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Conference on Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XI

作者： Ho, Nicolas Emond, Frederic Babin, Francois Healy, Dave Simard, Jean-Robert Buteau, Sylvie McFee, John E. INO 2740 Einstein St Quebec City PQ G1P 4S4 Canada MDA Richmond BC V6V 2J3 Canada Def Res & Dev Canada Valcartier Quebec City PQ G3J 1X5 Canada Def Res & Dev Canada Suffield Medicine Hat AB T1A 8K6 Canada

ISBN: (纸本)9780819481290

We have developed a small, relatively lightweight and efficient short range (<100 m) LIDAR instrument for remotely detecting harmful bioagents. The system is based on a pulsed, eye-safe, 355 nm laser exciting aerosols which then fluoresce with a typical spectrum. The system makes use of a novel technology for continuously monitoring for the presence of unusual concentrations of bioaerosols at a precise remote location within the monitored area, with response within seconds. Fluorescence is spectrally resolved over 32 channels capable of photon counting. Results show a sensitivity level of 40 ACPLA of Bacillus Globigii, an anthrax simulant, at a distance of 100 m (assumed worst case where 1 ppl = 1 ACPLA) considering particle sizes between 0.5 and 10 mu m, with a geometric mean at 1 um. The apparatus has been tested in the field during three test and evaluation campaigns with multiple bioagents and public security products. Preliminary results show that the system is able to distinguish between harmful bioagents and naturally occurring ones. A classification algorithm was successfully tested with a single type of bioagent;experiments for daytime measurements are discussed.

关键词： Standoff detection biological warfare agents lidar classification algorithm spectroscopy laser induced fluorescence

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Detection and classification of Marine Mammal Clicks

Detection and Classification of Marine Mammal Clicks

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Washington State Conference and Trade Center (WSCTC)

作者： McGaughey, Donald R. Marcotte, David Korenberg, Michael J. Theriault, James A. Royal Mil Coll Canada Box 17000 Stn Forces Kingston ON K7K 7B4 Canada ADAC Victoria BC Canada Queens Univ Dept Elect & Comp Engn Kingston ON K7L 3N6 Canada DRDC Atlan Dartmouth NS B2Y 3Z7 Canada

ISBN: (纸本)9781424443338

Detection of marine mammals within an influence zone of episodal anthropogenic noise source is critical to insure the safety of the animals. Marine mammal clicks are closely modeled by AM/FM signals. The Teager-Kaiser energy operator followed by a threshold detector provides an effective means of detecting AM/FM signals. classification of the species generating the click is done by finding the maximum cross-covariance between the power spectral density (PSD) of the received click and the PSDs of clicks which have been identified. The classification algorithm will create a new library entry when the cross-covariance is below a predefined threshold. Five species of marine mammals were classified using data from the 3rd International Workshop on Detection, classification and Localization of Marine Mammals (DCL). This paper presented a computationally-inexpensive marine mammal detection and classification algorithm with high probabilities of detection and correct classification.

关键词： 3rd international workshop on detection AM-FM signals Teager-Kaiser energy operator acoustic noise acoustic signal detection bioacoustics classification algorithm episodal anthropogenic noise source marine mammal classification marine mammal clicks marine mammal detection marine mammal localization power spectral density threshold detector

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Experimental bifurcation diagram of a circuit-implemented neuron model

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PHYSICS LETTERS A 2010年第45期374卷 4589-4593页

作者： Linaro, D. Poggi, T. Storace, M. Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

An experimental bifurcation diagram of a circuit implementing an approximation of the Hindmarsh-Rose (HR) neuron model is presented. Measured asymptotic time series of circuit voltages are automatically classified through an ad hoc algorithm. The resulting two-dimensional experimental bifurcation diagram evidences a good match with respect to the numerical results available for both the approximated and original HR model. Moreover, the experimentally obtained current-frequency curve is very similar to that of the original model. The obtained results are both a proof of concept of a quite general method developed in the last few years for the approximation and implementation of nonlinear dynamical systems and a first step towards the realisation in silica of HR neuron networks with tunable parameters. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Experimental bifurcation diagram Hardware neuron model classification algorithm

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Learning rates of gradient descent algorithm for classification

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2009年第1期224卷 182-192页

作者： Dong, Xue-Mei Chen, Di-Rong Beijing Univ Aeronaut & Astronaut Dept Math Beijing 100083 Peoples R China Minist Educ LMIB Beijing 100083 Peoples R China

In this paper, a stochastic gradient descent algorithm is proposed for the binary classification problems based on general convex loss functions. It has computational superiority over the existing algorithms when the sample size is large. Under some reasonable assumptions on the hypothesis space and the underlying distribution, the learning rate of the algorithm has been established, which is faster than that of closely related algorithms. (C) 2008 Elsevier B.V. All rights reserved.

关键词： classification algorithm Stochastic gradient descent Reproducing kernel Hilbert space Learning rates computational complexity

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Application of Ant Colony Optimization-SVM in Fault Diagnosis for Rectifier Circuit

Application of Ant Colony Optimization-SVM in Fault Diagnosi...

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2nd IEEE International Conference on Information and Financial Engineering (ICIFE)

作者： Xu Binghui Taizhou Vocat & Tech Coll Taizhou 318000 Peoples R China

ISBN: (纸本)9781424469284

Failure of rectifier circuit has the characteristics of latency and complexity, which leads to the difficulty to fault diagnosis for rectifier circuit. A new method of optimizing support vector machine (SVM) by using ant colony optimization algorithm is presented to fault diagnosis for rectifier circuit in the paper. The experimental object is provided and the six ACO-SVM classifiers are developed to identify the following seven states of the experimental object. The testing results demonstrate that the ACO-SVM classifier has higher diagnostic accuracy than normal support vector machine and BP neural network.

关键词： rectifier circuit ant colony optimization classifier fault diagnosis classification algorithm

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Three-Fold Cross-Validation of Parkinsonian Brain Patterns

Three-Fold Cross-Validation of Parkinsonian Brain Patterns

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Spetsieris, Phoebe G. Dhawan, Vijay Eidelberg, David N Shore LIJ Hlth Syst Ctr Neurosci Feinstein Inst Med Res Manhasset NY 11030 USA

ISBN: (纸本)9781424441242

Abnormal physiological networks of brain areas in disease can be identified by applying specialized multivariate computational algorithms based on principal component analysis to functional image data. Here we demonstrate the reproducibility of network patterns derived using positron emission tomography (PET) data in independent populations of parkinsonian patients for a large, clinically validated data set comprised of subjects with idiopathic Parkinson's disease (iPD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Correlation of voxel values of network patterns derived for the same condition in different data sets was high. To further illustrate the validity of these networks, we performed single subject differential diagnosis of prospective test subjects to determine the most probable case based on a subject's network scores expressed for each of these distinct parkinsonian syndromes. Three-fold cross-validation was performed to determine accuracy and positive predictive rates based on networks derived in separate folds of the composite data set. A logistic regression based classification algorithm was used to train in each fold and test in the remaining two folds. Combined accuracy for each of the three folds ranged from 82% to 93% in the training set and was approximately 81% for prospective test subjects.

关键词： abnormal physiological networks brain classification algorithm diseases idiopathic Parkinson disease image classification logistic regression medical image processing multiple system atrophy network pattern voxel values neural nets neurophysiology parkinsonian brain patterns parkinsonian syndromes positron emission tomography positron emission tomography progressive supranuclear palsy regression analysis single subject differential diagnosis

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Transformer Fault Diagnosis Based on Support Vector Machine

Transformer Fault Diagnosis Based on Support Vector Machine

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3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT)

作者： Zhang, Yan Zhang, Bide Yuan, Yuchun Pei, Zichun Wang, Yan Xihua Univ Inst Elect & Informat Chengdu 610039 Peoples R China

ISBN: (纸本)9781424455379

Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power system. Analysis of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve classification problem of nonlinearity and small sample. However, SVM has rarely been applied to diagnosis transformer fault by analysis the dissolved gases content in power transformer. In this study, support vector machine is proposed to analysis dissolved gases content in power transformer oil, among which cross-validation is used to determine free parameters of support vector machine. The experimental data from the electric power company in Sichuan are used to illustrate the performance of proposed SVM model. The experimental results indicate that the proposed SVM model can achieve very good diagnosis accuracy under the circumstances of small sample. Consequently, the SVM model is a proper alternative for diagnosing power transformer fault.

关键词： support vector machine classification algorithm cross-validation free parameters fault diagnosis

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A General Decision Layer Text classification Fusion Model

A General Decision Layer Text Classification Fusion Model

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2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

作者： Xiao-Dan Zhang Institute of Scientific and Technical Information of China Beijing 100038 P.R.China

An general decision layer text classification fusion model for higher precision, is proposed, which based on model theory of information fusion, and different classification algorithm of the feature layer fusion centre having different pre-processing, their classification results input into the decision layer fusion centre separately. And the final classification result output from decision layer fusion centre. KNN, SVM and BP Net are used in feature layer, and D-S Theory is used in decision layer. The model is realized in the experiment. From the experiment and contrast, the text classification fusion model can improve the classification precision effectively.

关键词： text classification decision layer classification fusion model classification algorithm information fusion

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Improving KNN Based Text classifications

Improving KNN Based Text Classifications

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The 2010 2nd International Conference on Future Computer and Communication(第二届未来计算机与通信国际会议 ICFCC 2010)

作者： Zongli Jiang Yi Deng School of Computer Science and Engineering Beijing University of Technology 100124 Beijing China

ISBN: (纸本)9781424458219

In a variety of text classification algorithm, KNN is a competitive one with simple implementation and high efficiency. However, with the expansion of the size of the text, the runtime of KNN will grow rapidly that cannot be afford. In this paper, we improve the KNN by introducing the kd-tree storage structure and reducing the sample space through the sample clestering methods. And experiment shows that the runtime of improved KNN algorithm reduce apparently.

关键词： classification algorithm KNN clustering

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Transformer Fault Diagnosis Based on Support Vector Machine

Transformer Fault Diagnosis Based on Support Vector Machine

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Yan Zhang,Bide Zhang,Yuchun Yuan Institute of Electrical and Information Xihua University Chengdu,China (610039) Zichun Pei,Yan Wang Institute of Electrical and Information Xihua University Chengdu,China (610039)

Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power *** of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training *** vector machine (SVM) has been successfully employed to solve classification problem of nonlinearity and small ***,SVM has rarely been applied to diagnosis transformer fault by analysis the dissolved gases content in power *** this study,support vector machine is proposed to analysis dissolved gases content in power transformer oil,among which cross-validation is used to determine free parameters of support vector *** experimental data from the electric power company in Sichuan are used to illustrate the performance of proposed SVM *** experimental results indicate that the proposed SVM model can achieve very good diagnosis accuracy under the circumstances of small ***,the SVM model is a proper alternative for diagnosing power transformer fault.

关键词： support vector machine classification algorithm cross-validation free parameters fault diagnosis

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