition. Classification features include frequency-shifted Mel-frequency cepstral coefficients (MFCCs) and log energy, spectrally motivated features which are commonly used in human speech processing. Experiments, including vocalization type classification and speaker identification, are performed on vocalizations collected from captive elephants in a naturalistic environment. The system classified vocalizations with accuracies of 94.3% and 82.5% for type classification and speaker identification classification experiments, respectively. Classification accuracy, statistical significance tests on the model parameters, and qualitative analysis support the effectiveness and robustness of this approach for vocalization analysis in nonhuman species. .

  11. Study on shift schedule saving energy of automatic transmission of ground vehicles

    Institute of Scientific and Technical Information of China (English)

    龚捷; 赵丁选; 陈鹰; 陈宁

    2004-01-01

    To improve ground vehicle efficiency, shift schedule energy saving was proposed for the ground vehicle automatic transmission by studying the function of the torque converter and transmission in the vehicular drivetrain. The shift schedule can keep the torque converter working in the high efficiency range under all the working conditions except in the low efficiency range on the left when the transmission worked at the lowest shift, and in the low efficiency range on the right when the transmission worked at the highest shift. The shift quality key factors were analysed. The automatic trans-mission's bench-test adopting this shift schedule was made on the automatic transmission's test-bed. The experimental results showed that the shift schedule was correct and that the shift quality was controllable.

  12. Study on shift schedule saving energy of automatic transmission of ground vehicles

    Institute of Scientific and Technical Information of China (English)

    龚捷; 赵丁选; 陈鹰; 陈宁

    2004-01-01

    To improve ground vehicle efficiency,shift schedule energy saving was proposed for the ground vehicle automatic transmission by studying the function of the torque converter and transmission in the vehicular drivetrain.The shift schedule can keep the torque converter working in the high efficiency range under all the working conditions except in the low efficiency range on the left when the transmission worked at the lowest shift,and in the low efficiency range on the right when the transmission worked at the highest shift.The shift quality key factors were analysed.The automatic transmission's bench-test adopting this shift schedule was made on the automatic transmission's test-bed.The experimental results showed that the shift schedule was correct and that the shift quality was controllable.

  13. 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 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.

  14. Automatic Fault Characterization via Abnormality-Enhanced Classification

    Energy Technology Data Exchange (ETDEWEB)

    Bronevetsky, G; Laguna, I; de Supinski, B R

    2010-12-20

    Enterprise and high-performance computing systems are growing extremely large and complex, employing hundreds to hundreds of thousands of processors and software/hardware stacks built by many people across many organizations. As the growing scale of these machines increases the frequency of faults, system complexity makes these faults difficult to detect and to diagnose. Current system management techniques, which focus primarily on efficient data access and query mechanisms, require system administrators to examine the behavior of various system services manually. Growing system complexity is making this manual process unmanageable: administrators require more effective management tools that can detect faults and help to identify their root causes. System administrators need timely notification when a fault is manifested that includes the type of fault, the time period in which it occurred and the processor on which it originated. Statistical modeling approaches can accurately characterize system behavior. However, the complex effects of system faults make these tools difficult to apply effectively. This paper investigates the application of classification and clustering algorithms to fault detection and characterization. We show experimentally that naively applying these methods achieves poor accuracy. Further, we design novel techniques that combine classification algorithms with information on the abnormality of application behavior to improve detection and characterization accuracy. Our experiments demonstrate that these techniques can detect and characterize faults with 65% accuracy, compared to just 5% accuracy for naive approaches.

  15. Internal combustion engine for vehicles with automatic gearbox. Brennkraftmaschine fuer Kraftfahrzeuge mit einem automatischen Getriebe

    Energy Technology Data Exchange (ETDEWEB)

    Hetmann, R.

    1982-04-19

    The invention refers to an internal combustion engine for vehicles with an automatic gearbox, where the internal combustion engine has a first group of cylinders and at least one second group of cylinders, and a device for affecting the fuel supply to the groups of cylinders, depending on the working parameters of the vehicle. The invention is characterised by the fact that the working parameters are the handbrake and footbrake of the vehicle, and that the device for affecting the fuel supply to the groups of cylinders when the footbrake or handbrake is operated makes it possible to supply fuel to only part of the groups of cylinders. The control switches of both braking systems are connected to the fuel supply control via a logic circuit. This arrangement of the system prevents damage when testing the braking speed of the automatic gearbox due to excessive loads.

  16. Ipsilateral coordination features for automatic classification of Parkinson's disease

    Science.gov (United States)

    Sarmiento, Fernanda; Atehortúa, Angélica; Martínez, Fabio; Romero, Eduardo

    2015-12-01

    A reliable diagnosis of the Parkinson Disease lies on the objective evaluation of different motor sub-systems. Discovering specific motor patterns associated to the disease is fundamental for the development of unbiased assessments that facilitate the disease characterization, independently of the particular examiner. This paper proposes a new objective screening of patients with Parkinson, an approach that optimally combines ipsilateral global descriptors. These ipsilateral gait features are simple upper-lower limb relationships in frequency and relative phase spaces. These low level characteristics feed a simple SVM classifier with a polynomial kernel function. The strategy was assessed in a binary classification task, normal against Parkinson, under a leave-one-out scheme in a population of 16 Parkinson patients and 7 healthy control subjects. Results showed an accuracy of 94;6% using relative phase spaces and 82;1% with simple frequency relations.

  17. Automatic Classification of Offshore Wind Regimes With Weather Radar Observations

    DEFF Research Database (Denmark)

    Trombe, Pierre-Julien; Pinson, Pierre; Madsen, Henrik

    2014-01-01

    Weather radar observations are called to play an important role in offshore wind energy. In particular, they can enable the monitoring of weather conditions in the vicinity of large-scale offshore wind farms and thereby notify the arrival of precipitation systems associated with severe wind...... and amplitude) using reflectivity observations from a single weather radar system. A categorical sequence of most likely wind regimes is estimated from a wind speed time series by combining a Markov-Switching model and a global decoding technique, the Viterbi algorithm. In parallel, attributes of precipitation...... systems are extracted from weather radar images. These attributes describe the global intensity, spatial continuity and motion of precipitation echoes on the images. Finally, a CART classification tree is used to find the broad relationships between precipitation attributes and wind regimes...

  18. Automatic Vehicle License Recognition Based on Video Vehicular Detection System

    Institute of Scientific and Technical Information of China (English)

    YANG Zhaoxuan; CHEN Yang; HE Yinghua; WU Jun

    2006-01-01

    Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system.Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algorithm based on Markov random field model is presented.Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold.The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%.When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system's requirement even under the noisy circumstance or uneven illumination.

  19. AUTOMATIC DETECTION AND CLASSIFICATION OF RETINAL VASCULAR LANDMARKS

    Directory of Open Access Journals (Sweden)

    Hadi Hamad

    2014-06-01

    Full Text Available The main contribution of this paper is introducing a method to distinguish between different landmarks of the retina: bifurcations and crossings. The methodology may help in differentiating between arteries and veins and is useful in identifying diseases and other special pathologies, too. The method does not need any special skills, thus it can be assimilated to an automatic way for pinpointing landmarks; moreover it gives good responses for very small vessels. A skeletonized representation, taken out from the segmented binary image (obtained through a preprocessing step, is used to identify pixels with three or more neighbors. Then, the junction points are classified into bifurcations or crossovers depending on their geometrical and topological properties such as width, direction and connectivity of the surrounding segments. The proposed approach is applied to the public-domain DRIVE and STARE datasets and compared with the state-of-the-art methods using proper validation parameters. The method was successful in identifying the majority of the landmarks; the average correctly identified bifurcations in both DRIVE and STARE datasets for the recall and precision values are: 95.4% and 87.1% respectively; also for the crossovers, the recall and precision values are: 87.6% and 90.5% respectively; thus outperforming other studies.

  20. Automatic classification of retinal vessels into arteries and veins

    Science.gov (United States)

    Niemeijer, Meindert; van Ginneken, Bram; Abràmoff, Michael D.

    2009-02-01

    Separating the retinal vascular tree into arteries and veins is important for quantifying vessel changes that preferentially affect either the veins or the arteries. For example the ratio of arterial to venous diameter, the retinal a/v ratio, is well established to be predictive of stroke and other cardiovascular events in adults, as well as the staging of retinopathy of prematurity in premature infants. This work presents a supervised, automatic method that can determine whether a vessel is an artery or a vein based on intensity and derivative information. After thinning of the vessel segmentation, vessel crossing and bifurcation points are removed leaving a set of vessel segments containing centerline pixels. A set of features is extracted from each centerline pixel and using these each is assigned a soft label indicating the likelihood that it is part of a vein. As all centerline pixels in a connected segment should be the same type we average the soft labels and assign this average label to each centerline pixel in the segment. We train and test the algorithm using the data (40 color fundus photographs) from the DRIVE database1 with an enhanced reference standard. In the enhanced reference standard a fellowship trained retinal specialist (MDA) labeled all vessels for which it was possible to visually determine whether it was a vein or an artery. After applying the proposed method to the 20 images of the DRIVE test set we obtained an area under the receiver operator characteristic (ROC) curve of 0.88 for correctly assigning centerline pixels to either the vein or artery classes.

  1. Automatic brain caudate nuclei segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder.

    Science.gov (United States)

    Igual, Laura; Soliva, Joan Carles; Escalera, Sergio; Gimeno, Roger; Vilarroya, Oscar; Radeva, Petia

    2012-12-01

    We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods.

  2. An embedded omnidirectional vision navigator for automatic guided vehicles

    Science.gov (United States)

    Feng, Weijia; Zhang, Baofeng; Röning, Juha; Cao, Zuoliang; Zong, Xiaoning

    2011-01-01

    Omnidirectional vision appears the definite significance since its advantage of acquiring full 360° horizontal field of vision information simultaneously. In this paper, an embedded original omnidirectional vision navigator (EOVN) based on fish-eye lens and embedded technology has been researched. Fish-eye lens is one of the special ways to establish omnidirectional vision. However, it appears with an unavoidable inherent and enormous distortion. A unique integrated navigation method which is conducted on the basis of targets tracking has been proposed. It is composed of multi-target recognition and tracking, distortion rectification, spatial location and navigation control. It is called RTRLN. In order to adapt to the different indoor and outdoor navigation environments, we implant mean-shift and dynamic threshold adjustment into the Particle Filter algorithm to improve the efficiency and robustness of tracking capability. RTRLN has been implanted in an independent development embedded platform. EOVN likes a smart crammer based on COMS+FPGA+DSP. It can guide various vehicles in outdoor environments by tracking the diverse marks hanging in the air. The experiments prove that the EOVN is particularly suitable for the guidance applications which need high requirements on precision and repeatability. The research achievements have a good actual applied inspection.

  3. Automatic classification and accurate size measurement of blank mask defects

    Science.gov (United States)

    Bhamidipati, Samir; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

    2015-07-01

    complexity of defects encountered. The variety arises due to factors such as defect nature, size, shape and composition; and the optical phenomena occurring around the defect. This paper focuses on preliminary characterization results, in terms of classification and size estimation, obtained by Calibre MDPAutoClassify tool on a variety of mask blank defects. It primarily highlights the challenges faced in achieving the results with reference to the variety of defects observed on blank mask substrates and the underlying complexities which make accurate defect size measurement an important and challenging task.

  4. Bond graph modeling, simulation, and reflex control of the Mars planetary automatic vehicle

    Science.gov (United States)

    Amara, Maher; Friconneau, Jean Pierre; Micaelli, Alain

    1993-01-01

    The bond graph modeling, simulation, and reflex control study of the Planetary Automatic Vehicle are considered. A simulator derived from a complete bond graph model of the vehicle is presented. This model includes both knowledge and representation models of the mechanical structure, the floor contact, and the Mars site. The MACSYMEN (French acronym for aided design method of multi-energetic systems) is used and applied to study the input-output power transfers. The reflex control is then considered. Controller architecture and locomotion specificity are described. A numerical stage highlights some interesting results of the robot and the controller capabilities.

  5. Research on a Chnese Text Automatic Classification Method%一种中文文本自动分类方法的研究

    Institute of Scientific and Technical Information of China (English)

    尹桂秀

    2002-01-01

    This article introduces a Chinese text automatic classification method, including its principle and classification process. The article focuses on some key theoretical problems, such as word classification, keyword collection and keyword matching.

  6. Gaussian Mixture Model and Deep Neural Network based Vehicle Detection and Classification

    Directory of Open Access Journals (Sweden)

    S Sri Harsha

    2016-09-01

    Full Text Available The exponential rise in the demand of vision based traffic surveillance systems have motivated academia-industries to develop optimal vehicle detection and classification scheme. In this paper, an adaptive learning rate based Gaussian mixture model (GMM algorithm has been developed for background subtraction of multilane traffic data. Here, vehicle rear information and road dash-markings have been used for vehicle detection. Performing background subtraction, connected component analysis has been applied to retrieve vehicle region. A multilayered AlexNet deep neural network (DNN has been applied to extract higher layer features. Furthermore, scale invariant feature transform (SIFT based vehicle feature extraction has been performed. The extracted 4096-dimensional features have been processed for dimensional reduction using principle component analysis (PCA and linear discriminant analysis (LDA. The features have been mapped for SVM-based classification. The classification results have exhibited that AlexNet-FC6 features with LDA give the accuracy of 97.80%, followed by AlexNet-FC6 with PCA (96.75%. AlexNet-FC7 feature with LDA and PCA algorithms has exhibited classification accuracy of 91.40% and 96.30%, respectively. On the contrary, SIFT features with LDA algorithm has exhibited 96.46% classification accuracy. The results revealed that enhanced GMM with AlexNet DNN at FC6 and FC7 can be significant for optimal vehicle detection and classification.

  7. An Automatic System of Vehicle Number-Plate Recognition Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper presents an automatic system of vehicle number-plate recognition based on neural networks. In this system, location of number-plate and recognition of characters in number-plate can be automatically completed. Pixel colors of Number-plate area are classified using neural network, then color features are extracted by analyzing scanning lines of the cross-section of number-plate. It takes full use of number-plate color features to locate number plate. Characters in number-plate can be effectively recognized using the neural networks. Experimental results show that the correct rate of number-plate location is close to 100%, and the time of number-plate location is less than 1 second. Moreover, recognition rate of characters is improved due to the known number-plate type. It is also observed that this system is not sensitive to variations of weather, illumination and vehicle speed. In addition, and also the size of number-plate need not to be known in prior. This system is of crucial significance to apply and spread the automatic system of vehicle number-plate recognition.

  8. Key issues in automatic classification of defects in post-inspection review process of photomasks

    Science.gov (United States)

    Pereira, Mark; Maji, Manabendra; Pai, Ravi R.; B. V. R., Samir; Seshadri, R.; Patil, Pradeepkumar

    2012-11-01

    The mask inspection and defect classification is a crucial part of mask preparation technology and consumes a significant amount of mask preparation time. As the patterns on a mask become smaller and more complex, the need for a highly precise mask inspection system with high detection sensitivity becomes greater. However, due to the high sensitivity, in addition to the detection of smaller defects on finer geometries, the inspection machine could report large number of false defects. The total number of defects becomes significantly high and the manual classification of these defects, where the operator should review each of the defects and classify them, may take huge amount of time. Apart from false defects, many of the very small real defects may not print on the wafer and user needs to spend time on classifying them as well. Also, sometimes, manual classification done by different operators may not be consistent. So, need for an automatic, consistent and fast classification tool becomes more acute in more advanced nodes. Automatic Defect Classification tool (NxADC) which is in advanced stage of development as part of NxDAT1, can automatically classify defects accurately and consistently in very less amount of time, compared to a human operator. Amongst the prospective defects as detected by the Mask Inspection System, NxADC identifies several types of false defects such as false defects due to registration error, false defects due to problems with CCD, noise, etc. It is also able to automatically classify real defects such as, pin-dot, pin-hole, clear extension, multiple-edges opaque, missing chrome, chrome-over-MoSi, etc. We faced a large set of algorithmic challenges during the course of the development of our NxADC tool. These include selecting the appropriate image alignment algorithm to detect registration errors (especially when there are sub-pixel registration errors or misalignment in repetitive patterns such as line space), differentiating noise from

  9. Automatic Ferrite Content Measurement based on Image Analysis and Pattern Classification

    Directory of Open Access Journals (Sweden)

    Hafiz Muhammad Tanveer

    2015-05-01

    Full Text Available The existing manual point counting technique for ferrite content measurement is a difficult time consuming method which has limited accuracy due to limited human perception and error induced by points on boundaries of grid spacing. In this paper, we present a novel algorithm, based on image analysis and pattern classification, to evaluate the volume fraction of ferrite in microstructure containing ferrite and austenite. The prime focus of the proposed algorithm is to solve the problem of ferrite content measurement using automatic binary classification approach. Classification of image data into two distinct classes, using optimum threshold finding method, is the key idea behind the new algorithm. Automation of the process to measure the ferrite content and to speed up specimen’s testing procedure is the main feature of the newly developed algorithm. Improved performance index by reducing error sources is reflected from obtained results and validated through the comparison with a well-known method of Ohtsu.

  10. Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

    Directory of Open Access Journals (Sweden)

    Jose Luis Rodríguez-Sotelo

    2014-12-01

    Full Text Available Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

  11. Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data

    Directory of Open Access Journals (Sweden)

    Qingqing Lu

    2014-01-01

    Full Text Available Ground penetrating radar (GPR is a powerful tool for detecting objects buried underground. However, the interpretation of the acquired signals remains a challenging task since an experienced user is required to manage the entire operation. Particularly difficult is the classification of the material type of underground objects in noisy environment. This paper proposes a new feature extraction method. First, discrete wavelet transform (DWT transforms A-Scan data and approximation coefficients are extracted. Then, fractional Fourier transform (FRFT is used to transform approximation coefficients into fractional domain and we extract features. The features are supplied to the support vector machine (SVM classifiers to automatically identify underground objects material. Experiment results show that the proposed feature-based SVM system has good performances in classification accuracy compared to statistical and frequency domain feature-based SVM system in noisy environment and the classification accuracy of features proposed in this paper has little relationship with the SVM models.

  12. Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm

    Directory of Open Access Journals (Sweden)

    Matthew P. Dannenberg

    2016-08-01

    Full Text Available Classifying land cover is perhaps the most common application of remote sensing, yet classification at frequent temporal intervals remains a challenging task due to radiometric differences among scenes, time and budget constraints, and semantic differences among class definitions from different dates. The automatic adaptive signature generalization (AASG algorithm overcomes many of these limitations by locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, which mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. We refined AASG to adapt stable site identification parameters to each individual land cover class, while also incorporating improved input data and a random forest classifier. In the Research Triangle region of North Carolina, our new version of AASG demonstrated an improved ability to update existing land cover classifications compared to the initial version of AASG, particularly for low intensity developed, mixed forest, and woody wetland classes. Topographic indices were particularly important for distinguishing woody wetlands from other forest types, while multi-seasonal imagery contributed to improved classification of water, developed, forest, and hay/pasture classes. These results demonstrate both the flexibility of the AASG algorithm and the potential for using it to produce high-quality land cover classifications that can utilize the entire temporal range of the Landsat archive in an automated fashion while maintaining consistent class definitions through time.

  13. Regenerative braking control strategy in mild hybrid electric vehicles equipped with automatic manual transmission

    Institute of Scientific and Technical Information of China (English)

    QIN Datong; YE Ming; LIU Zhenjun

    2007-01-01

    The actual regenerative braking force of an integrated starter/generator (ISG),which is varied with desired braking deceleration and vehicle speed,is calculated based on an analysis of the required deceleration,maximum braking force of ISG,engine braking force and state of charge (SOC) of battery.Braking force distribution strategies are presented according to the actual regenerative braking force of ISG.To recover the vehicle's kinetic energy maximally,braking shift rules for a mild hybrid electric vehicle (HEV) equipped with automatic manual transmission (AMT) are brought forward and effects of transmission ratios are considered.A test-bed is built up and regenerative braking tests are carried out.The results show that power recovered by the braking shift rules is more than that recovered by the normal braking control rules.

  14. Statistical classification of road pavements using near field vehicle rolling noise measurements.

    Science.gov (United States)

    Paulo, Joel Preto; Coelho, J L Bento; Figueiredo, Mário A T

    2010-10-01

    Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

  15. An atlas-based fuzzy connectedness method for automatic tissue classification in brain MRI

    Institute of Scientific and Technical Information of China (English)

    ZHOU Yongxin; BAI Jing

    2006-01-01

    A framework incorporating a subject-registered atlas into the fuzzy connectedness (FC) method is proposed for the automatic tissue classification of 3D images of brain MRI. The pre-labeled atlas is first registered onto the subject to provide an initial approximate segmentation. The initial segmentation is used to estimate the intensity histograms of gray matter and white matter. Based on the estimated intensity histograms, multiple seed voxels are assigned to each tissue automatically. The normalized intensity histograms are utilized in the FC method as the intensity probability density function (PDF) directly. Relative fuzzy connectedness technique is adopted in the final classification of gray matter and white matter. Experimental results based on the 20 data sets from IBSR are included, as well as comparisons of the performance of our method with that of other published methods. This method is fully automatic and operator-independent. Therefore, it is expected to find wide applications, such as 3D visualization, radiation therapy planning, and medical database construction.

  16. Detecting cognitive impairment by eye movement analysis using automatic classification algorithms.

    Science.gov (United States)

    Lagun, Dmitry; Manzanares, Cecelia; Zola, Stuart M; Buffalo, Elizabeth A; Agichtein, Eugene

    2011-09-30

    The Visual Paired Comparison (VPC) task is a recognition memory test that has shown promise for the detection of memory impairments associated with mild cognitive impairment (MCI). Because patients with MCI often progress to Alzheimer's Disease (AD), the VPC may be useful in predicting the onset of AD. VPC uses noninvasive eye tracking to identify how subjects view novel and repeated visual stimuli. Healthy control subjects demonstrate memory for the repeated stimuli by spending more time looking at the novel images, i.e., novelty preference. Here, we report an application of machine learning methods from computer science to improve the accuracy of detecting MCI by modeling eye movement characteristics such as fixations, saccades, and re-fixations during the VPC task. These characteristics are represented as features provided to automatic classification algorithms such as Support Vector Machines (SVMs). Using the SVM classification algorithm, in tandem with modeling the patterns of fixations, saccade orientation, and regression patterns, our algorithm was able to automatically distinguish age-matched normal control subjects from MCI subjects with 87% accuracy, 97% sensitivity and 77% specificity, compared to the best available classification performance of 67% accuracy, 60% sensitivity, and 73% specificity when using only the novelty preference information. These results demonstrate the effectiveness of applying machine-learning techniques to the detection of MCI, and suggest a promising approach for detection of cognitive impairments associated with other disorders.

  17. Vehicle-to-Grid Automatic Load Sharing with Driver Preference in Micro-Grids

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yubo; Nazaripouya, Hamidreza; Chu, Chi-Cheng; Gadh, Rajit; Pota, Hemanshu R.

    2014-10-15

    Integration of Electrical Vehicles (EVs) with power grid not only brings new challenges for load management, but also opportunities for distributed storage and generation. This paper comprehensively models and analyzes distributed Vehicle-to-Grid (V2G) for automatic load sharing with driver preference. In a micro-grid with limited communications, V2G EVs need to decide load sharing based on their own power and voltage profile. A droop based controller taking into account driver preference is proposed in this paper to address the distributed control of EVs. Simulations are designed for three fundamental V2G automatic load sharing scenarios that include all system dynamics of such applications. Simulation results demonstrate that active power sharing is achieved proportionally among V2G EVs with consideration of driver preference. In additional, the results also verify the system stability and reactive power sharing analysis in system modelling, which sheds light on large scale V2G automatic load sharing in more complicated cases.

  18. Classification of underwater targets from autonomous underwater vehicle sampled bistatic acoustic scattered fields.

    Science.gov (United States)

    Fischell, Erin M; Schmidt, Henrik

    2015-12-01

    One of the long term goals of autonomous underwater vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and post-processing and/or image interpretation. A vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target has been developed for onboard, fully autonomous classification with lower cost-per-vehicle. To achieve the high-quality, densely sampled three-dimensional (3D) bistatic scattering data required by this research, vehicle sampling behaviors and an acoustic payload for precision timed data acquisition with a 16 element nose array were demonstrated. 3D bistatic scattered field data were collected by an AUV around spherical and cylindrical targets insonified by a 7-9 kHz fixed source. The collected data were compared to simulated scattering models. Classification and confidence estimation were shown for the sphere versus cylinder case on the resulting real and simulated bistatic amplitude data. The final models were used for classification of simulated targets in real time in the LAMSS MOOS-IvP simulation package [M. Benjamin, H. Schmidt, P. Newman, and J. Leonard, J. Field Rob. 27, 834-875 (2010)].

  19. Automatic training sample selection for a multi-evidence based crop classification approach

    DEFF Research Database (Denmark)

    Chellasamy, Menaka; Ferre, Ty; Greve, Mogens Humlekrog

    three Multi-Layer Perceptron (MLP) neural networks trained separately with spectral, texture and vegetation indices; classification labels were then assigned based on Endorsement Theory. The present study proposes an approach to feed this ensemble classifier with automatically selected training samples...... are classified using the multi-evidence approach. The study is implemented with WorldView-2 multispectral imagery acquired for a study area containing 10 crop classes. The proposed approach is compared with the multi-evidence approach based on training samples selected randomly and border samples based...

  20. Computational text analysis and reading comprehension exam complexity towards automatic text classification

    CERN Document Server

    Liontou, Trisevgeni

    2014-01-01

    This book delineates a range of linguistic features that characterise the reading texts used at the B2 (Independent User) and C1 (Proficient User) levels of the Greek State Certificate of English Language Proficiency exams in order to help define text difficulty per level of competence. In addition, it examines whether specific reader variables influence test takers' perceptions of reading comprehension difficulty. The end product is a Text Classification Profile per level of competence and a formula for automatically estimating text difficulty and assigning levels to texts consistently and re

  1. Using fuzzy logic for automatic control: Case study of a problem of cereals samples classification

    Directory of Open Access Journals (Sweden)

    Lakhoua Najeh Mohamed

    2009-01-01

    Full Text Available The aim of this paper is to present the use of fuzzy logic for automatic control of industrial systems particularly the way to approach a problem of classification. We present a case study of a grading system of cereals that allows us to determine the price of transactions of cereals in Tunisia. Our contribution in this work consists in proposing not only an application of the fuzzy logic on the grading system of cereals but also a methodology enabling the proposing of a new grading system based on the concept of 'Grade' while using the fuzzy logic techniques. .

  2. Automatic detection and classification of obstacles with applications in autonomous mobile robots

    Science.gov (United States)

    Ponomaryov, Volodymyr I.; Rosas-Miranda, Dario I.

    2016-04-01

    Hardware implementation of an automatic detection and classification of objects that can represent an obstacle for an autonomous mobile robot using stereo vision algorithms is presented. We propose and evaluate a new method to detect and classify objects for a mobile robot in outdoor conditions. This method is divided in two parts, the first one is the object detection step based on the distance from the objects to the camera and a BLOB analysis. The second part is the classification step that is based on visuals primitives and a SVM classifier. The proposed method is performed in GPU in order to reduce the processing time values. This is performed with help of hardware based on multi-core processors and GPU platform, using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.

  3. Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring

    Science.gov (United States)

    Bello, Juan Pablo; Farnsworth, Andrew; Robbins, Matt; Keen, Sara; Klinck, Holger; Kelling, Steve

    2016-01-01

    Automatic classification of animal vocalizations has great potential to enhance the monitoring of species movements and behaviors. This is particularly true for monitoring nocturnal bird migration, where automated classification of migrants’ flight calls could yield new biological insights and conservation applications for birds that vocalize during migration. In this paper we investigate the automatic classification of bird species from flight calls, and in particular the relationship between two different problem formulations commonly found in the literature: classifying a short clip containing one of a fixed set of known species (N-class problem) and the continuous monitoring problem, the latter of which is relevant to migration monitoring. We implemented a state-of-the-art audio classification model based on unsupervised feature learning and evaluated it on three novel datasets, one for studying the N-class problem including over 5000 flight calls from 43 different species, and two realistic datasets for studying the monitoring scenario comprising hundreds of thousands of audio clips that were compiled by means of remote acoustic sensors deployed in the field during two migration seasons. We show that the model achieves high accuracy when classifying a clip to one of N known species, even for a large number of species. In contrast, the model does not perform as well in the continuous monitoring case. Through a detailed error analysis (that included full expert review of false positives and negatives) we show the model is confounded by varying background noise conditions and previously unseen vocalizations. We also show that the model needs to be parameterized and benchmarked differently for the continuous monitoring scenario. Finally, we show that despite the reduced performance, given the right conditions the model can still characterize the migration pattern of a specific species. The paper concludes with directions for future research. PMID:27880836

  4. Automatically inferred Markov network models for classification of chromosomal band pattern structures.

    Science.gov (United States)

    Granum, E; Thomason, M G

    1990-01-01

    A structural pattern recognition approach to the analysis and classification of metaphase chromosome band patterns is presented. An operational method of representing band pattern profiles as sharp edged idealized profiles is outlined. These profiles are nonlinearly scaled to a few, but fixed number of "density" levels. Previous experience has shown that profiles of six levels are appropriate and that the differences between successive bands in these profiles are suitable for classification. String representations, which focuses on the sequences of transitions between local band pattern levels, are derived from such "difference profiles." A method of syntactic analysis of the band transition sequences by dynamic programming for optimal (maximal probability) string-to-network alignments is described. It develops automatic data-driven inference of band pattern models (Markov networks) per class, and uses these models for classification. The method does not use centromere information, but assumes the p-q-orientation of the band pattern profiles to be known a priori. It is experimentally established that the method can build Markov network models, which, when used for classification, show a recognition rate of about 92% on test data. The experiments used 200 samples (chromosome profiles) for each of the 22 autosome chromosome types and are designed to also investigate various classifier design problems. It is found that the use of a priori knowledge of Denver Group assignment only improved classification by 1 or 2%. A scheme for typewise normalization of the class relationship measures prove useful, partly through improvements on average results and partly through a more evenly distributed error pattern. The choice of reference of the p-q-orientation of the band patterns is found to be unimportant, and results of timing of the execution time of the analysis show that recent and efficient implementations can process one cell in less than 1 min on current standard

  5. Correlation analysis-based image segmentation approach for automatic agriculture vehicle

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    It is important to segment image correctly to extract guidance information for automatic agriculture vehicle. If we can make the computer know where the crops are, we can extract the guidance line easily. Images were divided into some rectangle small windows, then a pair of 1-D arrays was constructed in each small windows. The correlation coefficients of every small window constructed the features to segment images. The results showed that correlation analysis is a potential approach for processing complex farmland for guidance system, and more correlation analysis methods must be researched.

  6. OPTIMAL TORQUE CONTROL STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLE WITH AUTOMATIC MECHANICAL TRANSMISSION

    Institute of Scientific and Technical Information of China (English)

    GU Yanchun; YIN Chengliang; ZHANG Jianwu

    2007-01-01

    In parallel hybrid electrical vehicle (PHEV) equipped with automatic mechanical transmission (AMT), the driving smoothness and the clutch abrasion are the primary considerations for powertrain control during gearshift and clutch operation. To improve these performance indexes of PHEV, a coordinated control system is proposed through the analyzing of HEV powertrain dynamic characteristics. Using the method of minimum principle, the input torque of transmission is optimized to improve the driving sinoothness of vehicle. Using the methods of fuzzy logic and fuzzy-PID, the engaging speed of clutch and the throttle opening of engine are manipulated to ensure the smoothness of clutch engagement and reduce the abrasion of clutch friction plates. The motor provides the difference between the required input torque of transmission and the torque transmitted through clutch plates. Results of simulation and experiments show that the proposed control strategy performs better than the contrastive control system, the smoothness of driving and the abrasion of clutch can be improved simultaneously.

  7. Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images

    Science.gov (United States)

    Wang, Liming; Zhang, Kai; Liu, Xiyang; Long, Erping; Jiang, Jiewei; An, Yingying; Zhang, Jia; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni; Li, Wangting; Lin, Haotian

    2017-01-01

    There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.

  8. Real-time classification of vehicles by type within infrared imagery

    Science.gov (United States)

    Kundegorski, Mikolaj E.; Akçay, Samet; Payen de La Garanderie, Grégoire; Breckon, Toby P.

    2016-10-01

    Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios.

  9. Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle.

    Science.gov (United States)

    Diaz-Varela, R A; Zarco-Tejada, P J; Angileri, V; Loudjani, P

    2014-02-15

    Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery.

  10. Poster abstract: A machine learning approach for vehicle classification using passive infrared and ultrasonic sensors

    KAUST Repository

    Warriach, Ehsan Ullah

    2013-01-01

    This article describes the implementation of four different machine learning techniques for vehicle classification in a dual ultrasonic/passive infrared traffic flow sensors. Using k-NN, Naive Bayes, SVM and KNN-SVM algorithms, we show that KNN-SVM significantly outperforms other algorithms in terms of classification accuracy. We also show that some of these algorithms could run in real time on the prototype system. Copyright © 2013 ACM.

  11. Automatic generation of 3D motifs for classification of protein binding sites

    Directory of Open Access Journals (Sweden)

    Herzyk Pawel

    2007-08-01

    Full Text Available Abstract Background Since many of the new protein structures delivered by high-throughput processes do not have any known function, there is a need for structure-based prediction of protein function. Protein 3D structures can be clustered according to their fold or secondary structures to produce classes of some functional significance. A recent alternative has been to detect specific 3D motifs which are often associated to active sites. Unfortunately, there are very few known 3D motifs, which are usually the result of a manual process, compared to the number of sequential motifs already known. In this paper, we report a method to automatically generate 3D motifs of protein structure binding sites based on consensus atom positions and evaluate it on a set of adenine based ligands. Results Our new approach was validated by generating automatically 3D patterns for the main adenine based ligands, i.e. AMP, ADP and ATP. Out of the 18 detected patterns, only one, the ADP4 pattern, is not associated with well defined structural patterns. Moreover, most of the patterns could be classified as binding site 3D motifs. Literature research revealed that the ADP4 pattern actually corresponds to structural features which show complex evolutionary links between ligases and transferases. Therefore, all of the generated patterns prove to be meaningful. Each pattern was used to query all PDB proteins which bind either purine based or guanine based ligands, in order to evaluate the classification and annotation properties of the pattern. Overall, our 3D patterns matched 31% of proteins with adenine based ligands and 95.5% of them were classified correctly. Conclusion A new metric has been introduced allowing the classification of proteins according to the similarity of atomic environment of binding sites, and a methodology has been developed to automatically produce 3D patterns from that classification. A study of proteins binding adenine based ligands showed that

  12. Vehicle Detection and Classification from High Resolution Satellite Images

    Science.gov (United States)

    Abraham, L.; Sasikumar, M.

    2014-11-01

    In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus the High Resolution Satellite Imagery (HRSI) proved to be a suitable alternative to aerial photogrammetric data to provide a new data source for object detection. Since the traffic rates in developing countries are enormously increasing, vehicle detection from satellite data will be a better choice for automating such systems. In this work, a novel technique for vehicle detection from the images obtained from high resolution sensors is proposed. Though we are using high resolution images, vehicles are seen only as tiny spots, difficult to distinguish from the background. But we are able to obtain a detection rate not less than 0.9. Thereafter we classify the detected vehicles into cars and trucks and find the count of them.

  13. Towards the Development of a Mobile Phonopneumogram: Automatic Breath-Phase Classification Using Smartphones.

    Science.gov (United States)

    Reyes, Bersain A; Reljin, Natasa; Kong, Youngsun; Nam, Yunyoung; Ha, Sangho; Chon, Ki H

    2016-09-01

    Correct labeling of breath phases is useful in the automatic analysis of respiratory sounds, where airflow or volume signals are commonly used as temporal reference. However, such signals are not always available. The development of a smartphone-based respiratory sound analysis system has received increased attention. In this study, we propose an optical approach that takes advantage of a smartphone's camera and provides a chest movement signal useful for classification of the breath phases when simultaneously recording tracheal sounds. Spirometer and smartphone-based signals were acquired from N = 13 healthy volunteers breathing at different frequencies, airflow and volume levels. We found that the smartphone-acquired chest movement signal was highly correlated with reference volume (ρ = 0.960 ± 0.025, mean ± SD). A simple linear regression on the chest signal was used to label the breath phases according to the slope between consecutive onsets. 100% accuracy was found for the classification of the analyzed breath phases. We found that the proposed classification scheme can be used to correctly classify breath phases in more challenging breathing patterns, such as those that include non-breath events like swallowing, talking, and coughing, and alternating or irregular breathing. These results show the feasibility of developing a portable and inexpensive phonopneumogram for the analysis of respiratory sounds based on smartphones.

  14. Automatic classification of background EEG activity in healthy and sick neonates

    Science.gov (United States)

    Löfhede, Johan; Thordstein, Magnus; Löfgren, Nils; Flisberg, Anders; Rosa-Zurera, Manuel; Kjellmer, Ingemar; Lindecrantz, Kaj

    2010-02-01

    The overall aim of our research is to develop methods for a monitoring system to be used at neonatal intensive care units. When monitoring a baby, a range of different types of background activity needs to be considered. In this work, we have developed a scheme for automatic classification of background EEG activity in newborn babies. EEG from six full-term babies who were displaying a burst suppression pattern while suffering from the after-effects of asphyxia during birth was included along with EEG from 20 full-term healthy newborn babies. The signals from the healthy babies were divided into four behavioural states: active awake, quiet awake, active sleep and quiet sleep. By using a number of features extracted from the EEG together with Fisher's linear discriminant classifier we have managed to achieve 100% correct classification when separating burst suppression EEG from all four healthy EEG types and 93% true positive classification when separating quiet sleep from the other types. The other three sleep stages could not be classified. When the pathological burst suppression pattern was detected, the analysis was taken one step further and the signal was segmented into burst and suppression, allowing clinically relevant parameters such as suppression length and burst suppression ratio to be calculated. The segmentation of the burst suppression EEG works well, with a probability of error around 4%.

  15. Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography

    Science.gov (United States)

    Abdolmanafi, Atefeh; Duong, Luc; Dahdah, Nagib; Cheriet, Farida

    2017-01-01

    Kawasaki disease (KD) is an acute childhood disease complicated by coronary artery aneurysms, intima thickening, thrombi, stenosis, lamellar calcifications, and disappearance of the media border. Automatic classification of the coronary artery layers (intima, media, and scar features) is important for analyzing optical coherence tomography (OCT) images recorded in pediatric patients. OCT has been known as an intracoronary imaging modality using near-infrared light which has recently been used to image the inner coronary artery tissues of pediatric patients, providing high spatial resolution (ranging from 10 to 20 μm). This study aims to develop a robust and fully automated tissue classification method by using the convolutional neural networks (CNNs) as feature extractor and comparing the predictions of three state-of-the-art classifiers, CNN, random forest (RF), and support vector machine (SVM). The results show the robustness of CNN as the feature extractor and random forest as the classifier with classification rate up to 96%, especially to characterize the second layer of coronary arteries (media), which is a very thin layer and it is challenging to be recognized and specified from other tissues. PMID:28271012

  16. Automatic Labelling and Selection of Training Samples for High-Resolution Remote Sensing Image Classification over Urban Areas

    Directory of Open Access Journals (Sweden)

    Xin Huang

    2015-12-01

    Full Text Available Supervised classification is the commonly used method for extracting ground information from images. However, for supervised classification, the selection and labelling of training samples is an expensive and time-consuming task. Recently, automatic information indexes have achieved satisfactory results for indicating different land-cover classes, which makes it possible to develop an automatic method for labelling the training samples instead of manual interpretation. In this paper, we propose a method for the automatic selection and labelling of training samples for high-resolution image classification. In this way, the initial candidate training samples can be provided by the information indexes and open-source geographical information system (GIS data, referring to the representative land-cover classes: buildings, roads, soil, water, shadow, and vegetation. Several operations are then applied to refine the initial samples, including removing overlaps, removing borders, and semantic constraints. The proposed sampling method is evaluated on a series of high-resolution remote sensing images over urban areas, and is compared to classification with manually labeled training samples. It is found that the proposed method is able to provide and label a large number of reliable samples, and can achieve satisfactory results for different classifiers. In addition, our experiments show that active learning can further enhance the classification performance, as active learning is used to choose the most informative samples from the automatically labeled samples.

  17. Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images

    Science.gov (United States)

    Ghaffarian, S.; Ghaffarian, S.

    2014-08-01

    This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB color space, which indicates the lightness of the image, and a novel double thresholding technique. Further, the training areas for supervised classification are selected by automatically determining a buffer zone on each building whose shadow is detected by using the shadow shape and the sun illumination direction. Thereafter, by calculating the statistic values of each buffer zone which is collected from the building areas the Improved Parallelepiped Supervised Classification is executed to detect the buildings. Standard deviation thresholding applied to the Parallelepiped classification method to improve its accuracy. Finally, simple morphological operations conducted for releasing the noises and increasing the accuracy of the results. The experiments were performed on set of high resolution Google Earth images. The performance of the proposed approach was assessed by comparing the results of the proposed approach with the reference data by using well-known quality measurements (Precision, Recall and F1-score) to evaluate the pixel-based and object-based performances of the proposed approach. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.4 % and 853 % overall pixel-based and object-based precision performances, respectively.

  18. Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses

    Science.gov (United States)

    Kocevar, Gabriel; Stamile, Claudio; Hannoun, Salem; Cotton, François; Vukusic, Sandra; Durand-Dubief, Françoise; Sappey-Marinier, Dominique

    2016-01-01

    Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS) patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles. Materials and Methods: Sixty-four MS patients [12 Clinical Isolated Syndrome (CIS), 24 Relapsing Remitting (RR), 24 Secondary Progressive (SP), and 17 Primary Progressive (PP)] along with 26 healthy controls (HC) underwent MR examination. T1 and diffusion tensor imaging (DTI) were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects' groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM) combined with Radial Basic Function (RBF) kernel. Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity, and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8, 91.8, 75.6, and 70.6%) were obtained for binary (HC-CIS, CIS-RR, RR-PP) and multi-class (CIS-RR-SP) classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6, 88.9, and 70.7%) were achieved for modularity with previous binary classification tasks. Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients' clinical profiles. PMID:27826224

  19. Automatic classification of endoscopic images for premalignant conditions of the esophagus

    Science.gov (United States)

    Boschetto, Davide; Gambaretto, Gloria; Grisan, Enrico

    2016-03-01

    Barrett's esophagus (BE) is a precancerous complication of gastroesophageal reflux disease in which normal stratified squamous epithelium lining the esophagus is replaced by intestinal metaplastic columnar epithelium. Repeated endoscopies and multiple biopsies are often necessary to establish the presence of intestinal metaplasia. Narrow Band Imaging (NBI) is an imaging technique commonly used with endoscopies that enhances the contrast of vascular pattern on the mucosa. We present a computer-based method for the automatic normal/metaplastic classification of endoscopic NBI images. Superpixel segmentation is used to identify and cluster pixels belonging to uniform regions. From each uniform clustered region of pixels, eight features maximizing differences among normal and metaplastic epithelium are extracted for the classification step. For each superpixel, the three mean intensities of each color channel are firstly selected as features. Three added features are the mean intensities for each superpixel after separately applying to the red-channel image three different morphological filters (top-hat filtering, entropy filtering and range filtering). The last two features require the computation of the Grey-Level Co-Occurrence Matrix (GLCM), and are reflective of the contrast and the homogeneity of each superpixel. The classification step is performed using an ensemble of 50 classification trees, with a 10-fold cross-validation scheme by training the classifier at each step on a random 70% of the images and testing on the remaining 30% of the dataset. Sensitivity and Specificity are respectively of 79.2% and 87.3%, with an overall accuracy of 83.9%.

  20. Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses

    Directory of Open Access Journals (Sweden)

    Gabriel Kocevar

    2016-10-01

    Full Text Available Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles.Materials and methods: Sixty-four MS patients (12 Clinical Isolated Syndrome (CIS, 24 Relapsing Remitting (RR, 24 Secondary Progressive (SP, and 17 Primary Progressive (PP along with 26 healthy controls (HC underwent MR examination. T1 and diffusion tensor imaging (DTI were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects’ groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM combined with Radial Basic Function (RBF kernel.Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8%, 91.8%, 75.6% and 70.6% were obtained for binary (HC-CIS, CIS-RR, RR-PP and multi-class (CIS-RR-SP classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6%, 88.9% and 70.7% were achieved for modularity with previous binary classification tasks.Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients’ clinical profiles.

  1. Electric vehicles performance estimation through a patterns extraction and classification methodology

    Science.gov (United States)

    Barré, Anthony; Suard, Frédéric; Gérard, Mathias; Riu, Delphine

    2015-01-01

    Direct estimation of battery performance is a major challenge as ageing process is a complex phenomenon not directly measurable. In this work a new methodology is provided to estimate global battery performances under real-life electric vehicle use. Such performances are estimated through battery signals patterns extraction. These signals patterns are used to identify physical degradation behavior of batteries. The analysis framework is composed of patterns extraction, clustering algorithms, summarizing data representation in the feature space of cluster distances and classification algorithms. This methodology is then applied on datasets, acquired from batteries used on electric vehicles, without controlled environmental conditions. The classification algorithm accuracy is studied on the obtained real data. The results suggest that battery signals patterns analysis provides an innovative technique for online estimation of the battery performance level. A detection of dysfunctions caused by ageing is also made, only based on battery signals pattern extracted during real vehicle accelerations.

  2. Vehicle Detection and Classification Using Passive Infrared Sensing

    KAUST Repository

    Odat, Enas M.

    2015-10-19

    We propose a new sensing device that can simultaneously monitor urban traffic congestion and another phenomenon of interest (flash floods on the present case). This sensing device is based on the combination of an ultrasonic rangefinder with one or multiple remote temperature sensors. We show an implementation of this device, and illustrate its performance in both traffic flow sensing. Field data shows that the sensor can detect vehicles with a 99% accuracy, in addition to estimating their speed and classifying them in function of their length. The same sensor can also monitor urban water levels with an accuracy of less than 2 cm.

  3. A case-comparison study of automatic document classification utilizing both serial and parallel approaches

    Science.gov (United States)

    Wilges, B.; Bastos, R. C.; Mateus, G. P.; Dantas, M. A. R.

    2014-10-01

    A well-known problem faced by any organization nowadays is the high volume of data that is available and the required process to transform this volume into differential information. In this study, a case-comparison study of automatic document classification (ADC) approach is presented, utilizing both serial and parallel paradigms. The serial approach was implemented by adopting the RapidMiner software tool, which is recognized as the worldleading open-source system for data mining. On the other hand, considering the MapReduce programming model, the Hadoop software environment has been used. The main goal of this case-comparison study is to exploit differences between these two paradigms, especially when large volumes of data such as Web text documents are utilized to build a category database. In the literature, many studies point out that distributed processing in unstructured documents have been yielding efficient results in utilizing Hadoop. Results from our research indicate a threshold to such efficiency.

  4. Automatic detection of photoresist residual layer in lithography using a neural classification approach

    KAUST Repository

    Gereige, Issam

    2012-09-01

    Photolithography is a fundamental process in the semiconductor industry and it is considered as the key element towards extreme nanoscale integration. In this technique, a polymer photo sensitive mask with the desired patterns is created on the substrate to be etched. Roughly speaking, the areas to be etched are not covered with polymer. Thus, no residual layer should remain on these areas in order to insure an optimal transfer of the patterns on the substrate. In this paper, we propose a nondestructive method based on a classification approach achieved by artificial neural network for automatic residual layer detection from an ellipsometric signature. Only the case of regular defect, i.e. homogenous residual layer, will be considered. The limitation of the method will be discussed. Then, an experimental result on a 400 nm period grating manufactured with nanoimprint lithography is analyzed with our method. © 2012 Elsevier B.V. All rights reserved.

  5. Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy

    Science.gov (United States)

    Xu, Chao; Zhou, Dongxiang; Zhai, Yongping; Liu, Yunhui

    2015-12-01

    This paper realizes the automatic segmentation and classification of Mycobacterium tuberculosis with conventional light microscopy. First, the candidate bacillus objects are segmented by the marker-based watershed transform. The markers are obtained by an adaptive threshold segmentation based on the adaptive scale Gaussian filter. The scale of the Gaussian filter is determined according to the color model of the bacillus objects. Then the candidate objects are extracted integrally after region merging and contaminations elimination. Second, the shape features of the bacillus objects are characterized by the Hu moments, compactness, eccentricity, and roughness, which are used to classify the single, touching and non-bacillus objects. We evaluated the logistic regression, random forest, and intersection kernel support vector machines classifiers in classifying the bacillus objects respectively. Experimental results demonstrate that the proposed method yields to high robustness and accuracy. The logistic regression classifier performs best with an accuracy of 91.68%.

  6. Automatic classification of a taxon-rich community recorded in the wild.

    Science.gov (United States)

    Potamitis, Ilyas

    2014-01-01

    There is a rich literature on automatic species identification of a specific target taxon as regards various vocalizing animals. Research usually is restricted to specific species--in most cases a single one. It is only very recently that the number of monitored species has started to increase for certain habitats involving birds. Automatic acoustic monitoring has not yet been proven to be generic enough to scale to other taxa and habitats than the ones described in the original research. Although attracting much attention, the acoustic monitoring procedure is neither well established yet nor universally adopted as a biodiversity monitoring tool. Recently, the multi-instance multi-label framework on bird vocalizations has been introduced to face the obstacle of simultaneously vocalizing birds of different species. We build on this framework to integrate novel, image-based heterogeneous features designed to capture different aspects of the spectrum. We applied our approach to a taxon-rich habitat that included 78 birds, 8 insect species and 1 amphibian. This dataset constituted the Multi-label Bird Species Classification Challenge-NIPS 2013 where the proposed approach achieved an average accuracy of 91.25% on unseen data.

  7. Automatic Detection of Cervical Cancer Cells by a Two-Level Cascade Classification System

    Directory of Open Access Journals (Sweden)

    Jie Su

    2016-01-01

    Full Text Available We proposed a method for automatic detection of cervical cancer cells in images captured from thin liquid based cytology slides. We selected 20,000 cells in images derived from 120 different thin liquid based cytology slides, which include 5000 epithelial cells (normal 2500, abnormal 2500, lymphoid cells, neutrophils, and junk cells. We first proposed 28 features, including 20 morphologic features and 8 texture features, based on the characteristics of each cell type. We then used a two-level cascade integration system of two classifiers to classify the cervical cells into normal and abnormal epithelial cells. The results showed that the recognition rates for abnormal cervical epithelial cells were 92.7% and 93.2%, respectively, when C4.5 classifier or LR (LR: logical regression classifier was used individually; while the recognition rate was significantly higher (95.642% when our two-level cascade integrated classifier system was used. The false negative rate and false positive rate (both 1.44% of the proposed automatic two-level cascade classification system are also much lower than those of traditional Pap smear review.

  8. Automatic identification and classification of muscle spasms in long-term EMG recordings.

    Science.gov (United States)

    Winslow, Jeffrey; Martinez, Adriana; Thomas, Christine K

    2015-03-01

    Spinal cord injured (SCI) individuals may be afflicted by spasticity, a condition in which involuntary muscle spasms are common. EMG recordings can be analyzed to quantify this symptom of spasticity but manual identification and classification of spasms are time consuming. Here, an algorithm was created to find and classify spasm events automatically within 24-h recordings of EMG. The algorithm used expert rules and time-frequency techniques to classify spasm events as tonic, unit, or clonus spasms. A companion graphical user interface (GUI) program was also built to verify and correct the results of the automatic algorithm or manually defined events. Eight channel EMG recordings were made from seven different SCI subjects. The algorithm was able to correctly identify an average (±SD) of 94.5 ± 3.6% spasm events and correctly classify 91.6 ± 1.9% of spasm events, with an accuracy of 61.7 ± 16.2%. The accuracy improved to 85.5 ± 5.9% and the false positive rate decreased to 7.1 ± 7.3%, respectively, if noise events between spasms were removed. On average, the algorithm was more than 11 times faster than manual analysis. Use of both the algorithm and the GUI program provide a powerful tool for characterizing muscle spasms in 24-h EMG recordings, information which is important for clinical management of spasticity.

  9. Neural Network Control-Based Drive Design of Servomotor and Its Application to Automatic Guided Vehicle

    Directory of Open Access Journals (Sweden)

    Ming-Shyan Wang

    2015-01-01

    Full Text Available An automatic guided vehicle (AGV is extensively used for productions in a flexible manufacture system with high efficiency and high flexibility. A servomotor-based AGV is designed and implemented in this paper. In order to steer the AGV to go along a predefined path with corner or arc, the conventional proportional-integral-derivative (PID control is used in the system. However, it is difficult to tune PID gains at various conditions. As a result, the neural network (NN control is considered to assist the PID control for gain tuning. The experimental results are first provided to verify the correctness of the neural network plus PID control for 400 W-motor control system. Secondly, the AGV includes two sets of the designed motor systems and CAN BUS transmission so that it can move along the straight line and curve paths shown in the taped videos.

  10. Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE.

    Science.gov (United States)

    Relan, D; MacGillivray, T; Ballerini, L; Trucco, E

    2014-01-01

    It is important to classify retinal blood vessels into arterioles and venules for computerised analysis of the vasculature and to aid discovery of disease biomarkers. For instance, zone B is the standardised region of a retinal image utilised for the measurement of the arteriole to venule width ratio (AVR), a parameter indicative of microvascular health and systemic disease. We introduce a Least Square-Support Vector Machine (LS-SVM) classifier for the first time (to the best of our knowledge) to label automatically arterioles and venules. We use only 4 image features and consider vessels inside zone B (802 vessels from 70 fundus camera images) and in an extended zone (1,207 vessels, 70 fundus camera images). We achieve an accuracy of 94.88% and 93.96% in zone B and the extended zone, respectively, with a training set of 10 images and a testing set of 60 images. With a smaller training set of only 5 images and the same testing set we achieve an accuracy of 94.16% and 93.95%, respectively. This experiment was repeated five times by randomly choosing 10 and 5 images for the training set. Mean classification accuracy are close to the above mentioned result. We conclude that the performance of our system is very promising and outperforms most recently reported systems. Our approach requires smaller training data sets compared to others but still results in a similar or higher classification rate.

  11. Support Vector Machine Model for Automatic Detection and Classification of Seismic Events

    Science.gov (United States)

    Barros, Vesna; Barros, Lucas

    2016-04-01

    The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support

  12. Classification Scheme for Random Longitudinal Road Unevenness Considering Road Waviness and Vehicle Response

    Directory of Open Access Journals (Sweden)

    Oldřich Kropáč

    2009-01-01

    Full Text Available A novel approach to the road unevenness classification based on the power spectral density with consideration of vehicle vibration response and broad interval of road waviness (road elevation PSD slope is presented. This approach enables transformation of two basic parameters of road profile elevation PSD (unevenness index, C, and waviness, w into a single-number indicator Cw when using a correction factor Kw accounting for w. For the road classification proposal two planar vehicle models (passenger car and truck, ten responses (reflecting ride comfort, dynamic load of road and cargo, ride safety and three different vehicle velocities have been considered. The minimum of ten estimated vibration response ranges sum for a broad waviness interval typical for real road sections (w = 1.5 to 3.5 has been used for the correction factor estimation. The introduced unevenness indicator, Cw, reflects the vehicle vibration response and seems to be a suitable alternative to the other currently used single-number indicators or as an extension of the road classification according to the ISO 8608: 1995, which is based on constant waviness value, w = 2.

  13. FOUR-PARAMETER AUTOMATIC TRANSMISSION TECHNOLOGY FOR CONSTRUCTION VEHICLE BASED ON ELMAN RECURSIVE NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hongyan; ZHAO Dingxuan; TANG Xinxing; Ding Chunfeng

    2008-01-01

    From the viewpoint of energy saving and improving transmission efficiency, the ZL50E wheel loader is taken as the study object. And the system model is analyzed based on the transmission system of the construction vehicle. A new four-parameter shift schedule is presented, which can keep the torque converter working in the high efficiency area. The control algorithm based on the Elman recursive neural network is applied, and four-parameter control system is developed which is based on industrial computer. The system is used to collect data accurately and control 4D180 power-shift gearbox of ZL50E wheel loader shift timely. An experiment is done on automatic transmission test-bed, and the result indicates that the control system could reliably and safely work and improve the efficiency of hydraulic torque converter. Four-parameter shift strategy that takes into account the power consuming of the working pump has important operating significance and reflects the actual working status of construction vehicle.

  14. A Novel OD Estimation Method Based on Automatic Vehicle Identification Data

    Science.gov (United States)

    Sun, Jian; Feng, Yu

    With the development and application of Automatic Vehicle Identification (AVI) technologies, a novel high resolution OD estimation method was proposed based on AVI detector information. 4 detected categories (Ox + Dy, Ox/Dy + Path(s), Ox/Dy, Path(s)) were divided at the first step. Then the initial OD matrix was updated using the Ox + Dy sample information considering the AVI detector errors. Referenced by particle filter, the link-path relationship data were revised using the last 3 categories information based on Bayesian inference and the possible trajectory and OD were determined using Monte Carlo random process at last. Finally, according to the current application of video detector in Shanghai, the North-South expressway was selected as the testbed which including 17 OD pairs and 9 AVI detectors. The results show that the calculated average relative error is 12.09% under the constraints that the simulation error is under 15% and the detector error is about 10%. It also shows that this method is highly efficient and can fully using the partial vehicle trajectory which can be satisfied with the dynamic traffic management application in reality.

  15. Classification Of Road Accidents From The Perspective Of Vehicle Safety Systems

    Directory of Open Access Journals (Sweden)

    Jirovský Václav

    2015-11-01

    Full Text Available Modern road accident investigation and database structures are focused on accident analysis and classification from the point of view of the accident itself. The presented article offers a new approach, which will describe the accident from the point of view of integrated safety vehicle systems. Seven main categories have been defined to specify the level of importance of automated system intervention. One of the proposed categories is a new approach to defining the collision probability of an ego-vehicle with another object. This approach focuses on determining a 2-D reaction space, which describes all possible positions of the vehicle or other moving object in the specified amount of time in the future. This is to be used for defining the probability of the vehicles interacting - when the intersection of two reaction spaces exists, an action has to be taken on the side of ego-vehicle. The currently used 1-D quantity of TTC (time-to-collision can be superseded by the new reaction space variable. Such new quantity, whose basic idea is described in the article, enables the option of counting not only with necessary braking time, but mitigation by changing direction is then easily feasible. Finally, transparent classification measures of a probable accident are proposed. Their application is highly effective not only during basic accident comparison, but also for an on-board safety system.

  16. Study on dynamic optimization of the double railway suspended vehicle for automatic transportation in the welding shop

    Institute of Scientific and Technical Information of China (English)

    Huang Dawei; Gao Xiuhua; Xing Hao; Liu Hongxue; Han Yanhe

    2007-01-01

    The 2DOF dynamic equations of the double railway suspended vehicle for automatic transportation in the welding shop are established. The sensitivities are analyzed. The parameter design is researched in ADAMS in terms of the inner railway radius, wheelbase, gauge, girder length of the double railway suspended vehicle for automatic transportation in the welding product line. The mutual-restriction among the design variables is discussed and the selective ranges of the variables are confirmed. The result shows that the stability of the double railway suspended vehicle for automatic transportation in the welding product line depends on parameters of the inner railway radius, wheelbase, gauge, girder length. The optimal results of the optimal objective and design variables have research significance for the virtual prototype of the double suspension railway automation vehicle. The optimal results are input into the simulation model iteratively and the simulation results are fed back to the physical prototype. The veracity and reliability of performance forecast are improved so that the manufacture cost of the double suspension railway automation vehicle is reduced significantly.

  17. Automatic classification of small bowel mucosa alterations in celiac disease for confocal laser endomicroscopy

    Science.gov (United States)

    Boschetto, Davide; Di Claudio, Gianluca; Mirzaei, Hadis; Leong, Rupert; Grisan, Enrico

    2016-03-01

    Celiac disease (CD) is an immune-mediated enteropathy triggered by exposure to gluten and similar proteins, affecting genetically susceptible persons, increasing their risk of different complications. Small bowels mucosa damage due to CD involves various degrees of endoscopically relevant lesions, which are not easily recognized: their overall sensitivity and positive predictive values are poor even when zoom-endoscopy is used. Confocal Laser Endomicroscopy (CLE) allows skilled and trained experts to qualitative evaluate mucosa alteration such as a decrease in goblet cells density, presence of villous atrophy or crypt hypertrophy. We present a method for automatically classifying CLE images into three different classes: normal regions, villous atrophy and crypt hypertrophy. This classification is performed after a features selection process, in which four features are extracted from each image, through the application of homomorphic filtering and border identification through Canny and Sobel operators. Three different classifiers have been tested on a dataset of 67 different images labeled by experts in three classes (normal, VA and CH): linear approach, Naive-Bayes quadratic approach and a standard quadratic analysis, all validated with a ten-fold cross validation. Linear classification achieves 82.09% accuracy (class accuracies: 90.32% for normal villi, 82.35% for VA and 68.42% for CH, sensitivity: 0.68, specificity 1.00), Naive Bayes analysis returns 83.58% accuracy (90.32% for normal villi, 70.59% for VA and 84.21% for CH, sensitivity: 0.84 specificity: 0.92), while the quadratic analysis achieves a final accuracy of 94.03% (96.77% accuracy for normal villi, 94.12% for VA and 89.47% for CH, sensitivity: 0.89, specificity: 0.98).

  18. Material classification and automatic content enrichment of images using supervised learning and knowledge bases

    Science.gov (United States)

    Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

    2011-02-01

    In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

  19. Progress toward automatic classification of human brown adipose tissue using biomedical imaging

    Science.gov (United States)

    Gifford, Aliya; Towse, Theodore F.; Walker, Ronald C.; Avison, Malcom J.; Welch, E. B.

    2015-03-01

    Brown adipose tissue (BAT) is a small but significant tissue, which may play an important role in obesity and the pathogenesis of metabolic syndrome. Interest in studying BAT in adult humans is increasing, but in order to quantify BAT volume in a single measurement or to detect changes in BAT over the time course of a longitudinal experiment, BAT needs to first be reliably differentiated from surrounding tissue. Although the uptake of the radiotracer 18F-Fluorodeoxyglucose (18F-FDG) in adipose tissue on positron emission tomography (PET) scans following cold exposure is accepted as an indication of BAT, it is not a definitive indicator, and to date there exists no standardized method for segmenting BAT. Consequently, there is a strong need for robust automatic classification of BAT based on properties measured with biomedical imaging. In this study we begin the process of developing an automated segmentation method based on properties obtained from fat-water MRI and PET-CT scans acquired on ten healthy adult subjects.

  20. Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features.

    Science.gov (United States)

    Magdy, Eman; Zayed, Nourhan; Fakhr, Mahmoud

    2015-01-01

    Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM) method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR) for classification step. Finally, K-nearest neighbour (KNN), support vector machine (SVM), naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.

  1. Automatic Classification of the Vestibulo-Ocular Reflex Nystagmus: Integration of Data Clustering and System Identification.

    Science.gov (United States)

    Ranjbaran, Mina; Smith, Heather L H; Galiana, Henrietta L

    2016-04-01

    The vestibulo-ocular reflex (VOR) plays an important role in our daily activities by enabling us to fixate on objects during head movements. Modeling and identification of the VOR improves our insight into the system behavior and improves diagnosis of various disorders. However, the switching nature of eye movements (nystagmus), including the VOR, makes dynamic analysis challenging. The first step in such analysis is to segment data into its subsystem responses (here slow and fast segment intervals). Misclassification of segments results in biased analysis of the system of interest. Here, we develop a novel three-step algorithm to classify the VOR data into slow and fast intervals automatically. The proposed algorithm is initialized using a K-means clustering method. The initial classification is then refined using system identification approaches and prediction error statistics. The performance of the algorithm is evaluated on simulated and experimental data. It is shown that the new algorithm performance is much improved over the previous methods, in terms of higher specificity.

  2. Groupwise conditional random forests for automatic shape classification and contour quality assessment in radiotherapy planning.

    Science.gov (United States)

    McIntosh, Chris; Svistoun, Igor; Purdie, Thomas G

    2013-06-01

    Radiation therapy is used to treat cancer patients around the world. High quality treatment plans maximally radiate the targets while minimally radiating healthy organs at risk. In order to judge plan quality and safety, segmentations of the targets and organs at risk are created, and the amount of radiation that will be delivered to each structure is estimated prior to treatment. If the targets or organs at risk are mislabelled, or the segmentations are of poor quality, the safety of the radiation doses will be erroneously reviewed and an unsafe plan could proceed. We propose a technique to automatically label groups of segmentations of different structures from a radiation therapy plan for the joint purposes of providing quality assurance and data mining. Given one or more segmentations and an associated image we seek to assign medically meaningful labels to each segmentation and report the confidence of that label. Our method uses random forests to learn joint distributions over the training features, and then exploits a set of learned potential group configurations to build a conditional random field (CRF) that ensures the assignment of labels is consistent across the group of segmentations. The CRF is then solved via a constrained assignment problem. We validate our method on 1574 plans, consisting of 17[Formula: see text] 579 segmentations, demonstrating an overall classification accuracy of 91.58%. Our results also demonstrate the stability of RF with respect to tree depth and the number of splitting variables in large data sets.

  3. Automatic type classification and speaker identification of african elephant (Loxodonta africana) vocalizations

    Science.gov (United States)

    Clemins, Patrick J.; Johnson, Michael T.

    2003-04-01

    This paper presents a system for automatically classifying African elephant vocalizations based on systems used for human speech recognition and speaker identification. The experiments are performed on vocalizations collected from captive elephants in a naturalistic environment. Features used for classification include Mel-Frequency Cepstral Coefficients (MFCCs) and log energy which are the most common features used in human speech processing. Since African elephants use lower frequencies than humans in their vocalizations, the MFCCs are computed using a shifted Mel-Frequency filter bank to emphasize the infrasound range of the frequency spectrum. In addition to these features, the use of less traditional features such as those based on fundamental frequency and the phase of the frequency spectrum is also considered. A Hidden Markov Model with Gaussian mixture state probabilities is used to model each type of vocalization. Vocalizations are classified based on type, speaker and estrous cycle. Experiments on continuous call type recognition, which can classify multiple vocalizations in the same utterance, are also performed. The long-term goal of this research is to develop a universal analysis framework and robust feature set for animal vocalizations that can be applied to many species.

  4. Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing.

    Science.gov (United States)

    Rahim, Sarni Suhaila; Palade, Vasile; Shuttleworth, James; Jayne, Chrisina

    2016-12-01

    Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

  5. Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.

    Science.gov (United States)

    Shi, Qi; Abdel-Aty, Mohamed; Yu, Rongjie

    2016-03-01

    In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature.

  6. New Navigation System for Automatic Guided Vehicles Using an Ultrasonic Sensor Array

    Science.gov (United States)

    Tabata, Katsuhiko; Nishida, Yoshifumi; Iida, Yoshihiro; Iwai, Toshiaki

    We propose a new navigation system for Automatic Guided Vehicles (AGV) used as a carrier in the factory. The guided marker of the navigation system is composed of ultrasonic transducers instead of the traditional markers such as electromagnetic tape, light reflective tape and so on. The proposed system is available to be used not only indoors but also outdoors and adaptable to a temporary route. The ultrasonic sensor is generically susceptible to noise, so that we make the following propositions. First, a phased array of the ultrasonic sensors is employed in searching a land marker to improve the signal-to-noise ratio. Second, the specific ID with 7bits is assigned as the land marker to avoid the system errors ascribable to an ultrasonic interference. In addition, the proposed system is quite compact in virtue of the embedded technology of a microcomputer and Field Programmable Gate Array (FPGA). This paper reports the development of the proto-type system of navigation system and confirmation of its fundamental performances.

  7. Automatic Parameter Tuning for the Morpheus Vehicle Using Particle Swarm Optimization

    Science.gov (United States)

    Birge, B.

    2013-01-01

    A high fidelity simulation using a PC based Trick framework has been developed for Johnson Space Center's Morpheus test bed flight vehicle. There is an iterative development loop of refining and testing the hardware, refining the software, comparing the software simulation to hardware performance and adjusting either or both the hardware and the simulation to extract the best performance from the hardware as well as the most realistic representation of the hardware from the software. A Particle Swarm Optimization (PSO) based technique has been developed that increases speed and accuracy of the iterative development cycle. Parameters in software can be automatically tuned to make the simulation match real world subsystem data from test flights. Special considerations for scale, linearity, discontinuities, can be all but ignored with this technique, allowing fast turnaround both for simulation tune up to match hardware changes as well as during the test and validation phase to help identify hardware issues. Software models with insufficient control authority to match hardware test data can be immediately identified and using this technique requires very little to no specialized knowledge of optimization, freeing model developers to concentrate on spacecraft engineering. Integration of the PSO into the Morpheus development cycle will be discussed as well as a case study highlighting the tool's effectiveness.

  8. Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images.

    Science.gov (United States)

    Haleem, Muhammad Salman; Han, Liangxiu; Hemert, Jano van; Fleming, Alan; Pasquale, Louis R; Silva, Paolo S; Song, Brian J; Aiello, Lloyd Paul

    2016-06-01

    Glaucoma is one of the leading causes of blindness worldwide. There is no cure for glaucoma but detection at its earliest stage and subsequent treatment can aid patients to prevent blindness. Currently, optic disc and retinal imaging facilitates glaucoma detection but this method requires manual post-imaging modifications that are time-consuming and subjective to image assessment by human observers. Therefore, it is necessary to automate this process. In this work, we have first proposed a novel computer aided approach for automatic glaucoma detection based on Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from all the existing methods, our approach can extract both geometric (e.g. morphometric properties) and non-geometric based properties (e.g. pixel appearance/intensity values, texture) from images and significantly increase the classification performance. Our proposed approach consists of three new major contributions including automatic localisation of optic disc, automatic segmentation of disc, and classification between normal and glaucoma based on geometric and non-geometric properties of different regions of an image. We have compared our method with existing approaches and tested it on both fundus and Scanning laser ophthalmoscopy (SLO) images. The experimental results show that our proposed approach outperforms the state-of-the-art approaches using either geometric or non-geometric properties. The overall glaucoma classification accuracy for fundus images is 94.4% and accuracy of detection of suspicion of glaucoma in SLO images is 93.9 %.

  9. Piloted Simulation Evaluation of a Model-Predictive Automatic Recovery System to Prevent Vehicle Loss of Control on Approach

    Science.gov (United States)

    Litt, Jonathan S.; Liu, Yuan; Sowers, Thomas S.; Owen, A. Karl; Guo, Ten-Huei

    2014-01-01

    This paper describes a model-predictive automatic recovery system for aircraft on the verge of a loss-of-control situation. The system determines when it must intervene to prevent an imminent accident, resulting from a poor approach. It estimates the altitude loss that would result from a go-around maneuver at the current flight condition. If the loss is projected to violate a minimum altitude threshold, the maneuver is automatically triggered. The system deactivates to allow landing once several criteria are met. Piloted flight simulator evaluation showed the system to provide effective envelope protection during extremely unsafe landing attempts. The results demonstrate how flight and propulsion control can be integrated to recover control of the vehicle automatically and prevent a potential catastrophe.

  10. Automatic approach to solve the morphological galaxy classification problem using the sparse representation technique and dictionary learning

    Science.gov (United States)

    Diaz-Hernandez, R.; Ortiz-Esquivel, A.; Peregrina-Barreto, H.; Altamirano-Robles, L.; Gonzalez-Bernal, J.

    2016-06-01

    The observation of celestial objects in the sky is a practice that helps astronomers to understand the way in which the Universe is structured. However, due to the large number of observed objects with modern telescopes, the analysis of these by hand is a difficult task. An important part in galaxy research is the morphological structure classification based on the Hubble sequence. In this research, we present an approach to solve the morphological galaxy classification problem in an automatic way by using the Sparse Representation technique and dictionary learning with K-SVD. For the tests in this work, we use a database of galaxies extracted from the Principal Galaxy Catalog (PGC) and the APM Equatorial Catalogue of Galaxies obtaining a total of 2403 useful galaxies. In order to represent each galaxy frame, we propose to calculate a set of 20 features such as Hu's invariant moments, galaxy nucleus eccentricity, gabor galaxy ratio and some other features commonly used in galaxy classification. A stage of feature relevance analysis was performed using Relief-f in order to determine which are the best parameters for the classification tests using 2, 3, 4, 5, 6 and 7 galaxy classes making signal vectors of different length values with the most important features. For the classification task, we use a 20-random cross-validation technique to evaluate classification accuracy with all signal sets achieving a score of 82.27 % for 2 galaxy classes and up to 44.27 % for 7 galaxy classes.

  11. THE APPLICATION OF RTK-GPS AND STEER-BY-WIRE TECHNOLOGY TO THE AUTOMATIC DRIVING OF VEHICLES AND AN EVALUATION OF DRIVER BEHAVIOR

    Directory of Open Access Journals (Sweden)

    Manabu OMAE

    2006-01-01

    Full Text Available Automatic vehicle driving has long been the subject of research efforts designed to improve the safety and efficiency of automobile transportation. In recent years, increasingly sophisticated sensors and automobiles have brought automatic driving systems closer to reality. In this paper we describe an attempt to apply real-time kinematic GPS (RTK-GPS, a highly precise positioning system, and steer-by-wire body technology, which has advanced greatly in recent years, to automatic driving. In addition, we also describe the results of research into human factors related to automatic driving, which will become more and more important as automatic driving is put to practical use.

  12. Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

    Science.gov (United States)

    Oh, Sang-Il; Kang, Hang-Bong

    2017-01-22

    To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

  13. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

    NARCIS (Netherlands)

    Ciompi, Francesco; de Hoop, Bartjan; van Riel, Sarah J; Chung, Kaman; Scholten, Ernst Th; Oudkerk, Matthijs; de Jong, Pim A; Prokop, Mathias; van Ginneken, Bram

    2015-01-01

    In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is

  14. Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.

    Directory of Open Access Journals (Sweden)

    Jingfang Liu

    2014-11-01

    Full Text Available User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews.We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure.In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier.By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.

  15. Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals

    Directory of Open Access Journals (Sweden)

    Tangermann Michael

    2011-08-01

    Full Text Available Abstract Background Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI or for Mental State Monitoring. While hand-optimized selection of source components derived from Independent Component Analysis (ICA to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. Methods We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM. The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT study, n = 12 that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP paradigm, n = 18; motor imagery BCI paradigm, n = 80 that used data with different channel setups and from new subjects. Results Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (10% Mean Squared Error (MSE on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. Conclusions We propose a universal and efficient classifier of

  16. Vision system for driving control using camera mounted on an automatic vehicle. Jiritsu sokosha no camera ni yoru shikaku system

    Energy Technology Data Exchange (ETDEWEB)

    Nishimori, K.; Ishihara, K.; Tokutaka, H.; Kishida, S.; Fujimura, K. (Tottori University, Tottori (Japan). Faculty of Engineering); Okada, M. (Mazda Corp., Hiroshima (Japan)); Hirakawa, S. (Fujitsu Corp., Tokyo (Japan))

    1993-11-30

    The present report explains a vision system, in which a CCD camera, used for the model vehicle automatically traveling by fuzzy control, is used as a vision sensor. The vision system is composed of input image processing module, situation recognition/analysis module to three-dimensionally recover the road, route-selecting navigation module to avoid the obstacle and vehicle control module. The CCD camera is used as a vision sensor to make the model vehicle automatically travel by fuzzy control with the above modules. In the present research, the traveling is controlled by treating the position and configuration of objective in image as a fuzzy inferential variable. Based on the above method, the traveling simulation gave the following knowledge: even with the image information only from the vision system, the application of fuzzy control facilitates the traveling. If the objective is clearly known, the control is judged able to be made even from vague image which does not necessitate the exact locative information. 4 refs., 11 figs.

  17. Automatic classification of the sub-techniques (gears) used in cross-country ski skating employing a mobile phone.

    Science.gov (United States)

    Stöggl, Thomas; Holst, Anders; Jonasson, Arndt; Andersson, Erik; Wunsch, Tobias; Norström, Christer; Holmberg, Hans-Christer

    2014-10-31

    The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

  18. Automatic Classification of the Sub-Techniques (Gears Used in Cross-Country Ski Skating Employing a Mobile Phone

    Directory of Open Access Journals (Sweden)

    Thomas Stöggl

    2014-10-01

    Full Text Available The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC ski-skating gears (G using Smartphone accelerometer data. Eleven XC skiers (seven men, four women with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01 with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

  19. Back-and-Forth Methodology for Objective Voice Quality Assessment: From/to Expert Knowledge to/from Automatic Classification of Dysphonia

    Science.gov (United States)

    Fredouille, Corinne; Pouchoulin, Gilles; Ghio, Alain; Revis, Joana; Bonastre, Jean-François; Giovanni, Antoine

    2009-12-01

    This paper addresses voice disorder assessment. It proposes an original back-and-forth methodology involving an automatic classification system as well as knowledge of the human experts (machine learning experts, phoneticians, and pathologists). The goal of this methodology is to bring a better understanding of acoustic phenomena related to dysphonia. The automatic system was validated on a dysphonic corpus (80 female voices), rated according to the GRBAS perceptual scale by an expert jury. Firstly, focused on the frequency domain, the classification system showed the interest of 0-3000 Hz frequency band for the classification task based on the GRBAS scale. Later, an automatic phonemic analysis underlined the significance of consonants and more surprisingly of unvoiced consonants for the same classification task. Submitted to the human experts, these observations led to a manual analysis of unvoiced plosives, which highlighted a lengthening of VOT according to the dysphonia severity validated by a preliminary statistical analysis.

  20. Investigation of Matlab® as platform in navigation and control of an Automatic Guided Vehicle utilising an omnivision sensor.

    Science.gov (United States)

    Kotze, Ben; Jordaan, Gerrit

    2014-08-25

    Automatic Guided Vehicles (AGVs) are navigated utilising multiple types of sensors for detecting the environment. In this investigation such sensors are replaced and/or minimized by the use of a single omnidirectional camera picture stream. An area of interest is extracted, and by using image processing the vehicle is navigated on a set path. Reconfigurability is added to the route layout by signs incorporated in the navigation process. The result is the possible manipulation of a number of AGVs, each on its own designated colour-signed path. This route is reconfigurable by the operator with no programming alteration or intervention. A low resolution camera and a Matlab® software development platform are utilised. The use of Matlab® lends itself to speedy evaluation and implementation of image processing options on the AGV, but its functioning in such an environment needs to be assessed.

  1. Investigation of Matlab® as Platform in Navigation and Control of an Automatic Guided Vehicle Utilising an Omnivision Sensor

    Directory of Open Access Journals (Sweden)

    Ben Kotze

    2014-08-01

    Full Text Available Automatic Guided Vehicles (AGVs are navigated utilising multiple types of sensors for detecting the environment. In this investigation such sensors are replaced and/or minimized by the use of a single omnidirectional camera picture stream. An area of interest is extracted, and by using image processing the vehicle is navigated on a set path. Reconfigurability is added to the route layout by signs incorporated in the navigation process. The result is the possible manipulation of a number of AGVs, each on its own designated colour-signed path. This route is reconfigurable by the operator with no programming alteration or intervention. A low resolution camera and a Matlab® software development platform are utilised. The use of Matlab® lends itself to speedy evaluation and implementation of image processing options on the AGV, but its functioning in such an environment needs to be assessed.

  2. MaNIAC-UAV - a methodology for automatic pavement defects detection using images obtained by Unmanned Aerial Vehicles

    Science.gov (United States)

    Henrique Castelo Branco, Luiz; César Lima Segantine, Paulo

    2015-09-01

    Intelligent Transportation Systems - ITS is a set of integrated technologies (Remote Sensing, Image Processing, Communications Systems and others) that aim to offer services and advanced traffic management for the several transportation modes (road, air and rail). Collect data on the characteristics and conditions of the road surface and keep them update is an important and difficult task that needs to be currently managed in order to reduce accidents and vehicle maintenance costs. Nowadays several roads and highways are paved, but usually there is insufficient updated data about current condition and status. There are different types of pavement defects on the roads and to keep them in good condition they should be constantly monitored and maintained according to pavement management strategy. This paper presents a methodology to obtain, automatically, information about the conditions of the highway asphalt pavement. Data collection was done through remote sensing using an UAV (Unmanned Aerial Vehicle) and the image processing and pattern recognition techniques through Geographic Information System.

  3. Comparative analysis of different implementations of a parallel algorithm for automatic target detection and classification of hyperspectral images

    Science.gov (United States)

    Paz, Abel; Plaza, Antonio; Plaza, Javier

    2009-08-01

    Automatic target detection in hyperspectral images is a task that has attracted a lot of attention recently. In the last few years, several algoritms have been developed for this purpose, including the well-known RX algorithm for anomaly detection, or the automatic target detection and classification algorithm (ATDCA), which uses an orthogonal subspace projection (OSP) approach to extract a set of spectrally distinct targets automatically from the input hyperspectral data. Depending on the complexity and dimensionality of the analyzed image scene, the target/anomaly detection process may be computationally very expensive, a fact that limits the possibility of utilizing this process in time-critical applications. In this paper, we develop computationally efficient parallel versions of both the RX and ATDCA algorithms for near real-time exploitation of these algorithms. In the case of ATGP, we use several distance metrics in addition to the OSP approach. The parallel versions are quantitatively compared in terms of target detection accuracy, using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center in New York, five days after the terrorist attack of September 11th, 2001, and also in terms of parallel performance, using a massively Beowulf cluster available at NASA's Goddard Space Flight Center in Maryland.

  4. 自动文本分类中的智能处理技术%The Intelligent Treatment Technology in Automatic Text Classification

    Institute of Scientific and Technical Information of China (English)

    孙晋文; 肖建国

    2003-01-01

    Text automatic classification has become an important technology along with development of Internet and the increment of information ,because of the complexity of text ,it is very difficult to achieve better effect only depend-ing on the different classification methods,it need to use multi- ways to resolve. Based on the retrospection of xtclassification,this paper gives a comprehensive ways to enhance the performance of text classification,which will pro-vide good instruction to the application of text classification.

  5. Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier

    Science.gov (United States)

    Provost, F.; Hibert, C.; Malet, J.-P.

    2017-01-01

    The deformation of slow-moving landslides developed in clays induces endogenous seismicity of mostly low-magnitude events (MLAlps) for the detection of four types of seismic sources. The automatic algorithm retrieves 93% of sensitivity in comparison to a manually interpreted catalog considered as reference.

  6. ASTErIsM - Application of topometric clustering algorithms in automatic galaxy detection and classification

    CERN Document Server

    Tramacere, A; Dubath, P; Kneib, J -P; Courbin, F

    2016-01-01

    We present a study on galaxy detection and shape classification using topometric clustering algorithms. We first use the DBSCAN algorithm to extract, from CCD frames, groups of adjacent pixels with significant fluxes and we then apply the DENCLUE algorithm to separate the contributions of overlapping sources. The DENCLUE separation is based on the localization of pattern of local maxima, through an iterative algorithm which associates each pixel to the closest local maximum. Our main classification goal is to take apart elliptical from spiral galaxies. We introduce new sets of features derived from the computation of geometrical invariant moments of the pixel group shape and from the statistics of the spatial distribution of the DENCLUE local maxima patterns. Ellipticals are characterized by a single group of local maxima, related to the galaxy core, while spiral galaxies have additional ones related to segments of spiral arms. We use two different supervised ensemble classification algorithms, Random Forest,...

  7. Parameter design and performance analysis of shift actuator for a two-speed automatic mechanical transmission for pure electric vehicles

    Directory of Open Access Journals (Sweden)

    Jianjun Hu

    2016-08-01

    Full Text Available Recent developments of pure electric vehicles have shown that pure electric vehicles equipped with two-speed or multi-speed gearbox possess higher energy efficiency by ensuring the drive motor operates at its peak performance range. This article presents the design, analysis, and control of a two-speed automatic mechanical transmission for pure electric vehicles. The shift actuator is based on a motor-controlled camshaft where a special geometric groove is machined, and the camshaft realizes the axial positions of the synchronizer sleeve for gear engaging, disengaging, and speed control of the drive motor. Based on the force analysis of shift process, the parameters of shift actuator and shift motor are designed. The drive motor’s torque control strategy before shifting, speed governing control strategy before engaging, shift actuator’s control strategy during gear engaging, and drive motor’s torque recovery strategy after shift process are proposed and implemented with a prototype. To validate the performance of the two-speed gearbox, a test bed was developed based on dSPACE that emulates various operation conditions. The experimental results indicate that the shift process with the proposed shift actuator and control strategy could be accomplished within 1 s under various operation conditions, with shift smoothness up to passenger car standard.

  8. Towards automatic lithological classification from remote sensing data using support vector machines

    Science.gov (United States)

    Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

    2010-05-01

    Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14

  9. Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum

    Science.gov (United States)

    Hernandez-Contreras, D.; Peregrina-Barreto, H.; Rangel-Magdaleno, J.; Ramirez-Cortes, J.; Renero-Carrillo, F.

    2015-11-01

    This paper presents a novel approach to characterize and identify patterns of temperature in thermographic images of the human foot plant in support of early diagnosis and follow-up of diabetic patients. Composed feature vectors based on 3D morphological pattern spectrum (pecstrum) and relative position, allow the system to quantitatively characterize and discriminate non-diabetic (control) and diabetic (DM) groups. Non-linear classification using neural networks is used for that purpose. A classification rate of 94.33% in average was obtained with the composed feature extraction process proposed in this paper. Performance evaluation and obtained results are presented.

  10. Design and construction of an automatic system for minimizing the risk of sinking of water vehicle

    Science.gov (United States)

    Sutradhar, Amit; Rashid, Md. Mahbubur; Helal-An-Nahiyan, Md.; Mandal, Manash Kumar

    2016-07-01

    This paper focuses on the reduction of the risk of water vehicle like launch, ferry, ship and boat from sinking which is a burning problem of Bangladesh now-a-days. Every year death toll is rising by leaps and bounds due to this unexpected phenomenon. The sinking mostly occurs due to overloading and lack of consciousness. That's why, an automated system is introduced here to make the travelers warned about the overloading situation through raising alarm before the vehicle starts to move on. The tolerance limit of the vehicle is determined based on the theory of buoyancy and floatation. Moreover, while moving on the water, the vehicle may get victim of sinking due to rough weather, low visibility or machineries breakdown. So water level indicator is used to determine the safe level of water. When water level rises up to the safe limit or just before crossing the safe limit, another alarm will warn the passengers which will sound quite different from the first alarm as stated before. And at once the on board GPS sensor will record the current position of the vehicle and transmit the location to the nearest rescue authority via GSM module in the form of text message which will help them to take necessary steps for the rescue of the passengers as soon as possible. Effective implementation of this method can reduce the accident as well as this research can also be a helpful tool to organize further researches in this field for the sake of humanity.

  11. Automatic classification of bengali sentences based on sense definitions present in bengali wordnet

    OpenAIRE

    Pal, Alok Ranjan; Saha, Diganta; Dash, Niladri Sekhar

    2015-01-01

    Based on the sense definition of words available in the Bengali WordNet, an attempt is made to classify the Bengali sentences automatically into different groups in accordance with their underlying senses. The input sentences are collected from 50 different categories of the Bengali text corpus developed in the TDIL project of the Govt. of India, while information about the different senses of particular ambiguous lexical item is collected from Bengali WordNet. In an experimental basis we hav...

  12. ASTErIsM: application of topometric clustering algorithms in automatic galaxy detection and classification

    Science.gov (United States)

    Tramacere, A.; Paraficz, D.; Dubath, P.; Kneib, J.-P.; Courbin, F.

    2016-12-01

    We present a study on galaxy detection and shape classification using topometric clustering algorithms. We first use the DBSCAN algorithm to extract, from CCD frames, groups of adjacent pixels with significant fluxes and we then apply the DENCLUE algorithm to separate the contributions of overlapping sources. The DENCLUE separation is based on the localization of pattern of local maxima, through an iterative algorithm, which associates each pixel to the closest local maximum. Our main classification goal is to take apart elliptical from spiral galaxies. We introduce new sets of features derived from the computation of geometrical invariant moments of the pixel group shape and from the statistics of the spatial distribution of the DENCLUE local maxima patterns. Ellipticals are characterized by a single group of local maxima, related to the galaxy core, while spiral galaxies have additional groups related to segments of spiral arms. We use two different supervised ensemble classification algorithms: Random Forest and Gradient Boosting. Using a sample of ≃24 000 galaxies taken from the Galaxy Zoo 2 main sample with spectroscopic redshifts, and we test our classification against the Galaxy Zoo 2 catalogue. We find that features extracted from our pipeline give, on average, an accuracy of ≃93 per cent, when testing on a test set with a size of 20 per cent of our full data set, with features deriving from the angular distribution of density attractor ranking at the top of the discrimination power.

  13. Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms.

    Science.gov (United States)

    Alaqtash, Murad; Sarkodie-Gyan, Thompson; Yu, Huiying; Fuentes, Olac; Brower, Richard; Abdelgawad, Amr

    2011-01-01

    An automated gait classification method is developed in this study, which can be applied to analysis and to classify pathological gait patterns using 3D ground reaction force (GRFs) data. The study involved the discrimination of gait patterns of healthy, cerebral palsy (CP) and multiple sclerosis subjects. The acquired 3D GRFs data were categorized into three groups. Two different algorithms were used to extract the gait features; the GRFs parameters and the discrete wavelet transform (DWT), respectively. Nearest neighbor classifier (NNC) and artificial neural networks (ANN) were also investigated for the classification of gait features in this study. Furthermore, different feature sets were formed using a combination of the 3D GRFs components (mediolateral, anterioposterior, and vertical) and their various impacts on the acquired results were evaluated. The best leave-one-out (LOO) classification accuracy 85% was achieved. The results showed some improvement through the application of a features selection algorithm based on M-shaped value of vertical force and the statistical test ANOVA of mediolateral and anterioposterior forces. The optimal feature set of six features enhanced the accuracy to 95%. This work can provide an automated gait classification tool that may be useful to the clinician in the diagnosis and identification of pathological gait impairments.

  14. Automatic Speech Segmentation Based On Audio and Optical Flow Visual Classification

    Directory of Open Access Journals (Sweden)

    Behnam Torabi

    2014-10-01

    Full Text Available Automatic speech segmentation as an important part of speech recognition system (ASR is highly noise dependent. Noise is made by changes in the communication channel, background, level of speaking etc. In recent years, many researchers have proposed noise cancelation techniques and have added visual features from speaker’s face to reduce the effect of noise on ASR systems. Removing noise from audio signals depends on the type of the noise; so it cannot be used as a general solution. Adding visual features improve this lack of efficiency, but advanced methods of this type need manual extraction of visual features. In this paper we propose a completely automatic system which uses optical flow vectors from speaker’s image sequence to obtain visual features. Then, Hidden Markov Models are trained to segment audio signals from image sequences and audio features based on extracted optical flow. The developed segmentation system based on such method acts totally automatic and become more robust to noise.

  15. 基于RFID的车辆自动化智能管理系统研究%Research of vehicle automatic management system based on RFID

    Institute of Scientific and Technical Information of China (English)

    张丽然; 沈胜利

    2012-01-01

    In order to solve the current residential parking problem,the residential vehicle automatic management system is designed based on ETC technology. With the analysis of the actual demand, the system includes three parts: vehicle out and in management, vehicle positioning management and vehicle parking management. The vehicle out and in management system could identify and confirm the vehicles going into the community automatically; vehicle positioning management system is responsible for tracking and positioning the vehicle in the community; vehicle parking management system would assign and unlock the parking space for the vehicle automatically. After practices, it proves that the system has good performance and practical value.%基于解决当前小区停车难问题的目的,采用ETC电子不停车收费相关技术,设计了小区车辆自动化管理系统;通过对实际需求的分析,所设计的系统主要包括3个部分:车辆出入管理、定位管理以及停车管理。其中,车辆出入管理系统对进入的车辆进行身份的自动识别和确认;车辆定位管理系统负责对在小区申行驶的车辆进行追踪定位;车辆停车管理系统则为进入的车辆自动分配车住和开启车位锁。经过实践的证明,本系统性能良好,具有较好的实用价值。

  16. miRFam: an effective automatic miRNA classification method based on n-grams and a multiclass SVM

    Directory of Open Access Journals (Sweden)

    Zhou Shuigeng

    2011-05-01

    Full Text Available Abstract Background MicroRNAs (miRNAs are ~22 nt long integral elements responsible for post-transcriptional control of gene expressions. After the identification of thousands of miRNAs, the challenge is now to explore their specific biological functions. To this end, it will be greatly helpful to construct a reasonable organization of these miRNAs according to their homologous relationships. Given an established miRNA family system (e.g. the miRBase family organization, this paper addresses the problem of automatically and accurately classifying newly found miRNAs to their corresponding families by supervised learning techniques. Concretely, we propose an effective method, miRFam, which uses only primary information of pre-miRNAs or mature miRNAs and a multiclass SVM, to automatically classify miRNA genes. Results An existing miRNA family system prepared by miRBase was downloaded online. We first employed n-grams to extract features from known precursor sequences, and then trained a multiclass SVM classifier to classify new miRNAs (i.e. their families are unknown. Comparing with miRBase's sequence alignment and manual modification, our study shows that the application of machine learning techniques to miRNA family classification is a general and more effective approach. When the testing dataset contains more than 300 families (each of which holds no less than 5 members, the classification accuracy is around 98%. Even with the entire miRBase15 (1056 families and more than 650 of them hold less than 5 samples, the accuracy surprisingly reaches 90%. Conclusions Based on experimental results, we argue that miRFam is suitable for application as an automated method of family classification, and it is an important supplementary tool to the existing alignment-based small non-coding RNA (sncRNA classification methods, since it only requires primary sequence information. Availability The source code of miRFam, written in C++, is freely and publicly

  17. Food Safety by Using Machine Learning for Automatic Classification of Seeds of the South-American Incanut Plant

    Science.gov (United States)

    Lemanzyk, Thomas; Anding, Katharina; Linss, Gerhard; Rodriguez Hernández, Jorge; Theska, René

    2015-02-01

    The following paper deals with the classification of seeds and seed components of the South-American Incanut plant and the modification of a machine to handle this task. Initially the state of the art is being illustrated. The research was executed in Germany and with a relevant part in Peru and Ecuador. Theoretical considerations for the solution of an automatically analysis of the Incanut seeds were specified. The optimization of the analyzing software and the separation unit of the mechanical hardware are carried out with recognition results. In a final step the practical application of the analysis of the Incanut seeds is held on a trial basis and rated on the bases of statistic values.

  18. Using Probe Vehicle Data for Automatic Extraction of Road Traffic Parameters

    Directory of Open Access Journals (Sweden)

    Roman Popescu Maria Alexandra

    2016-12-01

    Full Text Available Through this paper the author aims to study and find solutions for automatic detection of traffic light position and for automatic calculation of the waiting time at traffic light. The first objective serves mainly the road transportation field, mainly because it removes the need for collaboration with local authorities to establish a national network of traffic lights. The second objective is important not only for companies which are providing navigation solutions, but especially for authorities, institutions, companies operating in road traffic management systems. Real-time dynamic determination of traffic queue length and of waiting time at traffic lights allow the creation of dynamic systems, intelligent and flexible, adapted to actual traffic conditions, and not to generic, theoretical models. Thus, cities can approach the Smart City concept by boosting, efficienting and greening the road transport, promoted in Europe through the Horizon 2020, Smart Cities, Urban Mobility initiative.

  19. Driver head displacement during (automatic) vehicle braking tests with varying levels of distraction

    NARCIS (Netherlands)

    Rooij, L. van; Pauwelussen, J.; Camp, O.M.G.C. op den; Janssen, J.M.

    2013-01-01

    Vehicle occupant behavior in emergency driving conditions has a large effect on traffic safety. Distraction is estimated to be the cause of 15-20% of all crashes. Additionally, the posture of the occupants prior to the possibly unavoidable crash is known to have a large effect on the injury reducing

  20. A Communication Protocol and Monitoring Policy for Input/Output Vehicles in an Automatic Storage and Retrieval System

    Institute of Scientific and Technical Information of China (English)

    LI Li; LI Wenfeng; LIAO Xiaoping; SU Wengui; LIN Yizhong

    2006-01-01

    The acquisition and processing of equipment information is pivotal to control and management of the automated storage and retrieval system. The work of this paper is based on the automatic storage and retrieval experimental system of Wuhan University of Technology. First, the output/input flow and the control information of storage/retrieval vehicle are studied and the plotting finite state machine model of the stacking crane is established. Then, the communication protocol between the center control management computer and the PLC of stacking crane is designed. Finally, the stacking crane's monitoring data, which include operating time, running states and real-time position status, are gained by analyzing the communication protocol. The detailed program for the acquisition and processing of monitoring information is developed. This method is suitable for the equipment monitoring of the whole system, and provides a platform for studying the intelligent control and optimal scheduling strategies of AS/RS.

  1. Semi-automatic supervised classification of minerals from x-ray mapping images

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Flesche, Harald; Larsen, Rasmus

    1998-01-01

    This paper addresses the problem of assessing the robustness with respect to change in parameters of an integrated training and classification routine for minerals commonly encountered in siliciclastic or carbonate rocks. Twelve chemical elements are mapped from thin sections by energy dispersive...... spectroscopy (EDS) in a scanning electron microscope (SEM). Extensions to traditional multivariate statistical methods are applied to perform the classification. Training sets are grown from one or a few seed points by a method that ensures spatial and spectral closeness of observations. Spectral closeness...... is obtained by excluding observations that have high Mahalanobis distances to the training class mean. Spatial closeness is obtained by requiring connectivity. The marginal effects of changes in the parameters that are input to the seed growing algorithm are evaluated. Initially, the seed is expanded...

  2. Automatic Defect Detection and Classification Technique from Image: A Special Case Using Ceramic Tiles

    CERN Document Server

    Rahaman, G M Atiqur

    2009-01-01

    Quality control is an important issue in the ceramic tile industry. On the other hand maintaining the rate of production with respect to time is also a major issue in ceramic tile manufacturing. Again, price of ceramic tiles also depends on purity of texture, accuracy of color, shape etc. Considering this criteria, an automated defect detection and classification technique has been proposed in this report that can have ensured the better quality of tiles in manufacturing process as well as production rate. Our proposed method plays an important role in ceramic tiles industries to detect the defects and to control the quality of ceramic tiles. This automated classification method helps us to acquire knowledge about the pattern of defect within a very short period of time and also to decide about the recovery process so that the defected tiles may not be mixed with the fresh tiles.

  3. Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images

    OpenAIRE

    Ghaffarian, S.

    2014-01-01

    This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB color space, which indicates the lightness of the image, and a novel double thresholding technique. Furth...

  4. Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data

    OpenAIRE

    Qingqing Lu; Jiexin Pu; Zhonghua Liu

    2014-01-01

    Ground penetrating radar (GPR) is a powerful tool for detecting objects buried underground. However, the interpretation of the acquired signals remains a challenging task since an experienced user is required to manage the entire operation. Particularly difficult is the classification of the material type of underground objects in noisy environment. This paper proposes a new feature extraction method. First, discrete wavelet transform (DWT) transforms A-Scan data and approximation coefficient...

  5. Automatic classification and pattern discovery in high-throughput protein crystallization trials.

    Science.gov (United States)

    Cumbaa, Christian; Jurisica, Igor

    2005-01-01

    Conceptually, protein crystallization can be divided into two phases search and optimization. Robotic protein crystallization screening can speed up the search phase, and has a potential to increase process quality. Automated image classification helps to increase throughput and consistently generate objective results. Although the classification accuracy can always be improved, our image analysis system can classify images from 1,536-well plates with high classification accuracy (85%) and ROC score (0.87), as evaluated on 127 human-classified protein screens containing 5,600 crystal images and 189,472 non-crystal images. Data mining can integrate results from high-throughput screens with information about crystallizing conditions, intrinsic protein properties, and results from crystallization optimization. We apply association mining, a data mining approach that identifies frequently occurring patterns among variables and their values. This approach segregates proteins into groups based on how they react in a broad range of conditions, and clusters cocktails to reflect their potential to achieve crystallization. These results may lead to crystallization screen optimization, and reveal associations between protein properties and crystallization conditions. We also postulate that past experience may lead us to the identification of initial conditions favorable to crystallization for novel proteins.

  6. HaploGrep: a fast and reliable algorithm for automatic classification of mitochondrial DNA haplogroups.

    Science.gov (United States)

    Kloss-Brandstätter, Anita; Pacher, Dominic; Schönherr, Sebastian; Weissensteiner, Hansi; Binna, Robert; Specht, Günther; Kronenberg, Florian

    2011-01-01

    An ongoing source of controversy in mitochondrial DNA (mtDNA) research is based on the detection of numerous errors in mtDNA profiles that led to erroneous conclusions and false disease associations. Most of these controversies could be avoided if the samples' haplogroup status would be taken into consideration. Knowing the mtDNA haplogroup affiliation is a critical prerequisite for studying mechanisms of human evolution and discovering genes involved in complex diseases, and validating phylogenetic consistency using haplogroup classification is an important step in quality control. However, despite the availability of Phylotree, a regularly updated classification tree of global mtDNA variation, the process of haplogroup classification is still time-consuming and error-prone, as researchers have to manually compare the polymorphisms found in a population sample to those summarized in Phylotree, polymorphism by polymorphism, sample by sample. We present HaploGrep, a fast, reliable and straight-forward algorithm implemented in a Web application to determine the haplogroup affiliation of thousands of mtDNA profiles genotyped for the entire mtDNA or any part of it. HaploGrep uses the latest version of Phylotree and offers an all-in-one solution for quality assessment of mtDNA profiles in clinical genetics, population genetics and forensics. HaploGrep can be accessed freely at http://haplogrep.uibk.ac.at.

  7. Automatic sleep classification using a data-driven topic model reveals latent sleep states

    DEFF Research Database (Denmark)

    Koch, Henriette; Christensen, Julie Anja Engelhard; Frandsen, Rune

    2014-01-01

    sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1 s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model....... The model was optimized using 50 subjects and validated on 76 subjects. Results: The optimized sleep model used six topics, and the topic probabilities changed smoothly during transitions. According to the manual scorings, the model scored an overall subject-specific accuracy of 68.3 +/- 7.44 (% mu +/-sigma...

  8. Automatic quality classification of entire electrocardiographic recordings obtained with a novel patch type recorder

    DEFF Research Database (Denmark)

    Saadi, Dorthe Bodholt; Hoppe, Karsten; Egstrup, Kenneth

    2014-01-01

    Recently, new patch type electrocardiogram (ECG) recorders have reached the market. These new devices possess a number of advantages compared to the traditional Holter recorders. This forms the basis of questions related to benefits and drawbacks of different ambulatory ECG recording techniques....... One of the important questions is the ability to obtain high clinical quality of the recordings during the entire monitoring period. It is thus desirable to be able to obtain an automatic estimate of the global quality of entire ECG recordings. The purpose of this pilot study is therefore to design...

  9. Automatic classification of apnea/hypopnea events through sleep/wake states and severity of SDB from a pulse oximeter.

    Science.gov (United States)

    Park, Jong-Uk; Lee, Hyo-Ki; Lee, Junghun; Urtnasan, Erdenebayar; Kim, Hojoong; Lee, Kyoung-Joung

    2015-09-01

    This study proposes a method of automatically classifying sleep apnea/hypopnea events based on sleep states and the severity of sleep-disordered breathing (SDB) using photoplethysmogram (PPG) and oxygen saturation (SpO2) signals acquired from a pulse oximeter. The PPG was used to classify sleep state, while the severity of SDB was estimated by detecting events of SpO2 oxygen desaturation. Furthermore, we classified sleep apnea/hypopnea events by applying different categorisations according to the severity of SDB based on a support vector machine. The classification results showed sensitivity performances and positivity predictive values of 74.2% and 87.5% for apnea, 87.5% and 63.4% for hypopnea, and 92.4% and 92.8% for apnea + hypopnea, respectively. These results represent better or comparable outcomes compared to those of previous studies. In addition, our classification method reliably detected sleep apnea/hypopnea events in all patient groups without bias in particular patient groups when our algorithm was applied to a variety of patient groups. Therefore, this method has the potential to diagnose SDB more reliably and conveniently using a pulse oximeter.

  10. Automatic Generation of Data Types for Classification of Deep Web Sources

    Energy Technology Data Exchange (ETDEWEB)

    Ngu, A H; Buttler, D J; Critchlow, T J

    2005-02-14

    A Service Class Description (SCD) is an effective meta-data based approach for discovering Deep Web sources whose data exhibit some regular patterns. However, it is tedious and error prone to create an SCD description manually. Moreover, a manually created SCD is not adaptive to the frequent changes of Web sources. It requires its creator to identify all the possible input and output types of a service a priori. In many domains, it is impossible to exhaustively list all the possible input and output data types of a source in advance. In this paper, we describe machine learning approaches for automatic generation of the data types of an SCD. We propose two different approaches for learning data types of a class of Web sources. The Brute-Force Learner is able to generate data types that can achieve high recall, but with low precision. The Clustering-based Learner generates data types that have a high precision rate, but with a lower recall rate. We demonstrate the feasibility of these two learning-based solutions for automatic generation of data types for citation Web sources and presented a quantitative evaluation of these two solutions.

  11. Multistation alarm system for eruptive activity based on the automatic classification of volcanic tremor: specifications and performance

    Science.gov (United States)

    Langer, Horst; Falsaperla, Susanna; Messina, Alfio; Spampinato, Salvatore

    2015-04-01

    With over fifty eruptive episodes (Strombolian activity, lava fountains, and lava flows) between 2006 and 2013, Mt Etna, Italy, underscored its role as the most active volcano in Europe. Seven paroxysmal lava fountains at the South East Crater occurred in 2007-2008 and 46 at the New South East Crater between 2011 and 2013. Month-lasting lava emissions affected the upper eastern flank of the volcano in 2006 and 2008-2009. On this background, effective monitoring and forecast of volcanic phenomena are a first order issue for their potential socio-economic impact in a densely populated region like the town of Catania and its surroundings. For example, explosive activity has often formed thick ash clouds with widespread tephra fall able to disrupt the air traffic, as well as to cause severe problems at infrastructures, such as highways and roads. For timely information on changes in the state of the volcano and possible onset of dangerous eruptive phenomena, the analysis of the continuous background seismic signal, the so-called volcanic tremor, turned out of paramount importance. Changes in the state of the volcano as well as in its eruptive style are usually concurrent with variations of the spectral characteristics (amplitude and frequency content) of tremor. The huge amount of digital data continuously acquired by INGV's broadband seismic stations every day makes a manual analysis difficult, and techniques of automatic classification of the tremor signal are therefore applied. The application of unsupervised classification techniques to the tremor data revealed significant changes well before the onset of the eruptive episodes. This evidence led to the development of specific software packages related to real-time processing of the tremor data. The operational characteristics of these tools - fail-safe, robustness with respect to noise and data outages, as well as computational efficiency - allowed the identification of criteria for automatic alarm flagging. The

  12. Automatic Registration of Wide Area Motion Imagery to Vector Road Maps by Exploiting Vehicle Detections.

    Science.gov (United States)

    Elliethy, Ahmed; Sharma, Gaurav

    2016-11-01

    To enrich large-scale visual analytics applications enabled by aerial wide area motion imagery (WAMI), we propose a novel methodology for accurately registering a geo-referenced vector roadmap to WAMI by using the locations of detected vehicles and determining a parametric transform that aligns these locations with the network of roads in the roadmap. Specifically, the problem is formulated in a probabilistic framework, explicitly allowing for spurious detections that do not correspond to on-road vehicles. The registration is estimated via the expectation-maximization (EM) algorithm as the planar homography that minimizes the sum of weighted squared distances between the homography-mapped detection locations and the corresponding closest point on the road network, where the weights are estimated posterior probabilities of detections being on-road vehicles. The weighted distance minimization is efficiently performed using the distance transform with the Levenberg-Marquardt nonlinear least-squares minimization procedure, and the fraction of spurious detections is estimated within the EM framework. The proposed method effectively sidesteps the challenges of feature correspondence estimation, applies directly to different imaging modalities, is robust to spurious detections, and is also more appropriate than feature matching for a planar homography. Results over three WAMI data sets captured by both visual and infrared sensors indicate the effectiveness of the proposed methodology: both visual comparison and numerical metrics for the registration accuracy are significantly better for the proposed method as compared with the existing alternatives.

  13. Automatic braking system modification for the Advanced Transport Operating Systems (ATOPS) Transportation Systems Research Vehicle (TSRV)

    Science.gov (United States)

    Coogan, J. J.

    1986-01-01

    Modifications were designed for the B-737-100 Research Aircraft autobrake system hardware of the Advanced Transport Operating Systems (ATOPS) Program at Langley Research Center. These modifications will allow the on-board flight control computer to control the aircraft deceleration after landing to a continuously variable level for the purpose of executing automatic high speed turn-offs from the runway. A bread board version of the proposed modifications was built and tested in simulated stopping conditions. Test results, for various aircraft weights, turnoff speed, winds, and runway conditions show that the turnoff speeds are achieved generally with errors less than 1 ft/sec.

  14. Study of automatic and manual terminal guidance and control systems for space shuttle vehicles. Volume 1: Sections 1 through 3

    Science.gov (United States)

    Osder, S.; Keller, R.

    1971-01-01

    The results of a study to analyze, design, and evaluate guidance and control systems are presented that start at an altitude of about 100,000 feet and bring the unpowered space shuttle orbiters to a precision horizontal landing. The systems under consideration included fully automatic versions which involve no pilot participation as well as various manual configurations that provide combinations of displays and control augmentation which permit the pilot to control the vehicle to a successful landing. Two classes of vehicles were studied: the low cross range or straight-wing orbiter and the high cross range or delta-wing (delta body) orbiter. The recommended navigation, guidance and control system is shown to be compatible with realistic physical constraints that would exist in space shuttlecraft and to be consistent with the 1971 avionics equipment state of the art. Aircraft capable of aerodynamically simulating the various candidate space shuttlecraft in their unpowered, terminal area descent were investigated, and flight test recommendations, including system mechanizations, are made.

  15. 汽车自动行驶装置设计%Design of vehicle automatic driving device

    Institute of Scientific and Technical Information of China (English)

    吴小邦

    2012-01-01

    The control of fire engines automatically traveling, mainly related to the key technologies of the steering, gear, brake and clutch control,its organization has used the machinery integration type or the air operated integration types of control,may be installed near the gear shift box in the vehicle and the bottom of the cab outside the narrow space. Use fire engine gas source and apply the pneumatic control localization,can be easy debugging and maintenance-free. Use single-power control solenoid valve, can be done for vehicles when it meet unexpected power outages,the every cylinder can be back to its original position.%对消防车自动行驶的控制,主要涉及转向、排挡、刹车和离合器控制等关键技术,其机构均采用机械集成式或气动集成控制形式,可安装在车辆的换挡箱附近、驾驶室外底部狭小空间;利用消防车的气源,应用气动控制定位,使调试更加方便、免维护;采用单电控电磁阀能确保在车辆意外断电时各气缸回复至原位.

  16. Algorithms for the Automatic Classification and Sorting of Conifers in the Garden Nursery Industry

    DEFF Research Database (Denmark)

    Petri, Stig

    , resulting in a prototype data acquisition system that can possibly be integrated into a production line (conveyor) system. The developed software includes the necessary functions for acquiring images, normalizing these, extracting features, creating and optimizing classification models, and evaluating...... was used as the basis for evaluating the constructed feature extraction algorithms. Through an analysis of the construction of a machine vision system suitable for classifying and sorting plants, the needs with regard to physical frame, lighting system, camera and software algorithms have been uncovered...

  17. Automatic detection and classification of damage zone(s) for incorporating in digital image correlation technique

    Science.gov (United States)

    Bhattacharjee, Sudipta; Deb, Debasis

    2016-07-01

    Digital image correlation (DIC) is a technique developed for monitoring surface deformation/displacement of an object under loading conditions. This method is further refined to make it capable of handling discontinuities on the surface of the sample. A damage zone is referred to a surface area fractured and opened in due course of loading. In this study, an algorithm is presented to automatically detect multiple damage zones in deformed image. The algorithm identifies the pixels located inside these zones and eliminate them from FEM-DIC processes. The proposed algorithm is successfully implemented on several damaged samples to estimate displacement fields of an object under loading conditions. This study shows that displacement fields represent the damage conditions reasonably well as compared to regular FEM-DIC technique without considering the damage zones.

  18. A framework for evaluating automatic indexing or classification in the context of retrieval

    DEFF Research Database (Denmark)

    Golub, Korajlka; Soergel, Dagobert; Buchanan, George

    2016-01-01

    subject indexing, hard scientific evidence of their performance in operating information environments is scarce. A major reason for this is that research is usually conducted in laboratory conditions, excluding the complexities of real-life systems and situations. The paper reviews and discusses issues......Tools for automatic subject assignment help deal with scale and sustainability in creating and enriching metadata, establishing more connections across and between resources and enhancing consistency. While some software vendors and experimental researchers claim the tools can replace manual...... with existing evaluation approaches such as problems of aboutness and relevance assessments, implying the need to use more than a single “gold standard” method when evaluating indexing and retrieval and proposes a comprehensive evaluation framework. The framework is informed by a systematic review...

  19. Statistical methods of automatic spectral classification and their application to the Hamburg/ESO Survey

    CERN Document Server

    Christlieb, N; Grasshoff, G; Christlieb, Norbert; Wisotzki, Lutz; Grasshoff, Gerd

    2002-01-01

    We employ classical statistical methods of multivariate classification for the exploitation of the stellar content of the Hamburg/ESO objective prism survey (HES). In a simulation study we investigate the precision of a three-dimensional classification (Teff, log g, [Fe/H]) achievable in the HES for stars in the effective temperature range 520010 (typically corresponding to B_J<16.5). The accuracies in log g and [Fe/H] are better than 0.68dex in the same S/N range. These precisions allow for a very efficient selection of metal-poor stars in the HES. We present a minimum cost rule for compilation of complete samples of objects of a given class, and a rejection rule for identification of corrupted or peculiar spectra. The algorithms we present are being used for the identification of other interesting objects in the HES data base as well, and they are applicable to other existing and future large data sets, such as those to be compiled by the DIVA and GAIA missions.

  20. Automatic classification of hepatocellular carcinoma images based on nuclear and structural features

    Science.gov (United States)

    Kiyuna, Tomoharu; Saito, Akira; Marugame, Atsushi; Yamashita, Yoshiko; Ogura, Maki; Cosatto, Eric; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

    2013-03-01

    Diagnosis of hepatocellular carcinoma (HCC) on the basis of digital images is a challenging problem because, unlike gastrointestinal carcinoma, strong structural and morphological features are limited and sometimes absent from HCC images. In this study, we describe the classification of HCC images using statistical distributions of features obtained from image analysis of cell nuclei and hepatic trabeculae. Images of 130 hematoxylin-eosin (HE) stained histologic slides were captured at 20X by a slide scanner (Nanozoomer, Hamamatsu Photonics, Japan) and 1112 regions of interest (ROI) images were extracted for classification (551 negatives and 561 positives, including 113 well-differentiated positives). For a single nucleus, the following features were computed: area, perimeter, circularity, ellipticity, long and short axes of elliptic fit, contour complexity and gray level cooccurrence matrix (GLCM) texture features (angular second moment, contrast, homogeneity and entropy). In addition, distributions of nuclear density and hepatic trabecula thickness within an ROI were also extracted. To represent an ROI, statistical distributions (mean, standard deviation and percentiles) of these features were used. In total, 78 features were extracted for each ROI and a support vector machine (SVM) was trained to classify negative and positive ROIs. Experimental results using 5-fold cross validation show 90% sensitivity for an 87.8% specificity. The use of statistical distributions over a relatively large area makes the HCC classifier robust to occasional failures in the extraction of nuclear or hepatic trabecula features, thus providing stability to the system.

  1. Shifting Rule Modification Strategy of Automatic Transmission Based on Driver-vehicle-road Environment

    Institute of Scientific and Technical Information of China (English)

    WU Guangqiang; ZHANG Deming

    2010-01-01

    Accidental or frequent shift often occurs when the shifting rule is built based on traditional two parameters (I.e., velocity and throttle), because the speed of engine varies slower than change of throttle opening. Currently, modifying shift point velocity value or throttle by throttle change rate is one of common methods, but the results are not so satisfactory in some working condition such as uphill. The reason is that these methods merely consider throttle change rate which is not enough for a car driving in driver-vehicle-road environment system. So a novel fuzzy control modification strategy is proposed to avoid or reduce those abnormal shift actions. It can adjust shifting rule by the change rate of throttle, current gear position and road environment information, while different gear position and driving environment get corresponding modification value. In order to compare the results of shifting actions, fuel consumption and braking distance, emergent braking in level road and extra-urban driving cycle(EUDC) working conditions with fuzzy shifting schedule modification strategy are simulated digitally. Furthermore, a hardware-in-the-loop simulation platform is introduced to verify its effect in slope road condition according to the ON/OFF numbers of solenoid valve in hydraulic system. The simulation results show that the problem of unexpected shift in those working conditions may be resolved by fuzzy modification strategy. At last, it is concluded that although there is some slight decline in power performance in uphill situation, this fuzzy modification strategy could correctly identify slope of road, decrease braking distance, improve vehicle comfort and fuel economy effectively and prolong the life of clutch system. So, this fuzzy logic shifting strategy provides important References for vehicle intelligent shifting schedule.

  2. Automatic method for thalamus parcellation using multi-modal feature classification.

    Science.gov (United States)

    Stough, Joshua V; Glaister, Jeffrey; Ye, Chuyang; Ying, Sarah H; Prince, Jerry L; Carass, Aaron

    2014-01-01

    Segmentation and parcellation of the thalamus is an important step in providing volumetric assessment of the impact of disease n brain structures. Conventionally, segmentation is carried out on T1-weighted magnetic resonance (MR) images and nuclear parcellation using diffusion weighted MR images. We present the first fully automatic method that incorporates both tissue contrasts and several derived fea-fractional anisotrophy, fiber orientation from the 5D Knutsson representation of the principal eigenvectors, and connectivity between the thalamus and the cortical lobes, as features. Combining these multiple information sources allows us to identify discriminating dimensions and thus parcellate the thalamic nuclei. A hierarchical random forest framework with a multidimensional feature per voxel, first distinguishes thalamus from background, and then separates each group of thalamic nuclei. Using a leave one out cross-validation on 12 subjects we have a mean Dice score of 0.805 and 0.799 for the left and right thalami, respectively. We also report overlap for the thalamic nuclear groups.

  3. Automatic phylogenetic classification of bacterial beta-lactamase sequences including structural and antibiotic substrate preference information.

    Science.gov (United States)

    Ma, Jianmin; Eisenhaber, Frank; Maurer-Stroh, Sebastian

    2013-12-01

    Beta lactams comprise the largest and still most effective group of antibiotics, but bacteria can gain resistance through different beta lactamases that can degrade these antibiotics. We developed a user friendly tree building web server that allows users to assign beta lactamase sequences to their respective molecular classes and subclasses. Further clinically relevant information includes if the gene is typically chromosomal or transferable through plasmids as well as listing the antibiotics which the most closely related reference sequences are known to target and cause resistance against. This web server can automatically build three phylogenetic trees: the first tree with closely related sequences from a Tachyon search against the NCBI nr database, the second tree with curated reference beta lactamase sequences, and the third tree built specifically from substrate binding pocket residues of the curated reference beta lactamase sequences. We show that the latter is better suited to recover antibiotic substrate assignments through nearest neighbor annotation transfer. The users can also choose to build a structural model for the query sequence and view the binding pocket residues of their query relative to other beta lactamases in the sequence alignment as well as in the 3D structure relative to bound antibiotics. This web server is freely available at http://blac.bii.a-star.edu.sg/.

  4. The Iqmulus Urban Showcase: Automatic Tree Classification and Identification in Huge Mobile Mapping Point Clouds

    Science.gov (United States)

    Böhm, J.; Bredif, M.; Gierlinger, T.; Krämer, M.; Lindenberg, R.; Liu, K.; Michel, F.; Sirmacek, B.

    2016-06-01

    Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling ~ 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.

  5. Love thy neighbour: automatic animal behavioural classification of acceleration data using the K-nearest neighbour algorithm.

    Directory of Open Access Journals (Sweden)

    Owen R Bidder

    Full Text Available Researchers hoping to elucidate the behaviour of species that aren't readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1 Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2 they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN. Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.

  6. An automatic classification technique for attenuation correction in positron emission tomography

    Energy Technology Data Exchange (ETDEWEB)

    Bettinardi, V.; Pagani, E.; Gilardi, M.C.; Landoni, C.; Riddell, C.; Rizzo, G.; Castiglioni, I.; Belluzzo, D.; Lucignani, G.; Fazio, F. [INB-CNR, Scientific Inst. H San Raffaele, Univ. of Milan (Italy); Schubert, S. [GE Medical System, Milwaukee, WI (United States)

    1999-05-01

    In this paper a clustering technique is proposed for attenuation correction (AC) in positron emission tomography (PET). The method is unsupervised and adaptive with respect to counting statistics in the transmission (TR) images. The technique allows the classification of pre- or post-injection TR images into main tissue components in terms of attenuation coefficients. The classified TR images are then forward projected to generate new TR sinograms to be used for AC in the reconstruction of the corresponding emission (EM) data. The technique has been tested on phantoms and clinical data of brain, heart and whole-body PET studies. The method allows: (a) reduction of noise propagation from TR into EM images, (b) reduction of TR scanning to a few minutes (3 min) with maintenance of the quantitative accuracy (within 6%) of longer acquisition scans (15-20 min), (c) reduction of the radiation dose to the patient, (d) performance of quantitative whole-body studies. (orig.) With 8 figs., 4 tabs., 25 refs.

  7. 集成自动分类的垂直搜索引擎及其应用%Automatic Classification Integrated Vertical Search Engines and Its Applications

    Institute of Scientific and Technical Information of China (English)

    傅丽君; 潘旭伟; 李娜

    2012-01-01

      Tosolve problems such as the low efficiency and the poor accuracy of information search in the vertical search engine (VSE) , the automatic classification technique is introduced into VSE to improve the efficiency and the accuracy of information search. After the concept and features of VSE are described, the benefits of automatic classification technique integrated into VSE are analyzed from VSE's characteristics: specialization, accuracy and deepness. Based on these, an architecture of the VSE integrated with automatic classification technique is built, and key technologies in the architecture are discussed and studied, which are automatic classification algorithm, auto-matic classification-integrated information search, browsing and recommendation. Finally, an example is introduced to demonstrate the implementation and the effects of automatic classification integrated VSE%  针对垂直搜索引擎存在检索效率低、检索结果不准确等问题,将自动分类引入到垂直搜索引擎中以提高信息查询的效率和准确性。在阐述垂直搜索引擎概念和特点的基础上,从垂直搜索引擎的专业、精确、深入三个特点出发,分析了自动分类在垂直搜索引擎中的作用,构建了集成自动分类的垂直搜索引擎系统框架,并对该框架中自动分类计算、集成自动分类的信息检索、集成自动分类的信息浏览和关联推荐等关键技术进行了探讨。最后,通过一个实例阐释了自动分类集成到垂直搜索引擎中的技术实现与应用效果。

  8. Automatic classification of long-term ambulatory ECG records according to type of ischemic heart disease

    Directory of Open Access Journals (Sweden)

    Smrdel Aleš

    2011-12-01

    Full Text Available Abstract Background Elevated transient ischemic ST segment episodes in the ambulatory electrocardiographic (AECG records appear generally in patients with transmural ischemia (e. g. Prinzmetal's angina while depressed ischemic episodes appear in patients with subendocardial ischemia (e. g. unstable or stable angina. Huge amount of AECG data necessitates automatic methods for analysis. We present an algorithm which determines type of transient ischemic episodes in the leads of records (elevations/depressions and classifies AECG records according to type of ischemic heart disease (Prinzmetal's angina; coronary artery diseases excluding patients with Prinzmetal's angina; other heart diseases. Methods The algorithm was developed using 24-hour AECG records of the Long Term ST Database (LTST DB. The algorithm robustly generates ST segment level function in each AECG lead of the records, and tracks time varying non-ischemic ST segment changes such as slow drifts and axis shifts to construct the ST segment reference function. The ST segment reference function is then subtracted from the ST segment level function to obtain the ST segment deviation function. Using the third statistical moment of the histogram of the ST segment deviation function, the algorithm determines deflections of leads according to type of ischemic episodes present (elevations, depressions, and then classifies records according to type of ischemic heart disease. Results Using 74 records of the LTST DB (containing elevated or depressed ischemic episodes, mixed ischemic episodes, or no episodes, the algorithm correctly determined deflections of the majority of the leads of the records and correctly classified majority of the records with Prinzmetal's angina into the Prinzmetal's angina category (7 out of 8; majority of the records with other coronary artery diseases into the coronary artery diseases excluding patients with Prinzmetal's angina category (47 out of 55; and correctly

  9. TEXT CLASSIFICATION FOR AUTOMATIC DETECTION OF E-CIGARETTE USE AND USE FOR SMOKING CESSATION FROM TWITTER: A FEASIBILITY PILOT.

    Science.gov (United States)

    Aphinyanaphongs, Yin; Lulejian, Armine; Brown, Duncan Penfold; Bonneau, Richard; Krebs, Paul

    2016-01-01

    Rapid increases in e-cigarette use and potential exposure to harmful byproducts have shifted public health focus to e-cigarettes as a possible drug of abuse. Effective surveillance of use and prevalence would allow appropriate regulatory responses. An ideal surveillance system would collect usage data in real time, focus on populations of interest, include populations unable to take the survey, allow a breadth of questions to answer, and enable geo-location analysis. Social media streams may provide this ideal system. To realize this use case, a foundational question is whether we can detect e-cigarette use at all. This work reports two pilot tasks using text classification to identify automatically Tweets that indicate e-cigarette use and/or e-cigarette use for smoking cessation. We build and define both datasets and compare performance of 4 state of the art classifiers and a keyword search for each task. Our results demonstrate excellent classifier performance of up to 0.90 and 0.94 area under the curve in each category. These promising initial results form the foundation for further studies to realize the ideal surveillance solution.

  10. Automatic Object-Oriented, Spectral-Spatial Feature Extraction Driven by Tobler’s First Law of Geography for Very High Resolution Aerial Imagery Classification

    Directory of Open Access Journals (Sweden)

    Zhiyong Lv

    2017-03-01

    Full Text Available Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

  11. Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation.

    Science.gov (United States)

    Perry, Daniel; Morris, Alan; Burgon, Nathan; McGann, Christopher; Macleod, Robert; Cates, Joshua

    2012-02-23

    Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts.

  12. Nonlinear automatic landing control of unmanned aerial vehicles on moving platforms via a 3D laser radar

    Energy Technology Data Exchange (ETDEWEB)

    Hervas, Jaime Rubio; Tang, Hui [School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798 (Singapore); Reyhanoglu, Mahmut [Physical Sciences Department, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114 (United States)

    2014-12-10

    This paper presents a motion tracking and control system for automatically landing Unmanned Aerial Vehicles (UAVs) on an oscillating platform using Laser Radar (LADAR) observations. The system itself is assumed to be mounted on a ship deck. A full nonlinear mathematical model is first introduced for the UAV. The ship motion is characterized by a Fourier transform based method which includes a realistic characterization of the sea waves. LADAR observation models are introduced and an algorithm to process those observations for yielding the relative state between the vessel and the UAV is presented, from which the UAV's state relative to an inertial frame can be obtained and used for feedback purposes. A sliding mode control algorithm is derived for tracking a landing trajectory defined by a set of desired waypoints. An extended Kalman filter (EKF) is proposed to account for process and observation noises in the design of a state estimator. The effectiveness of the control algorithm is illustrated through a simulation example.

  13. Classification

    Science.gov (United States)

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  14. Study of automatic and manual terminal guidance and control systems for space shuttle vehicles. Volume 2: Section 4 through appendix B

    Science.gov (United States)

    Osder, S.; Keller, R.

    1971-01-01

    Guidance and control design studies that were performed for three specific space shuttle candidate vehicles are described. Three types of simulation were considered. The manual control investigations and pilot evaluations of the automatic system performance is presented. Recommendations for systems and equipment, both airborne and ground-based, necessary to flight test the guidance and control concepts for shuttlecraft terminal approach and landing are reported.

  15. Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

    Science.gov (United States)

    Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

    2017-01-01

    Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying

  16. Semi-automatic classification of skeletal morphology in genetically altered mice using flat-panel volume computed tomography.

    Directory of Open Access Journals (Sweden)

    Christian Dullin

    2007-07-01

    Full Text Available Rapid progress in exploring the human and mouse genome has resulted in the generation of a multitude of mouse models to study gene functions in their biological context. However, effective screening methods that allow rapid noninvasive phenotyping of transgenic and knockout mice are still lacking. To identify murine models with bone alterations in vivo, we used flat-panel volume computed tomography (fpVCT for high-resolution 3-D imaging and developed an algorithm with a computational intelligence system. First, we tested the accuracy and reliability of this approach by imaging discoidin domain receptor 2- (DDR2- deficient mice, which display distinct skull abnormalities as shown by comparative landmark-based analysis. High-contrast fpVCT data of the skull with 200 microm isotropic resolution and 8-s scan time allowed segmentation and computation of significant shape features as well as visualization of morphological differences. The application of a trained artificial neuronal network to these datasets permitted a semi-automatic and highly accurate phenotype classification of DDR2-deficient compared to C57BL/6 wild-type mice. Even heterozygous DDR2 mice with only subtle phenotypic alterations were correctly determined by fpVCT imaging and identified as a new class. In addition, we successfully applied the algorithm to classify knockout mice lacking the DDR1 gene with no apparent skull deformities. Thus, this new method seems to be a potential tool to identify novel mouse phenotypes with skull changes from transgenic and knockout mice on the basis of random mutagenesis as well as from genetic models. However for this purpose, new neuronal networks have to be created and trained. In summary, the combination of fpVCT images with artificial neuronal networks provides a reliable, novel method for rapid, cost-effective, and noninvasive primary screening tool to detect skeletal phenotypes in mice.

  17. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box.

    Science.gov (United States)

    Ciompi, Francesco; de Hoop, Bartjan; van Riel, Sarah J; Chung, Kaman; Scholten, Ernst Th; Oudkerk, Matthijs; de Jong, Pim A; Prokop, Mathias; van Ginneken, Bram

    2015-12-01

    In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.

  18. Kardashev's Classification at 50+: A Fine Vehicle with Room for Improvement

    CERN Document Server

    Cirkovic, Milan M

    2016-01-01

    We review the history and status of the famous classification of extraterrestrial civilizations given by the great Russian astrophysicist Nikolai Semenovich Kardashev, roughly half a century after it has been proposed. While Kardashev's classification (or Kardashev's scale) has often been seen as oversimplified, and multiple improvements, refinements, and alternatives to it have been suggested, it is still one of the major tools for serious theoretical investigation of SETI issues. During these 50+ years, several attempts at modifying or reforming the classification have been made; we review some of them here, together with presenting some of the scenarios which present difficulties to the standard version. Recent results in both theoretical and observational SETI studies, especially the G-hat infrared survey (2014-2015), have persuasively shown that the emphasis on detectability inherent in Kardashev's classification obtains new significance and freshness. Several new movements and conceptual frameworks, suc...

  19. Vehicle passes detector based on multi-sensor analysis

    Science.gov (United States)

    Bocharov, D.; Sidorchuk, D.; Konovalenko, I.; Koptelov, I.

    2015-02-01

    The study concerned deals with a new approach to the problem of detecting vehicle passes in vision-based automatic vehicle classification system. Essential non-affinity image variations and signals from induction loop are the events that can be considered as detectors of an object presence. We propose several vehicle detection techniques based on image processing and induction loop signal analysis. Also we suggest a combined method based on multi-sensor analysis to improve vehicle detection performance. Experimental results in complex outdoor environments show that the proposed multi-sensor algorithm is effective for vehicles detection.

  20. Development and Testing of an Automatic Transmission Shift Schedule Algorithm for Vehicle Simulation (SAE Paper 2015-01-1142)

    Science.gov (United States)

    The Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) modeling tool was created by EPA to estimate greenhouse gas (GHG) emissions of light-duty vehicles. ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of analyzing various vehicle type...

  1. Study on the Framework of Vehicle Automatic Navigation System%车辆自动导航系统基本框架研究

    Institute of Scientific and Technical Information of China (English)

    张可; 刘小明; 王笑京

    2001-01-01

    讨论了车辆自动导航系统的基本框架,将其划分为路网数据库管理、车辆定位、路线优化、路线引导4个子系统,并提出了各个子系统需要实现的功能,以及实现这些功能所需的关键技术%As one of the important research aspect of IntelligentTransportation System (ITS), the technique of vehicle automatic navigation has shown great prospect for application. This paper discusses the framework of the vehicle automatic navigation system, which could be divided into four subsystems: the database management subsystem for the road networks, the vehicle positioning subsystem, the route planning subsystem and the route guidance subsystem. The functions of each subsystem and the key techniques for realizing these functions are proposed.

  2. Unmanned Surface Vehicle Automatic Navigation Based On GPS%基于 GPS 定位的无人艇自主导航

    Institute of Scientific and Technical Information of China (English)

    陈永泽; 舒军勇; 王真亮; 谢能刚

    2016-01-01

    This paper mainly investigated unmanned surface vehicle automatic navigation based on GPS.According to the GPS received position coordinates and planning target point coordinates,this paper put forward an automatic navigation algorithm.Experimental sample of unmanned surface vehicle was established and actual trajectory was gained.By the actual trajectory and the comparison of theoretical trajectory as a result,we proved that the unmanned surface vehicle navigation algorithm has good robustness.%研究了基于 GPS 定位技术的无人艇自主导航。根据 GPS 接收到的位置坐标和规划的目标点坐标,提出一种自主导航算法。研制了无人艇实验样船,得到了无人艇实际航行轨迹。实际航行轨迹和理论航行轨迹的对比结果表明:该无人艇自主导航算法具有良好的鲁棒性。

  3. Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring

    Science.gov (United States)

    In this paper, we examine the potential of using a small unmanned aerial vehicle (UAV) for rangeland inventory, assessment and monitoring. Imagery with 8-cm resolution was acquired over 290 ha in southwestern Idaho. We developed a semi-automated orthorectification procedure suitable for handling lar...

  4. An automatic segmentation method for building facades from vehicle-borne LiDAR point cloud data based on fundamental geographical data

    Science.gov (United States)

    Li, Yongqiang; Mao, Jie; Cai, Lailiang; Zhang, Xitong; Li, Lixue

    2016-03-01

    In this paper, the author proposed a segmentation method based on the fundamental geographic data, the algorithm describes as following: Firstly, convert the coordinate system of fundamental geographic data to that of vehicle- borne LiDAR point cloud though some data preprocessing work, and realize the coordinate system between them; Secondly, simplify the feature of fundamental geographic data, extract effective contour information of the buildings, then set a suitable buffer threshold value for building contour, and segment out point cloud data of building facades automatically; Thirdly, take a reasonable quality assessment mechanism, check and evaluate of the segmentation results, control the quality of segmentation result. Experiment shows that the proposed method is simple and effective. The method also has reference value for the automatic segmentation for surface features of other types of point cloud.

  5. Design of Automatic Guide Vehicle Based on AT89S52%基于AT89S52的自动导引车设计

    Institute of Scientific and Technical Information of China (English)

    何裕源; 何晓晖

    2013-01-01

    介绍一种自动导引车(Automatic Guided Vehicle)的设计方法,小车采用红外传感器为导引装置,直流电机为驱动装置,以AT89S52为主控核心,指导自动导引车自动识别正确轨迹并实现运行.该系统电气原理简单,可靠性能高,为自动导引车在工业中应用开发提供参考.

  6. 基于语义模板的文档自动分类模型研究%Study on Automatic Classification Model of Documents Based on Semantics

    Institute of Scientific and Technical Information of China (English)

    李海蓉

    2012-01-01

    简要介绍语义模板的概念.提出基于语义模板向量空间的文档自动分类模型。利用支持向量机(SVM,Support Vector Machine)分类算法对文档测试集进行基于语义模板空间、词向量空间的分类实验,实验结果表明,基于语义模板空间的文本分类性能比基于词向量空间的分类性能要高。%Simply introduced the concepts of semantic pattern, and proposed an automatic classification of Documents based on semantic pattern vector space. With the SVM classification algorithm carrying out classification experiment to document test corpus based on semantic pattern vector space and based on word vector space. Experimental results show that text classification performance based on semantic pattern vector space is higher than that based on word vector space.

  7. A Summary of Research on Automatic Text Classification Technologies%文本自动分类技术研究综述

    Institute of Scientific and Technical Information of China (English)

    庞观松; 蒋盛益

    2012-01-01

    Research results in automatic text classification in resent years are summarized and discussed from the perspective of text representation,feature selection,classification algorithm,commonly-used benchmark corpuses and evaluation indices.It's believed that short-text classification and multilingual text organization are the newly-emerging important and urgent problems.This paper focuses on discussing these two problems as well as several other key problems such as class imbalance,hierarchical classification and labeled corpus bottleneck.Finally,the paper summarizes and forecasts these researches.%文章从文本表示、特征选择、分类算法、常用基准语料以及评估指标等方面对近年来的研究成果进行综述并讨论。认为短文本分类和多语言文本分类管理是新出现的重要且紧迫的问题,并对这两个问题以及数据集偏斜、多层分类、标注瓶颈等几个关键问题进行重点讨论。最后总结并展望这些研究内容。

  8. The vehicle routing problem: State of the art classification and review

    OpenAIRE

    Braekers, Kris; Ramaekers, Katrien; Van Nieuwenhuyse, Inneke

    2016-01-01

    Over the past decades, the Vehicle Routing Problem (VRP) and its variants have grown ever more popular in the academic literature. Yet, the problem characteristics and assumptions vary widely and few literature reviews have made an effort to classify the existing articles accordingly. In this article, we present a taxonomic review of the VRP literature published between 2009 and June 2015. Based on an adapted version of an existing comprehensive taxonomy, we classify 277 articles and analyze ...

  9. 使用SOFM方法进行恒星光谱自动分类%Automatic Classification of Stellar Spectra Using SOFM Method

    Institute of Scientific and Technical Information of China (English)

    薛建桥; 李启斌; 赵永恒

    2000-01-01

    In this paper, an automatic classification method of stellar spectrausing the Self-Organization Feature Mapping (SOFM) method is given.The SOFM is an unsupervised learning algorithm of Artificial NeuralNetwork (ANN). It allows the data to be organized onto a feature graphwhile conserving most of the topological features of the originaldata space. We used this method to classify stellar spectraautomatically. The result is very similar to the Harvard sequencewith an accuracy comparable to that obtained by human experts. TheSOFM should be a useful method for on-line classification of stellarspectrum samples of very large size. The SOFM can beexecuted automatically, so it can be applied to process largenumbers of target spectra.%SOFM是人工神经网络的非监督学习算法,可以将数据组织到一个特征图上,而保存大多数原始数据空间的拓扑特征. 使用这种方法进行恒星光谱自动分类, 分类结果与哈佛序列十分相似.SOFM方法应该是进行大数量恒星光谱样本在线分类的有用方法,它能够自动执行,因此可用于处理大数量天体光谱.

  10. Real-time adaptive off-road vehicle navigation and terrain classification

    Science.gov (United States)

    Muller, Urs A.; Jackel, Lawrence D.; LeCun, Yann; Flepp, Beat

    2013-05-01

    We are developing a complete, self-contained autonomous navigation system for mobile robots that learns quickly, uses commodity components, and has the added benefit of emitting no radiation signature. It builds on the au­tonomous navigation technology developed by Net-Scale and New York University during the Defense Advanced Research Projects Agency (DARPA) Learning Applied to Ground Robots (LAGR) program and takes advantage of recent scientific advancements achieved during the DARPA Deep Learning program. In this paper we will present our approach and algorithms, show results from our vision system, discuss lessons learned from the past, and present our plans for further advancing vehicle autonomy.

  11. Experimental analysis on classification of unmanned aerial vehicle images using the probabilistic latent semantic analysis

    Science.gov (United States)

    Yi, Wenbin; Tang, Hong

    2009-10-01

    In this paper, we present a novel algorithm to classify UAV images through the image annotation which is a semi-supervised method. During the annotation process, we first divide whole image into different sizes of blocks and generate suitable visual words which are the K-means clustering centers or just pixels in small size image block. Then, given a set of image blocks for each semantic concept as training data, learning is based on the Probabilistic Latent Semantic Analysis (PLSA). The probability distributions of visual words in every document can be learned through the PLSA model. The labeling of every document (image block) is done by computing the similarity of its feature distribution to the distribution of the training documents with the Kullback-Leibler (K-L) divergence. Finally, the classification of the UAV images will be done by combining all the image blocks in every block size. The UAV images using in our experiments was acquired during Sichuan earthquake in 2008. The results show that smaller size block image will get better classification results.

  12. A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning

    DEFF Research Database (Denmark)

    Olesen, Alexander Neergaard; Christensen, Julie Anja Engelhard; Sørensen, Helge Bjarup Dissing;

    2016-01-01

    (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen’s kappa of 0.74 indicating substantial agreement between...

  13. Automatic classification of gammas-gamma coincidence matrices; Clasificacion automatizada de matrices de coincidencias Gamma-Gamma

    Energy Technology Data Exchange (ETDEWEB)

    Los Arcos Merino, J. M.; Gonzalez, J. A.

    1978-07-01

    The information obtained during a coincidence experiment, recorded on magnetic tape by a MULTI-8 minicomputer, is transferred to a new tape in 36 bit words, using the program LEC0M8. The classification in two dimensional matrix form is carried out off-line, on a magnetic disk file, by the program CLAFI. On finishing classification one obtains a copy of the coincidence matrix on the second magnetic tape. Both programs are written to be processed in that order with the UNIVAC 1106 computer of J.E.N. (Author) 4 refs.

  14. 基于建模仿真的战车分类算法研究%Research on the Military Vehicle Classification Algorithm Based on Modeling and Simulation

    Institute of Scientific and Technical Information of China (English)

    马云飞

    2014-01-01

    Recognition and classification of military vehicle is an important research content of information acquirement in battlefield. In order to collect data and study military vehicle classification algorithm, real external field experiment mode is popularly used, however it needs long time and expensive costs. In this paper, the tank model, armored vehicle model and truck model are built in the virtual battlefield simulation platform. The noise signals, magnetic field signals and vibration signals of the military vehicles in simulation environment are collected and used as the sample data for the research of military vehicle classification algorithm. In the same time this paper designs a classification algorithm of military vehicle based on the one-to-one multi-class SVM, and gives out an adjustment strategy for classifier parameters based on the cross-validation method. The experiment results show that, compared to AdaBoost algorithm, the present algorithm has higher classification accuracy on military vehicles.%战车类型的识别分类是现代情报获取的重要研究内容。为了获得数据并研究战车分类算法,常进行外场真实实验,但其时间长、耗资巨大。本文在虚拟战场仿真平台上建立坦克、装甲车、运兵车三种战车模型。利用仿真环境中的战车噪声、磁场、振动特征信号作为样本数据,进行战车的分类算法研究。同时基于一对一多分类支持向量机,设计了一种战车分类算法,并给出了分类器交叉验证参数调整策略。实验表明,相比于AdaBoost算法,文章提出的战车分类算法的分类准确率较高。

  15. Large Scale Automatic Analysis and Classification of Roof Surfaces for the Installation of Solar Panels Using a Multi-Sensor Aerial Platform

    Directory of Open Access Journals (Sweden)

    Luis López-Fernández

    2015-09-01

    Full Text Available A low-cost multi-sensor aerial platform, aerial trike, equipped with visible and thermographic sensors is used for the acquisition of all the data needed for the automatic analysis and classification of roof surfaces regarding their suitability to harbor solar panels. The geometry of a georeferenced 3D point cloud generated from visible images using photogrammetric and computer vision algorithms, and the temperatures measured on thermographic images are decisive to evaluate the areas, tilts, orientations and the existence of obstacles to locate the optimal zones inside each roof surface for the installation of solar panels. This information is complemented with the estimation of the solar irradiation received by each surface. This way, large areas may be efficiently analyzed obtaining as final result the optimal locations for the placement of solar panels as well as the information necessary (location, orientation, tilt, area and solar irradiation to estimate the productivity of a solar panel from its technical characteristics.

  16. Minimum Hamiltonian Ascent Trajectory Evaluation (MASTRE) program (update to automatic flight trajectory design, performance prediction, and vehicle sizing for support of Shuttle and Shuttle derived vehicles) engineering manual

    Science.gov (United States)

    Lyons, J. T.

    1993-01-01

    The Minimum Hamiltonian Ascent Trajectory Evaluation (MASTRE) program and its predecessors, the ROBOT and the RAGMOP programs, have had a long history of supporting MSFC in the simulation of space boosters for the purpose of performance evaluation. The ROBOT program was used in the simulation of the Saturn 1B and Saturn 5 vehicles in the 1960's and provided the first utilization of the minimum Hamiltonian (or min-H) methodology and the steepest ascent technique to solve the optimum trajectory problem. The advent of the Space Shuttle in the 1970's and its complex airplane design required a redesign of the trajectory simulation code since aerodynamic flight and controllability were required for proper simulation. The RAGMOP program was the first attempt to incorporate the complex equations of the Space Shuttle into an optimization tool by using an optimization method based on steepest ascent techniques (but without the min-H methodology). Development of the complex partial derivatives associated with the Space Shuttle configuration and using techniques from the RAGMOP program, the ROBOT program was redesigned to incorporate these additional complexities. This redesign created the MASTRE program, which was referred to as the Minimum Hamiltonian Ascent Shuttle TRajectory Evaluation program at that time. Unique to this program were first-stage (or booster) nonlinear aerodynamics, upper-stage linear aerodynamics, engine control via moment balance, liquid and solid thrust forces, variable liquid throttling to maintain constant acceleration limits, and a total upgrade of the equations used in the forward and backward integration segments of the program. This modification of the MASTRE code has been used to simulate the new space vehicles associated with the National Launch Systems (NLS). Although not as complicated as the Space Shuttle, the simulation and analysis of the NLS vehicles required additional modifications to the MASTRE program in the areas of providing

  17. Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

    Directory of Open Access Journals (Sweden)

    Cíntia Matsuda Toledo

    Full Text Available Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario.OBJECTIVE: The aims were to describe how to: (i develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii automatically identify the features that best distinguish the groups.METHODS: The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age. In this study, the descriptions by 144 of the subjects studied in Toledo18 were used, which included 200 healthy Brazilians of both genders.RESULTS AND CONCLUSION:A Support Vector Machine (SVM with a radial basis function (RBF kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS is a strong candidate to replace manual feature selection methods.

  18. Classification of Water Masses and Targeted Sampling of Ocean Plankton Populations by an Autonomous Underwater Vehicle

    Science.gov (United States)

    Zhang, Y.; Ryan, J. P.; Bellingham, J. G.; Harvey, J.; McEwen, R.; Chavez, F.; Scholin, C.

    2011-12-01

    Autonomous underwater vehicles (AUVs) are playing an increasingly active role in oceanographic surveys due to their mobility, efficiency, and growing intelligence. The Dorado AUV is equipped with a comprehensive suite of in situ sensors and ten 1.8-liter water samplers (called "gulpers"). During an October 2010 experiment in Monterey Bay, the AUV ran our autonomous peak-capture algorithm to acquire chlorophyll/backscatter peak samples from a phytoplankton bloom, allowing biologists to successfully monitor fluctuations in harmful microalgae (Psuedo-nitzschia spp.), the toxin they produce (domoic acid), and co-occurring zooplankton (invertebrate larvae and copepods) over space and time. For further investigations of the complex marine ecosystem in northern Monterey Bay, we set a more challenging goal: when the AUV flies from an upwelling shadow region (stratified water column) through an upwelling front into newly upwelled water, can it autonomously distinguish among water columns with different vertical structures and accordingly sample plankton populations on either side of, as well as within, the upwelling front? To achieve this goal, we have developed two new algorithms, one for distinguishing upwelling water columns from stratified water columns based on the vertical homogeneity of temperature, and the other for detecting an upwelling front based on the horizontal gradient of temperature. For acquiring targeted water samples, the 10 gulpers are appropriately allocated to the two distinct water columns and the front. Lockout time intervals between triggerings are set to prevent "dense triggerings". During our June 2011 experiment, the Dorado AUV flew westward from an upwelling shadow region (stratified water column) through an upwelling front, and into an upwelling water column. Three gulpers were allocated to the stratified water column, four to the front, and the remaining three to the upwelling water column. The AUV successfully detected and acquired targeted

  19. Research of brain-computer interface automatic vehicle system based on SSVEP%基于SSVEP的脑-机接口自动车系统研究

    Institute of Scientific and Technical Information of China (English)

    赵丽; 孙永; 马彦臻; 何洋

    2011-01-01

    This paper mainly carried out proposes the research of SSVEP brain-computer interface automatic vehicle control systems,which describes the principles of the visual evoked potentials that used in brain-computer interface,and the single-chip is used to designs visual stimulation. Base on the LABVIEW platform, it also uses Hilbert Huang Transform to extract evoked potential vector continuously,which produces brain-computer interface control signals that can be applied to automatic vehicle control system to control the car around before and after exercise. According to a lot of experiments to verify,this sistem can send out the control commands that the correct rate is higher than 83% and can also send a command less than 5 seconds compared with the average time based on SSVEP,so it proves that the system is feasible and has a high application value.%阐述了视觉诱发电位用于脑-机接口的原理,系统采用单片机设计视觉刺激器,同时在LABVIEW平台上,利用希尔伯特黄变换实时提取诱发电位向量,产生脑机接口控制信号,并用于自动车控制系统,从而控制小车的前后左右运动.通过大量实验验证,设计的基于稳态视觉诱发电位的脑-机接口自动车控制系统,发送控制命令正确率高于83%,发送一个命令的平均时间低于5 s,证明该系统的方案是可行的,具有较高的应用价值.

  20. Development of a Two-Speed Automatic Transmission for Pure Electric Vehicle%纯电动车两挡自动变速器的研发

    Institute of Scientific and Technical Information of China (English)

    黄伟; 王耀南; 冯坤

    2011-01-01

    为了改善纯电动汽车驱动系统的性能,开发一种用于纯电动运动多功能汽车的两级自动变速器.应用速比选择原则、换挡控制策略,研究其对于整车性能的影响.仿真结果表明:设计的两挡自动变速器可降低对电机最大转矩和最高转速的需求,减少机械传动噪音,降低变速器输入转速,优化电机的工作转速区间,提高动力传动系统效率.台架试验表明:采用电机主动同步控制技术,在挡位切换过程中能减少动力中断时问,获得较好的换挡品质.%A two-speed automatic transmission used in electric sport utility vehicle was developed to improve fhe performance of the propulsion system of the vehicle.The motor active synchronization were researched using gear ratio choice principle and shift change control algorithm.Simulation results show that the two-speed automatic transmission reduces the maximum torque demand,the motor running speed and mechanical noise,and improves driveline efficiency.The bench tests show thaf the motor active synchronization control reduces the power interruption time,and helps the transmission achieve a high shifting quality.

  1. Automatic welding quality classification for the spot welding based on the Hopfield associative memory neural network and Chernoff face description of the electrode displacement signal features

    Science.gov (United States)

    Zhang, Hongjie; Hou, Yanyan; Zhao, Jian; Wang, Lijing; Xi, Tao; Li, Yafeng

    2017-02-01

    To develop an automatic welding quality classification method for the spot welding based on the Chernoff face image created by the electrode displacement signal features, an effective pattern feature extraction method was proposed by which the Chernoff face images were converted to binary ones, and each binary image could be characterized by a binary matrix. According to expression categories on the Chernoff face images, welding quality was classified into five levels and each level just corresponded to a kind of expression. The Hopfield associative memory neural network was used to build a welding quality classifier in which the pattern feature matrices of some weld samples with different welding quality levels were remembered as the stable states. When the pattern feature matrix of a test weld is input into the classifier, it can be converged to the most similar stable state through associative memory, thus, welding quality corresponding to this finally locked stable state can represent the welding quality of the test weld. The classification performance test results show that the proposed method significantly improves the applicability and efficiency of the Chernoff faces technique for spot welding quality evaluation and it is feasible, effective and reliable.

  2. A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels

    Directory of Open Access Journals (Sweden)

    Kopriva Ivica

    2011-12-01

    Full Text Available Abstract Background Bioinformatics data analysis is often using linear mixture model representing samples as additive mixture of components. Properly constrained blind matrix factorization methods extract those components using mixture samples only. However, automatic selection of extracted components to be retained for classification analysis remains an open issue. Results The method proposed here is applied to well-studied protein and genomic datasets of ovarian, prostate and colon cancers to extract components for disease prediction. It achieves average sensitivities of: 96.2 (sd = 2.7%, 97.6% (sd = 2.8% and 90.8% (sd = 5.5% and average specificities of: 93.6% (sd = 4.1%, 99% (sd = 2.2% and 79.4% (sd = 9.8% in 100 independent two-fold cross-validations. Conclusions We propose an additive mixture model of a sample for feature extraction using, in principle, sparseness constrained factorization on a sample-by-sample basis. As opposed to that, existing methods factorize complete dataset simultaneously. The sample model is composed of a reference sample representing control and/or case (disease groups and a test sample. Each sample is decomposed into two or more components that are selected automatically (without using label information as control specific, case specific and not differentially expressed (neutral. The number of components is determined by cross-validation. Automatic assignment of features (m/z ratios or genes to particular component is based on thresholds estimated from each sample directly. Due to the locality of decomposition, the strength of the expression of each feature across the samples can vary. Yet, they will still be allocated to the related disease and/or control specific component. Since label information is not used in the selection process, case and control specific components can be used for classification. That is not the case with standard factorization methods. Moreover, the component selected by proposed method

  3. Automatic classification of lung tumour heterogeneity according to a visual-based score system in dynamic contrast enhanced CT sequences

    Science.gov (United States)

    Bevilacqua, Alessandro; Baiocco, Serena

    2016-03-01

    Computed tomography (CT) technologies have been considered for a long time as one of the most effective medical imaging tools for morphological analysis of body parts. Contrast Enhanced CT (CE-CT) also allows emphasising details of tissue structures whose heterogeneity, inspected through visual analysis, conveys crucial information regarding diagnosis and prognosis in several clinical pathologies. Recently, Dynamic CE-CT (DCE-CT) has emerged as a promising technique to perform also functional hemodynamic studies, with wide applications in the oncologic field. DCE-CT is based on repeated scans over time performed after intravenous administration of contrast agent, in order to study the temporal evolution of the tracer in 3D tumour tissue. DCE-CT pushes towards an intensive use of computers to provide automatically quantitative information to be used directly in clinical practice. This requires that visual analysis, representing the gold-standard for CT image interpretation, gains objectivity. This work presents the first automatic approach to quantify and classify the lung tumour heterogeneities based on DCE-CT image sequences, so as it is performed through visual analysis by experts. The approach developed relies on the spatio-temporal indices we devised, which also allow exploiting temporal data that enrich the knowledge of the tissue heterogeneity by providing information regarding the lesion status.

  4. Vehicles Recognition Using Fuzzy Descriptors of Image Segments

    CERN Document Server

    Płaczek, Bartłomiej

    2011-01-01

    In this paper a vision-based vehicles recognition method is presented. Proposed method uses fuzzy description of image segments for automatic recognition of vehicles recorded in image data. The description takes into account selected geometrical properties and shape coefficients determined for segments of reference image (vehicle model). The proposed method was implemented using reasoning system with fuzzy rules. A vehicles recognition algorithm was developed based on the fuzzy rules describing shape and arrangement of the image segments that correspond to visible parts of a vehicle. An extension of the algorithm with set of fuzzy rules defined for different reference images (and various vehicle shapes) enables vehicles classification in traffic scenes. The devised method is suitable for application in video sensors for road traffic control and surveillance systems.

  5. Modularity, adaptability and evolution in the AUTOPIA architecture for control of autonomous vehicles. Updating Mechatronics of Automatic Cars

    OpenAIRE

    Pérez Rastelli, Joshué; González, Carlos; Milanés, Vicente; Onieva, Enrique; Godoy, Jorge; Pedro, Teresa De

    2009-01-01

    International audience; Computer systems to carry out control algorithms on autonomous vehicles have been developed in recent years. However, the advances in peripheral devices allow connecting the actuator controllers to the control system by means of standard communication links (USB, CAN, Ethernet ... ).The goal is to permit the use of standard computers. In this paper, we present the evolution of AUTOPIA architecture and its modularity and adaptability to move the old system based on ISA ...

  6. 自动情感文本分类研究综述%A Literature Review on Automatic Sentiment Classification

    Institute of Scientific and Technical Information of China (English)

    夏火松; 彭柳艳; 余梦麟

    2011-01-01

    Sentiment classification and its applications have been witnessed a booming interest in nowadays research,it is a cross research of natural language processing, machine learning and psychology, and has practical applications in many fields, such as product reputation analysis, public opinion tracking, blogger interests analysis, and so on. This paper gives an overview of the current study on sentiment classification of domestic and international, divides research directions of existing literature into four categories, then describe these four categories in detail, and analyzes the key issues in this field, then proposes a general framework for sentiment classification, finally, discuss the shortcomings of current study, and predicts the development trend.%情感分类及其应用是目前研究的一个热点,是自然语言处理,机器学习和心理学等多学科交叉的研究课题,在很多领域都有实际的应用,如产品的声誉分析,舆情跟踪,博客兴趣分析等.论文对情感分类目前国内外的研究概貌进行了分析,将现有文献中的研究方向分为四个类别,并对这四个类别分别进行了描述,对情感分类中的关键问题进行了研究,提出了情感分类的一般框架,最后对目前研究中存在的不足进行了讨论,对情感分类研究的发展方向进行了展望.

  7. Practical issues in automatic 3D reconstruction and navigation applications using man-portable or vehicle-mounted sensors

    Science.gov (United States)

    Harris, Chris; Stennett, Carl

    2012-09-01

    The navigation of an autonomous robot vehicle and person localisation in the absence of GPS both rely on using local sensors to build a model of the 3D environment. Accomplishing such capabilities is not straightforward - there are many choices to be made of sensor and processing algorithms. Roke Manor Research has broad experience in this field, gained from building and characterising real-time systems that operate in the real world. This includes developing localization for planetary and indoor rovers, model building of indoor and outdoor environments, and most recently, the building of texture-mapped 3D surface models.

  8. Automatic classification and robust identification of vestibulo-ocular reflex responses: from theory to practice: introducing GNL-HybELS.

    Science.gov (United States)

    Ghoreyshi, Atiyeh; Galiana, Henrietta

    2011-10-01

    The Vestibulo-Ocular Reflex (VOR) stabilizes images of the world on our retinae when our head moves. Basic daily activities are thus impaired if this reflex malfunctions. During the past few decades, scientists have modeled and identified this system mathematically to diagnose and treat VOR deficits. However, traditional methods do not analyze VOR data comprehensively because they disregard the switching nature of nystagmus; this can bias estimates of VOR dynamics. Here we propose, for the first time, an automated tool to analyze entire VOR responses (slow and fast phases), without a priori classification of nystagmus segments. We have developed GNL-HybELS (Generalized NonLinear Hybrid Extended Least Squares), an algorithmic tool to simultaneously classify and identify the responses of a multi-mode nonlinear system with delay, such as the horizontal VOR and its alternating slow and fast phases. This algorithm combines the procedures of Generalized Principle Component Analysis (GPCA) for classification, and Hybrid Extended Least Squares (HybELS) for identification, by minimizing a cost function in an optimization framework. It is validated here on clean and noisy VOR simulations and then applied to clinical VOR tests on controls and patients. Prediction errors were less than 1 deg for simulations and ranged from .69 deg to 2.1 deg for the clinical data. Nonlinearities, asymmetries, and dynamic parameters were detected in normal and patient data, in both fast and slow phases of the response. This objective approach to VOR analysis now allows the design of more complex protocols for the testing of oculomotor and other hybrid systems.

  9. Implementation of Fuzzy Logic with High Security Registration Plate (HSRP for Vehicle Classification and Checking in TollPlaza

    Directory of Open Access Journals (Sweden)

    V. Sathya

    2012-10-01

    Full Text Available In Automobile Industries, to use of High Security Registration plate (HSRP is still a challenging problem. There are more options to misuse the vehicle and exchange its engine, chassis, gear box, axle etc., In an existing system, the Regional Transport Office (RTO only determine an abstract of the vehicle and its owner. The vehicles are classified using piezo sensor and inductive loop systems. The toll-plaza is used only collected fees from the vehicles for maintain the quality roads. There are no authorized agencies allotted to identify the vehicle checking and no possibilities to control the vehicle overloading. The proposed system, toll-plaza will be act as a multi-plaza. Vehicles are classified with weight and speed. Then it is checking in toll-plaza either passed or checked. In this paper, The system uses illumination (such as Infrared and a camera to take the image of the front or rear of the vehicle, then an extracts the plate information. This data is used for enforcement and it can be used to open a gate if the vehicle is checked with RTO data in toll-plaza. In vehicle checking, we develop new rules using a fuzzy logic to improve the performance. The features of this system are implemented in the upgrading vehicles only. It is used to control the overloading to maintain road safety and to identify the theft vehicle to reduce the crime and terrorism. As Bharat Stage Emission (BSE standard vehicles are implemented in India very aggressively. The emission standard vehicles are serviced only in authorized service centre not for doing and end root machines.

  10. Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis.

    Science.gov (United States)

    Möller, Christiane; Pijnenburg, Yolande A L; van der Flier, Wiesje M; Versteeg, Adriaan; Tijms, Betty; de Munck, Jan C; Hafkemeijer, Anne; Rombouts, Serge A R B; van der Grond, Jeroen; van Swieten, John; Dopper, Elise; Scheltens, Philip; Barkhof, Frederik; Vrenken, Hugo; Wink, Alle Meije

    2016-06-01

    Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD

  11. Optimal Line Pressure Control for an Automatic Transmission-Based Parallel Hybrid Electric Vehicle considering Mode Change and Gear Shift

    Directory of Open Access Journals (Sweden)

    Minseok Song

    2014-05-01

    Full Text Available An optimal line pressure control algorithm was proposed for the fuel economy improvement of an AT-based parallel hybrid electric vehicle (HEV. By performing lever analysis at each gear step, the required line pressure was obtained considering the torque ratio of the friction elements. In addition, the required line pressure of the mode clutch was calculated. Based on these results, the optimal line pressure map at each gear step of the EV and HEV modes was presented. Using the line pressure map, an optimal line pressure was performed for the AT input torque and mode. To investigate the proposed line pressure control algorithm, a HEV performance simulator was developed based on the powertrain model of the target HEV, and fuel economy improvement was evaluated. Simulation results showed that as the gear step became higher, the optimal line pressure control could reduce the hydraulic power loss, which gave a 2.2% fuel economy improvement compared to the existing line pressure control for the FTP-72 mode.

  12. High-Resolution, Semi-Automatic Fault Mapping Using Umanned Aerial Vehicles and Computer Vision: Mapping from an Armchair

    Science.gov (United States)

    Micklethwaite, S.; Vasuki, Y.; Turner, D.; Kovesi, P.; Holden, E.; Lucieer, A.

    2012-12-01

    Our ability to characterise fractures depends upon the accuracy and precision of field techniques, as well as the quantity of data that can be collected. Unmanned Aerial Vehicles (UAVs; otherwise known as "drones") and photogrammetry, provide exciting new opportunities for the accurate mapping of fracture networks, over large surface areas. We use a highly stable, 8 rotor, UAV platform (Oktokopter) with a digital SLR camera and the Structure-from-Motion computer vision technique, to generate point clouds, wireframes, digital elevation models and orthorectified photo mosaics. Furthermore, new image analysis methods such as phase congruency are applied to the data to semiautomatically map fault networks. A case study is provided of intersecting fault networks and associated damage, from Piccaninny Point in Tasmania, Australia. Outcrops >1 km in length can be surveyed in a single 5-10 minute flight, with pixel resolution ~1 cm. Centimetre scale precision can be achieved when selected ground control points are measured using a total station. These techniques have the potential to provide rapid, ultra-high resolution mapping of fracture networks, from many different lithologies; enabling us to more accurately assess the "fit" of observed data relative to model predictions, over a wide range of boundary conditions.igh resolution DEM of faulted outcrop (Piccaninny Point, Tasmania) generated using the Oktokopter UAV (inset) and photogrammetric techniques.

  13. Automatic speed control of highway traffic

    Science.gov (United States)

    Klingman, E. E.

    1973-01-01

    Vehicle control system monitors all vehicles in its range, and automatically slows down speeding vehicles by activating governor in vehicle. System determines only maximum speed; speeds below maximum are controlled by vehicle operator. Loss of transmitted signal or activation of emergency over-ride will open fuel line and return control to operator.

  14. 空客A320系列飞机APS3200型APU自动关车故障分析%Failure Analysis of APS3200 Type APU Automatic Vehicle Shutdown of the Airbus A320 Series

    Institute of Scientific and Technical Information of China (English)

    张绪勤

    2016-01-01

    In this paper, by analyzing the troubleshooting process of APS3200 Type APU Automatic Vehicle Shutdown of the Airbus A320 Series occurring repeatedly, it proposed troubleshooting and maintenance tips to the APU shutdown automatic vehicle shutdown appearing in the operation of the aircraft power supply switching.%本文通过对空客A320系列飞机APS3200型APU反复出现自动关车故障的排故过程进行分析,对飞机运行中供电切换时出现APU自动关车现象提出排故建议和维护提示。

  15. 基于深度卷积神经网络的车型识别研究%Deep convolution neural networks for vehicle classification

    Institute of Scientific and Technical Information of China (English)

    邓柳; 汪子杰

    2016-01-01

    近年来,深度学习中的卷积神经网络已经广泛运用于图像识别领域,它不仅显著提升了识别准确率,同时在特征提取速度方面也优于许多传统方法。针对高速公路环境下的车型识别问题,引入卷积神经网络(CNNs)理论,设计相应特征提取算法,并结合 SVM分类器构建识别系统。通过对高速公路上主要三种车型(小车、客车、货车)的分类实验显示,该方法在识别精度及速度上均取得了较显著的提高。%In recent years,the deep convolution neural network (CNN),a state-of-the-art deep learning method,has been widely used in the field of image recognition.It can not only significantly improve the recognition accuracy,but also superior to many traditional algorithms in terms of feature extraction speed.This paper firstly introduced the CNN for the highway vehi-cle recognition.It constructed a vehicle recognition system by using a proposed deep CNN based feature extraction method and the SVMclassifier.The classification results of three major types of vehicles (cars,buses,trucks)on the highway show that significant improvements are achieved in both classification accuracy and speed.

  16. Continuous Active Sonar for Undersea Vehicles Final Report: Input of Factor Graphs into the Detection, Classification, and Localization Chain and Continuous Active SONAR in Undersea Vehicles

    Science.gov (United States)

    2015-12-31

    Kk( ’- e usmg l ment zk- Hx;;) ~ ~ ~~M = pk-HT(HP Gain I kHT +R)-1 J Figure 16: An image of the recursive process of the Kalman filter...the areas of continuous active sonar (CAS) and unmanned underwater vehicle signal processing . Closed-form factor graph formulations for detection...quantified . [This report is excerpted from the thesis submitted by Brandi N. Gross in partial fulfillment of the requirements for the degree of Master of

  17. CLASSIFICATION OF URBAN FEATURE FROM UNMANNED AERIAL VEHICLE IMAGES USING GASVM INTEGRATION AND MULTI-SCALE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    M. Modiri

    2015-12-01

    Full Text Available The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

  18. Tissue Classification

    DEFF Research Database (Denmark)

    Van Leemput, Koen; Puonti, Oula

    2015-01-01

    Computational methods for automatically segmenting magnetic resonance images of the brain have seen tremendous advances in recent years. So-called tissue classification techniques, aimed at extracting the three main brain tissue classes (white matter, gray matter, and cerebrospinal fluid), are now...... well established. In their simplest form, these methods classify voxels independently based on their intensity alone, although much more sophisticated models are typically used in practice. This article aims to give an overview of often-used computational techniques for brain tissue classification...

  19. AUTOMATIC REMOTE SENSING IMAGE CLASSIFICATION ALGORITHM BASED ONFCM AND BP NEURAL NETWORK%基于模糊C均值和BP神经网络的遥感影像自动分类算法

    Institute of Scientific and Technical Information of China (English)

    黄奇瑞

    2015-01-01

    针对非监督分类算法分类精度不高、监督法分类算法的训练样本需要人工选择且容易误选的问题,提出了一种基于模糊C均值聚类( FCM)和BP神经网络相结合的遥感影像自动分类算法. 首先利用FCM对影像进行初始聚类,然后根据聚类结果,由该算法自动选取其中的纯净像元作为训练样本,并送入BP网络进行学习,用最终训练得到的BP神经网络分类器对TM遥感影像进行分类,实验结果表明该算法具有较高的分类精度,能够满足大尺度地物类别判定的需要.%As for the problems that low classification accuracy of non-supervise classification algorithm and training sample of super-vise classification algorithm needs manual selection which is easy to be made wrongly, there is an automatic classfication algorithm of remote sensing image which is based on the combination of FCM and BP neural network. First, this paper uses FCM to make initial clusters of images. Then in accordance with the results of clusters, this paper picks out the endmembers which are automatically select-ed by the algorithm as the traaning samples, sends the samples to study in BP network and uses the BP neural network classifier which is got from the final training to classify the TM remote sensing images. The result shows that the algorithm owns high accuracy which could meet the requirements of determination of object types in a large scale.

  20. Automatic vehicle detection using spaceborne optical remote sensing images in city area%城市街区星载光学遥感图像车辆目标自动检测方法

    Institute of Scientific and Technical Information of China (English)

    李昭慧; 张建奇

    2014-01-01

    It is difficult to detect vehicles in city area by using paceborne optical remote sensing images, because the background in city area is too complex. In this paper, an automatic vehicle detection method was proposed to address the issue by using background segmentation method. Firstly, the physical property of the vegetation was analyzed and used to suppress the vegetation background of a scene by using the multi- spectral information of the scene. Next, the reflectance characteristics of city area cover types were analyzed. Based on the reflectance characteristics of building roofs and roads, the building background in the scene was removed by employing the binary morphological method on the panchromatic band image. Finally, the famous RX algorithm was introduced to detect the vehicles on the vegetation and building background suppressed image. The proposed method is applied to the actual Quickbird image for vehicle target detection. The results show that the proposed method has strong robustness, high efficiency, and automatic characteristics, and can be used for vehicle detection in city area.%针对星载光学遥感图像城市街区复杂背景问题,提出一种车辆目标自动检测方法。首先,利用场景中植被背景的物理属性,通过多光谱波段抑制场景中的植被背景,然后,在分析城市街区地物形态反射率特性的基础上,利用全色波段并结合二值形态学方法抑制场景中的建筑物,最后,引入著名的RX算法对抑制后的图像进行车辆目标检测。将文中提出的方法应用于实际Quickbird影像的车辆目标检测,结果表明所提出的方法具有鲁棒性强,执行效率高,不需要人工辅助等方面的特点,可用于城市街区车辆目标的自动检测。

  1. Automatic Water Sensor Window Opening System

    KAUST Repository

    Percher, Michael

    2013-12-05

    A system can automatically open at least one window of a vehicle when the vehicle is being submerged in water. The system can include a water collector and a water sensor, and when the water sensor detects water in the water collector, at least one window of the vehicle opens.

  2. AUTOMATIC CLASSIFICATION OF STRUCTURAL MRI FOR DIAGNOSIS OF NEURODEGENERATIVE DISEASES Clasificación automática de IRM estructural para el diagnóstico de enfermedades neurodegenerativas

    Directory of Open Access Journals (Sweden)

    GLORIA DÍAZ

    Full Text Available This paper presents an automatic approach which classifies structural Magnetic Resonance images into pathological or healthy controls. A classification model was trained to find the boundaries that allow to separate the study groups. The method uses the deformation values from a set of regions, automatically identified as relevant, in a process that selects the statistically significant regions of a t-test under the restriction that this significance must be spatially coherent within a neighborhood of 5 voxels. The proposed method was assessed to distinguish healthy controls from schizophrenia patients. Classification results showed accuracy between 74% and 89%, depending on the stage of the disease and number of training samples.Este artículo presenta un método automático para la clasificación de individuos en grupos patológicos o controles sanos haciendo uso de imágenes de resonancia magnética. El método propuesto usa los valores de deformación del sujeto analizado a un cerebro plantilla, para entrenar un modelo de clasificación capaz de identificar las fronteras que separan los grupos de estudio en un espacio de características dado. Con el fin de reducir la dimensionalidad del problema, un conjunto de regiones relevantes es automáticamente extraído en un proceso que selecciona las regiones estadísticamente significativas en una prueba t-student, con la restricción de mantener coherencia en dicha significancia en una vecindad de 5 voxeles. El método propuesto fue evaluado en la clasificación de pacientes con esquizofrenia y sujetos sanos. Los resultados mostraron un desempeño entre el 74 y el 89%, el cual depende principalmente del número de muestras empleadas para el entrenamiento del modelo.

  3. Automatic sequences

    CERN Document Server

    Haeseler, Friedrich

    2003-01-01

    Automatic sequences are sequences which are produced by a finite automaton. Although they are not random they may look as being random. They are complicated, in the sense of not being not ultimately periodic, they may look rather complicated, in the sense that it may not be easy to name the rule by which the sequence is generated, however there exists a rule which generates the sequence. The concept automatic sequences has special applications in algebra, number theory, finite automata and formal languages, combinatorics on words. The text deals with different aspects of automatic sequences, in particular:· a general introduction to automatic sequences· the basic (combinatorial) properties of automatic sequences· the algebraic approach to automatic sequences· geometric objects related to automatic sequences.

  4. GA(M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design.

    Science.gov (United States)

    Pérez-Castillo, Yunierkis; Lazar, Cosmin; Taminau, Jonatan; Froeyen, Mathy; Cabrera-Pérez, Miguel Ángel; Nowé, Ann

    2012-09-24

    Computer-aided drug design has become an important component of the drug discovery process. Despite the advances in this field, there is not a unique modeling approach that can be successfully applied to solve the whole range of problems faced during QSAR modeling. Feature selection and ensemble modeling are active areas of research in ligand-based drug design. Here we introduce the GA(M)E-QSAR algorithm that combines the search and optimization capabilities of Genetic Algorithms with the simplicity of the Adaboost ensemble-based classification algorithm to solve binary classification problems. We also explore the usefulness of Meta-Ensembles trained with Adaboost and Voting schemes to further improve the accuracy, generalization, and robustness of the optimal Adaboost Single Ensemble derived from the Genetic Algorithm optimization. We evaluated the performance of our algorithm using five data sets from the literature and found that it is capable of yielding similar or better classification results to what has been reported for these data sets with a higher enrichment of active compounds relative to the whole actives subset when only the most active chemicals are considered. More important, we compared our methodology with state of the art feature selection and classification approaches and found that it can provide highly accurate, robust, and generalizable models. In the case of the Adaboost Ensembles derived from the Genetic Algorithm search, the final models are quite simple since they consist of a weighted sum of the output of single feature classifiers. Furthermore, the Adaboost scores can be used as ranking criterion to prioritize chemicals for synthesis and biological evaluation after virtual screening experiments.

  5. Automatic Fingerprint Classification by GA-Based Neural Network%基于遗传算法的神经网络指纹自动分类

    Institute of Scientific and Technical Information of China (English)

    黄席樾; 马笑潇; 沈志熙; 汪鹏; 周欣

    2001-01-01

    研究指纹的自动分类问题对解决大容量指纹库的匹配实时性有着重要的意义。笔者提出了一种新的指纹自动分类方法。该方法通过求取指纹方向图抽取了指纹的纹形特征,并将其送入神经网络进行分类识别,网络连接权系数采用遗传算法进行学习寻优,克服了单纯BP算法训练时间长、易陷入局部极值的缺点,同时提高了网络全局收敛的效率。测试结果表明,基于遗传算法的多层前向神经网络分类器对指纹图象的分类有良好的性能。%Fingerprint classification can provide an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint-matching time for large database. In the paper, by combining genetic algorithm and neural network is presented a fingerprint classification algorithm which is able to achieve an accurate classification. By inputting the global feature represented by directional image to three layer neural network trained by genetic algorithm, the fingerprints were classified into six categories: whorl, right loop, left loop, arch, double loop and undiscerning type successfully.

  6. Semi-automatic extraction of supra-glacial features using fuzzy logic approach for object-oriented classification on WorldView-2 imagery

    Science.gov (United States)

    Jawak, Shridhar D.; Palanivel, Yogesh V.; Alvarinho, Luis J.

    2016-04-01

    High resolution satellite data provide high spatial, spectral and contextual information. Spatial and contextual information of image objects are in demand to extract the information from high resolution satellite data. The supraglacial environment includes several features that are present on the surface of the glacier. The extraction of features from supraglacial environment is quite challenging using pixel-based image analysis. To overcome this, objectoriented approach is implemented. This paper aims at the extraction of geo-information from the supraglacial environment from high resolution satellite image by object-oriented image analysis using the fuzzy logic approach. The object-oriented image analysis involves the multiresolution segmentation for the creation of objects followed by the classification of objects using the fuzzy logic approach. The multiresolution segmentation is executed on the pixel level initially which merges pixels for the creation of objects thus minimizing their heterogeneity. This is followed by the development of rule sets for the classification of various features such as blue ice, debris, snow from the supraglacial environment in WorldView-2 data. The area of extracted feature is compared with the reference data and misclassified area of each feature using various bands is determined. The present object oriented classification achieved an overall accuracy of ≈ 92% for classifying supraglacial features. Finally, it is suggested that Red band is quite effective in the extraction of blue ice and snow, while NIR1 band is effective in debris extraction.

  7. Automatic acquisition and classification system for agricultural network information based on Web data%基于Web数据的农业网络信息自动采集与分类系统

    Institute of Scientific and Technical Information of China (English)

    段青玲; 魏芳芳; 张磊; 肖晓琰

    2016-01-01

    The purpose of this study is to obtain agricultural web information efficiently, and to provide users with personalized service through the integration of agricultural resources scattered in different sites and the fusion of heterogeneous environmental data. The research in this paper has improved some key information technologies, which are agricultural web data acquisition and extraction technologies, text classification based on support vector machine (SVM) and heterogeneous data collection based on the Internet of things (IOT). We first add quality target seed site into the system, and get website URL (uniform resource locator) and category information. The web crawler program can save original pages. The de-noised web page can be obtained through HTML parser and regular expressions, which create custom Node Filter objects. Therefore, the system builds a document object model (DOM) tree before digging out data area. According to filtering rules, the target data area can be identified from a plurality of data regions with repeated patterns. Next, the structured data can be extracted after property segmentation. Secondly, we construct linear SVM classification model, and realize agricultural text classification automatically. The procedures of our model include 4 steps. First of all, we use segment tool ICTCLAS to carry out the word segment and part-of-speech (POS) tagging, followed by combining agricultural key dictionary and document frequency adjustment rule to choose feature words, and building a feature vector and calculating inverse document frequency (IDF) weight value for feature words; lastly we design adaptive classifier of SVM algorithm. Finally, the perception data of different format collected by the sensor are transmitted to the designated server as the source data through the wireless sensor network. Relational database in accordance with specified acquisition frequency can be achieved through data conversion and data filtering. The key step of

  8. 基于自动语义标注和集成学习的Web服务分类%Web Service Classification Based on Automatic Semantic Annotation and Ensemble Learning

    Institute of Scientific and Technical Information of China (English)

    李元杰; 曹健; 胡亮

    2013-01-01

    随着Web服务技术的发展,它们在互联网上发布的数量正在快速增长,智能地去识别每个Web服务成为了高效运用网络的关键,而识别Web服务的第一步就是对它们进行准确地分类.于是对海量的Web服务进行分类成为一项工作量庞大的任务.于是,为了能够更有效的利用这些Web服务,需要自动对Web服务进行分类.本文以常见的WSDL为例进行研究,由于Web服务的描述采用了WSDL文件,使之无法用传统的文本分类手段.该文中介绍了一种将WSDL文件处理后通过本体匹配进行自动的语义标注,运用Naive Bayes、SVM、REPTree三种分类方法,进而运用集成学习进行分类的方法,在951个Web服务集合上进行19个类别的分类实验中,其准确率达到了87.39%.%With the development of Web Service Technology, the quantity of the web services published on the Internet is increasing rapidly. Recognizing each web service intelligently becomes the key of efficiently using Internet. And the first step of recognization is to classify the web services accurately. To classify a huge amount of web services becomes a difficulty job. Therefore, in order to support applications of web services more effectively, an automatic web service classification method is needed. In this paper, the common WSDL files are regarded as the study object. Since web service is described by WSDL, the traditional document classification method can not be applied directly. In the paper, a new method is proposed which applies automatic web service semantic annotation and uses three classification method: Naive Bayes, SVM and REPTree, furthermore ensemble learning is applied. According to the experiment done on 951 WSDL files and 19 categories, the accuracy was 87.39%

  9. Supervised Mineral Classification with Semi-automatic Training and Validation Set Generation in Scanning Electron Microscope Energy Dispersive Spectroscopy Images of Thin Sections

    DEFF Research Database (Denmark)

    Flesche, Harald; Nielsen, Allan Aasbjerg; Larsen, Rasmus

    2000-01-01

    This paper addresses the problem of classifying minerals common in siliciclastic and carbonate rocks. Twelve chemical elements are mapped from thin sections by energy dispersive spectroscopy in a scanning electron microscope (SEM). Extensions to traditional multivariate statistical methods...... are applied to perform the classification. First, training and validation sets are grown from one or a few seed points by a method that ensures spatial and spectral closeness of observations. Spectral closeness is obtained by excluding observations that have high Mahalanobis distances to the training class...

  10. Imbalanced Classification Approaches to Automatic Single-Document Summarization%基于非平衡数据分类的单文档自动文摘方法

    Institute of Scientific and Technical Information of China (English)

    倪维健; 刘彤; 曾庆田; 赵华; 汤建渝

    2012-01-01

    Machine learning based automatic document summarization approaches have drawn increasing attentions in the natural language processing literature. However, neither of them takes the im-balanced class distribution in automatic document summarization into account, I. E. , the number of the sentences in summary is much fewer than that of in the whole document. It is obvious that the highly imbalanced data distribution will degrade the effectiveness of the conventional machine learning algorithms. This paper addresses the problem of automatic document summarization from a perspective of imbalanced classification and proposes two learning strategies to deal with the highly imbalanced distributed data in automatic single-document summarization effectively. The experimental results on the DUC 2001 data set show the significant performance improvements of our approaches in terms of F1 and ROUGH-2.%自动文摘是自然语言处理领域的一个重要研究话题,基于机器学习的自动文摘方法则是该项研究中的一个热点.然而,自动文摘问题中的数据分布有一个重要现象,即文摘句子与非文摘句子的数量相差非常悬殊,该现象将给传统机器学习算法的应用效果带来负面影响.为此,本文针对自动文摘中句子类别分布严重不平衡这一现象,以支持向量机算法为基础,设计了两种有效的处理非平衡自动文摘数据的分类方法.在第一种方法中,将传统支持向量机中正负类平衡的分类间隔转换为不平衡的分类间隔;在第二种方法中,通过将数据集进行切分,设计了一种支持向量机集成学习算法.通过在DUC2001数据集上的实验证明,本文设计的两种基于非平衡数据分类的单文档自动文摘方法显著优于基于传统分类算法的自动文摘方法.

  11. 纯电动汽车自动同步换挡系统设计%Automatic Synchronization Shift of Pure Electric Vehicle System Design and Research

    Institute of Scientific and Technical Information of China (English)

    张进; 沈安文

    2011-01-01

    The transmission of the pure electric vehicle has important sense, can improve the electric starting performance and accelerating ability, climbing performance, high-speed performance and improve the maximum mileage of electric vehicle. Through the comparison and analysis of several existing transmission advantages and disadvantages, learn these types of transmission systems based on self-5development and design of a synchronous system (AST) .the article describes in detail the internal structure of AST systems, structural design and work principle, AST system experiment platform and gear shift strategy and process to achieve, through experimental results obtained a good description of AST system, excellent performance, with high practical value.%变速器对纯电动汽车具有重要意义,可以改善电动汽车的起步性能、加速性能、爬坡性能、高速性能以及提高电动汽车最高续航里程.本文对比分析现有几种变速器的优缺点,借鉴这几种变速器系统的基础上,自行开发设计自动同步换挡变速器系统(AST),文章详细介绍AST系统的内部结构,结构设计与工作原理,AST系统的实验平台以及AST换挡策略和换挡过程实现,通过实验得到的实验结果很好的说明AST系统优异性能,具有很高的实用价值.

  12. Automatic classification of squamosal abnormality in micro-CT images for the evaluation of rabbit fetal skull defects using active shape models

    Science.gov (United States)

    Chen, Antong; Dogdas, Belma; Mehta, Saurin; Bagchi, Ansuman; Wise, L. David; Winkelmann, Christopher

    2014-03-01

    High-throughput micro-CT imaging has been used in our laboratory to evaluate fetal skeletal morphology in developmental toxicology studies. Currently, the volume-rendered skeletal images are visually inspected and observed abnormalities are reported for compounds in development. To improve the efficiency and reduce human error of the evaluation, we implemented a framework to automate the evaluation process. The framework starts by dividing the skull into regions of interest and then measuring various geometrical characteristics. Normal/abnormal classification on the bone segments is performed based on identifying statistical outliers. In pilot experiments using rabbit fetal skulls, the majority of the skeletal abnormalities can be detected successfully in this manner. However, there are shape-based abnormalities that are relatively subtle and thereby difficult to identify using the geometrical features. To address this problem, we introduced a model-based approach and applied this strategy on the squamosal bone. We will provide details on this active shape model (ASM) strategy for the identification of squamosal abnormalities and show that this method improved the sensitivity of detecting squamosal-related abnormalities from 0.48 to 0.92.

  13. Research on Bench-marking Analysis Model of Competitive Intelligence Integrated Text Automatic Classification%融合文本自动分类的竞争情报定标比超分析模型研究

    Institute of Scientific and Technical Information of China (English)

    张玉峰; 黄姮

    2011-01-01

    本文分析了传统定标比超方法的思想和缺陷,提出将传统情报分析方法与智能分析技术相结合,构建了融合文本自动分类的竞争情报定标比超分析模型。本文提出构建定标比超内容层次指标体系,将其作为文本自动分类的分类体系。两种方法相辅相成、相互优化,实现竞争情报的良性循环型、科学的智能分析。进而,深入研究了该模型的功能任务和情报分析过程与算法。最后,从科学性、时效性、全面性、准确性和动态性方面对该模型进行了性能评价。%This paper analyses the theories and defects of traditional bench-marking and proposes a bench-marking analysis model of competitive intelligence integrated text automatic classification,which integrates traditional intelligence analytical method and inte

  14. 基于流形模糊双支持向量机的恒星光谱分类方法%Automatic Classification Method of Star Spectra Data Based on Manifold Fuzzy Twin Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    刘忠宝; 高艳云; 王建珍

    2015-01-01

    支持向量机(support vector machine ,SVM )具有良好的学习性能和泛化能力,因而被广泛应用于恒星光谱分类中。然而实际应用面临的数据规模往往很大,SVM 便暴露出计算量大、分类速度慢等问题。为了解决上述问题,Jayadeva等提出双支持向量机(twin support vector machine ,TWSVM ),将计算时间减少至SVM的1/4。然后上述方法仅关注数据的全局特征,对每类数据的局部特征并未关注。鉴于此,提出基于流形模糊双支持向量机(manifold fuzzy twin support vector machine ,MF-TSVM)的恒星光谱分类方法。利用流形判别分析获得数据的全局特征和局部特征,模糊隶属度函数的引入将各类数据区别对待,尽可能减少噪声点和奇异点对分类结果的影响。与C-SVM ,KNN等传统分类方法在SDSS恒星光谱数据集上的比较实验表明了该方法的有效性。%Support vector machine (SVM ) with good leaning ability and generalization is widely used in the star spectra data clas-sification .But when the scale of data becomes larger ,the shortages of SVM appear :the calculation amount is quite large and the classification speed is too slow .In order to solve the above problems ,twin support vector machine (TWSVM ) was proposed by Jayadeva .The advantage of TSVM is that the time cost is reduced to 1/4 of that of SVM .While all the methods mentioned above only focus on the global characteristics and neglect the local characteristics .In view of this ,an automatic classification method of star spectra data based on manifold fuzzy twin support vector machine (MF-TSVM ) is proposed in this paper .In MF-TSVM ,manifold-based discriminant analysis (MDA) is used to obtain the global and local characteristics of the input data and the fuzzy membership is introduced to reduce the influences of noise and singular data on the classification results .Compara-tive experiments with current classification

  15. Study on Ground Automatic Identification Technology for Intelligent Vehicle Based on Vision Sensor%基于视觉传感器的自主车辆地面自动辨识技术研究

    Institute of Scientific and Technical Information of China (English)

    崔根群; 余建明; 赵娴; 赵丛琳

    2011-01-01

    The ground automatic identification technology for intelligent vehicle is iaking Leobor-Edu autonomous vehicle as a test vector and using DH-HV2003UC-T vision sensor to collect image infarmaiion of five common lane roads( cobbled road, concrete road, dirt road, grass road, tile road) , then using MATLAB image processing module to perform coding compression, recovery reconstruction, smoothing, sharpening, enhancement, feature extraction and other related processing,then using MATLAB BP neural network module to carry on pattern recognition.Through analyzing the pattern recognition result, lt shows that the objective error is 20%, the road recognition rate has reached the intended requirement in the system,and it can be universally applied in the smart vehicle or robots and other related fields.%谊自主车辆地面自动辨识技术是以Leobot-Edu自主车辆作为试验载体,并应用DH-HV2003UC-T视觉传感器对常见的5种行车路面(石子路面、水泥路面、土壤路面、草地路面、砖地路面)进行图像信息的采集,应用Matlab图像处理模块对其依次进行压缩编码、复原重建、平滑、锐化、增强、特征提取等相关处理后,再应用Matlab BP神经网络模块进行模式识别.通过对模式识别结果分析可知,网络训练目标的函数误差为20%,该系统路面识别率达到预定要求,可以在智能车辆或移动机器人等相关领域普及使用.

  16. A Study on Traction Control for Automatic Transmission Vehicle on Low-adhesion Road%自动变速车辆低附着路面牵引力控制研究

    Institute of Scientific and Technical Information of China (English)

    史俊武; 鲁统利; 李小伟; 张建武

    2011-01-01

    A new traction control system for automatic transmission vehicle is proposed based on concerted control of throttle opening and transmission gear position. In view of the non-linearity and uncertainties of slip ratio control system, the feedback linearization and sliding mode control are applied to the system by means of differential geometry theory. Furthermore, by using dynamic programming, the throttle opening of engine and the gear position of transmission, at which the required driving torque can be produced, are worked out. The simulation results indicate that the traction control system proposed is characterized by its simple structure and good performance.%提出了一种基于发动机节气门开度与变速器挡位协调控制的自动变速车辆牵引力控制系统.针对滑移率控制系统的非线性与参数不确定性,根据微分几何理论对其进行了反馈线性化并施加滑模控制.同时利用动态规划方法求得能产生所需驱动转矩的节气门开度与变速器挡位.仿真结果表明,此种牵引力控制系统具有设计简单和控制效果良好等特点.

  17. Design on Automatic Heave Compensation Hydraulic System of Remotely Operated Vehicle Based on Neuron PID Control%基于神经元PID的水下机器人自动升沉补偿液压系统设计

    Institute of Scientific and Technical Information of China (English)

    何新英; 吴家鸣

    2015-01-01

    针对母船的升沉运动会影响到带缆遥控水下机器人的安全作业和收放功能,提出了利用液压绞车进行水下机器人自动升沉补偿的方案。设计了带缆遥控水下机器人升沉补偿液压系统,控制系统采用了神经元自适应PID控制算法。并在Matlab中进行了仿真,仿真结果表明,该系统能够较好的实现水下机器人的升沉补偿运动。%The supporting ship heave motion affects the remotely operated vehicle safety operation and storage function,using hydraulic winch for automatic heave compensation of ROV was presented in this paper,The heave compensation hydraulic system of ROV has been designed, which the neuron adaptive PID control algorithm has been adopted. And has been simulate in mat lab,the simulation result show that the system can realize the ROV heave compensation movement.

  18. 自动识别环境下车辆的出行矩阵估计新方法%A New Method of OD Estimation Based On Automatic Vehicle Identification Data

    Institute of Scientific and Technical Information of China (English)

    孙剑; 冯羽

    2011-01-01

    鉴于以视频牌照识别系统为代表的车辆自动识别(automatic vehicle identification,AVI)技术在我国逐步应用的现实,提出了利用AVI检测信息估计高精度车辆起讫点矩阵(OD- matrix)的新方法.该方法首先将检测的车辆信息分为4类(起讫点已知、起点或终点及部分路径已知、仅知起点或终点、仅知部分路径),然后利用第1类信息根据AVI检测误差直接扩样更新基础OD矩阵;利用第2,3,4类信息,参照粒子滤波算法思想,基于贝叶斯估计理论修正更新路段-路径流量关系,进而用蒙特卡罗随机过程确定可能路径以及OD;最后根据AVI获得的路径流量信息反向验算校正OD.根据上海市目前视频牌照识别系统的应用现状,选择以南北高架快速路为研究对象,根据牌照识别系统检测的动态车辆信息,对布设9个视频检测器的南北高架沿线17个出入口的OD进行了估计应用.结果表明,在路网仿真模型误差≤15%、AVI设施覆盖率为27.2%以及检测误差在10%的前提下,运用本方法,OD估计的总体平均相对误差仅为11.09%.该方法能充分利用AVI检测的个体车辆不完整路径信息,且计算效率高,可满足实际动态交通管理的需求.%With the development and application of video license plate recognition system which represented the automatic vehicle identification (AVI) technologies in China,a novel high resolution OD estimation method was proposed based on AVI detector information. 4 detected categories (Ox + Dy, Ox/Dy + (8), Ox/Dy、 P(8)) were divided at the first step. Then the initial OD matrix was updated by using the Ox + Dy sample information considering the AVI detector errors. Referenced by particle filter, the link-path relationship data were revised by using the last 3 categories information based on Bayesian inference and the possible trajectory and OD were determined with the Monte Carlo random process. Then the OD was corrected

  19. 面向应急响应的无人机图像快速自动拼接%Fast Automatic Stitching for Images of Unmanned Aerial Vehicle in Emergency Response

    Institute of Scientific and Technical Information of China (English)

    吴俣; 余涛; 谢东海

    2013-01-01

    With the demand of emergency response, in order to obtain full cover image of remote sensing area fast, automatically and stably, this paper proposes a suitable stitching method of image sequences from unmanned aerial vehicle (UAV). A position and orientation system with low accuracy and a non-metric camera without calibration mounted on UAV can photograph the interested area to attain the image sequences and fast automatic registration. With the global ground points and the corresponding projection points, the whole optimum error equations are calculated using Levenberg-Marquardt algorithm for nonlinear iteration of the Homography matrices and unrestrained projective centers. Then, the parallel geometric seeking algorithm of stitching line is applied; image interpolation and image fusion are processed for whole automatic image outputting. Through the disaster emergency response experiment based on UAV remote sensing, the 0. 05 m resolution full cover image with less than 3 pixel of positioning accuracy are obtained from 450 images in an hour. The fast automatic algorithm proposed in this paper is appropriate for image sequences from UAV, which can provide effective technology support for emergency response.%根据应急响应的需求,为了快速、自动且稳定地获取遥感区域的全覆盖图像,提出一种直接适用于无人机序列图像的拼接方法.利用无人机平台上的低精度飞行控制系统与无标定的非量测相机获取序列图像,并进行快速自动配准;根据全局地面点在多视图像上的投影点集,基于单应矩阵和自由投影中心建立整体优化误差方程,并利用Levenberg-Marquardt非线性迭代求解;再根据并行几何定位拼接线的快速处理方法进行图像插值和融合,实现全流程自动化的图像输出.通过灾害应急响应的无人机遥感实验结果表明,在1h内处理完成450幅序列图像,获得定位精度小于3个像素的0.05m分辨率图像;文中方法

  20. 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…

  1. 基于自回归模型和关联向量机的癫痫脑电信号自动分类%Automatic Classification of Epileptic EEG Signals Based on AR Model and Relevance Vector Machine

    Institute of Scientific and Technical Information of China (English)

    韩敏; 孙磊磊; 洪晓军; 韩杰

    2011-01-01

    癫痫脑电信号自动分类方法的研究具有重要意义.基于自回归模型和关联向量机,实现癫痫脑电信号的自动分类.采用自回归模型,进行脑电信号特征提取;通过引入主成分分析和线性判别分析两种特征变换方法,降低特征空间维数;采用关联向量机作为分类器,提高模型稀疏性并可以得到概率式输出.在对波恩大学癫痫研究中心脑电信号的分类中,所提出的方法最高准确率可以达到99.875%;在将特征空间维数降至原始维数的1/15时,分类准确率仍可达到99.500%;采用关联向量机作为分类器,模型稀疏性大幅提高,与支持向量机相比,同等条件下关联向量数仅为支持向量数的几十分之一.所提方法可以很好地应用于癫痫脑电信号的自动分类.%Automatic classification system of epileptic EEG signals is one very important issue. In this paper a new epileptic EEG signal classification method was proposed on the basis of AR model and relevance vector machine. AR model was used to extract EEG features, and then principle components analysis and linear discriminant analysis were adopted to reduce the dimensionality of feature space. In order to obtain a sparser model and a model with probabilistic outputs, relevance vector machine was chosen as classifier. A publicly-available database was used to test the proposed method: the highest accuracy obtained in this paper is 99. 875% ; and even if the dimensionality of feature space is reduced to 1/15 of the original dimensionality, the classification accuracy was still able to reach 99. 500% . The introduction of relevance vector machine makes the model sparser; the number of relevance vectors is just a few tenths of that of support vectors. The results mentioned above suggest that the method can be well applied in epileptic EEG signal classification.

  2. Music classification with MPEG-7

    Science.gov (United States)

    Crysandt, Holger; Wellhausen, Jens

    2003-01-01

    Driven by increasing amount of music available electronically the need and possibility of automatic classification systems for music becomes more and more important. Currently most search engines for music are based on textual descriptions like artist or/and title. This paper presents a system for automatic music description, classification and visualization for a set of songs. The system is designed to extract significant features of a piece of music in order to find songs of similar genre or a similar sound characteristics. The description is done with the help of MPEG-7 only. The classification and visualization is done with the self organizing map algorithm.

  3. Research on algorithm of automatically recognizing andpositioning road manhole covers based on vehicle-mounted sensors%基于车载传感器的路面井盖自动定位识别算法研究

    Institute of Scientific and Technical Information of China (English)

    刘建华

    2011-01-01

    The fast recognizing and positioning of municipal manhole covers is an important problem needed to be addressed for promoting digital management of modern cities. In view of the above problem, this paper proposed an algorithm, to automatically recognize and position road manhole covers under complex background in natural scene based on vehicle-mounted sensors. Taking the elliptical geometrical characteristic of manhole cover in perspective image captured by vehicle-mounted sensors as criterion, the algorithm firstly extracted edge information by employing vector edge detection method. Secondly constructed a contour list with boundary through contour tracing, then imitated and quickly generated all the possible elliptical targets in the contour list by means of least square fitting method. Subsequently eliminated the elliptical targets without relationship corresponding to road manhole covers according to their shape features, and last formed accurate results of recognition and position. Experiment shows that generally the algorithm is able to achieve real-time manhole cover targets recognition rapidly and effectively for images conforming to quality standard of data capturing.%市政井盖快速定位与识别是提升现代城市部件空间数字化管理水平需要解决的重要问题,针对该问题提出基于车载传感器的复杂背景下路面井盖目标自动定位识别算法.该算法以车载传感器获取的透视图像中井盖所具有的椭圆形几何特征为判据,先利用矢量边缘检测方法提取边缘信息,再运用轮廓跟踪法将边缘构造成轮廓链表,然后通过最小二乘法拟合与快速生成轮廓链表中可能存在的椭圆目标,并根据井盖的形状特征排除透视图中与路面井盖无对应关系的虚假椭圆目标,最终形成高精度定位识别结果.实证研究表明,对达到数据采集质量标准的图像,在一般情况下该算法能较好地实现其中市政井盖的实时定位识别.

  4. Intelligent lunar Vehicle Infrared Tracing Algorithm for Automatic Control Strategies and Research%智能探月车红外寻迹自动控制策略研究

    Institute of Scientific and Technical Information of China (English)

    钱昕; 殷庆纵; 王栋

    2012-01-01

    In Intelligent Lunar Vehicle's infrared tracking automatic control, using reflective infrared sensor array for the analog photoelectric sensor detects the path. Process the sensor data and relative error through the detection of nonlinear analog A / D sampling, real ?time process and non-linearized the control parameters in the PID variable scale incremental control algorithm; In addition, incremental PD formula can be derived by fuzzy control technology and the sensitivity and stability of the car tracing can be improved by adjusting the parameters in programming. It has been proved by that this method can shorten response time of the lunar exploration vehicle, reduce the random interference, optimize the tracking line, effectively eliminate oscillation and slow response of turn, at the meantime it can also improve mobility.%智能探月车红外循迹自动控制中主要采用反射式红外传感器的模拟光电传感器阵列进行路径检测;通过A/D采样方式检测非线性模拟量,对传感器采样数据及相对误差进行处理,引入变尺度增量式PID控制算法,对控制参数采用实时非线性整定,实现智能闭环控制;另外,应用模糊控制技术推导增量式PD计算公式,通过编程设定可调节参数以改善小车寻迹的灵敏度和稳定性;实践证明该方法可以缩短探月车的控制响应时间,降低随机干扰,优化循迹路线,有效地克服其在行驶中易产生振荡、转弯时反应迟钝、机动性差的缺点.

  5. Real-time people and vehicle detection from UAV imagery

    Science.gov (United States)

    Gaszczak, Anna; Breckon, Toby P.; Han, Jiwan

    2011-01-01

    A generic and robust approach for the real-time detection of people and vehicles from an Unmanned Aerial Vehicle (UAV) is an important goal within the framework of fully autonomous UAV deployment for aerial reconnaissance and surveillance. Here we present an approach for the automatic detection of vehicles based on using multiple trained cascaded Haar classifiers with secondary confirmation in thermal imagery. Additionally we present a related approach for people detection in thermal imagery based on a similar cascaded classification technique combining additional multivariate Gaussian shape matching. The results presented show the successful detection of vehicle and people under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance of the detector is optimized to reduce the overall false positive rate by aiming at the detection of each object of interest (vehicle/person) at least once in the environment (i.e. per search patter flight path) rather than every object in each image frame. Currently the detection rate for people is ~70% and cars ~80% although the overall episodic object detection rate for each flight pattern exceeds 90%.

  6. Single-Frame Terrain Mapping Software for Robotic Vehicles

    Science.gov (United States)

    Rankin, Arturo L.

    2011-01-01

    This software is a component in an unmanned ground vehicle (UGV) perception system that builds compact, single-frame terrain maps for distribution to other systems, such as a world model or an operator control unit, over a local area network (LAN). Each cell in the map encodes an elevation value, terrain classification, object classification, terrain traversability, terrain roughness, and a confidence value into four bytes of memory. The input to this software component is a range image (from a lidar or stereo vision system), and optionally a terrain classification image and an object classification image, both registered to the range image. The single-frame terrain map generates estimates of the support surface elevation, ground cover elevation, and minimum canopy elevation; generates terrain traversability cost; detects low overhangs and high-density obstacles; and can perform geometry-based terrain classification (ground, ground cover, unknown). A new origin is automatically selected for each single-frame terrain map in global coordinates such that it coincides with the corner of a world map cell. That way, single-frame terrain maps correctly line up with the world map, facilitating the merging of map data into the world map. Instead of using 32 bits to store the floating-point elevation for a map cell, the vehicle elevation is assigned to the map origin elevation and reports the change in elevation (from the origin elevation) in terms of the number of discrete steps. The single-frame terrain map elevation resolution is 2 cm. At that resolution, terrain elevation from 20.5 to 20.5 m (with respect to the vehicle's elevation) is encoded into 11 bits. For each four-byte map cell, bits are assigned to encode elevation, terrain roughness, terrain classification, object classification, terrain traversability cost, and a confidence value. The vehicle s current position and orientation, the map origin, and the map cell resolution are all included in a header for each

  7. Automatic Classification of Kepler Threshold Crossing Events

    CERN Document Server

    McCauliff, Sean; Catanzarite, Joseph; Burke, Christopher J; Coughlin, Jeffrey L; Twicken, Joseph D; Tenenbaum, Peter; Seader, Shawn; Li, Jie; Cote, Miles

    2014-01-01

    The Kepler Science Operations Center detects interesting, exoplanet transit-like signals while searching over 211,000 distinct light curves. The mission has produced four catalogs of interesting objects with planet transit-like features known as Kepler Objects of Interest (KOI). The total number of objects with transit-like features identified in the light curves has increased to as many as approximately 18,000, just examining the first three years of data. This number of significant detections has become difficult for human beings to inspect by eye in a thorough and timely fashion. In order to accelerate the process by which new planet candidates are classified and to provide an independent assessment of planet candidates, we propose a machine learning approach to establish a preliminary list of planetary candidates ranked from most credible to least credible. The classifier must distinguish between three classes of detections: non-transiting phenomena, astrophysical false positives, and planet candidates. W...

  8. Automatic Classification of Digitally Modulated Signals.

    Science.gov (United States)

    1987-12-01

    the bandwidth of a sinusoid approaches zero as the observation time becomes infinite ( Stremler ,1982:87). In practice, the bandwith will be small, but...straightforward manner. Detailed explanations of these operations can be found in the references(Couch,1983;Schwartz, 1980; Stremler ,1982). Since the... Stremler ,1982: 279). The variance of its envelope is zero and therefore, R is equal to zero. For amplitude modulation, the information is conveyed by

  9. Automatic Reading

    Institute of Scientific and Technical Information of China (English)

    胡迪

    2007-01-01

    <正>Reading is the key to school success and,like any skill,it takes practice.A child learns to walk by practising until he no longer has to think about how to put one foot in front of the other.The great athlete practises until he can play quickly,accurately and without thinking.Ed- ucators call it automaticity.

  10. Method of information extraction of marbling image characteristic and automatic classification for beef%牛肉大理石花纹图像特征信息提取及自动分级方法

    Institute of Scientific and Technical Information of China (English)

    周彤; 彭彦昆

    2013-01-01

    . Light intensity was regulated through a light controller, and the distance between the camera lens and the beef samples was adjusted though translation stages in the image acquisition device. Collected images were automatically stored in the computer for further image processing. First, some methods such as image denoising, background removal, and image enhancement were adopted to preprocess the image to obtain a region of interest (ROI). In this step, the image was cropped to separate the beef from the background. Then, an iteration method was used to segment the beef area, obtain the beef marbling area and fat area. The redundant fat area was removed to extract an effective rib-eye region. Ten characteristic parameters of beef marbling namely, the rate of marbling area in the rib-eye region, the number of large grain fat, medium grain fat, small grain fat, total grain fat, the density of large grain fat, medium grain fat, small grain fat, total grain fat and, the evenness degree of fat distribution in the rib-eye region can reflect the amount of marbling and its distribution. So they were used to establish principal component regression (PCR) model. The PCR model result yielded a correction coefficient (Rv) of 0.88 and a standard error of prediction (SEP) of 0.56. And the PCR model showed that the rate of the marbling area in the rib-eye region had the greatest effect on the grade of beef marbling. Fisher discriminant functions were constructed based on the PCR model results to classify the grade of beef marbling. Experimental results showed that the classification accuracy was 97.0%in the calibration set and 91.2%in the prediction set. On this basis, a software system was developed for the automatic grading of beef marbling. A corresponding hardware device was also developed, controlled by the software system for real time application. The speed and accuracy of the algorithm were verified with theoretical analysis and a practical test. Through tests, the average

  11. An Experimental Comparative Study on Three Classification Algorithms

    Institute of Scientific and Technical Information of China (English)

    蔡巍; 王永成; 李伟; 尹中航

    2003-01-01

    Classification algorithm is one of the key techniques to affect text automatic classification system's performance, play an important role in automatic classification research area. This paper comparatively analyzed k-NN. VSM and hybrid classification algorithm presented by our research group. Some 2000 pieces of Internet news provided by ChinaInfoBank are used in the experiment. The result shows that the hybrid algorithm's performance presented by the groups is superior to the other two algorithms.

  12. MOTOR VEHICLE SAFETY RESEARCH METHODOLOGY

    Directory of Open Access Journals (Sweden)

    A. Stepanov

    2015-07-01

    Full Text Available The issues of vehicle safety are considered. The methodology of approach to analyzing and solving the problem of safety management of vehicles and overall traffic is offered. The distinctive features of organization and management of vehicle safety are shown. There has been drawn a conclusion that the methodological approach to solving traffic safety problems is reduced to selection and classification of safety needs.

  13. STUDY ON SHIFT SCHEDULE OF AUTOMATIC TRANSMISSION TO IMPROVE ENGINEERING VEHICULAR EFFICIENCY

    Institute of Scientific and Technical Information of China (English)

    Gong Jie; Zhao Dingxuan; Huang Haidong; Gong Wenbin; Chen Ying

    2004-01-01

    New shift schedule for automatic transmission is proposed from the point of view of saving energy.The bench-test of automatic shift adopting this shift schedule is done on automatic transmission's test-bed.The experimental results show the shift schedule is correct.This shift schedule has enriched the theory of vehicle automatic maneuvering and will improve the efficiency of hydrodynamic drive system of the vehicle.

  14. Optimum Route Planning and Scheduling for Unmanned Aerial Vehicles

    Science.gov (United States)

    2008-12-01

    Vehicle Routing Problem , VRP 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT Unclassified 18. SECURITY CLASSIFICATION OF THIS PAGE...23 B. VEHICLE ROUTING PROBLEM ........................................................ 26 C. ORIENTEERING PROBLEM AND PRIZE...Team Orienteering Problem TSP Traveling Salesman Problem VRP Vehicle Routing Problem VRPPD VRP with Pick-Up And Delivering VRPTW VRP with Time

  15. Classification of iconic images

    OpenAIRE

    Zrianina, Mariia; Kopf, Stephan

    2016-01-01

    Iconic images represent an abstract topic and use a presentation that is intuitively understood within a certain cultural context. For example, the abstract topic “global warming” may be represented by a polar bear standing alone on an ice floe. Such images are widely used in media and their automatic classification can help to identify high-level semantic concepts. This paper presents a system for the classification of iconic images. It uses a variation of the Bag of Visual Words approach wi...

  16. Supervised Ensemble Classification of Kepler Variable Stars

    CERN Document Server

    Bass, Gideon

    2016-01-01

    Variable star analysis and classification is an important task in the understanding of stellar features and processes. While historically classifications have been done manually by highly skilled experts, the recent and rapid expansion in the quantity and quality of data has demanded new techniques, most notably automatic classification through supervised machine learning. We present an expansion of existing work on the field by analyzing variable stars in the {\\em Kepler} field using an ensemble approach, combining multiple characterization and classification techniques to produce improved classification rates. Classifications for each of the roughly 150,000 stars observed by {\\em Kepler} are produced separating the stars into one of 14 variable star classes.

  17. TEXT AREA IDENTIFICATION FOR RECOGNIZING DESTINATION PLACES FROM VEHICLES

    Directory of Open Access Journals (Sweden)

    Selvanayaki K.S

    2014-07-01

    Full Text Available Nowadays, automatic detection of text from the vehicles is an important problem in many applications. Text information present in an image can be easily understood by both human and computer. It has wide applications such as license plate reading, sign detection, identification of destination places, mobile text recognition and so on. This problem is challenging due to complex backgrounds, the non-uniform illuminations, variations of text font, size and line orientation. Once the text is identified, it can be analyzed, recognized and interpreted. Hence, there is a need for a better algorithm for detection and localization of text from vehicles. A method is proposed for detecting text from vehicles. The method makes use of features such as Histogram of oriented Gradients (HOG and Local Binary Pattern (LBP. These features are stored which can be further used for feature matching at the time of classification. After the text region is being detected, it can be further subjected to character segmentation and recognition thereby identifying the destination places. The ability to recognize text area from the vehicles, especially buses has obvious applications like traffic management in the bus stands. The obtained results are verified and performance parameters like speed, precision and recall are determined.

  18. Fast Probabilistic Fusion of 3d Point Clouds via Occupancy Grids for Scene Classification

    Science.gov (United States)

    Kuhn, Andreas; Huang, Hai; Drauschke, Martin; Mayer, Helmut

    2016-06-01

    High resolution consumer cameras on Unmanned Aerial Vehicles (UAVs) allow for cheap acquisition of highly detailed images, e.g., of urban regions. Via image registration by means of Structure from Motion (SfM) and Multi View Stereo (MVS) the automatic generation of huge amounts of 3D points with a relative accuracy in the centimeter range is possible. Applications such as semantic classification have a need for accurate 3D point clouds, but do not benefit from an extremely high resolution/density. In this paper, we, therefore, propose a fast fusion of high resolution 3D point clouds based on occupancy grids. The result is used for semantic classification. In contrast to state-of-the-art classification methods, we accept a certain percentage of outliers, arguing that they can be considered in the classification process when a per point belief is determined in the fusion process. To this end, we employ an octree-based fusion which allows for the derivation of outlier probabilities. The probabilities give a belief for every 3D point, which is essential for the semantic classification to consider measurement noise. For an example point cloud with half a billion 3D points (cf. Figure 1), we show that our method can reduce runtime as well as improve classification accuracy and offers high scalability for large datasets.

  19. STUDY ON SHIFT SCHEDULE AND SIMULATION OF AUTOMATIC TRANSMISSION

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    From the point of view of saving energy, a new shift schedule and auto-controlling strategy for automatic transmission are proposed. In order to verify this shift schedule,a simulation program using a software package of Matlab/Simulink is developed. The simulation results show the shift schedule is correct. This shift schedule has enriched the theory of vehicle automatic maneuvering and will improve the efficiency of hydrodynanic drive system of the vehicle.

  20. Experiences in automatic keywording of particle physics literature

    CERN Document Server

    Montejo Ráez, Arturo

    2001-01-01

    Attributing keywords can assist in the classification and retrieval of documents in the particle physics literature. As information services face a future with less available manpower and more and more documents being written, the possibility of keyword attribution being assisted by automatic classification software is explored. A project being carried out at CERN (the European Laboratory for Particle Physics) for the development and integration of automatic keywording is described.

  1. Vehicle to Vehicle Services

    DEFF Research Database (Denmark)

    Brønsted, Jeppe Rørbæk

    2008-01-01

    , mobility, and availability of services. The dissertation consists of two parts. Part I gives an overview of service oriented architecture for pervasive computing systems and describes the contributions of the publications listed in part II. We investigate architecture for vehicular technology applications......As computing devices, sensors, and actuators pervade our surroundings, new applications emerge with accompanying research challenges. In the transportation domain vehicles are being linked by wireless communication and equipped with an array of sensors and actuators that make is possible to provide...... location aware infotainment, increase safety, and lessen environmental strain. This dissertation is about service oriented architecture for pervasive computing with an emphasis on vehicle to vehicle applications. If devices are exposed as services, applications can be created by composing a set of services...

  2. An Automatic Sample Collection Method for Object-oriented Classification of Remotely Sensed Imageries Based on Transfer Learning%迁移学习支持下的遥感影像对象级分类样本自动选择方法

    Institute of Scientific and Technical Information of China (English)

    吴田军; 骆剑承; 夏列钢; 杨海平; 沈占锋; 胡晓东

    2014-01-01

    For the large-scale remote sensing applications,the automatic classification of remotely sensed imageries is still a challenge.For example,the artificial sample collection scheme cannot meet the needs of automatic information extraction from the remotely sensed imageries.In order to establish a prior knowl-edge-based and fully automatic classification method,an automatic sample collection method for object-oriented classification,with the introduction of data mining to the process of information extraction,is proposed.Firstly,the unchanged landmarks are located.Then the prior class knowledge from old interpreted thematic images is transferred to the new target images,and the above knowledge is then used to rebuild the relationship between landmark classes and their spatial-spectral features.The results show that,with the assist of preliminary thematic data,the approach can automatically obtain reliable object samples for object-oriented classification.The accuracy of the classified land-cover types and the efficiency of object-oriented classification are both improved.%面向遥感大范围应用的目标,自动化程度仍是遥感影像分类面临的重要问题,样本的人工选择难以适应当前土地覆盖信息自动化提取的实际应用需求。为了构建一套基于先验知识的遥感影像全自动分类流程,本文将空间信息挖掘技术引入到遥感信息提取过程中,提出一种面向遥感影像对象级分类的样本自动选择方法。该方法通过变化检测将不变地物标示在新的目标影像上,并将过去解译的地物类别知识迁移至新的影像上,建立新的特征与地物关系,从而完成历史专题数据辅助下目标影像的自动化对象级分类。试验结果表明,在已有历史专题层的图斑知识指导下,该方法能有效地自动选择适用于新影像分类的可靠样本,获得较好的信息提取效果,提高了对象级分类的效率。

  3. Two Systems for Automatic Music Genre Recognition

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    2012-01-01

    We re-implement and test two state-of-the-art systems for automatic music genre classification; but unlike past works in this area, we look closer than ever before at their behavior. First, we look at specific instances where each system consistently applies the same wrong label across multiple t...

  4. Research of the Influences of Input Parameters on the Result of Vehicles Collision Simulation

    Directory of Open Access Journals (Sweden)

    Vuk Bogdanović

    2012-05-01

    Full Text Available Vehicle collisions are complex processes which are determined by a large number of different parameters. The development of computer programs for simulation has made the collision analysis and reconstruction procedure easier, as well as the possibility to realise the influences of different parameters on collision processes, which was not possible while using classical methods. The quality of results of vehicle collision simulation and reconstruction is expressed by an error which is determined on the basis of the difference between vehicles stopping positions, which was obtained by the simulation of established vehicles stopping positions in real collisions. Being acquainted with the influence of collision parameters on the simulation error enables the development of more reliable models for automatic optimisation of the collision process and reduction of the number of iterations in the procedure of a collision reconstruction. Within the scope of this paper, the analysis and classification of different collision parameters have been carried out. It has been done by the degree of the influence on the error in the simulation process in the software package Virtual CRASH. Varying twenty different collision parameters on the sample of seven crash tests, their influence on the distance, trajectory and angular error has been analysed, and ten parameters with the highest level of influence (centre of gravity position from front axle of vehicle 1, restitution coefficient, collision place in longitudinal direction, collision place in transverse direction, centre of gravity height-vehicle2, centre of gravity height-vehicle1, collision angle, contact plane angle, slowing down the vehicle and vehicle movement direction have been distinguished.

  5. Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle

    Directory of Open Access Journals (Sweden)

    Xiangwei Xing

    2014-01-01

    Full Text Available As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC has attracted much attention in synthetic aperture radar (SAR automatic target recognition (ATR recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA, in which the correlation between the vehicle’s aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle’s aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

  6. 汽车自动变速器故障诊断专家系统的研究%Research on the Fault Diagnoses Expert System of Vehicle Automatic Transmission

    Institute of Scientific and Technical Information of China (English)

    蒋鸣雷

    2013-01-01

    Automatic transmission is a system integrating the machine, electricity and liquid. The fault diagnosis of automatic transmission is very dif icult. At present, the fault diagnosis expert system is the development direction of automatic transmission diagnostic technology. By using the production rule and the frame representation, knowledge base and the fault tree of automatic transmission is are constructed, and the expert system is designed by applying forward reasoning mechanism. The fault diagnosis expert system of automatic transmission is developed by using Visual Basic 6 and Microsoft Access 2003 as the programming language.%  汽车自动变速器是集机、电、液于一体的系统,其故障诊断难度大,故障诊断专家系统是目前汽车自动变速器故障诊断技术的发展方向。利用产生式规则和框架表示法构建知识库,建立自动变速器工作异常故障树并应用正向推理机制对专家系统进行了设计,以Visual Basic 6.0、Microsoft Access 2003为编程语言开发了自动变速器故障诊断专家系统。

  7. Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

    Science.gov (United States)

    Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

    2009-01-01

    The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

  8. Control design and implementation for a line tracker vehicle

    OpenAIRE

    Prats Martinho, Ivan Odair

    2016-01-01

    The aim of this project is to implement control algorithms in a line tracker vehicle. The vehicle should be built and controlled to track a path and the evaluation of the different control performances should be reported. The main goal of this work is to build a two-wheeled robot emulating a vehicle in a road. The aim of the overall project is to apply control techniques to different scenarios of communicated vehicles, such as: platooning, autonomous vehicles, overtaking maneuvers, automat...

  9. Failure of classical traffic flow theories: Stochastic highway capacity and automatic driving

    Science.gov (United States)

    Kerner, Boris S.

    2016-05-01

    In a mini-review Kerner (2013) it has been shown that classical traffic flow theories and models failed to explain empirical traffic breakdown - a phase transition from metastable free flow to synchronized flow at highway bottlenecks. The main objective of this mini-review is to study the consequence of this failure of classical traffic-flow theories for an analysis of empirical stochastic highway capacity as well as for the effect of automatic driving vehicles and cooperative driving on traffic flow. To reach this goal, we show a deep connection between the understanding of empirical stochastic highway capacity and a reliable analysis of automatic driving vehicles in traffic flow. With the use of simulations in the framework of three-phase traffic theory, a probabilistic analysis of the effect of automatic driving vehicles on a mixture traffic flow consisting of a random distribution of automatic driving and manual driving vehicles has been made. We have found that the parameters of automatic driving vehicles can either decrease or increase the probability of the breakdown. The increase in the probability of traffic breakdown, i.e., the deterioration of the performance of the traffic system can occur already at a small percentage (about 5%) of automatic driving vehicles. The increase in the probability of traffic breakdown through automatic driving vehicles can be realized, even if any platoon of automatic driving vehicles satisfies condition for string stability.

  10. Physics of Automatic Target Recognition

    CERN Document Server

    Sadjadi, Firooz

    2007-01-01

    Physics of Automatic Target Recognition addresses the fundamental physical bases of sensing, and information extraction in the state-of-the art automatic target recognition field. It explores both passive and active multispectral sensing, polarimetric diversity, complex signature exploitation, sensor and processing adaptation, transformation of electromagnetic and acoustic waves in their interactions with targets, background clutter, transmission media, and sensing elements. The general inverse scattering, and advanced signal processing techniques and scientific evaluation methodologies being used in this multi disciplinary field will be part of this exposition. The issues of modeling of target signatures in various spectral modalities, LADAR, IR, SAR, high resolution radar, acoustic, seismic, visible, hyperspectral, in diverse geometric aspects will be addressed. The methods for signal processing and classification will cover concepts such as sensor adaptive and artificial neural networks, time reversal filt...

  11. Vehicle License Plate Recognition Syst

    Directory of Open Access Journals (Sweden)

    Meenakshi,R. B. Dubey

    2012-12-01

    Full Text Available The vehicle license plate recognition system has greater efficiency for vehicle monitoring in automatic zone access control. This Plate recognition system will avoid special tags, since all vehicles possess a unique registration number plate. A number of techniques have been used for car plate characters recognition. This system uses neural network character recognition and pattern matching of characters as two character recognition techniques. In this approach multilayer feed-forward back-propagation algorithm is used. The performance of the proposed algorithm has been tested on several car plates and provides very satisfactory results.

  12. Meta Classification for Variable Stars

    CERN Document Server

    Pichara, Karim; León, Daniel

    2016-01-01

    The need for the development of automatic tools to explore astronomical databases has been recognized since the inception of CCDs and modern computers. Astronomers already have developed solutions to tackle several science problems, such as automatic classification of stellar objects, outlier detection, and globular clusters identification, among others. New science problems emerge and it is critical to be able to re-use the models learned before, without rebuilding everything from the beginning when the science problem changes. In this paper, we propose a new meta-model that automatically integrates existing classification models of variable stars. The proposed meta-model incorporates existing models that are trained in a different context, answering different questions and using different representations of data. Conventional mixture of experts algorithms in machine learning literature can not be used since each expert (model) uses different inputs. We also consider computational complexity of the model by ...

  13. Music Genre Classification Systems - A Computational Approach

    OpenAIRE

    Ahrendt, Peter; Hansen, Lars Kai

    2006-01-01

    Automatic music genre classification is the classification of a piece of music into its corresponding genre (such as jazz or rock) by a computer. It is considered to be a cornerstone of the research area Music Information Retrieval (MIR) and closely linked to the other areas in MIR. It is thought that MIR will be a key element in the processing, searching and retrieval of digital music in the near future. This dissertation is concerned with music genre classification systems and in particular...

  14. [Classification of viruses by computer].

    Science.gov (United States)

    Ageeva, O N; Andzhaparidze, O G; Kibardin, V M; Nazarova, G M; Pleteneva, E A

    1982-01-01

    The study used the information mass containing information on 83 viruses characterized by 41 markers. The suitability of one of the variants of cluster analysis for virus classification was demonstrated. It was established that certain stages of automatic allotment of viruses into groups by the degree of similarity of their properties end the formation of groups which consist of viruses sufficiently close to each other by their properties and are sufficiently isolated. Comparison of these groups with the classification proposed by the ICVT established their correspondence to individual families. Analysis of the obtained classification system permits sufficiently grounded conclusions to be drawn with regard to the classification position of certain viruses, the classification of which has not yet been completed by the ICVT.

  15. Image analysis techniques associated with automatic data base generation.

    Science.gov (United States)

    Bond, A. D.; Ramapriyan, H. K.; Atkinson, R. J.; Hodges, B. C.; Thomas, D. T.

    1973-01-01

    This paper considers some basic problems relating to automatic data base generation from imagery, the primary emphasis being on fast and efficient automatic extraction of relevant pictorial information. Among the techniques discussed are recursive implementations of some particular types of filters which are much faster than FFT implementations, a 'sequential similarity detection' technique of implementing matched filters, and sequential linear classification of multispectral imagery. Several applications of the above techniques are presented including enhancement of underwater, aerial and radiographic imagery, detection and reconstruction of particular types of features in images, automatic picture registration and classification of multiband aerial photographs to generate thematic land use maps.

  16. Navigation System for Reusable Launch Vehicle

    OpenAIRE

    Schlotterer, Markus

    2008-01-01

    PHOENIX is a downscaled experimental vehicle to demonstrate automatic landing capabilities of future Reusable Launch Vehicles (RLVs). PHOENIX has flown in May 2004 at NEAT (North European Aerospace Test range) in Vidsel, Sweden. As the shape of the vehicle has been designed for re-entry, the dynamics are very high and almost unstable. This requires a fast and precise GNC system. This paper describes the navigation system and the navigation filter of PHOENIX. The system is introduced and the h...

  17. Feasible Path Planning for Autonomous Vehicles

    Directory of Open Access Journals (Sweden)

    Vu Trieu Minh

    2014-01-01

    Full Text Available The objective of this paper is to find feasible path planning algorithms for nonholonomic vehicles including flatness, polynomial, and symmetric polynomial trajectories subject to the real vehicle dynamical constraints. Performances of these path planning methods are simulated and compared to evaluate the more realistic and smoother generated trajectories. Results show that the symmetric polynomial algorithm provides the smoothest trajectory. Therefore, this algorithm is recommended for the development of an automatic control for autonomous vehicles.

  18. An Automatic Number Plate Recognition System under Image Processing

    OpenAIRE

    Sarbjit Kaur

    2016-01-01

    Automatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image , it display the number plate information into text. Mainly the ANPR system consists of 4 phases: - Acquisition of Vehicle Image and Pre-Processing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. The overall accuracy and efficiency of whol...

  19. 基于Google Earth的ETM+遥感图像自动分类方法%Automatic Classification Method of ETM + Remote Sensing Images Based on Google Earth

    Institute of Scientific and Technical Information of China (English)

    李文庆; 姜琦刚; 邢宇; 吴淞; 印影; 刘舒; 崔璨

    2012-01-01

    为了快速准确识别地物、设计野外路线并减少踏勘后对前期解译工作的修改,本文参考Google Earth软件提供的高分辨率遥感图像,利用ETM+解译生成训练样本,然后采用最大似然监督分类算法进行ETM+图像分类.结果表明:与非监督分类和非监督-监督混合分类方法相比,基于Google Earth高分辨率遥感图像的ETM最大似然监督分类方法效果好、精度高,是一种经济、高效的技术手段,可用于初步识别地物分布情况、设计野外路线和勘查点等工作,对野外工作具有一定的指导意义;不同融合方式、不同波段组合的图像分类结果明显不同,该区域ETM+图像R(5)G(4)B(3)波段组合、PCA融合图像的分类总精度最好.%Through referring the high - resolution remote sensing images provided by Google Earth, the training samples were generated by the manual interpretation of the Landsat ETM+ images. The samples were used to conduct ETM+ image classification by using the maximum likelihood supervised classification algorithm. The results showed that; in comparison with the methods of non -supervised classification and unsupervised - supervision mixed classification, the ETM maximum likelihood supervised classification method based on Google Earth high - resolution remote sensing images worked well with high precision, which was an economical and efficient technical means. It could be used to roughly identify the distribution of surface feature, and to design field routes and exploration points, which had a certain guiding significance on field work. The classification results of different fusion methods and different band combinations of images were significantly different. The overall classification accuracy of ETM+ images with R(5)G(4)B(3) band combination and PCA fused image in this region was the best.

  20. Automatic Kurdish Dialects Identification

    Directory of Open Access Journals (Sweden)

    Hossein Hassani

    2016-02-01

    Full Text Available Automatic dialect identification is a necessary Lan guage Technology for processing multi- dialect languages in which the dialects are linguis tically far from each other. Particularly, this becomes crucial where the dialects are mutually uni ntelligible. Therefore, to perform computational activities on these languages, the sy stem needs to identify the dialect that is the subject of the process. Kurdish language encompasse s various dialects. It is written using several different scripts. The language lacks of a standard orthography. This situation makes the Kurdish dialectal identification more interesti ng and required, both form the research and from the application perspectives. In this research , we have applied a classification method, based on supervised machine learning, to identify t he dialects of the Kurdish texts. The research has focused on two widely spoken and most dominant Kurdish dialects, namely, Kurmanji and Sorani. The approach could be applied to the other Kurdish dialects as well. The method is also applicable to the languages which are similar to Ku rdish in their dialectal diversity and differences.

  1. MULTI-LABEL CLASSIFICATION OF PRODUCT REVIEWS USING STRUCTURED SVM

    Directory of Open Access Journals (Sweden)

    Jincy B. Chrystal

    2015-05-01

    Full Text Available Most of the text classification problems are associated with multiple class labels and hence automatic text classification is one of the most challenging and prominent research area. Text classification is the problem of categorizing text documents into different classes. In the multi-label classification scenario, each document is associated may have more than one label. The real challenge in the multi-label classification is the labelling of large number of text documents with a subset of class categories. The feature extraction and classification of such text documents require an efficient machine learning algorithm which performs automatic text classification. This paper describes the multi-label classification of product review documents using Structured Support Vector Machine.

  2. Text Classification using Data Mining

    CERN Document Server

    Kamruzzaman, S M; Hasan, Ahmed Ryadh

    2010-01-01

    Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification using data mining that requires fewer documents for training. Instead of using words, word relation i.e. association rules from these words is used to derive feature set from pre-classified text documents. The concept of Naive Bayes classifier is then used on derived features and finally only a single concept of Genetic Algorithm has been added for final classification. A system based on the...

  3. Comparative Analysis of Vehicle Make and Model Recognition Techniques

    Directory of Open Access Journals (Sweden)

    Faiza Ayub Syed

    2014-03-01

    Full Text Available Vehicle Make and Model Recognition (VMMR has emerged as a significant element of vision based systems because of its application in access control systems, traffic control and monitoring systems, security systems and surveillance systems, etc. So far a number of techniques have been developed for vehicle recognition. Each technique follows different methodology and classification approaches. The evaluation results highlight the recognition technique with highest accuracy level. In this paper we have pointed out the working of various vehicle make and model recognition techniques and compare these techniques on the basis of methodology, principles, classification approach, classifier and level of recognition After comparing these factors we concluded that Locally Normalized Harris Corner Strengths (LHNS performs best as compared to other techniques. LHNS uses Bayes and K-NN classification approaches for vehicle classification. It extracts information from frontal view of vehicles for vehicle make and model recognition.

  4. 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......) is subsequently used for classification. This classifier is rather simple; current research investigates more advanced methods of classification....

  5. Study on Automatic Logistics Vehicle Positioning System Based on Web-GIS Technology%基于Web-GIS技术的物流自动车辆定位系研究

    Institute of Scientific and Technical Information of China (English)

    贺媛媛; 赵秦; 马伯元; 聂海荣; 栗惠英

    2013-01-01

    利用地理信息系统的分析功能来完善并提高物流系统的分析功能,研究在物流业车辆定位中引入Web-GIS技术,利用GIS的空间地理信息对车辆进行定位,并利用道路信息,以及路况情况来辅助决策配送车辆的调配和路线规划,给出了基于Web-GIS技术的物流自动车辆定位系统的框架,分析了其可行性.%In this paper,using the analytic function of GIS to boost the performance of the logistics systems,we introduced the Web-GIS technology into logistics vehicle positioning,using the spatial geographical information to position the vehicles and using the road status information to assist dispatching and routing of the distribution vehicles.At the end,we presented the framework of the system and analyzed its feasibility.

  6. 基于几何矩预分类的无人机遥感图像自动配准方法%Automatic Unmanned Aerial Vehicle(UAV) Image Registration Based on Geometric Moment For Pre-classifying

    Institute of Scientific and Technical Information of China (English)

    鲁云飞; 赵红颖; 刘大平; 晏磊

    2011-01-01

    Recently, image registration technology is one of the rapid development field in image processing area. In remote sensing field, it is a significant step for image fusion, moving detection, image correction, image mosaic and so on. Although there are many methods for image registration in the world, different methods apply to different kinds of images, most time the methods are selected by human intervening but not automatically. It becomes a key issue that how to combine the advantages of different methods to achieve automatic image registration, especially for UAV images. In this paper, a pre-classifying method based on geometric moment is proposed after the comparison of image registration methods based on SIFT and SURF feature extraction, in order to decide which image registration method is the best one, thereby, achieve the whole automatic process. The experiments show that this automatic image registration method makes sure a good matching effect and at the same time it broaden the types of applicative images.%图像配准技术是近些年来图像处理领域发展迅速的研究方向之一.在遥感领域内,图像配准更是实现图像融合、运动检测、图像校正、图像拼接等应用的一个关键步骤.尽管国内外目前在图像配准方面提出了很多方法,但不同方法适用的图像范围不同,很多时候需要人工干预进行方法的选择.尤其对于无人机这种快速、实时获取图像的新型遥感平台,如何集合不同方法的优点以实现图像自动配准成为了关键性问题.本文在比较分析了基于SIFT和SURF特征提取图像配准方法的各自优势后,提出基于几何矩的方法对图像进行预先分类,从而决定将其分配给何种方法进行配准,实现全程自动化.实验证明,这种图像自动配准方法在拓宽了图像应用范围的同时保证了良好的配准效果.

  7. 75 FR 15621 - Federal Motor Vehicle Safety Standards; Theft Protection and Rollaway Prevention

    Science.gov (United States)

    2010-03-30

    ... National Highway Traffic Safety Administration 49 CFR Part 571 RIN 2127-AK38 Federal Motor Vehicle Safety... Federal Motor Vehicle Safety Standard No. 114 that certain motor vehicles with an automatic transmission..., or ``Act'') was signed into law.\\1\\ This Act relates to several aspects of motor vehicle...

  8. Electric vehicles

    Science.gov (United States)

    1990-03-01

    Quiet, clean, and efficient, electric vehicles (EVs) may someday become a practical mode of transportation for the general public. Electric vehicles can provide many advantages for the nation's environment and energy supply because they run on electricity, which can be produced from many sources of energy such as coal, natural gas, uranium, and hydropower. These vehicles offer fuel versatility to the transportation sector, which depends almost solely on oil for its energy needs. Electric vehicles are any mode of transportation operated by a motor that receives electricity from a battery or fuel cell. EVs come in all shapes and sizes and may be used for different tasks. Some EVs are small and simple, such as golf carts and electric wheel chairs. Others are larger and more complex, such as automobile and vans. Some EVs, such as fork lifts, are used in industries. In this fact sheet, we will discuss mostly automobiles and vans. There are also variations on electric vehicles, such as hybrid vehicles and solar-powered vehicles. Hybrid vehicles use electricity as their primary source of energy, however, they also use a backup source of energy, such as gasoline, methanol or ethanol. Solar-powered vehicles are electric vehicles that use photovoltaic cells (cells that convert solar energy to electricity) rather than utility-supplied electricity to recharge the batteries. These concepts are discussed.

  9. PLC Based Automatic Multistoried Car Parking System

    OpenAIRE

    2014-01-01

    This project work presents the study and design of PLC based Automatic Multistoried Car Parking System. Multistoried car parking is an arrangement which is used to park a large number of vehicles in least possible place. For making this arrangement in a real plan very high technological instruments are required. In this project a prototype of such a model is made. This prototype model is made for accommodating twelve cars at a time. Availability of the space for parking is detecte...

  10. Support vector machine for automatic pain recognition

    Science.gov (United States)

    Monwar, Md Maruf; Rezaei, Siamak

    2009-02-01

    Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

  11. A Continuously Updated, Global Land Classification Map Project

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose to demonstrate a fully automatic capability for generating a global, high resolution (30 m) land classification map, with continuous updates from...

  12. A complete discrimination system for polynomials with complex coefficients and its automatic generation

    Institute of Scientific and Technical Information of China (English)

    梁松新; 张景中

    1999-01-01

    By establishing a complete discrimination system for polynomials, the problem of complete root classification for polynomials with complex coefficients is utterly solved, furthermore, the algorithm obtained is made into a general program in Maple, which enables the complete discrimination system and complete root classification of a polynomial to be automatically generated by computer, without any human intervention. Besides, by using the automatic generation of root classification, a method to determine the positive definiteness of a polynomial in one or two indeterminates is automatically presented.

  13. Classification of sports types from tracklets

    DEFF Research Database (Denmark)

    Gade, Rikke; Moeslund, Thomas B.

    Automatic analysis of video is important in order to process and exploit large amounts of data, e.g. for sports analysis. Classification of sports types is one of the first steps to- wards a fully automatic analysis of the activities performed at sports arenas. In this work we test the idea...... experiments we use 30 2-minutes thermal video sequences from each of five different sports types. By applying a 10- fold cross validation we obtain a correct classification rate of 94.5 %....

  14. Semi-automatic knee cartilage segmentation

    Science.gov (United States)

    Dam, Erik B.; Folkesson, Jenny; Pettersen, Paola C.; Christiansen, Claus

    2006-03-01

    Osteo-Arthritis (OA) is a very common age-related cause of pain and reduced range of motion. A central effect of OA is wear-down of the articular cartilage that otherwise ensures smooth joint motion. Quantification of the cartilage breakdown is central in monitoring disease progression and therefore cartilage segmentation is required. Recent advances allow automatic cartilage segmentation with high accuracy in most cases. However, the automatic methods still fail in some problematic cases. For clinical studies, even if a few failing cases will be averaged out in the overall results, this reduces the mean accuracy and precision and thereby necessitates larger/longer studies. Since the severe OA cases are often most problematic for the automatic methods, there is even a risk that the quantification will introduce a bias in the results. Therefore, interactive inspection and correction of these problematic cases is desirable. For diagnosis on individuals, this is even more crucial since the diagnosis will otherwise simply fail. We introduce and evaluate a semi-automatic cartilage segmentation method combining an automatic pre-segmentation with an interactive step that allows inspection and correction. The automatic step consists of voxel classification based on supervised learning. The interactive step combines a watershed transformation of the original scan with the posterior probability map from the classification step at sub-voxel precision. We evaluate the method for the task of segmenting the tibial cartilage sheet from low-field magnetic resonance imaging (MRI) of knees. The evaluation shows that the combined method allows accurate and highly reproducible correction of the segmentation of even the worst cases in approximately ten minutes of interaction.

  15. AUOTOMATIC CLASSIFICATION OF POINT CLOUDS EXTRACTED FROM ULTRACAM STEREO IMAGES

    OpenAIRE

    M. Modiri; Masumi, M.; A. Eftekhari

    2015-01-01

    Automatic extraction of building roofs, street and vegetation are a prerequisite for many GIS (Geographic Information System) applications, such as urban planning and 3D building reconstruction. Nowadays with advances in image processing and image matching technique by using feature base and template base image matching technique together dense point clouds are available. Point clouds classification is an important step in automatic features extraction. Therefore, in this study, the classific...

  16. Improving Cluster Analysis with Automatic Variable Selection Based on Trees

    Science.gov (United States)

    2014-12-01

    ANALYSIS WITH AUTOMATIC VARIABLE SELECTION BASED ON TREES by Anton D. Orr December 2014 Thesis Advisor: Samuel E. Buttrey Second Reader...DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE IMPROVING CLUSTER ANALYSIS WITH AUTOMATIC VARIABLE SELECTION BASED ON TREES 5. FUNDING NUMBERS 6...2006 based on classification and regression trees to address problems with determining dissimilarity. Current algorithms do not simultaneously address

  17. Unification of automatic target tracking and automatic target recognition

    Science.gov (United States)

    Schachter, Bruce J.

    2014-06-01

    The subject being addressed is how an automatic target tracker (ATT) and an automatic target recognizer (ATR) can be fused together so tightly and so well that their distinctiveness becomes lost in the merger. This has historically not been the case outside of biology and a few academic papers. The biological model of ATT∪ATR arises from dynamic patterns of activity distributed across many neural circuits and structures (including retina). The information that the brain receives from the eyes is "old news" at the time that it receives it. The eyes and brain forecast a tracked object's future position, rather than relying on received retinal position. Anticipation of the next moment - building up a consistent perception - is accomplished under difficult conditions: motion (eyes, head, body, scene background, target) and processing limitations (neural noise, delays, eye jitter, distractions). Not only does the human vision system surmount these problems, but it has innate mechanisms to exploit motion in support of target detection and classification. Biological vision doesn't normally operate on snapshots. Feature extraction, detection and recognition are spatiotemporal. When vision is viewed as a spatiotemporal process, target detection, recognition, tracking, event detection and activity recognition, do not seem as distinct as they are in current ATT and ATR designs. They appear as similar mechanism taking place at varying time scales. A framework is provided for unifying ATT and ATR.

  18. Automatic Fiscal Stabilizers

    Directory of Open Access Journals (Sweden)

    Narcis Eduard Mitu

    2013-11-01

    Full Text Available Policies or institutions (built into an economic system that automatically tend to dampen economic cycle fluctuations in income, employment, etc., without direct government intervention. For example, in boom times, progressive income tax automatically reduces money supply as incomes and spendings rise. Similarly, in recessionary times, payment of unemployment benefits injects more money in the system and stimulates demand. Also called automatic stabilizers or built-in stabilizers.

  19. Automatic differentiation bibliography

    Energy Technology Data Exchange (ETDEWEB)

    Corliss, G.F. (comp.)

    1992-07-01

    This is a bibliography of work related to automatic differentiation. Automatic differentiation is a technique for the fast, accurate propagation of derivative values using the chain rule. It is neither symbolic nor numeric. Automatic differentiation is a fundamental tool for scientific computation, with applications in optimization, nonlinear equations, nonlinear least squares approximation, stiff ordinary differential equation, partial differential equations, continuation methods, and sensitivity analysis. This report is an updated version of the bibliography which originally appeared in Automatic Differentiation of Algorithms: Theory, Implementation, and Application.

  20. Visual traffic surveillance framework: classification to event detection

    Science.gov (United States)

    Ambardekar, Amol; Nicolescu, Mircea; Bebis, George; Nicolescu, Monica

    2013-10-01

    Visual traffic surveillance using computer vision techniques can be noninvasive, automated, and cost effective. Traffic surveillance systems with the ability to detect, count, and classify vehicles can be employed in gathering traffic statistics and achieving better traffic control in intelligent transportation systems. However, vehicle classification poses a difficult problem as vehicles have high intraclass variation and relatively low interclass variation. Five different object recognition techniques are investigated: principal component analysis (PCA)+difference from vehicle space, PCA+difference in vehicle space, PCA+support vector machine, linear discriminant analysis, and constellation-based modeling applied to the problem of vehicle classification. Three of the techniques that performed well were incorporated into a unified traffic surveillance system for online classification of vehicles, which uses tracking results to improve the classification accuracy. To evaluate the accuracy of the system, 31 min of traffic video containing multilane traffic intersection was processed. It was possible to achieve classification accuracy as high as 90.49% while classifying correctly tracked vehicles into four classes: cars, SUVs/vans, pickup trucks, and buses/semis. While processing a video, our system also recorded important traffic parameters such as the appearance, speed, trajectory of a vehicle, etc. This information was later used in a search assistant tool to find interesting traffic events.

  1. Improving Statistical Language Model Performance with Automatically Generated Word Hierarchies

    CERN Document Server

    McMahon, J; Mahon, John Mc

    1995-01-01

    An automatic word classification system has been designed which processes word unigram and bigram frequency statistics extracted from a corpus of natural language utterances. The system implements a binary top-down form of word clustering which employs an average class mutual information metric. Resulting classifications are hierarchical, allowing variable class granularity. Words are represented as structural tags --- unique $n$-bit numbers the most significant bit-patterns of which incorporate class information. Access to a structural tag immediately provides access to all classification levels for the corresponding word. The classification system has successfully revealed some of the structure of English, from the phonemic to the semantic level. The system has been compared --- directly and indirectly --- with other recent word classification systems. Class based interpolated language models have been constructed to exploit the extra information supplied by the classifications and some experiments have sho...

  2. Automatic Detection and Classification of Epileptic EEG Based on Detrended Fluctuation Analysis and Extreme Learning Machine%基于 EEG 去趋势波动分析和极限学习机的癫痫发作自动检测与分类识别

    Institute of Scientific and Technical Information of China (English)

    刘小峰; 张翔; 王雪

    2015-01-01

    Epilepsy is one of the most common neurological diseases.Automatic detection and accurate identification of epileptic seizure based on electroencephalogram ( EEG) plays an important role in the dia gnosis and treatment of epileptic seizures.In this paper, EEG signals were decomposed into a number of intrinsic mode functions ( IMFs) by empirical mode decomposition ( EMD) , and then the detrended fluc-tuation index, mean and standard deviation ( SD) of IMFs of lower scales were calculated.The three pa-rameters were combined into a feature vector and fed into an extreme learning machine ( ELM) classifier. The proposed method was validated on the EEG data sets from Bonn University and Boston Children's Hospital, involving healthy subjects and epileptics.Results show that the proposed method of automatic detection and rapid identification requires fewer training samples while achieving a higher recognition rate (≥95%),indicating that it is a promising tool for automatic detection and classification of epileptic sei-zures.%癫痫是一种常发的中枢神经失调疾病。基于脑电( EEG)的癫痫发作自动检测与准确识别在临床诊断和治疗上具有重要意义。本文首先采用经验模态分解(EMD)将被试者脑电信号分解成多个固有模态函数(IMF),然后计算低尺度IMF的去趋势波动指数、均值和标准差并组成特征向量,再由极限学习机( ELM)进行自动分类。经使用波恩大学和波士顿儿童医院的脑电数据集(含健康志愿者与癫痫患者)检测验证,结果表明本文所提出的自动检测与快速识别方法仅需较少训练样本即可达到较高的癫痫发作准确识别率(≥95%),具有较好临床应用价值。

  3. Study on Automatic Determination of Foot- arch Classification Parameters, Their Reliabilities and Classifying Consistency%人体足弓类型评价参数自动提取、可靠性分析及分类结果一致性研究

    Institute of Scientific and Technical Information of China (English)

    邱海; 熊树平; 孙娜; 屠晏欣

    2012-01-01

    为了实现对人体足弓类型进行科学评价和分类,本文研究了如何从二维脚印图中快速地自动提取系列足弓类型评价参数,利用实验设计的方法对系列评价参数进行了可靠性、相关性和分类结果一致性的分析.研究表明足弓系数和脚印系数比比值系数有更好可靠性,三种参数存在很好的线性相关性,通过它们进行脚型分类的结果有较好的一致性.研究结果对鞋类产品设计、运动员与士兵的选拨、足部保健等有重要的实际意义.%In order to evaluate and classify foot - arch type scientifically, this paper proposed a method to automatically determine foot - arch classification parameters from two - dimensional footprints. An experimental study was conducted to evaluate the reliabilities and correlation of each parameter and the classifying consistency. The results showed that the reliabilities of arch index and footprint index are higher than those of ratio index; each parameter does correlate with each other well and the foot - arch type classification results from three investigated parameters have good consistency. The research output could be important in several aspects such as the design of comfortable footwear products, the screening process of athletes and soldiers, and the health care of foot.

  4. Automated Periodontal Diseases Classification System

    Directory of Open Access Journals (Sweden)

    Aliaa A. A. Youssif

    2012-01-01

    Full Text Available This paper presents an efficient and innovative system for automated classification of periodontal diseases, The strength of our technique lies in the fact that it incorporates knowledge from the patients' clinical data, along with the features automatically extracted from the Haematoxylin and Eosin (H&E stained microscopic images. Our system uses image processing techniques based on color deconvolution, morphological operations, and watershed transforms for epithelium & connective tissue segmentation, nuclear segmentation, and extraction of the microscopic immunohistochemical features for the nuclei, dilated blood vessels & collagen fibers. Also, Feedforward Backpropagation Artificial Neural Networks are used for the classification process. We report 100% classification accuracy in correctly identifying the different periodontal diseases observed in our 30 samples dataset.

  5. Automatic classification of Deep Web sources based on KNN algorithm%基于K-近邻算法的Deep Web数据源的自动分类

    Institute of Scientific and Technical Information of China (English)

    张智; 顾韵华

    2011-01-01

    To meet the need of Deep Web query, an algorithm for classification of Deep Web sources based on KNN is put forward. The algorithm extracts the form features from Web pages, and makes the form features vector normal. Then the algorithm classifies Deep Web pages by computing distance. The experimental results show that the algorithm has improved in precision and recall.%针对Deep Web的查询需求,提出了一种基于K-近邻算法的Deep Web数据源的自动分类方法.该算法在对Deep Web网页进行表单特征提取及规范化的基础上,基于距离对Deep Web网页所属的目标主题进行判定.实验结果表明:基于K-近邻分类算法可以较有效地进行DeepWeb数据源的自动分类,并得到较高的查全率和查准率.

  6. Depth Level Control System using Peripheral Interface Controller for Underwater Vehicle

    Directory of Open Access Journals (Sweden)

    Muhamad Fadli Ghani

    2013-01-01

    Full Text Available This research explained on a design and development of an Automatic Depth Control System for underwater vehicle. Definition of underwater vehicle is a robotic sub-sea that is a part of the emerging field of autonomous and unmanned vehicles. This project shows the implementation’s development of an Automatic Depth Control System on a test prototyping vehicle especially involved small-scale and low cost sub-sea robots. The Automatic Depth Control System assembled with mechanical system and module of electronic system for development of a controller.

  7. Path Tracking Control of Automatic Parking Cloud Model considering the Influence of Time Delay

    Directory of Open Access Journals (Sweden)

    Yiding Hua

    2017-01-01

    Full Text Available This paper establishes the kinematic model of the automatic parking system and analyzes the kinematic constraints of the vehicle. Furthermore, it solves the problem where the traditional automatic parking system model fails to take into account the time delay. Firstly, based on simulating calculation, the influence of time delay on the dynamic trajectory of a vehicle in the automatic parking system is analyzed under the transverse distance Dlateral between different target spaces. Secondly, on the basis of cloud model, this paper utilizes the tracking control of an intelligent path closer to human intelligent behavior to further study the Cloud Generator-based parking path tracking control method and construct a vehicle path tracking control model. Moreover, tracking and steering control effects of the model are verified through simulation analysis. Finally, the effectiveness and timeliness of automatic parking controller in the aspect of path tracking are tested through a real vehicle experiment.

  8. Mediation and Automatization.

    Science.gov (United States)

    Hutchins, Edwin

    This paper discusses the relationship between the mediation of task performance by some structure that is not inherent in the task domain itself and the phenomenon of automatization, in which skilled performance becomes effortless or phenomenologically "automatic" after extensive practice. The use of a common simple explicit mediating…

  9. Digital automatic gain control

    Science.gov (United States)

    Uzdy, Z.

    1980-01-01

    Performance analysis, used to evaluated fitness of several circuits to digital automatic gain control (AGC), indicates that digital integrator employing coherent amplitude detector (CAD) is best device suited for application. Circuit reduces gain error to half that of conventional analog AGC while making it possible to automatically modify response of receiver to match incoming signal conditions.

  10. Automatic Differentiation Package

    Energy Technology Data Exchange (ETDEWEB)

    2007-03-01

    Sacado is an automatic differentiation package for C++ codes using operator overloading and C++ templating. Sacado provide forward, reverse, and Taylor polynomial automatic differentiation classes and utilities for incorporating these classes into C++ codes. Users can compute derivatives of computations arising in engineering and scientific applications, including nonlinear equation solving, time integration, sensitivity analysis, stability analysis, optimization and uncertainity quantification.

  11. Control of Multiple Robotic Sentry Vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Feddema, J.; Klarer, P.; Lewis, C.

    1999-04-01

    As part of a project for the Defense Advanced Research Projects Agency, Sandia National Laboratories is developing and testing the feasibility of using of a cooperative team of robotic sentry vehicles to guard a perimeter and to perform surround and diversion tasks. This paper describes on-going activities in the development of these robotic sentry vehicles. To date, we have developed a robotic perimeter detection system which consists of eight ''Roving All Terrain Lunar Explorer Rover'' (RATLER{trademark}) vehicles, a laptop-based base-station, and several Miniature Intrusion Detection Sensors (MIDS). A radio frequency receiver on each of the RATLER vehicles alerts the sentry vehicles of alarms from the hidden MIDS. When an alarm is received, each vehicle decides whether it should investigate the alarm based on the proximity of itself and the other vehicles to the alarm. As one vehicle attends an alarm, the other vehicles adjust their position around the perimeter to better prepare for another alarm. We have also demonstrated the ability to drive multiple vehicles in formation via tele-operation or by waypoint GPS navigation. This is currently being extended to include mission planning capabilities. At the base-station, the operator can draw on an aerial map the goal regions to be surrounded and the repulsive regions to be avoided. A potential field path planner automatically generates a path from the vehicles' current position to the goal regions while avoiding the repulsive regions and the other vehicles. This path is previewed to the operator before the regions are downloaded to the vehicles. The same potential field path planner resides on the vehicle, except additional repulsive forces from on-board proximity sensors guide the vehicle away from unplanned obstacles.

  12. Unmanned Aerial Vehicle Serial Aerial Image Automatic Registration Based on Improved SIFT Algorithm%改进SIFT算法的小型无人机航拍图像自动配准

    Institute of Scientific and Technical Information of China (English)

    熊自明; 万刚; 闫鹤; 李明

    2012-01-01

    针对小型无人机航拍图像视点离散、视角变化有一定运动规律的特点,首先对航拍图像进行数据预处理,结合Harris特征点和SIFT特征向量的优势,提取Harris特征点、计算特征点的特征半径和SIFT特征向量,并利用PCA降低特征向量的维数;然后采用最邻近(NN)方法进行特征匹配,利用BBF算法搜索特征的最邻近以提高匹配速度;最后采用PROSAC算法提纯特征点匹配对并精确计算运动模型参数,实现了图像的自动配准.实验证明,该图像配准方法在准确性、效率方面较经典的SIFT算法有较大的提高.%Due to the disperse and regular of view points and the view angle of UAV Aerial Image, the image data was preconditioned at first, then the Harris feature points with SIFT feature vectors were combined, Harris feature points were extracted, the characteristics radius of feature points and SIFT feature vector was calculated, and PCA (Principal Component Analysis) was used to reduce the dimension of SIFT feature vectors. And then the most close method (NN) was used to feature matching, the BBF algorithm was applied to search the nearest neighbor feature for improving the matching speed. Finally, the PROSAC algorithm was used to purify initial feature point matching pairs, and motion model parameters were calculated, the image automatic registration was achieved. The results of experiment proved that such algorithm was more efficient and exact than the classic SIFT algorithm.

  13. Prototype Selection for Classification in Standard and Generalized Dissimilarity Spaces

    NARCIS (Netherlands)

    Plasencia Calaña, Y.

    2015-01-01

    Automatic pattern classification for a given problem domain aims at assigning a class or category membership to a new unseen object from the same domain. This is performed in three main stages: data preprocessing, representation and classification. The data preprocessing highly depends on the data t

  14. Abandoned vehicles

    CERN Multimedia

    Relations with the Host States Service

    2004-01-01

    The services in charge of managing the CERN site have recently noted an increase in the number of abandoned vehicles. This poses a risk from the point of view of safety and security and, on the eve of several important events in honour of CERN's fiftieth anniversary, is detrimental to the Organization's image. Owners of vehicles that have been left immobile for some time on the CERN site, including on the external car park by the flags, are therefore invited to contact the Reception and Access Control Service (service-parking-longterm@cern.ch) before 1st October 2004 and, where appropriate, move their vehicle to a designated long-term parking area. After this date, any vehicle whose owner has failed to respond to this request and which is without a number plate, has been stationary for several weeks or is out of service, may be impounded at the owner's risk and expense. Relations with the Host States Service Tel. 72848

  15. Sports Type Classification using Signature Heatmaps

    DEFF Research Database (Denmark)

    Gade, Rikke; Moeslund, Thomas B.

    2013-01-01

    Automatic classification of activities in a sports arena is important in order to analyse and optimise the use of the arenas. In this work we classify five sports types based only on occupancy heatmaps produced from position data. Due to privacy issues we use thermal imaging for detecting people...... and then calculate their positions on the court us- ing homography. Heatmaps are produced by summarising Gaussian distributions respresenting people over 10-minute periods. Before classification the heatmaps are projected to a low-dimensional discriminative space using the principle of Fisherfaces. Our result using...... two weeks of video are very promising with a correct classification of 90.76 %....

  16. Performance of an Automated-Mixed-Traffic-Vehicle /AMTV/ System. [urban people mover

    Science.gov (United States)

    Peng, T. K. C.; Chon, K.

    1978-01-01

    This study analyzes the operation and evaluates the expected performance of a proposed automatic guideway transit system which uses low-speed Automated Mixed Traffic Vehicles (AMTV's). Vehicle scheduling and headway control policies are evaluated with a transit system simulation model. The effect of mixed-traffic interference on the average vehicle speed is examined with a vehicle-pedestrian interface model. Control parameters regulating vehicle speed are evaluated for safe stopping and passenger comfort.

  17. Visual Alphabets: Video Classification by End Users

    NARCIS (Netherlands)

    Israël, Menno; Broek, van den Egon L.; Putten, van der Peter; Uyl, den Marten J.; Petrushin, Valery A.; Khan, Latifur

    2006-01-01

    The work presented here introduces a real-time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification c

  18. Research Dynamics of the Classification Methods of Remote Sensing Images

    Institute of Scientific and Technical Information of China (English)

    Yan; ZHANG; Baoguo; WU; Dong; WANG

    2013-01-01

    As the key technology of extracting remote sensing information,the classification of remote sensing images has always been the research focus in the field of remote sensing. The paper introduces the classification process and system of remote sensing images. According to the recent research status of domestic and international remote sensing classification methods,the new study dynamics of remote sensing classification,such as artificial neural networks,support vector machine,active learning and ensemble multi-classifiers,were introduced,providing references for the automatic and intelligent development of remote sensing images classification.

  19. 四轴车辆全轮转向之自动模式研究%A Research on the Automatic Mode of All-wheel Steering for Four-axle Vehicle

    Institute of Scientific and Technical Information of China (English)

    陈思忠; 郑凯锋

    2015-01-01

    在第2轴转角比例于第1轴转角,第3轴转角比例于第4轴转角的条件下,从理论上分析了全轮转向与双前桥转向之间的差异。接着以质心侧偏角为零,设计了控制器一和控制器二两种控制器,并再次分析了双前桥转向、带控制器一的全轮转向和带控制器二的全轮转向三者之间的区别与联系,为四轴车辆的全轮转向技术的研究提供了理论参考。%Under the condition of that the wheel turning angles in second and third axles are respectively proportional to those of first and fourth axles, the differences between all wheel steering and double-front-axle steer-ing are analyzed theoretically. Then with the sideslip angle of mass center set to zero, controller 1 and controller 2 are designed, and the differences and connections between double-front-axle steering, all wheel steering with con-troller 1 and all wheel steering with controller 2 are analyzed again, providing theoretical references for the research on the all wheel steering technique for four-axle vehicle.

  20. Vision-based Vehicle Detection Survey

    Directory of Open Access Journals (Sweden)

    Alex David S

    2016-03-01

    Full Text Available Nowadays thousands of drivers and passengers were losing their lives every year on road accident, due to deadly crashes between more than one vehicle. There are number of many research focuses were dedicated to the development of intellectual driver assistance systems and autonomous vehicles over the past decade, which reduces the danger by monitoring the on-road environment. In particular, researchers attracted towards the on-road detection of vehicles in recent years. Different parameters have been analyzed in this paper which includes camera placement and the various applications of monocular vehicle detection, common features and common classification methods, motion- based approaches and nighttime vehicle detection and monocular pose estimation. Previous works on the vehicle detection listed based on camera poisons, feature based detection and motion based detection works and night time detection.

  1. HEp-2 Cell Classification via Fusing Texture and Shape Information

    OpenAIRE

    Qi, Xianbiao; Zhao, Guoying; Li, Chun-Guang; Guo, Jun; Pietikäinen, Matti

    2015-01-01

    Indirect Immunofluorescence (IIF) HEp-2 cell image is an effective evidence for diagnosis of autoimmune diseases. Recently computer-aided diagnosis of autoimmune diseases by IIF HEp-2 cell classification has attracted great attention. However the HEp-2 cell classification task is quite challenging due to large intra-class variation and small between-class variation. In this paper we propose an effective and efficient approach for the automatic classification of IIF HEp-2 cell image by fusing ...

  2. Small-Scale Helicopter Automatic Autorotation: Modeling, Guidance, and Control

    NARCIS (Netherlands)

    Taamallah, S.

    2015-01-01

    Our research objective consists in developing a, model-based, automatic safety recovery system, for a small-scale helicopter Unmanned Aerial Vehicle (UAV) in autorotation, i.e. an engine OFF flight condition, that safely flies and lands the helicopter to a pre-specified ground location. In pursuit o

  3. Automatic recognition of lactating sow behaviors through depth image processing

    Science.gov (United States)

    Manual observation and classification of animal behaviors is laborious, time-consuming, and of limited ability to process large amount of data. A computer vision-based system was developed that automatically recognizes sow behaviors (lying, sitting, standing, kneeling, feeding, drinking, and shiftin...

  4. Automotive Control Systems: For Engine, Driveline, and Vehicle

    Science.gov (United States)

    Kiencke, Uwe; Nielsen, Lars

    Advances in automotive control systems continue to enhance safety and comfort and to reduce fuel consumption and emissions. Reflecting the trend to optimization through integrative approaches for engine, driveline, and vehicle control, this valuable book enables control engineers to understand engine and vehicle models necessary for controller design, and also introduces mechanical engineers to vehicle-specific signal processing and automatic control. The emphasis on measurement, comparisons between performance and modeling, and realistic examples derive from the authors' unique industrial experience

  5. Transporter Classification Database (TCDB)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

  6. electric vehicle

    Directory of Open Access Journals (Sweden)

    W. R. Lee

    1999-01-01

    Full Text Available A major problem facing battery-powered electric vehicles is in their batteries: weight and charge capacity. Thus, a battery-powered electric vehicle only has a short driving range. To travel for a longer distance, the batteries are required to be recharged frequently. In this paper, we construct a model for a battery-powered electric vehicle, in which driving strategy is to be obtained such that the total travelling time between two locations is minimized. The problem is formulated as an optimization problem with switching times and speed as decision variables. This is an unconventional optimization problem. However, by using the control parametrization enhancing technique (CPET, it is shown that this unconventional optimization is equivalent to a conventional optimal parameter selection problem. Numerical examples are solved using the proposed method.

  7. Vehicle Travel Information System (VTRIS) - Data Download Tool

    Data.gov (United States)

    Department of Transportation — The VTRIS W-Tables are designed to provide a standard format for presenting the outcome of the Vehicle Weighing and Classification efforts at truck weigh sites. The...

  8. Word Automaticity of Tree Automatic Scattered Linear Orderings Is Decidable

    CERN Document Server

    Huschenbett, Martin

    2012-01-01

    A tree automatic structure is a structure whose domain can be encoded by a regular tree language such that each relation is recognisable by a finite automaton processing tuples of trees synchronously. Words can be regarded as specific simple trees and a structure is word automatic if it is encodable using only these trees. The question naturally arises whether a given tree automatic structure is already word automatic. We prove that this problem is decidable for tree automatic scattered linear orderings. Moreover, we show that in case of a positive answer a word automatic presentation is computable from the tree automatic presentation.

  9. An open-set detection evaluation methodology for automatic emotion recognition in speech

    NARCIS (Netherlands)

    Truong, K.P.; Leeuwen, D.A. van

    2007-01-01

    In this paper, we present a detection approach and an ‘open-set’ detection evaluation methodology for automatic emotion recognition in speech. The traditional classification approach does not seem to be suitable and flexible enough for typical emotion recognition tasks. For example, classification d

  10. Line matching for automatic change detection algorithm

    Science.gov (United States)

    Dhollande, Jérôme; Monnin, David; Gond, Laetitia; Cudel, Christophe; Kohler, Sophie; Dieterlen, Alain

    2012-06-01

    During foreign operations, Improvised Explosive Devices (IEDs) are one of major threats that soldiers may unfortunately encounter along itineraries. Based on a vehicle-mounted camera, we propose an original approach by image comparison to detect signicant changes on these roads. The classic 2D-image registration techniques do not take into account parallax phenomena. The consequence is that the misregistration errors could be detected as changes. According to stereovision principles, our automatic method compares intensity proles along corresponding epipolar lines by extrema matching. An adaptive space warping compensates scale dierence in 3D-scene. When the signals are matched, the signal dierence highlights changes which are marked in current video.

  11. Research on automatic human chromosome image analysis

    Science.gov (United States)

    Ming, Delie; Tian, Jinwen; Liu, Jian

    2007-11-01

    Human chromosome karyotyping is one of the essential tasks in cytogenetics, especially in genetic syndrome diagnoses. In this thesis, an automatic procedure is introduced for human chromosome image analysis. According to different status of touching and overlapping chromosomes, several segmentation methods are proposed to achieve the best results. Medial axis is extracted by the middle point algorithm. Chromosome band is enhanced by the algorithm based on multiscale B-spline wavelets, extracted by average gray profile, gradient profile and shape profile, and calculated by the WDD (Weighted Density Distribution) descriptors. The multilayer classifier is used in classification. Experiment results demonstrate that the algorithms perform well.

  12. Feature Extraction in Radar Target Classification

    Directory of Open Access Journals (Sweden)

    Z. Kus

    1999-09-01

    Full Text Available This paper presents experimental results of extracting features in the Radar Target Classification process using the J frequency band pulse radar. The feature extraction is based on frequency analysis methods, the discrete-time Fourier Transform (DFT and Multiple Signal Characterisation (MUSIC, based on the detection of Doppler effect. The analysis has turned to the preference of DFT with implemented Hanning windowing function. We assumed to classify targets-vehicles into two classes, the wheeled vehicle and tracked vehicle. The results show that it is possible to classify them only while moving. The feature of the class results from a movement of moving parts of the vehicle. However, we have not found any feature to classify the wheeled and tracked vehicles while non-moving, although their engines are on.

  13. Empirical evaluation of three machine learning method for automatic classification of neoplastic diagnoses Evaluación empírica de tres métodos de aprendizaje automático para clasificar automáticamente diagnósticos de neoplasias

    Directory of Open Access Journals (Sweden)

    José Luis Jara

    2011-12-01

    Full Text Available Diagnoses are a valuable source of information for evaluating a health system. However, they are not used extensively by information systems because diagnoses are normally written in natural language. This work empirically evaluates three machine learning methods to automatically assign codes from the International Classification of Diseases (10th Revision to 3,335 distinct diagnoses of neoplasms obtained from UMLS®. This evaluation is conducted on three different types of preprocessing. The results are encouraging: a well-known rule induction method and maximum entropy models achieve 90% accuracy in a balanced cross-validation experiment.Los diagnósticos médicos son una fuente valiosa de información para evaluar el funcionamiento de un sistema de salud. Sin embargo, su utilización en sistemas de información se ve dificultada porque éstos se encuentran normalmente escritos en lenguaje natural. Este trabajo evalúa empíricamente tres métodos de Aprendizaje Automático para asignar códigos de acuerdo a la Clasificación Internacional de Enfermedades (décima versión a 3.335 diferentes diagnósticos de neoplasias extraídos desde UMLS®. Esta evaluación se realiza con tres tipos distintos de preprocesamiento. Los resultados son alentadores: un conocido método de inducción de reglas de decisión y modelos de entropía máxima obtienen alrededor de 90% accuracy en una validación cruzada balanceada.

  14. Comparative analysis of XE-5000 automatic hematology analyzer and artificial microscopy in detecting white blood cell classification%XE-5000全自动血细胞分析仪检测白细胞分类与人工镜检结果对比分析

    Institute of Scientific and Technical Information of China (English)

    陈建霞; 黄衍锋; 张旭

    2015-01-01

    目的:探讨XE-5000全自动血细胞分析仪检测白细胞(WBC)分类的准确性。方法随机收集本院2012年6月~2013年6月门诊、住院患者及健康体检者共208例,根据年龄进行分组,其中成人组100例、儿童组60例、新生儿组48例,分别用Sysmex XE-5000与人工镜检法对不同年龄组的静脉血进行WBC分类,采用统计学软件对两种方法的结果进行对比分析,并进行相关性分析。结果除嗜碱粒细胞外,成人组仪器法与人工镜检法检测WBC分类计数结果比较,差异无统计学意义(P>0.05),且呈正相关(P0.05),且呈正相关(P0.05),and had a positive correla-tion (P0.05),and had a positive correlation (P<0.05). Conclusion Using the Sysmex XE-5000 automatic blood cell analyzer to detect the white blood cell classification has a good per-formance and it is suitable to detect a large quantity of samples quickly and effectively,but it can not completely re-place the artificial microscopy.If the two methods are combined,it can ensure the accuracy and reliability of test results.

  15. Odor Classification using Agent Technology

    Directory of Open Access Journals (Sweden)

    Sigeru OMATU

    2014-03-01

    Full Text Available In order to measure and classify odors, Quartz Crystal Microbalance (QCM can be used. In the present study, seven QCM sensors and three different odors are used. The system has been developed as a virtual organization of agents using an agent platform called PANGEA (Platform for Automatic coNstruction of orGanizations of intElligent Agents. This is a platform for developing open multi-agent systems, specifically those including organizational aspects. The main reason for the use of agents is the scalability of the platform, i.e. the way in which it models the services. The system models functionalities as services inside the agents, or as Service Oriented Approach (SOA architecture compliant services using Web Services. This way the adaptation of the odor classification systems with new algorithms, tools and classification techniques is allowed.

  16. Stability Control of Vehicle Emergency Braking with Tire Blowout

    Directory of Open Access Journals (Sweden)

    Qingzhang Chen

    2014-01-01

    Full Text Available For the stability control and slowing down the vehicle to a safe speed after tire failure, an emergency automatic braking system with independent intellectual property is developed. After the system has received a signal of tire blowout, the automatic braking mode of the vehicle is determined according to the position of the failure tire and the motion state of vehicle, and a control strategy for resisting tire blowout additional yaw torque and deceleration is designed to slow down vehicle to a safe speed in an expected trajectory. The simulating test system is also designed, and the testing results show that the vehicle can be quickly stabilized and kept in the original track after tire blowout with the emergency braking system described in the paper.

  17. Automatic Program Development

    DEFF Research Database (Denmark)

    by members of the IFIP Working Group 2.1 of which Bob was an active member. All papers are related to some of the research interests of Bob and, in particular, to the transformational development of programs and their algorithmic derivation from formal specifications. Automatic Program Development offers......Automatic Program Development is a tribute to Robert Paige (1947-1999), our accomplished and respected colleague, and moreover our good friend, whose untimely passing was a loss to our academic and research community. We have collected the revised, updated versions of the papers published in his...... honor in the Higher-Order and Symbolic Computation Journal in the years 2003 and 2005. Among them there are two papers by Bob: (i) a retrospective view of his research lines, and (ii) a proposal for future studies in the area of the automatic program derivation. The book also includes some papers...

  18. Automatic text summarization

    CERN Document Server

    Torres Moreno, Juan Manuel

    2014-01-01

    This new textbook examines the motivations and the different algorithms for automatic document summarization (ADS). We performed a recent state of the art. The book shows the main problems of ADS, difficulties and the solutions provided by the community. It presents recent advances in ADS, as well as current applications and trends. The approaches are statistical, linguistic and symbolic. Several exemples are included in order to clarify the theoretical concepts.  The books currently available in the area of Automatic Document Summarization are not recent. Powerful algorithms have been develop

  19. Automatic Camera Control

    DEFF Research Database (Denmark)

    Burelli, Paolo; Preuss, Mike

    2014-01-01

    Automatically generating computer animations is a challenging and complex problem with applications in games and film production. In this paper, we investigate howto translate a shot list for a virtual scene into a series of virtual camera configurations — i.e automatically controlling the virtual...... camera. We approach this problem by modelling it as a dynamic multi-objective optimisation problem and show how this metaphor allows a much richer expressiveness than a classical single objective approach. Finally, we showcase the application of a multi-objective evolutionary algorithm to generate a shot...

  20. Equipment Proposal for the Autonomous Vehicle Systems Laboratory at UIW

    Science.gov (United States)

    2015-04-29

    SECURITY CLASSIFICATION OF: This final technical report details the status of the Autonomous Vehicle Systems (AVS) Research and Education Laboratory...Approved for Public Release; Distribution Unlimited Final Report: Equipment Proposal for the Autonomous Vehicle Systems Laboratory at UIW The views...P.O. Box 12211 Research Triangle Park, NC 27709-2211 Final Report for UIW Autonomous Vehicle Systems Research and Education Laboratory REPORT

  1. Overview of Stochastic Vehicle Routing Problems

    Institute of Scientific and Technical Information of China (English)

    郭耀煌; 谢秉磊; 郭强

    2002-01-01

    Stochastic vehicle routing problems (VRPs) play important roles in logistics, though they have not been studied systematically yet. The paper summaries the definition, properties and classification of stochastic VRPs, makes further discussion about two strategies in stochastic VRPs, and at last overviews dynamic and stochastic VRPs.

  2. Automatic image cropping for republishing

    Science.gov (United States)

    Cheatle, Phil

    2010-02-01

    Image cropping is an important aspect of creating aesthetically pleasing web pages and repurposing content for different web or printed output layouts. Cropping provides both the possibility of improving the composition of the image, and also the ability to change the aspect ratio of the image to suit the layout design needs of different document or web page formats. This paper presents a method for aesthetically cropping images on the basis of their content. Underlying the approach is a novel segmentation-based saliency method which identifies some regions as "distractions", as an alternative to the conventional "foreground" and "background" classifications. Distractions are a particular problem with typical consumer photos found on social networking websites such as FaceBook, Flickr etc. Automatic cropping is achieved by identifying the main subject area of the image and then using an optimization search to expand this to form an aesthetically pleasing crop. Evaluation of aesthetic functions like auto-crop is difficult as there is no single correct solution. A further contribution of this paper is an automated evaluation method which goes some way towards handling the complexity of aesthetic assessment. This allows crop algorithms to be easily evaluated against a large test set.

  3. Music Genre Classification Systems - A Computational Approach

    DEFF Research Database (Denmark)

    Ahrendt, Peter

    2006-01-01

    Automatic music genre classification is the classification of a piece of music into its corresponding genre (such as jazz or rock) by a computer. It is considered to be a cornerstone of the research area Music Information Retrieval (MIR) and closely linked to the other areas in MIR. It is thought...... that MIR will be a key element in the processing, searching and retrieval of digital music in the near future. This dissertation is concerned with music genre classification systems and in particular systems which use the raw audio signal as input to estimate the corresponding genre. This is in contrast...... to systems which use e.g. a symbolic representation or textual information about the music. The approach to music genre classification systems has here been system-oriented. In other words, all the different aspects of the systems have been considered and it is emphasized that the systems should...

  4. Evaluation for Uncertain Image Classification and Segmentation

    CERN Document Server

    Martin, Arnaud; Arnold-Bos, Andreas

    2008-01-01

    Each year, numerous segmentation and classification algorithms are invented or reused to solve problems where machine vision is needed. Generally, the efficiency of these algorithms is compared against the results given by one or many human experts. However, in many situations, the location of the real boundaries of the objects as well as their classes are not known with certainty by the human experts. Furthermore, only one aspect of the segmentation and classification problem is generally evaluated. In this paper we present a new evaluation method for classification and segmentation of image, where we take into account both the classification and segmentation results as well as the level of certainty given by the experts. As a concrete example of our method, we evaluate an automatic seabed characterization algorithm based on sonar images.

  5. Vehicle Real-time Location Based on Visual Perception Model

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.

  6. Automatic 3-D Point Cloud Classification of Urban Environments

    Science.gov (United States)

    2008-12-01

    paper, we address the problem of automated interpretation of 3-D point clouds from scenes of urban and natural environments; our analysis is...over 10 km of traverse. We implemented three geometric features com- monly used in spectral analysis of point clouds . We de- fine λ2 ≥ λ1 ≥ λ0 to be

  7. AUTOMATIC CLASSIFICATION OF STRUCTURAL MRI FOR DIAGNOSIS OF NEURODEGENERATIVE DISEASES

    Directory of Open Access Journals (Sweden)

    GLORIA DÍAZ

    2010-01-01

    Full Text Available Este artículo presenta un método automático para la clasificación de individuos en grupos patológicos o controles sanos haciendo uso de imágenes de resonancia mag- nética. El método propuesto usa los valores de deformación del sujeto analizado a un cerebro plantilla, para entrenar un modelo de clasificación capaz de identificar las fronteras que separan los grupos de estudio en un espacio de características dado. Con el fin de reducir la dimensionalidad del problema, un conjunto de regiones relevantes es automáticamente extraído en un proceso que selecciona las regiones estadística- mente significativas en una prueba t-student, con la restricción de mantener coherencia en dicha significancia en una vecindad de 5 voxeles. El método propuesto fue evaluado en la clasificación de pacientes con esquizofrenia y sujetos sanos. Los resultados mos- traron un desempeño entre el 74 y el 89%, el cual depende principalmente del número de muestras empleadas para el entrenamiento del modelo.

  8. AutoFACT: An Automatic Functional Annotation and Classification Tool

    Directory of Open Access Journals (Sweden)

    Lang B Franz

    2005-06-01

    Full Text Available Abstract Background Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets. Results We present AutoFACT, a fully automated and customizable annotation tool that assigns biologically informative functions to a sequence. Key features of this tool are that it (1 analyzes nucleotide and protein sequence data; (2 determines the most informative functional description by combining multiple BLAST reports from several user-selected databases; (3 assigns putative metabolic pathways, functional classes, enzyme classes, GeneOntology terms and locus names; and (4 generates output in HTML, text and GFF formats for the user's convenience. We have compared AutoFACT to four well-established annotation pipelines. The error rate of functional annotation is estimated to be only between 1–2%. Comparison of AutoFACT to the traditional top-BLAST-hit annotation method shows that our procedure increases the number of functionally informative annotations by approximately 50%. Conclusion AutoFACT will serve as a useful annotation tool for smaller sequencing groups lacking dedicated bioinformatics staff. It is implemented in PERL and runs on LINUX/UNIX platforms. AutoFACT is available at http://megasun.bch.umontreal.ca/Software/AutoFACT.htm.

  9. Automatic classification of images with appendiceal orifice in colonoscopy videos.

    Science.gov (United States)

    Cao, Yu; Liu, Danyu; Tavanapong, Wallapak; Wong, Johnny; Oh, JungHwan; de Groen, Piet C

    2006-01-01

    Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. In current practice, videos captured from colonoscopic procedures are not routinely stored for either manual or automated post-procedure analysis. In this paper, we introduce new algorithms for automated detection of the presence of the shape of the opening of the appendix in a colonoscopy video frame. The appearance of the appendix in colonoscopy videos indicates traversal of the colon, which is an important measurement for evaluating the quality of colonoscopic procedures. The proposed techniques are valuable for (1) establishment of an effective content-based retrieval system to facilitate endoscopic research and education; and (2) assessment and improvement of the procedural skills of endoscopists, both in training and practice.

  10. HUMAN-SIMULATING VEHICLE STEERING CONTROL ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    XU Youchun; LI Keqiang; CHANG Ming; CHEN Jun

    2006-01-01

    A new vehicle steering control algorithm is presented. Unlike the traditional methods do,the algorithm uses a sigmoid function to describe the principle of the human driver's steering strategy.Based on this function, a human simulating vehicle steering model, human-simulating steering control(HS) algorithm is designed. In order to improve the adaptability to different environments, a parameter adaptive adjustment algorithm is presented. This algorithm can online modify the value of the key parameters of the HS real time. HS controller is used on a vehicle equipped with computer vision system and computer controlled steering actuator system, the result from the automatic vehicle steering experiment shows that the HS algorithm gives good performance at different speed, even at the maximum speed of 172 km/h.

  11. Automatic Complexity Analysis

    DEFF Research Database (Denmark)

    Rosendahl, Mads

    1989-01-01

    One way to analyse programs is to to derive expressions for their computational behaviour. A time bound function (or worst-case complexity) gives an upper bound for the computation time as a function of the size of input. We describe a system to derive such time bounds automatically using abstract...

  12. Exploring Automatization Processes.

    Science.gov (United States)

    DeKeyser, Robert M.

    1996-01-01

    Presents the rationale for and the results of a pilot study attempting to document in detail how automatization takes place as the result of different kinds of intensive practice. Results show that reaction times and error rates gradually decline with practice, and the practice effect is skill-specific. (36 references) (CK)

  13. Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

    Directory of Open Access Journals (Sweden)

    Boon-Giin Lee

    2014-09-01

    Full Text Available Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals.

  14. Automatic identification of artifacts in electrodermal activity data.

    Science.gov (United States)

    Taylor, Sara; Jaques, Natasha; Chen, Weixuan; Fedor, Szymon; Sano, Akane; Picard, Rosalind

    2015-01-01

    Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.

  15. Automaticity and Reading: Perspectives from the Instance Theory of Automatization.

    Science.gov (United States)

    Logan, Gordon D.

    1997-01-01

    Reviews recent literature on automaticity, defining the criteria that distinguish automatic processing from non-automatic processing, and describing modern theories of the underlying mechanisms. Focuses on evidence from studies of reading and draws implications from theory and data for practical issues in teaching reading. Suggests that…

  16. Text Classification: A Sequential Reading Approach

    CERN Document Server

    Dulac-Arnold, Gabriel; Gallinari, Patrick

    2011-01-01

    We propose to model the text classification process as a sequential decision process. In this process, an agent learns to classify documents into topics while reading the document sentences sequentially and learns to stop as soon as enough information was read for deciding. The proposed algorithm is based on a modelisation of Text Classification as a Markov Decision Process and learns by using Reinforcement Learning. Experiments on four different classical mono-label corpora show that the proposed approach performs comparably to classical SVM approaches for large training sets, and better for small training sets. In addition, the model automatically adapts its reading process to the quantity of training information provided.

  17. Automated source classification using a Kohonen network

    CERN Document Server

    Mahonen, P; Mahonen, Petri; Hakala, Pasi

    1995-01-01

    We report progress in the development of automatic star/galaxy classifier for processing images generated by large galaxy surveys like APM. Our classification method is based on neural networks using the Kohonen Self-Organizing Map approach. Our method is novel, since it does not need supervised learning, i.e. human factor, in training. The analysis presented here concentrates on separating point sources (stars) from extended ones. Using simple numerical experiments we compare our method of image classification to the more traditional (PSF-fitting) approach of DAOFIND.

  18. Automatic morphometry of nerve histological sections.

    Science.gov (United States)

    Romero, E; Cuisenaire, O; Denef, J F; Delbeke, J; Macq, B; Veraart, C

    2000-04-15

    A method for the automatic segmentation, recognition and measurement of neuronal myelinated fibers in nerve histological sections is presented. In this method, the fiber parameters i.e. perimeter, area, position of the fiber and myelin sheath thickness are automatically computed. Obliquity of the sections may be taken into account. First, the image is thresholded to provide a coarse classification between myelin and non-myelin pixels. Next, the resulting binary image is further simplified using connected morphological operators. By applying semantic rules to the zonal graph axon candidates are identified. Those are either isolated or still connected. Then, separation of connected fibers is performed by evaluating myelin sheath thickness around each candidate area with an Euclidean distance transformation. Finally, properties of each detected fiber are computed and false positives are removed. The accuracy of the method is assessed by evaluating missed detection, false positive ratio and comparing the results to the manual procedure with sampling. In the evaluated nerve surface, a 0.9% of false positives was found, along with 6.36% of missed detections. The resulting histograms show strong correlation with those obtained by manual measure. The noise introduced by this method is significantly lower than the intrinsic sampling variability. This automatic method constitutes an original tool for morphometrical analysis.

  19. Vision-Based Leader Vehicle Trajectory Tracking for Multiple Agricultural Vehicles.

    Science.gov (United States)

    Zhang, Linhuan; Ahamed, Tofael; Zhang, Yan; Gao, Pengbo; Takigawa, Tomohiro

    2016-04-22

    The aim of this study was to design a navigation system composed of a human-controlled leader vehicle and a follower vehicle. The follower vehicle automatically tracks the leader vehicle. With such a system, a human driver can control two vehicles efficiently in agricultural operations. The tracking system was developed for the leader and the follower vehicle, and control of the follower was performed using a camera vision system. A stable and accurate monocular vision-based sensing system was designed, consisting of a camera and rectangular markers. Noise in the data acquisition was reduced by using the least-squares method. A feedback control algorithm was used to allow the follower vehicle to track the trajectory of the leader vehicle. A proportional-integral-derivative (PID) controller was introduced to maintain the required distance between the leader and the follower vehicle. Field experiments were conducted to evaluate the sensing and tracking performances of the leader-follower system while the leader vehicle was driven at an average speed of 0.3 m/s. In the case of linear trajectory tracking, the RMS errors were 6.5 cm, 8.9 cm and 16.4 cm for straight, turning and zigzag paths, respectively. Again, for parallel trajectory tracking, the root mean square (RMS) errors were found to be 7.1 cm, 14.6 cm and 14.0 cm for straight, turning and zigzag paths, respectively. The navigation performances indicated that the autonomous follower vehicle was able to follow the leader vehicle, and the tracking accuracy was found to be satisfactory. Therefore, the developed leader-follower system can be implemented for the harvesting of grains, using a combine as the leader and an unloader as the autonomous follower vehicle.

  20. Automatic land vehicle navigation using road map data

    Energy Technology Data Exchange (ETDEWEB)

    Schindwolf, R.

    1984-06-01

    A land navigation system has been developed that provides accurate navigation data while it is traveling on mapped roads. The system is autonomous and consists of a simple dead-reckoning navigator that is updated with stored road map data. Simulation and preliminary test results indicate that accuracies on the order of 50 feet can be achieved. Accuracy is independent of time.

  1. Classification in context

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper surveys classification research literature, discusses various classification theories, and shows that the focus has traditionally been on establishing a scientific foundation for classification research. This paper argues that a shift has taken place, and suggests that contemporary...... classification research focus on contextual information as the guide for the design and construction of classification schemes....

  2. Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

    Science.gov (United States)

    Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongxue; Ma, Xiaoxue

    2015-04-01

    Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class.

  3. Current challenges in autonomous vehicle development

    Science.gov (United States)

    Connelly, J.; Hong, W. S.; Mahoney, R. B., Jr.; Sparrow, D. A.

    2006-05-01

    The field of autonomous vehicles is a rapidly growing one, with significant interest from both government and industry sectors. Autonomous vehicles represent the intersection of artificial intelligence (AI) and robotics, combining decision-making with real-time control. Autonomous vehicles are desired for use in search and rescue, urban reconnaissance, mine detonation, supply convoys, and more. The general adage is to use robots for anything dull, dirty, dangerous or dumb. While a great deal of research has been done on autonomous systems, there are only a handful of fielded examples incorporating machine autonomy beyond the level of teleoperation, especially in outdoor/complex environments. In an attempt to assess and understand the current state of the art in autonomous vehicle development, a few areas where unsolved problems remain became clear. This paper outlines those areas and provides suggestions for the focus of science and technology research. The first step in evaluating the current state of autonomous vehicle development was to develop a definition of autonomy. A number of autonomy level classification systems were reviewed. The resulting working definitions and classification schemes used by the authors are summarized in the opening sections of the paper. The remainder of the report discusses current approaches and challenges in decision-making and real-time control for autonomous vehicles. Suggested research focus areas for near-, mid-, and long-term development are also presented.

  4. On the relevance of spectral features for instrument classification

    DEFF Research Database (Denmark)

    Nielsen, Andreas Brinch; Sigurdsson, Sigurdur; Hansen, Lars Kai

    2007-01-01

    Automatic knowledge extraction from music signals is a key component for most music organization and music information retrieval systems. In this paper, we consider the problem of instrument modelling and instrument classification from the rough audio data. Existing systems for automatic instrument...... classification operate normally on a relatively large number of features, from which those related to the spectrum of the audio signal are particularly relevant. In this paper, we confront two different models about the spectral characterization of musical instruments. The first assumes a constant envelope...

  5. Automatic Classification Method of Star Spectra Data Based on Manifold-Based Discriminant Anaysis and Support Vector Machine%流形判别分析和支持向量机的恒星光谱数据自动分类方法

    Institute of Scientific and Technical Information of China (English)

    刘忠宝; 王召巴; 赵文娟

    2014-01-01

    Although Support Vector Machine (SVM) is widely used in astronomy ,it only takes the margin between classes into consideration while neglects the data distribution in each class ,which seriously limits the classification efficiency .In view of this ,a novel automatic classification method of star spectra data based on manifold-based discriminant analysis (MDA ) and SVM is proposed in this paper .Two important concepts in MDA ,manifold-based within-class scatter (MWCS) and manifold-based between-class scatter (MBCS) ,are introduced in the proposed method ,the separating hyperplane found by which ensures MWCS is minimized and MBCS is maximized .Based on the above analysis ,the corresponding optimal problem can be estab-lished ,and then MDA transforms the original optimization problem to the QP dual form and we can obtain the support vectors and decision function .The classes of test samples are decided by the decision function .The advantage of the proposed method is that it not only focuses on the information between classes and distribution characteristics ,but also preserves the manifold struc-ture of each class .Experiments on SDSS star spectra datasets verify the effectiveness of the proposed method .%尽管经典的分类方法支持向量机SVM在天文学领域广泛应用,但其只考虑类间的绝对间隔而忽略类内的分布性状,因而分类性能有待于进一步提升。鉴于此,提出一种新颖的基于流形判别分析和支持向量机的恒星光谱数据自动分类方法。该方法引入流形判别分析的两个重要概念:基于流形的类内离散度 MW和基于流形的类间离散度MB 。所提方法找到的分类面同时保证 MW 最小且MB 最大。可建立相应最优化问题,然后将原最优化问题转化为QP对偶形式求得支持向量和判别函数,最后利用判别函数判断测试样本的类属。该方法的最大优势在于进行分类决策时,不仅考虑样本的类间信息和分布

  6. Shadow detection and removal in RGB VHR images for land use unsupervised classification

    Science.gov (United States)

    Movia, A.; Beinat, A.; Crosilla, F.

    2016-09-01

    Nowadays, high resolution aerial images are widely available thanks to the diffusion of advanced technologies such as UAVs (Unmanned Aerial Vehicles) and new satellite missions. Although these developments offer new opportunities for accurate land use analysis and change detection, cloud and terrain shadows actually limit benefits and possibilities of modern sensors. Focusing on the problem of shadow detection and removal in VHR color images, the paper proposes new solutions and analyses how they can enhance common unsupervised classification procedures for identifying land use classes related to the CO2 absorption. To this aim, an improved fully automatic procedure has been developed for detecting image shadows using exclusively RGB color information, and avoiding user interaction. Results show a significant accuracy enhancement with respect to similar methods using RGB based indexes. Furthermore, novel solutions derived from Procrustes analysis have been applied to remove shadows and restore brightness in the images. In particular, two methods implementing the so called "anisotropic Procrustes" and the "not-centered oblique Procrustes" algorithms have been developed and compared with the linear correlation correction method based on the Cholesky decomposition. To assess how shadow removal can enhance unsupervised classifications, results obtained with classical methods such as k-means, maximum likelihood, and self-organizing maps, have been compared to each other and with a supervised clustering procedure.

  7. 3D laser methods for calibrating and localising robotic vehicles

    OpenAIRE

    Mark Sheehan

    2013-01-01

    This thesis is about the construction and automatic target-less calibration of a 3D laser sensor; this is then used to localise an autonomous vehicle without using other sensors. Two novel contributions to our knowledge of robotics are presented here. The first is an automatic calibration routine, which is capable of learning its calibration parameters using only data from a 3D laser scanner. Targets with known dimensions are not required, as has previously been the case. The second main ...

  8. 混合动力汽车再生制动的归类及其应用%Classification and Its Application of Regenerative Braking for Hybrid Electric Vehicle

    Institute of Scientific and Technical Information of China (English)

    盖福祥; 杜家益; 张彤

    2011-01-01

    According to different braking control strategies, the regenerative braking systems of hybrid electric vehicle (HEV) are classified into three categories: series braking with the best braking effect, series braking with the maximum energy regeneration rate and parallel braking, which are then analyzed respectively. A control strategy is put forward based on SOC, vehicle speed and the position of braking pedal, and is applied to a typical parallel HEV. The test results show that the braking control strategy proposed can recovery more braking energy with better braking feeling on the premise of safety assurance.%按不同的制动控制策略,将混合动力汽车再生制动系统分为具有最佳制动效果的串联制动、具有最佳能最回收率的串联制动和并联制动3种类型,并分别对它们进行了分析.提出了基于SOC、车速和制动踏板位置,动态地控制冉生制动转矩的控制策略,并将其应用于一款并联混合动力汽车上.测试结果表明:所制定的制动控制策略,可在保证安伞的前提下,更多地回收制动能量,并有较好的制动感觉.

  9. Automaticity or active control

    DEFF Research Database (Denmark)

    Tudoran, Ana Alina; Olsen, Svein Ottar

    aspects of the construct, such as routine, inertia, automaticity, or very little conscious deliberation. The data consist of 2962 consumers participating in a large European survey. The results show that habit strength significantly moderates the association between satisfaction and action loyalty, and......This study addresses the quasi-moderating role of habit strength in explaining action loyalty. A model of loyalty behaviour is proposed that extends the traditional satisfaction–intention–action loyalty network. Habit strength is conceptualised as a cognitive construct to refer to the psychological......, respectively, between intended loyalty and action loyalty. At high levels of habit strength, consumers are more likely to free up cognitive resources and incline the balance from controlled to routine and automatic-like responses....

  10. Automatic Ultrasound Scanning

    DEFF Research Database (Denmark)

    Moshavegh, Ramin

    Medical ultrasound has been a widely used imaging modality in healthcare platforms for examination, diagnostic purposes, and for real-time guidance during surgery. However, despite the recent advances, medical ultrasound remains the most operator-dependent imaging modality, as it heavily relies...... on the user adjustments on the scanner interface to optimize the scan settings. This explains the huge interest in the subject of this PhD project entitled “AUTOMATIC ULTRASOUND SCANNING”. The key goals of the project have been to develop automated techniques to minimize the unnecessary settings...... on the scanners, and to improve the computer-aided diagnosis (CAD) in ultrasound by introducing new quantitative measures. Thus, four major issues concerning automation of the medical ultrasound are addressed in this PhD project. They touch upon gain adjustments in ultrasound, automatic synthetic aperture image...

  11. Automatic trend estimation

    CERN Document Server

    Vamos¸, C˘alin

    2013-01-01

    Our book introduces a method to evaluate the accuracy of trend estimation algorithms under conditions similar to those encountered in real time series processing. This method is based on Monte Carlo experiments with artificial time series numerically generated by an original algorithm. The second part of the book contains several automatic algorithms for trend estimation and time series partitioning. The source codes of the computer programs implementing these original automatic algorithms are given in the appendix and will be freely available on the web. The book contains clear statement of the conditions and the approximations under which the algorithms work, as well as the proper interpretation of their results. We illustrate the functioning of the analyzed algorithms by processing time series from astrophysics, finance, biophysics, and paleoclimatology. The numerical experiment method extensively used in our book is already in common use in computational and statistical physics.

  12. Topological characterization of safe coordinated vehicle motions

    Energy Technology Data Exchange (ETDEWEB)

    MILGRAM.R. JAMES; KAUFMAN,STEPHEN G.

    2000-04-03

    This paper characterizes the homotopy properties and the global topology of the space of positions of vehicles which are constrained to travel without intersecting on a network of paths. The space is determined by the number of vehicles and the network. Paths in the space correspond to simultaneous non-intersecting motions of all vehicles. The authors therefore focus on computing the homotopy type of the space, and show how to do so in the general case. Understanding the homotopy type of the space is the central issue in controlling the vehicles, as it gives a complete description of the distinct ways that vehicles may move safely on the network. The authors exhibit graphs, products of graphs, and amalgamations of products of graphs that are homotopy equivalent to the full configuration space, and are far simpler than might be expected. The results indicate how a control system for such a network of vehicles (such as a fleet of automatically guided vehicles guided by wires buried in a factory floor) may be implemented.

  13. Radar clutter classification

    Science.gov (United States)

    Stehwien, Wolfgang

    1989-11-01

    The problem of classifying radar clutter as found on air traffic control radar systems is studied. An algorithm based on Bayes decision theory and the parametric maximum a posteriori probability classifier is developed to perform this classification automatically. This classifier employs a quadratic discriminant function and is optimum for feature vectors that are distributed according to the multivariate normal density. Separable clutter classes are most likely to arise from the analysis of the Doppler spectrum. Specifically, a feature set based on the complex reflection coefficients of the lattice prediction error filter is proposed. The classifier is tested using data recorded from L-band air traffic control radars. The Doppler spectra of these data are examined; the properties of the feature set computed using these data are studied in terms of both the marginal and multivariate statistics. Several strategies involving different numbers of features, class assignments, and data set pretesting according to Doppler frequency and signal to noise ratio were evaluated before settling on a workable algorithm. Final results are presented in terms of experimental misclassification rates and simulated and classified plane position indicator displays.

  14. Phenotype classification of zebrafish embryos by supervised learning.

    Science.gov (United States)

    Jeanray, Nathalie; Marée, Raphaël; Pruvot, Benoist; Stern, Olivier; Geurts, Pierre; Wehenkel, Louis; Muller, Marc

    2015-01-01

    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.

  15. Phenotype classification of zebrafish embryos by supervised learning.

    Directory of Open Access Journals (Sweden)

    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.

  16. Automatic food decisions

    DEFF Research Database (Denmark)

    Mueller Loose, Simone

    Consumers' food decisions are to a large extent shaped by automatic processes, which are either internally directed through learned habits and routines or externally influenced by context factors and visual information triggers. Innovative research methods such as eye tracking, choice experiments...... and food diaries allow us to better understand the impact of unconscious processes on consumers' food choices. Simone Mueller Loose will provide an overview of recent research insights into the effects of habit and context on consumers' food choices....

  17. Discriminative Chemical Patterns: Automatic and Interactive Design.

    Science.gov (United States)

    Bietz, Stefan; Schomburg, Karen T; Hilbig, Matthias; Rarey, Matthias

    2015-08-24

    The classification of molecules with respect to their inhibiting, activating, or toxicological potential constitutes a central aspect in the field of cheminformatics. Often, a discriminative feature is needed to distinguish two different molecule sets. Besides physicochemical properties, substructures and chemical patterns belong to the descriptors most frequently applied for this purpose. As a commonly used example of this descriptor class, SMARTS strings represent a powerful concept for the representation and processing of abstract chemical patterns. While their usage facilitates a convenient way to apply previously derived classification rules on new molecule sets, the manual generation of useful SMARTS patterns remains a complex and time-consuming process. Here, we introduce SMARTSminer, a new algorithm for the automatic derivation of discriminative SMARTS patterns from preclassified molecule sets. Based on a specially adapted subgraph mining algorithm, SMARTSminer identifies structural features that are frequent in only one of the given molecule classes. In comparison to elemental substructures, it also supports the consideration of general and specific SMARTS features. Furthermore, SMARTSminer is integrated into an interactive pattern editor named SMARTSeditor. This allows for an intuitive visualization on the basis of the SMARTSviewer concept as well as interactive adaption and further improvement of the generated patterns. Additionally, a new molecular matching feature provides an immediate feedback on a pattern's matching behavior across the molecule sets. We demonstrate the utility of the SMARTSminer functionality and its integration into the SMARTSeditor software in several different classification scenarios.

  18. Automatic detection of laughter

    NARCIS (Netherlands)

    Truong, K.P.; Leeuwen, D.A. van

    2005-01-01

    In the context of detecting ‘paralinguistic events’ with the aim to make classification of the speaker’s emotional state possible, a detector was developed for one of the most obvious ‘paralinguistic events’, namely laughter. Gaussian Mixture Models were trained with Perceptual Linear Prediction fea

  19. Automatization of lexicographic work

    Directory of Open Access Journals (Sweden)

    Iztok Kosem

    2013-12-01

    Full Text Available A new approach to lexicographic work, in which the lexicographer is seen more as a validator of the choices made by computer, was recently envisaged by Rundell and Kilgarriff (2011. In this paper, we describe an experiment using such an approach during the creation of Slovene Lexical Database (Gantar, Krek, 2011. The corpus data, i.e. grammatical relations, collocations, examples, and grammatical labels, were automatically extracted from 1,18-billion-word Gigafida corpus of Slovene. The evaluation of the extracted data consisted of making a comparison between the time spent writing a manual entry and a (semi-automatic entry, and identifying potential improvements in the extraction algorithm and in the presentation of data. An important finding was that the automatic approach was far more effective than the manual approach, without any significant loss of information. Based on our experience, we would propose a slightly revised version of the approach envisaged by Rundell and Kilgarriff in which the validation of data is left to lower-level linguists or crowd-sourcing, whereas high-level tasks such as meaning description remain the domain of lexicographers. Such an approach indeed reduces the scope of lexicographer’s work, however it also results in the ability of bringing the content to the users more quickly.

  20. Meta-classification for Variable Stars

    Science.gov (United States)

    Pichara, Karim; Protopapas, Pavlos; León, Daniel

    2016-03-01

    The need for the development of automatic tools to explore astronomical databases has been recognized since the inception of CCDs and modern computers. Astronomers already have developed solutions to tackle several science problems, such as automatic classification of stellar objects, outlier detection, and globular clusters identification, among others. New scientific problems emerge, and it is critical to be able to reuse the models learned before, without rebuilding everything from the beginning when the sciencientific problem changes. In this paper, we propose a new meta-model that automatically integrates existing classification models of variable stars. The proposed meta-model incorporates existing models that are trained in a different context, answering different questions and using different representations of data. A conventional mixture of expert algorithms in machine learning literature cannot be used since each expert (model) uses different inputs. We also consider the computational complexity of the model by using the most expensive models only when it is necessary. We test our model with EROS-2 and MACHO data sets, and we show that we solve most of the classification challenges only by training a meta-model to learn how to integrate the previous experts.

  1. AUTOMATIC EXTRACTION OF BUILDING OUTLINE FROM HIGH RESOLUTION AERIAL IMAGERY

    Directory of Open Access Journals (Sweden)

    Y. Wang

    2016-06-01

    Full Text Available In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately. It consists of automatic generation of high quality point cloud from the imagery, building detection from point cloud, classification of building roof and generation of building outline. Point cloud is generated from the imagery automatically using semi-global image matching technology. Buildings are detected from the differential surface generated from the point cloud. Further classification of building roof is performed in order to generate accurate building outline. Finally classified building roof is converted into vector format. Numerous tests have been done on images in different locations and results are presented in the paper.

  2. Automatic Extraction of Building Outline from High Resolution Aerial Imagery

    Science.gov (United States)

    Wang, Yandong

    2016-06-01

    In this paper, a new approach for automated extraction of building boundary from high resolution imagery is proposed. The proposed approach uses both geometric and spectral properties of a building to detect and locate buildings accurately. It consists of automatic generation of high quality point cloud from the imagery, building detection from point cloud, classification of building roof and generation of building outline. Point cloud is generated from the imagery automatically using semi-global image matching technology. Buildings are detected from the differential surface generated from the point cloud. Further classification of building roof is performed in order to generate accurate building outline. Finally classified building roof is converted into vector format. Numerous tests have been done on images in different locations and results are presented in the paper.

  3. Complete automatic target cuer/recognition system for tactical forward-looking infrared images

    Science.gov (United States)

    Ernisse, Brian E.; Rogers, Steven K.; DeSimio, Martin P.; Raines, Richard A.

    1997-09-01

    A complete forward-looking IR (FLIR) automatic target cuer/recognizer (ATC/R) is presented. The data used for development and testing of this ATC/R are first generation FLIR images collected using a F-15E. The database contains thousands of images with various mission profiles and target arrangements. The specific target of interest is a mobile missile launcher, the primary target. The goal is to locate all vehicles (secondary targets) within a scene and identify the primary targets. The system developed and tested includes an image segmenter, region cluster algorithm, feature extractor, and classifier. Conventional image processing algorithms in conjunction with neural network techniques are used to form a complete ATC/R system. The conventional techniques include hit/miss filtering, difference of Gaussian filtering, and region clustering. A neural network (multilayer perceptron) is used for classification. These algorithms are developed, tested and then combined into a functional ATC/R system. Overall primary target detection rate (cuer) is 84% with a 69% primary target identification (recognizer) rate at ranges relevant to munitions release. Furthermore, the false alarm rate (a nontarget cued as a target) in only 2.3 per scene. The research is being completed with a 10 flight test profile using third generation FLIR images.

  4. Contour classification in thermographic images for detection of breast cancer

    Science.gov (United States)

    Okuniewski, Rafał; Nowak, Robert M.; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Oleszkiewicz, Witold

    2016-09-01

    Thermographic images of breast taken by the Braster device are uploaded into web application which uses different classification algorithms to automatically decide whether a patient should be more thoroughly examined. This article presents the approach to the task of classifying contours visible on thermographic images of breast taken by the Braster device in order to make the decision about the existence of cancerous tumors in breast. It presents the results of the researches conducted on the different classification algorithms.

  5. Automatic Design of a Maglev Controller in State Space

    Science.gov (United States)

    1991-12-01

    conventional trains with steel wheels on steel rails. Several experimen- tal maglev systems in Germany and Japan have demonstrated that this mode of...Design of a Maglev Controller in State Space Feng Zhao Richard Thornton Abstract We describe the automatic synthesis of a global nonlinear controller for...the global switching points of the controller is presented. The synthesized control system can stabilize the maglev vehicle with large initial displace

  6. Irregular and adaptive sampling for automatic geophysic measure systems

    Science.gov (United States)

    Avagnina, Davide; Lo Presti, Letizia; Mulassano, Paolo

    2000-07-01

    In this paper a sampling method, based on an irregular and adaptive strategy, is described. It can be used as automatic guide for rovers designed to explore terrestrial and planetary environments. Starting from the hypothesis that a explorative vehicle is equipped with a payload able to acquire measurements of interesting quantities, the method is able to detect objects of interest from measured points and to realize an adaptive sampling, while badly describing the not interesting background.

  7. Automatic infarct planimetry by means of swarm-based clustering

    OpenAIRE

    Van Vuuren, Pieter A.; Van Vuuren, Derick

    2014-01-01

    Infarct planimetry is an important tool in cardiology research. At present this technique entails that infarct size is manually determined from scanned images of prepared heart sections. Existing attempts at automating infarct planimetry are limited in that they require user input in the form of starting points for region growing algorithms or template values for classification algorithms. In this paper a new automatic infarct planimetry (AIP) algorithm is presented. The ...

  8. Automatic design of decision-tree algorithms with evolutionary algorithms.

    Science.gov (United States)

    Barros, Rodrigo C; Basgalupp, Márcio P; de Carvalho, André C P L F; Freitas, Alex A

    2013-01-01

    This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.

  9. Vehicle Development Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — FUNCTION: Supports the development of prototype deployment platform vehicles for offboard countermeasure systems. DESCRIPTION: The Vehicle Development Laboratory is...

  10. Vehicle Development Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — FUNCTION: Supports the development of prototype deployment platform vehicles for offboard countermeasure systems.DESCRIPTION: The Vehicle Development Laboratory is...

  11. Automatic voltage imbalance detector

    Science.gov (United States)

    Bobbett, Ronald E.; McCormick, J. Byron; Kerwin, William J.

    1984-01-01

    A device for indicating and preventing damage to voltage cells such as galvanic cells and fuel cells connected in series by detecting sequential voltages and comparing these voltages to adjacent voltage cells. The device is implemented by using operational amplifiers and switching circuitry is provided by transistors. The device can be utilized in battery powered electric vehicles to prevent galvanic cell damage and also in series connected fuel cells to prevent fuel cell damage.

  12. Classification rules for Indian Rice diseases

    Directory of Open Access Journals (Sweden)

    A. Nithya

    2011-01-01

    Full Text Available Many techniques have been developed for learning rules and relationships automatically from diverse data sets, to simplify the often tedious and error-prone process of acquiring knowledge from empirical data. Decision tree is one of learning algorithm which posses certain advantages that make it suitable for discovering the classification rule for data mining applications. Normally Decision trees widely used learning method and do not require any prior knowledge of data distribution, works well on noisy data .It has been applied to classify Rice disease based on the symptoms. This paper intended to discover classification rules for the Indian rice diseases using the c4.5 decision trees algorithm. Expert systems have been used in agriculture since the early 1980s. Several systems have been developed in different countries including the USA, Europe, and Egypt for plant-disorder diagnosis, management and other production aspects. This paper explores what Classification rule can do in the agricultural domain.

  13. Automatic Configuration in NTP

    Institute of Scientific and Technical Information of China (English)

    Jiang Zongli(蒋宗礼); Xu Binbin

    2003-01-01

    NTP is nowadays the most widely used distributed network time protocol, which aims at synchronizing the clocks of computers in a network and keeping the accuracy and validation of the time information which is transmitted in the network. Without automatic configuration mechanism, the stability and flexibility of the synchronization network built upon NTP protocol are not satisfying. P2P's resource discovery mechanism is used to look for time sources in a synchronization network, and according to the network environment and node's quality, the synchronization network is constructed dynamically.

  14. Classification of the web

    DEFF Research Database (Denmark)

    Mai, Jens Erik

    2004-01-01

    This paper discusses the challenges faced by investigations into the classification of the Web and outlines inquiries that are needed to use principles for bibliographic classification to construct classifications of the Web. This paper suggests that the classification of the Web meets challenges...

  15. Cluster Based Text Classification Model

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

    2011-01-01

    We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases...

  16. A Survey of Vehicle Routing Problem

    Directory of Open Access Journals (Sweden)

    Cao Wujun

    2017-01-01

    Full Text Available In recent years, vehicle routing problem (VRP has become an important content in logistics management research, and has been widely used in transportation system, logistics distribution system and express delivery system. In this paper, we discuss the classification of VRP, and summarize the common constraints of VRP, model algorithm and the main research results in recent years. Finally, we analyzes the future of VRP, and it is considered that the intelligent vehicle routing problem and intelligent heuristic algorithm will be an important field of future research.

  17. Transmissions in vehicles 2010

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2010-07-01

    Within the international VDI congress 'Gears in vehicles 2010' of the VDI Wissensforum GmbH (Duesseldorf, Federal Republic of Germany) between 22nd and 23rd June, 2010, in Friedrichshafen (Federal Republic of Germany), the following lectures were held: (1) 8HP70H - The moldhybrid transmission from ZF - Cjallenges and achievements (P. Gutmann); (2) GETRAG boosted range extender - A highly flexible electric powertrain for maximum CO{sub 2} reduction (S. Huepkes); (3) E-Transmission between full-hybrid and E-drive (P. Tenberge); (4) Reducing NO{sub x} and particulate emissions in electrified drivelines (R. Kuberczyk); (5) Simulation aided HEV and EV development: from the component to the whole powertrain (A. Gacometti); (6) Investigations on operating behaviour of the optimized CVT hybrid driveline (B.-R. Hoehn); (7) Customer-oriented dimensioning of electrified drivetrains (M. Eghtessad); (8) Decentralized optimal control strategy for parallel hybrid electric vehicles (A. Frenkel); (9) The new generation 6-speed automatic transmission AF40 (G. Bednarek); (10) Customized mechatronic solutions for integrated transmission control units (M. Wieczorek); (11) The optimal automatic transmission for front-transverse applications - Planetary transmissions or dual clutch transmissions? (G. Gumpoltsberger); (12) The new shift-by-wire gearshift lever for the Audi A8 - Requirements and concept (T. Guttenbergere); (13) The new shift-by-wire gearshift lever for the Audi A8 - Realization (A. Giefer); (14) Fuel-efficient transmissions of the future: Calculation of the efficiency factor for vehicle transmissions (B. Volpert); (15) HT-ACM: A new polymer generation for static and dynamic gearbox sealing solutions (E. Osen); (16) 'Energy efficiency equipped solutions by SKF' for power train applications - A contribution to CO{sub 2} - emission reduction and sustainability (T. Bobke); (17) 6-Ratio planetary shift transmission controlled by 4 external brakes, and design

  18. GSM-GPS Based Intelligent Security and Control System for Vehicle

    Directory of Open Access Journals (Sweden)

    Mr. Kiran Gaikwad

    2013-05-01

    Full Text Available The revolution of Mobile and Technology has made ‘GSM based vehicle security system’. The vehicle security system is prominent worldwide. But it is not so much secure system. Every vehicle owner wants maximum protection of his vehicle; otherwise thief can easily trap the vehicle. So, by combing the idea of mobile and vehicle security system GSM based vehicle security system can be designed. So this GSM-GPS based vehicle security system works when someone tries to steal your vehicle. This paper deals with the design {&} development of an embedded system, which is being used to prevent/control the theft of a vehicle. The instrument is an embedded system based on GSM and GPS technology. The instrument is installed in the engine of the vehicle. The main objective of this instrument is to protect the vehicle from any unauthorized access, through entering a protected password and intimate the status and location of the same vehicle to the authorize person (owner using Global System for Mobile Communication (GSM and Global Positioning System (GPS technology. Here owner of vehicle can control system through Cell phone or a personal computer (PC. In this system new concept is inclusion of RTC (Real Time Clock by which vehicle can be permanently off depending upon date and time set. This system is intelligent because it performs many tasks automatically and also control vehicle on/off from a distance

  19. 76 FR 61103 - Draft Guidance for Industry and Food and Drug Administration Staff; De Novo Classification...

    Science.gov (United States)

    2011-10-03

    ... Food, Drug, and Cosmetic Act (FD&C Act), also known as the de novo classification process. FDA is... HUMAN SERVICES Food and Drug Administration Draft Guidance for Industry and Food and Drug Administration Staff; De Novo Classification Process (Evaluation of Automatic Class III Designation);...

  20. Automatic and efficient driving strategies while approaching a traffic light

    CERN Document Server

    Treiber, Martin

    2014-01-01

    Vehicle-infrastructure communication opens up new ways to improve traffic flow efficiency at signalized intersections. In this study, we assume that equipped vehicles can obtain information about switching times of relevant traffic lights in advance. This information is used to improve traffic flow by the strategies 'early braking', 'anticipative start', and 'flying start'. The strategies can be implemented in driver-information mode, or in automatic mode by an Adaptive Cruise Controller (ACC). Quality criteria include cycle-averaged capacity, driving comfort, fuel consumption, travel time, and the number of stops. By means of simulation, we investigate the isolated strategies and the complex interactions between the strategies and between equipped and non-equipped vehicles. As universal approach to assess equipment level effects we propose relative performance indexes and found, at a maximum speed of 50 km/h, improvements of about 15% for the number of stops and about 4% for the other criteria. All figures d...

  1. Automatic target tracking on multi-resolution terrain

    Institute of Scientific and Technical Information of China (English)

    WAN Ming; ZHANG Wei; MURRAY Marie O.; KAUFMAN Arie

    2006-01-01

    We propose a high-performance path planning algorithm for automatic target tracking in the applications of real-time simulation and visualization of large-scale terrain datasets, with a large number of moving objects (such as vehicles) tracking multiple moving targets. By using a modified Dijkstra's algorithm, an optimal path between each vehicle-target pair over a weighted grid-presented terrain is computed and updated to eliminate the problem of local minima and losing of tracking. Then, a dynamic path re-planning strategy using multi-resolution representation of a dynamic updating region is proposed to achieve high-performance by trading-off precision for efficiency, while guaranteeing accuracy. Primary experimental results showed that our algorithm successfully achieved 10 to 96 frames per second interactive path-replanning rates during a terrain simulation scenario with 10 to 100 vehicles and multiple moving targets.

  2. Classification of independent components of EEG into multiple artifact classes

    DEFF Research Database (Denmark)

    Frølich, Laura; Andersen, Tobias; Mørup, Morten

    2015-01-01

    neural and five nonneural types of components. Between subjects within studies, high classification performances were obtained. Between studies, however, classification was more difficult. For neural versus nonneural classifications, performance was on par with previous results obtained by others. We...... found that automatic separation of multiple artifact classes is possible with a small feature set. Our method can reduce manual workload and allow for the selective removal of artifact classes. Identifying artifacts during EEG recording may be used to instruct subjects to refrain from activity causing...

  3. Comparison of automatic control systems

    Science.gov (United States)

    Oppelt, W

    1941-01-01

    This report deals with a reciprocal comparison of an automatic pressure control, an automatic rpm control, an automatic temperature control, and an automatic directional control. It shows the difference between the "faultproof" regulator and the actual regulator which is subject to faults, and develops this difference as far as possible in a parallel manner with regard to the control systems under consideration. Such as analysis affords, particularly in its extension to the faults of the actual regulator, a deep insight into the mechanism of the regulator process.

  4. VEHICLE SIMULATION MODEL FOR DEVELOPING AN INTELLIGENT SLOPE SHIFT STRATEGY

    Institute of Scientific and Technical Information of China (English)

    Jin Hui; Ge Anlin

    2004-01-01

    With the rapid development of electronics and the growing demand for higher vehicle performance,intelligent shift technology is becoming increasingly important,and it promises to be a developing trend in vehicle automatic transmissions.A new simulation model is presented,which includes engine,powertrain,tire and vehicle dynamics models.Based on the model,simulation experiments are conducted to investigate the slope shift strategy.The data and conclusions obtained from the simulations are valuable contributions to the development of an intelligent slope shift strategy.

  5. Morphological classification of nanoceramic aggregates

    Science.gov (United States)

    Crosta, Giovanni F.; Kang, Bongwoo; Ospina, Carolina; Sung, Changmo

    2005-01-01

    Aluminum silicate nanoaggregates grown at near-room temperature on an organic template under a variety of experimental conditions have been imaged by transmission electron microscopy. Images have been automatically classified by an algorithm based on "spectrum enhancement", multivariate statistics and supervised optimization. Spectrum enhancement consists of subtracting, in the log scale, a known function of wavenumber from the angle averaged power spectral density of the image. Enhanced spectra of each image, after polynomial interpolation, have been regarded as morphological descriptors and as such submitted to principal components analysis nested with a multiobjective parameter optimization algorithm. The latter has maximized pairwise discrimination between classes of materials. The role of the organic template and of a reaction parameter on aggregate morphology has been assessed at two magnification scales. Classification results have also been related to crystal structure data derived from selected area electron diffraction patterns.

  6. A Multistep Framework for Vision Based Vehicle Detection

    Directory of Open Access Journals (Sweden)

    Hai Wang

    2014-01-01

    Full Text Available Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. In this work, a multistep framework for vision based vehicle detection is proposed. In the first step, for vehicle candidate generation, a novel geometrical and coarse depth information based method is proposed. In the second step, for candidate verification, a deep architecture of deep belief network (DBN for vehicle classification is trained. In the last step, a temporal analysis method based on the complexity and spatial information is used to further reduce miss and false detection. Experiments demonstrate that this framework is with high true positive (TP rate as well as low false positive (FP rate. On road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets.

  7. Structuring automated vehicle guidance knowledge in The Netherlands

    NARCIS (Netherlands)

    Hoedemaeker, M.; Bastiaensen, E.G.H.J.; Zwaneveld, P.J.

    2000-01-01

    This paper describes the set-up of a AVG knowledge database from the AVV Transport Research Centre from the Dutch Ministry of Transport, Public Works and Water Management. The database provides a categorised overview of all research performed in The Netherlands in the area of Automatic Vehicle Guida

  8. Artificial Neural Network Approach in Radar Target Classification

    Directory of Open Access Journals (Sweden)

    N. K. Ibrahim

    2009-01-01

    Full Text Available Problem statement: This study unveils the potential and utilization of Neural Network (NN in radar applications for target classification. The radar system under test is a special of it kinds and known as Forward Scattering Radar (FSR. In this study the target is a ground vehicle which is represented by typical public road transport. The features from raw radar signal were extracted manually prior to classification process using Neural Network (NN. Features given to the proposed network model are identified through radar theoretical analysis. Multi-Layer Perceptron (MLP back-propagation neural network trained with three back-propagation algorithm was implemented and analyzed. In NN classifier, the unknown target is sent to the network trained by the known targets to attain the accurate output. Approach: Two types of classifications were analyzed. The first one is to classify the exact type of vehicle, four vehicle types were selected. The second objective is to grouped vehicle into their categories. The proposed NN architecture is compared to the K Nearest Neighbor classifier and the performance is evaluated. Results: Based on the results, the proposed NN provides a higher percentage of successful classification than the KNN classifier. Conclusion/Recommendation: The result presented here show that NN can be effectively employed in radar classification applications.

  9. Automatic Fixture Planning

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    Fixture planning is a crucial problem in the field of fixture design. In this paper, the research scope and research methods of the computer-aided fixture planning are presented. Based on positioning principles of typical workparts, an ANN algorithm, namely Hopfield algorithm, is adopted for the automatic fixture planning. Also, this paper leads a deep research into the selection of positioning and clamping surfaces (or points) on workparts using positioning-clamping-surface-selecting rules and matrix evaluation of deterministic workpart positioning. In the end of this paper, the methods to select positioning and clamping elements from database and the layout algorithm to assemble the selected fixture elements into a tangible fixture are developed.

  10. Pneumatic brake control for precision stopping of heavy-duty vehicles

    OpenAIRE

    Bu, Fanping; Tan, Han-Shue

    2007-01-01

    Precision stopping is an important automated vehicle control function that is critical in applications such as precision bus docking, automated truck or bus fueling, as well as automatic intersection, or toll booth stopping. The initial applications of this technology are most likely to be applied to heavy-duty vehicles such as buses or trucks. Such applications require specific attention to brake control since the characteristics of a typical pneumatic brake system of a heavy vehicle is inhe...

  11. Transportation Modes Classification Using Sensors on Smartphones.

    Science.gov (United States)

    Fang, Shih-Hau; Liao, Hao-Hsiang; Fei, Yu-Xiang; Chen, Kai-Hsiang; Huang, Jen-Wei; Lu, Yu-Ding; Tsao, Yu

    2016-08-19

    This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user's transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.

  12. Transportation Modes Classification Using Sensors on Smartphones

    Directory of Open Access Journals (Sweden)

    Shih-Hau Fang

    2016-08-01

    Full Text Available This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user’s transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.

  13. Automatic keywording of High Energy Physics

    CERN Document Server

    Dallman, David Peter

    1999-01-01

    Bibliographic databases were developed from the traditional library card catalogue in order to enable users to access library documents via various types of bibliographic information, such as title, author, series or conference date. In addition these catalogues sometimes contained some form of indexation by subject, such as the Universal (or Dewey) Decimal Classification used for books. With the introduction of the eprint archives, set up by the High Energy Physics (HEP) Community in the early 90s, huge collections of documents in several fields have been made available on the World Wide Web. These developments however have not yet been followed up from a keywording point of view. We will see in this paper how important it is to attribute keywords to all documents in the area of HEP Grey Literature. As libraries are facing a future with less and less manpower available and more and more documents, we will explore the possibility of being helped by automatic classification software. We will specifically menti...

  14. Moving Vehicle Detection and Tracking Algorithm in Traffic Video

    Directory of Open Access Journals (Sweden)

    Shisong Zhu

    2013-06-01

    Full Text Available Aiming at the defects and shortages of traditional moving vehicles detection algorithms, by the analysis and comparison of the existing detection algorithms, we propose an algorithm that combined with frames with symmetric difference and background difference to detect moving vehicle in this paper. First, two different difference images by using frames with symmetric difference and background difference are gained respectively and two binary images can be gained by the appropriate threshold, then the contour of moving vehicles can be extracted by applying OR operation in the two binary images. Finally, the precise moving vehicles will be gained by mathematic morphological methods. In this paper we use Harris operator, Feature Points such as edges and corners are extracted, followed by block-matching to track the Feature Points in successive viedo frames. Many vehicles can be tracked at the same time automatically since  the  information is obtained from video sequences.

  15. Enhancing Accuracy of Plant Leaf Classification Techniques

    Directory of Open Access Journals (Sweden)

    C. S. Sumathi

    2014-03-01

    Full Text Available Plants have become an important source of energy, and are a fundamental piece in the puzzle to solve the problem of global warming. Living beings also depend on plants for their food, hence it is of great importance to know about the plants growing around us and to preserve them. Automatic plant leaf classification is widely researched. This paper investigates the efficiency of learning algorithms of MLP for plant leaf classification. Incremental back propagation, Levenberg–Marquardt and batch propagation learning algorithms are investigated. Plant leaf images are examined using three different Multi-Layer Perceptron (MLP modelling techniques. Back propagation done in batch manner increases the accuracy of plant leaf classification. Results reveal that batch training is faster and more accurate than MLP with incremental training and Levenberg– Marquardt based learning for plant leaf classification. Various levels of semi-batch training used on 9 species of 15 sample each, a total of 135 instances show a roughly linear increase in classification accuracy.

  16. Classification of Scenes into Indoor/Outdoor

    Directory of Open Access Journals (Sweden)

    R. Raja

    2014-12-01

    Full Text Available Effective model for scene classification is essential, to access the desired images from large scale databases. This study presents an efficient scene classification approach by integrating low level features, to reduce the semantic gap between the visual features and richness of human perception. The objective of the study is to categorize an image into indoor or outdoor scene using relevant low level features such as color and texture. The color feature from HSV color model, texture feature through GLCM and entropy computed from UV color space forms the feature vector. To support automatic scene classification, Support Vector Machine (SVM is implemented on low level features for categorizing a scene into indoor/outdoor. Since the combination of these image features exhibit a distinctive disparity between images containing indoor or outdoor scenes, the proposed method achieves better performance in terms of classification accuracy of about 92.44%. The proposed method has been evaluated on IITM- SCID2 (Scene Classification Image Database and dataset of 3442 images collected from the web.

  17. 77 FR 50969 - Approval and Promulgation of Air Quality Implementation Plans; Maryland; Low Emission Vehicle...

    Science.gov (United States)

    2012-08-23

    ... means as the former 1-hour ozone standard) albeit with slightly different area names and classifications..., purchase, lease, rent, acquire, or receive a motor vehicle for titling or registration in Maryland....

  18. Classification of cultivated plants.

    NARCIS (Netherlands)

    Brandenburg, W.A.

    1986-01-01

    Agricultural practice demands principles for classification, starting from the basal entity in cultivated plants: the cultivar. In establishing biosystematic relationships between wild, weedy and cultivated plants, the species concept needs re-examination. Combining of botanic classification, based

  19. A ROBUST GA/KNN BASED HYPOTHESIS VERIFICATION SYSTEM FOR VEHICLE DETECTION

    Directory of Open Access Journals (Sweden)

    Nima Khairdoost

    2015-03-01

    Full Text Available Vehicle detection is an important issue in driver assistance systems and self-guided vehicles that includes two stages of hypothesis generation and verification. In the first stage, potential vehicles are hypothesized and in the second stage, all hypothesis are verified. The focus of this work is on the second stage. We extract Pyramid Histograms of Oriented Gradients (PHOG features from a traffic image as candidates of feature vectors to detect vehicles. Principle Component Analysis (PCA and Linear Discriminant Analysis (LDA are applied to these PHOG feature vectors as dimension reduction and feature selection tools parallelly. After feature fusion, we use Genetic Algorithm (GA and cosine similarity-based K Nearest Neighbor (KNN classification to improve the performance and generalization of the features. Our tests show good classification accuracy of more than 97% correct classification on realistic on-road vehicle images.

  20. Automatic computational models of acoustical category features: Talking versus singing

    Science.gov (United States)

    Gerhard, David

    2003-10-01

    The automatic discrimination between acoustical categories has been an increasingly interesting problem in the fields of computer listening, multimedia databases, and music information retrieval. A system is presented which automatically generates classification models, given a set of destination classes and a set of a priori labeled acoustic events. Computational models are created using comparative probability density estimations. For the specific example presented, the destination classes are talking and singing. Individual feature models are evaluated using two measures: The Kologorov-Smirnov distance measures feature separation, and accuracy is measured using absolute and relative metrics. The system automatically segments the event set into a user-defined number (n) of development subsets, and runs a development cycle for each set, generating n separate systems, each of which is evaluated using the above metrics to improve overall system accuracy and to reduce inherent data skew from any one development subset. Multiple features for the same acoustical categories are then compared for underlying feature overlap using cross-correlation. Advantages of automated computational models include improved system development and testing, shortened development cycle, and automation of common system evaluation tasks. Numerical results are presented relating to the talking/singing classification problem.

  1. Automatic aircraft recognition

    Science.gov (United States)

    Hmam, Hatem; Kim, Jijoong

    2002-08-01

    Automatic aircraft recognition is very complex because of clutter, shadows, clouds, self-occlusion and degraded imaging conditions. This paper presents an aircraft recognition system, which assumes from the start that the image is possibly degraded, and implements a number of strategies to overcome edge fragmentation and distortion. The current vision system employs a bottom up approach, where recognition begins by locating image primitives (e.g., lines and corners), which are then combined in an incremental fashion into larger sets of line groupings using knowledge about aircraft, as viewed from a generic viewpoint. Knowledge about aircraft is represented in the form of whole/part shape description and the connectedness property, and is embedded in production rules, which primarily aim at finding instances of the aircraft parts in the image and checking the connectedness property between the parts. Once a match is found, a confidence score is assigned and as evidence in support of an aircraft interpretation is accumulated, the score is increased proportionally. Finally a selection of the resulting image interpretations with the highest scores, is subjected to competition tests, and only non-ambiguous interpretations are allowed to survive. Experimental results demonstrating the effectiveness of the current recognition system are given.

  2. Energy Star Concepts for Highway Vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Greene, D.L.

    2003-06-24

    The authors of this report, under the sponsorship of the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) Weatherization and Intergovernmental Program, have investigated the possible application of Energy Star ratings to passenger cars and light trucks. This study establishes a framework for formulating and evaluating Energy Star rating methods that is comprised of energy- and environmental-based metrics, potential vehicle classification systems, vehicle technology factors, and vehicle selection criteria. The study tests several concepts and Energy Star rating methods using model-year 2000 vehicle data--a spreadsheet model has been developed to facilitate these analyses. This study tests two primary types of rating systems: (1) an outcome-based system that rates vehicles based on fuel economy, GHG emissions, and oil use and (2) a technology-based system that rates vehicles based on the energy-saving technologies they use. Rating methods were evaluated based on their ability to select vehicles with high fuel economy, low GHG emissions, and low oil use while preserving a full range of service (size and acceleration) and body style choice. This study concludes that an Energy Star rating for passenger cars and light trucks is feasible and that several methods could be used to achieve reasonable tradeoffs between low energy use and emissions and diversity in size, performance, and body type. It also shows that methods that consider only fuel economy, GHG emissions, or oil use will not select a diverse mix of vehicles. Finally, analyses suggest that methods that encourage the use of technology only, may result in increases in acceleration power and weight rather than reductions in oil use and GHG emissions and improvements in fuel economy.

  3. AUTOMOTIVE DIESEL MAINTENANCE 2. UNIT VI, AUTOMATIC TRANSMISSIONS--PLANETARY GEARING.

    Science.gov (United States)

    Human Engineering Inst., Cleveland, OH.

    THIS MODULE OF A 25-MODULE COURSE IS DESIGNED TO ACQUAINT THE TRAINEE WITH THE OPERATION OF PLANETARY GEARS IN AUTOMATIC TRANSMISSIONS USED ON DIESEL POWERED VEHICLES. TOPICS ARE (1) PURPOSE OF PLANETARY GEARING, (2) POWER TRANSMISSION THROUGH A PLANETARY SYSTEM, (3) HYDRAMATIC TRANSMISSION, (4) HYDRAULIC SYSTEM, AND (5) GEAR FAILURE AND…

  4. Automatic target recognition in synthetic aperture sonar images for autonomous mine hunting

    NARCIS (Netherlands)

    Quesson, B.A.J.; Sabel, J.C.; Bouma, H.; Dekker, R.J.; Lengrand-Lambert, J.

    2010-01-01

    The future of Mine Countermeasures (MCM) operations lies with unmanned platforms where Automatic Target Recognition (ATR) is an essential step in making the mine hunting process autonomous. At TNO, a new ATR method is currently being developed for use on an Autonomous Underwater Vehicle (AUV), using

  5. Failure of classical traffic flow theories: Stochastic highway capacity and automatic driving

    CERN Document Server

    Kerner, Boris S

    2016-01-01

    In a mini-review [Physica A {\\bf 392} (2013) 5261--5282] it has been shown that classical traffic flow theories and models failed to explain empirical traffic breakdown -- a phase transition from metastable free flow to synchronized flow at highway bottlenecks. The main objective of this mini-review is to study the consequence of this failure of classical traffic-flow theories for an analysis of empirical stochastic highway capacity as well as for the effect of automatic driving vehicles and cooperative driving on traffic flow. To reach this goal, we show a deep connection between the understanding of empirical stochastic highway capacity and a reliable analysis of automatic driving vehicles in traffic flow. With the use of simulations in the framework of three-phase traffic theory, a probabilistic analysis of the effect of automatic driving vehicles on a mixture traffic flow consisting of a random distribution of automatic driving and manual driving vehicles has been made. We have found that the parameters o...

  6. Electronic amplifiers for automatic compensators

    CERN Document Server

    Polonnikov, D Ye

    1965-01-01

    Electronic Amplifiers for Automatic Compensators presents the design and operation of electronic amplifiers for use in automatic control and measuring systems. This book is composed of eight chapters that consider the problems of constructing input and output circuits of amplifiers, suppression of interference and ensuring high sensitivity.This work begins with a survey of the operating principles of electronic amplifiers in automatic compensator systems. The succeeding chapters deal with circuit selection and the calculation and determination of the principal characteristics of amplifiers, as

  7. The Automatic Telescope Network (ATN)

    CERN Document Server

    Mattox, J R

    1999-01-01

    Because of the scheduled GLAST mission by NASA, there is strong scientific justification for preparation for very extensive blazar monitoring in the optical bands to exploit the opportunity to learn about blazars through the correlation of variability of the gamma-ray flux with flux at lower frequencies. Current optical facilities do not provide the required capability.Developments in technology have enabled astronomers to readily deploy automatic telescopes. The effort to create an Automatic Telescope Network (ATN) for blazar monitoring in the GLAST era is described. Other scientific applications of the networks of automatic telescopes are discussed. The potential of the ATN for science education is also discussed.

  8. Automatic calibration and neural networks for robot guidance

    Science.gov (United States)

    Sethuramasamyraja, Balaji; Ghaffari, Masoud; Hall, Ernest L.

    2003-10-01

    An autonomous robot must be able to sense its environment and react appropriately in a variable environment. The University of Cincinnati Robot team is actively involved in building a small, unmanned, autonomously guided vehicle for the International Ground Robotics Contest organized by Association for Unmanned Vehicle Systems International (AUVSI) each year. The unmanned vehicle is supposed to follow an obstacle course bounded by two white/yellow lines, which are four inches thick and 10 feet apart. The navigation system for one of the University of Cincinnati"s designs, Bearcat, uses 2 CCD cameras and an image-tracking device for the front end processing of the image captured by the cameras. The three dimensional world co-ordinates were reduced to two dimensional image coordinates as a result of the transformations taking place from the ground plane to the image plane. A novel automatic calibration system was designed to transform the image co-ordinates back to world co-ordinates for navigation purposes. The purpose of this paper is to simplify this tedious calibration using an artificial neural network. Image processing is used to automatically detect calibration points. Then a back projection neural algorithm is used to learn the relationships between the image coordinates and three-dimensional coordinates. This transformation is the main focus of this study. Using these algorithms, the robot built with this design is able to track and follow the lines successfully.

  9. A Disaster Document Classification Technique Using Domain Specific Ontologies

    Directory of Open Access Journals (Sweden)

    Qazi Mudassar Ilyas

    2015-12-01

    Full Text Available Manual data collection and entry is one of the bottlenecks in conventional disaster management information systems. Time is a critical factor in emergency situations and timely data collection and processing may help in saving several lives. An effective disaster management system needs to collect data from World Wide Web automatically. A prerequisite for data collection process is document classification mechanism to classify a particular document into different categories. Ontologies are formal bodies of knowledge used to capture machine understandable semantics of a domain of interest and have been used successfully to support document classification in various domains. This paper presents an ontology-based document classification technique for automatic data collection in a disaster management system. A general ontology of disasters is used that contains the description of several natural and man-made disasters. The proposed technique augments the conventional classification measures with the ontological knowledge to improve the precision of classification. A preliminary implementation of the proposed technique shows promising results with up to 10% overall improvement in precision when compared with conventional classification methods.

  10. Quantifying the Visual Impact of Classification Boundaries in Choropleth Maps.

    Science.gov (United States)

    Zhang, Yifan; Maciejewski, Ross

    2017-01-01

    One critical visual task when using choropleth maps is to identify spatial clusters in the data. If spatial units have the same color and are in the same neighborhood, this region can be visually identified as a spatial cluster. However, the choice of classification method used to create the choropleth map determines the visual output. The critical map elements in the classification scheme are those that lie near the classification boundary as those elements could potentially belong to different classes with a slight adjustment of the classification boundary. Thus, these elements have the most potential to impact the visual features (i.e., spatial clusters) that occur in the choropleth map. We present a methodology to enable analysts and designers to identify spatial regions where the visual appearance may be the result of spurious data artifacts. The proposed methodology automatically detects the critical boundary cases that can impact the overall visual presentation of the choropleth map using a classification metric of cluster stability. The map elements that belong to a critical boundary case are then automatically assessed to quantify the visual impact of classification edge effects. Our results demonstrate the impact of boundary elements on the resulting visualization and suggest that special attention should be given to these elements during map design.

  11. Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis.

    NARCIS (Netherlands)

    Vos, P.C.; Barentsz, J.O.; Karssemeijer, N.; Huisman, H.J.

    2012-01-01

    In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performe

  12. Meta-Learning Approach for Automatic Parameter Tuning: A Case Study with Educational Datasets

    Science.gov (United States)

    Molina, M. M.; Luna, J. M.; Romero, C.; Ventura, S.

    2012-01-01

    This paper proposes to the use of a meta-learning approach for automatic parameter tuning of a well-known decision tree algorithm by using past information about algorithm executions. Fourteen educational datasets were analysed using various combinations of parameter values to examine the effects of the parameter values on accuracy classification.…

  13. Automatic parameter extraction for the 16,000 galaxies in the ESO/Uppsala catalogue

    NARCIS (Netherlands)

    Lauberts, A.; Valentijn, E. A.

    1983-01-01

    Under a restriction for minimum angular diameter of not less than 1 arcmin, corresponding to the 15th magnitude and allowing morphological classification of structure, 16,000 galaxies have been brought together in the single volume of the ESO/Uppsala catalog (1982). Attention is given to the automat

  14. Electric Vehicle Charging Modeling

    OpenAIRE

    Grahn, Pia

    2014-01-01

    With an electrified passenger transportation fleet, carbon dioxide emissions could be reduced significantly depending on the electric power production mix. Increased electric power consumption due to electric vehicle charging demands of electric vehicle fleets may be met by increased amount of renewable power production in the electrical systems. With electric vehicle fleets in the transportation system there is a need for establishing an electric vehicle charging infrastructure that distribu...

  15. Vehicle detection and tracking based on phase-correlation

    Institute of Scientific and Technical Information of China (English)

    Yi He(何毅); Xin Yang(杨新)

    2004-01-01

    This paper presents vehicle detection and tracking algorithms based on real-time background (RTB) and phase-correlation (PC) in the video sequence of urban highway with fixed camera. Firstly moving objects are obtained by subtracting RTB from serial images. After classification, PC is used to determine corresponding regions of moving objects between consecutive frames. The problems of vehicles' merging and splitting, sudden stop, and restart are also considered. Experiments show that the method is practical and can realize real-time detection and tracking of vehicles on highway.

  16. Classification, disease, and diagnosis.

    Science.gov (United States)

    Jutel, Annemarie

    2011-01-01

    Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study.

  17. Road-rail vehicle

    NARCIS (Netherlands)

    Evers, J.J.M.

    2002-01-01

    A transport vehicle equipped with a number of first wheel sets, having wheels provided with tires, to which steering means and driving means, if any, are coupled to enable the transport vehicle to be moved over a road surface. The transport vehicle further comprises at least one second wheel set, ha

  18. Electric vehicles: Driving range

    Science.gov (United States)

    Kempton, Willett

    2016-09-01

    For uptake of electric vehicles to increase, consumers' driving-range needs must be fulfilled. Analysis of the driving patterns of personal vehicles in the US now shows that today's electric vehicles can meet all travel needs on almost 90% of days from a single overnight charge.

  19. The Electric Vehicle Development

    DEFF Research Database (Denmark)

    Wang, Jingyu; Yingqi, Liu; Kokko, Ari

    2014-01-01

    in three aspects-city environment, government and stakeholders. Then the paper discusses the promotion ways and role of government and consumer. Finally, the paper offers some suggestions to promote electric vehicles in China: focusing on feasibility and adaptability of electric vehicles, playing...... government`s leading role, improving low-awareness and acceptance of electric vehicles and focusing on user requirements....

  20. Automotive vehicle sensors

    Energy Technology Data Exchange (ETDEWEB)

    Sheen, S.H.; Raptis, A.C.; Moscynski, M.J.

    1995-09-01

    This report is an introduction to the field of automotive vehicle sensors. It contains a prototype data base for companies working in automotive vehicle sensors, as well as a prototype data base for automotive vehicle sensors. A market analysis is also included.