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Marking the Ends of ECG T-Waves for Assessing Cardiac Safety: algorithms and Experts

STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2010年第3期2卷 359-367页

作者： Zhou, Yingchun Sedransk, Nell E China Normal Univ Sch Finance & Stat Shanghai 200062 Peoples R China Natl Inst Stat Sci Res Triangle Pk NC 27709 USA

The prolongation of QT interval on electrocardiogram (ECG) is the current measure for cardiac safety that is used in drug development and drug approval. Although in thorough QT studies pharmaceutical companies need to measure QT intervals for thousands of beats, they mainly rely on experts to mark the QT interval end-points. However, selected beats of data show that the difference between two experts' marks can easily exceed 10 milliseconds. Note that for QT analyses presented to the FDA, if the maximal difference over all time points between QT measures comparing control to drug exceeds 10 milliseconds, the question of cardiac safety requires further discussion. Indeed experts appear to use the slope and curvature of the waveform differently in judging the end of the T-wave. This article developes a bayesian approach combining both slope and curvature information. We show that the difference between the automatic bayesian marks and either of the experts' marks is not statistically larger than the difference between two experts' marks, thus this approach is successful in closely approximating the experts' results in marking T-wave end, and it is much faster and cost efficient. Being algorithmic, our method offers the opportunity to be more consistent.

关键词： bayesian algorithm Functional data analysis QT interval

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Modal estimation in underwater acoustics by data-driven structured sparse decompositions 29

Modal estimation in underwater acoustics by data-driven stru...

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29th European Signal Processing Conference (EUSIPCO)

作者： Dorffer, Clement Paviet-Salomon, Thomas Le Chenadec, Gilles Dremeau, Angelique Lab STICC & ENSTA Bretagne UMR CNRS 6285 Brest France

ISBN: (纸本)9789082797060

In underwater acoustics, shallow water environments act as modal dispersive waveguides when considering lowfrequency sources. In this context, propagating signals can be described as a sum of few modal components, each of them propagating according to its own wavenumber. Estimating these wavenumbers is of key interest to understand the propagating environment as well as the emitting source. To solve this problem, we proposed recently a bayesian approach exploiting a sparsity-inforcing prior. When dealing with broadband sources, this model can be further improved by integrating the particular dependence linking the wavenumbers from one frequency to the other. In this contribution, we propose to resort to a new approach relying on a restricted Boltzmann machine, exploited as a generic structured sparsity-inforcing model. This model, derived from deep bayesian networks, can indeed be efficiently learned on physically realistic simulated data using well-known and proven algorithms.

关键词： Underwater modal estimation structured sparse approximation Restricted Boltzmann Machine bayesian algorithm

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Filtering Spam by Using Factors Hyperbolic Trees

Filtering Spam by Using Factors Hyperbolic Trees

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IEEE Global Telecommunications Conference (GLOBECOM 08)

作者： Hou, Hailong Chen, Yan Beyah, Raheem Zhang, Yan-Qing Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

ISBN: (纸本)9781424423248

Most of current anti-spam techniques, like the bayesian anti-spam algorithm, primarily use lexical matching for filtering unsolicited bulk E-mails (UBE) and unsolicited commercial E-mails (UCE). However, precision of spam filtering is usually low when the lexical matching algorithms are used in real dynamic environments. For example, an E-mail of refrigerator advertisements is useful for most families, but it is useless for Eskimos. The lexical matching anti-spam algorithms cannot distinguish such processed E-mails that are junk to most people but are useful for others. We propose a Factors Hyperbolic Tree (FHT) based algorithm that, unlike the lexical matching algorithms, handles spam filtering in a dynamic environment by considering various relevant factors. The new Ranked Term Frequency (RTF) algorithm is proposed to extract indicators from E-mails that are related to environmental factors. Type-1 and Type-2 fuzzy logic systems are used to evaluate the indicators and determine whether E-mails are spam based on the environmental factors. Additionally, weights of factors in a FHT database are continuously updated according to dynamic conditional factors in a real environment. Simulation results show that the FHT algorithm filters out spam with high precision. Furthermore, the FHT algorithm is more efficient than other methods when it filters E-mails with complex influencing factors. The main contribution of this paper is that the FHT based algorithm can filter E-mails based on influencing factors instead of matched words to allow dynamic filtering of spam E-mails.

关键词： spam bayesian algorithm Ranked Term Frequency fuzzy logic factors hyperbolic trees

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Compressive Sensing of Neural Action Potentials Using Nano Platinum Black Modified Microelectrode Array

Compressive Sensing of Neural Action Potentials Using Nano P...

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8th Annual IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS)

作者： Zhou, Shuai Zhang, Bingchen Xu, Shengwei Wang, Mixia Dai, Bowei Hong, Wen Wu, Yirong Cai, Xinxia Chinese Acad Sci Inst Elect State Key Lab Transducer Technol Beijing Peoples R China

ISBN: (纸本)9781467363525;9781467363518

Long-term wireless neural recording systems which are subject to stringent power consumption, are highly desired to reduce the rate of data transmission and computation complexity. In this paper, we propose using a combination of on-chip neural action potentials ('spikes') detection system and compressive sensing (CS) techniques to reduce the power required for data transmission and a random Bernoulli matrix to reduce the computation complexity consequently further reduce the power consumption. By analyzing the data detected by nano platinum black modified microelectrode array implanted in the hippocampus of the Sprague-Dawley (SD) rat, we prove that spikes are compressible in the wavelet domain. We use the bayesian CS algorithm to reconstruct them. Our results show that the mean compression ratio is 26: 1 achieved for 16-dB SNDR recovery using this mechanism.

关键词： compressive sensing spikes wavelet bayesian algorithm

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Condition Monitoring and Failure prognostic of Wind Turbine Blades

Condition Monitoring and Failure prognostic of Wind Turbine ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Rezamand, Milad Kordestani, Mojtaba Orchard, Marcos Carriveau, Rupp Ting, David Saif, Mehrdad Univ Windsor Fac Engn Windsor ON Canada Univ Chile Dept Elect Engn Santiago Chile

ISBN: (纸本)9781665442077

Condition Monitoring (CM) has become an essential tool in complex engineering systems like wind turbines. They can prevent unexpected failures and contribute to a more reliable system. Information attained from monitoring can be employed for maintenance scheduling, hence, minimizing maintenance costs. Remaining Useful Life (RUL) is a critical aspect of CM. This paper introduces a new RUL prediction method for wind turbine blades using a novel fuzzy-based failure dynamic modeling via a Supervisory Control and Data Acquisition (SCADA) system. For this goal, a recursive Principal Component Analysis (PCA) is employed to compress the SCADA data and extract real-time Principal Components (PCs). Next, a wavelet-based Probability Density Function (PDF) is applied to obtain the probability of staying healthy from the extracted PCs. It is anticipated that blade degradation will lead to a subsequent decline in the PDF curve. A failure trajectory is then captured by transforming the PDF into the PC's surface. Subsequently, the T-S fuzzy system is utilized to form the mathematical model of degradation from this failure trajectory. Next, a bayesian algorithm is adaptively administered to predict the RUL. Experimental test results on Canadian wind farms explain a high performance of the proposed failure prognosis method in comparison with a bayesian algorithm.

关键词： Remaining useful life wind turbine blade probability density function Takagi-Sugeno fuzzy system bayesian algorithm

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An Integration of AHP Approach and Bayes Classification algorithm in Supplier Selection

An Integration of AHP Approach and Bayes Classification Algo...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Chan, F. T. S. Chung, S. H. Chow, J. C. L. Niu, B. Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479909865

Weight of criteria can only be changed through discussion or analysis of experts under traditional supplier selection method, which brings up two problems: (1) human effect problem and (2) incapability in timely decision making on updates of weights. In this paper, a new supplier selection method based on integration of Analytic Hierarchy Process, the bayesian Classifier algorithm and dynamic probabilities (AHP-BCA) is proposed. The method makes predictions with the probabilities of occurrences of criteria values based on historical records to avoid any human effects in decision making. It is also equipped with an instant self-update function to instantly update the probability values with new data, and be ready for next calculation. A simulation experiment is conducted to compare the performance of the proposed approach with a remarkable traditional approach in literature with historical data. Results show that the proposed approach can outperform

关键词： Analytic Hierarchy Process bayesian algorithm Supplier Selection

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A Substation Fault Diagnosis Method Based on IEC61850

A Substation Fault Diagnosis Method Based on IEC61850

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Gao Zhanjun Wang Junshan Gao Nuo Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250100 Shandong Peoples R China Shandong Jianzhu Univ Shandong Jianzhu Univ Sch Informat & Elect Jinan Shandong Peoples R China

ISBN: (纸本)9781467380409

Substations play an important role in power systems. The application of the IEC6I850 standard changes the situation of the traditional substation greatly. The information in the intelligent substation is rich, but it is difficult for the operators to digest them in a short time without an effective assistant tool. A fault diagnosis method which uses the alarming information of the primary and secondary system of the intelligent substation is proposed in this paper. This method first uses the bayesian based algorithm to find the possible faulty set HI. And at the same time, the information entropy difference algorithm is used to obtain the fault assumption set H2. Then the fault assumption set H2 is used to calculate the fault correctness and a decision fusion algorithm can locate the fault components accurately. Finally, a fault scenario is served for demonstrating the feasibility and validity of the method presented.

关键词： Intelligent Substation Alarm Information bayesian algorithm Information Entropy Difference Decision Fusion

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WiSLAM: improving FootSLAM with WiFi

WiSLAM: improving FootSLAM with WiFi

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Bruno, Luigi Robertson, Patrick Univ Salerno Dept Informat & Elect Engn Fisciano Italy German Aerosp Ctr DLR Inst Communicat & Navigat Wessling Germany

ISBN: (纸本)9781457718045

We address Simultaneous Localization and Mapping (SLAM) for pedestrians by means of WiFi signal strength measurements. In our system odometric data from foot mounted Inertial Measurements Units are fused with received signal strength (RSS) measurements of IEEE 802.11. To do this, we assign a probabilistic model to RSS measurements, and adopt the bayesian framework on which FootSLAM and PlaceSLAM are based. Computational aspects are also accounted in order to provide a practical implementation of the algorithm. Simulative and experimental examples of WiSLAM are shown to underline the effectiveness of our proposal.

关键词： SLAM Received Signal Strengths Inertial Measurements Units bayesian algorithm

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A Non Parametric Partial Histogram Bayes Learning algorithm for Classification Applications 4

A Non Parametric Partial Histogram Bayes Learning Algorithm ...

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4th IEEE International Conference on Control System Computing and Engineering (ICCSCE 2014)

作者： Lawend, Haider O. Muad, Anuar M. Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia

ISBN: (纸本)9781479956869

In many applications such as dealing with database, continuous environment and humanoid robots, the machine often deals with large amount of data every day of work. Dealing with large amount of data requires fast as well as accurate learning algorithms to do the classification. A new supervised non parametric Partial Histogram Bayes learning algorithm (PHBayes) is proposed and presented in this paper. The proposed algorithm was tested on image database and compared with other standard algorithms like Naive Bayes, Gaussian Mixture Model based Classifier, 1st Nearest Neighbor and Nearest Class Mean for classification purpose. The experimental results showed that the proposed algorithm is faster as well as more accurate compare with other algorithms, which makes it worthy to be considered in classification applications.

关键词： bayesian algorithm non parametric algorithm classification class histogram representation

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Trust Evaluation based Content Filtering in Social Interactive Data

Trust Evaluation based Content Filtering in Social Interacti...

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International Conference on Cloud Computing and Big Data (CLOUDCOM-ASIA)

作者： Xu, Lingshan Zheng, Xianghan Rong, Chunming Fuzhou Univ Coll Math & Comp Sci Fuzhou 350002 Peoples R China Univ Stavanger Coll Comp & Informat Technol Stavanger Norway

ISBN: (纸本)9781479928293

A new Cloud-based Trust Awareness and Interaction Model (CTAIM) is proposed for trust evaluation based content filtering in social interactive data. The research is based on diversified information from social interactive data to analyse people's intention and evaluate people's trust. The proposed model is composed of bayesian content filtering algorithm and bayesian inference algorithm in Dirichlet distribution, and it's capable to provide 3rd party trustworthiness evaluation according to node behavior and interaction history with high-efficiency, security, and neutrality. Additionally, MapReduce-based computing and HBase storage framework are implemented for parallel computing among mass interactive data.

关键词： social interactive data cloud computing trustworthiness evaluation bayesian algorithm

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