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Post-Saccadic Event Related Potential toward a New Technique for Information Retrieval

Post-Saccadic Event Related Potential toward a New Technique...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Hideaki Touyama Toyama Prefectural University

ISBN: (纸本)9781467317139

In this paper, toward a brain-computer interface for information retrieval, we performed preliminary experiments on post-saccadic event related potential (ERP) during web browsing. Two channel electroencephalogram was analyzed with five subjects during simple string retrieval tasks using web browser with active eye movements, i.e., saccadic eye movements. After the saccadic eye movements the ERP signals were observed in target string retrievals, whereas not observed in standard string retrievals. The classification algorithm was applied and the average decoding performance of single-shot post-saccadic ERP could be 78.2% and 77.6% in recall and precision, respectively. The eye-tracking was also studied and the eye gaze points were confirmed to be on and around target strings when the ERP was elicited. This result suggests that the post-saccadic ERP with eye-tracking leads to a possibility of the use in the brain-computer interface for information retrieval.

关键词： Brain-Computer Interface Saccadic Eye Movement ElectroEncephaloGram (EEG) Event Related Potential (ERP) Information Retrieval Web Browsing NeuroMarketing Classification information retrieval Enterprise resource planning Brain-Computer Interfaces Event-related potentials new technique Eye Movements sorting algorithm Saccades Cosmic strings Web Browser single-shot Computer interfaces

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Survey and proposal on packet classification algorithms

Survey and proposal on packet classification algorithms

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IEEE Workshop on High Performance Switching and Routing

作者： Hyesook Lim Department of Electronics Engineering Ewha Womans University Seoul South Korea

ISBN: (数字)9781424469710

ISBN: (纸本)9781424469697

Presents a collection of slides covering the following topics: packet classification; ternary CAM; linear search; rule set characteristics; hierarchical trie; grid of tries; performance evaluation; hierarchical set pruning binary search tree; refined HSP-BST; performance simulation; bit vector; cross product; tuple space search; rectangle search; tuple pruning; bloom filter; area based quad trie; and hierarchical intelligent cutting.

关键词： packet classification sorting algorithm algorithms

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Activity Level Classification algorithm Using SHIMMER Wearable Sensors for Individuals with Rheumatoid Arthritis

Activity Level Classification Algorithm Using SHIMMER Wearab...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Emma Fortune Marie Tierney Cliodhna Ni Scanaill Ala Bourke Norelee Kennedy John Nelson Intel Health Research and Innovation Group Leixlip Co. Dept. of Physiotherapy University of Limerick Dept. of Electronic and Computer Engineering University of Limerick

ISBN: (纸本)9781424441211

In rheumatoid arthritis (RA) it is believed that symptoms associated with the progression of the disease result in a reduction in the physical activity level of the patient. One of the key flaws of the research surrounding this hypothesis to date is the use of non-validated physical activity outcomes measures. In this study, an algorithm to estimate physical activity levels in patients as they perform a simulated protocol of typical activities of daily living using SHIMMER kinematic sensors, incorporating tri-axial gyroscopes and accelerometers, is proposed. The results are validated against simultaneously recorded energy expenditure data and the defined activity protocol and demonstrate that SHIMMER can be used to accurately estimate physical activity levels in RA populations.

关键词： Physical Activity Level ACTIVITY LEVELS Rheumatoid Arthritis Wearable sensors sorting algorithm Motor Activity accelerometers Energy Expenditure Activities of Daily Living Right Ascension

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On Flight Route Planning Method of the Terrain Avoidance System

On Flight Route Planning Method of the Terrain Avoidance Sys...

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2014中国制导、导航与控制学术会议

作者： Dongcai Qu Dehai Yu Yuguang Feng Binwen Lu Department of Control Engineering Naval Aeronautical and Astronautical UniversityYantaiShandong ProvinceChina264001 Train DepartmentNaval Aeronautical and Astronautical University YantaiShandong ProvinceChina264001 Department of Ordnance Science and TechnologyNaval Aeronautical and Astronautical UniversityYantaiShandong ProvinceChina264001 College of Mechatronic Engineering and AutomationNational Univerisity of Defense TechnologyChangshaHunan ProvinceChina410000

ISBN: (纸本)9781479946983

It is an important step for flight route planning in the process of air terrain avoidance.A reference air route that can guarantee the flight safety,meet flight quality is the basis to accomplish the flying *** paper research the air route planning for flight terrain avoidance,classify and model to the terrain sampled point by using k mean-value algorithm,and set up the feasible point *** then using the Dijkstra algorithm to find a high quality air route,based on considering the restrained conditions of *** the simulation,the feasibility and rationality of the scheme was verified

关键词： air safety aircraft control collision avoidance pattern clustering Dijkstra algorithm air terrain avoidance feasible point space flight conditions flight quality flight route planning method flight safety flying task high quality air route k mean-value algorithm reference air route terrain avoidance system terrain sampled point Atmospheric modeling Classification algorithms Clustering algorithms Educational institutions Heart Path planning Planning flight safety air safety Aircraft control Atmospheric models flight conditions flight line sorting algorithm Clustering algorithms Educational institutions Dijkstra's algorithm Path planning Collision avoidance Airlines Cluster Analysis Heart

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A New Approximate Maximal Margin Classification algorithm 00

A New Approximate Maximal Margin Classification Algorithm

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Annual Conference on Neural Information Processing Systems

作者： Claudio Gentile DSI Universita' di Milano Via Comelico 39 20135 Milano Italy

ISBN: (纸本)0262122413

A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. Norm p ≥ 2 for a set of linearly separable data. Our algorithm, called ALMA_p (Approximate Large Margin algorithm w.r.t. Norm p), takes O ((p-1) X~2/α~2γ~2) corrections to separate the data with p-norm margin larger than (1 ― α) γ, where γ is the p-norm margin of the data and X is a bound on the p-norm of the instances. ALMA_p avoids quadratic (or higher-order) programming methods. It is very easy to implement and is as fast as on-line algorithms, such as Rosenblatt's perceptron. We report on some experiments comparing ALMA_p to two incremental algorithms: Perceptron and Li and Long's ROMMA. Our algorithm seems to perform quite better than both. The accuracy levels achieved by ALMA_p are slightly inferior to those obtained by Support vector Machines (SVMs). On the other hand, ALMA_p is quite faster and easier to implement than standard SVMs training algorithms.

关键词： algorithms Tumor margin status Fasting sorting algorithm Neural Networks (Computer) Lithium Approximate Maximal

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Research on Object Self-Organizing Iterative Cluster algorithm of Emitter Identification

Research on Object Self-Organizing Iterative Cluster Algorit...

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International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Xiaoxuan Wang Lianwang Diao Xin Xu Sci. & Technol. on Inf. Syst. Eng. Lab. China Electronics Technology Group Corp 28th Research Institute Nanjing Jiangsu CN

ISBN: (纸本)9781479986477

A novel sorting algorithm based on clustering is proposed to resolve problems such as low sorting precision and even failure which are caused by conventional radar signal sorting algorithm applied in present high-density signal environment. According to the characters of electromagnetic parameters, the advantages and disadvantages of the traditional clustering algorithms in data mining have been discussed in this paper. By using traditional clustering algorithm, users have to specify in advance how many clusters are being sought and it cannot be processed well if signal samples are not in spherical cluster distribution. An improved clustering algorithm based on self-organizing iterative analytic is presented in this paper, which avoid relying on prior knowledge or depending on signals' distribution. Simulation result proves the validity and practicability of this algorithm. It provides a new way for object clustering problem in emitter identification.

关键词： data mining iterative methods pattern clustering radar computing radar signal processing sorting electromagnetic parameter characters emitter identification low sorting precision object self-organizing iterative cluster algorithm radar signal sorting algorithm Clustering Analysis Clustering Evaluation Emitter Identification K-means data mining Cluster Analysis radar computing Clustering algorithms Radar signal processing iterative methods sorting programs grade sorting algorithm RADAR SIGNALS emitters

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Three-Phase Modular Multilevel Converter (MMC) for Low-voltage Applications: Improved Modulation Technique Toward Less Capacitance Requirement

Three-Phase Modular Multilevel Converter (MMC) for Low-volta...

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IEEE Conference on Power Electronics and Renewable Energy (CPERE)

作者： Tarek Younis Paolo Mattavelli Paolo Magnone Igino Toigo Michele Corradin University of Padova Vicenza Italy Socomec Vicenza Italy

ISBN: (纸本)9781728109114

Modular Multilevel Converter (MMC) has become one of the promising topologies in medium and high power applications. Modularity, scalability, and reliability are the main features that give the MMC some advantages over other multilevel topologies when considering high number of levels. This paper presents an alternative modulation technique for a modified modular multilevel converter (M-MMC) topology for low-voltage applications. By using level-shifted carrier (LSC) modulation and the proposed sorting algorithm, the capacitance requirement for this topology can be reduced. Finally, the proposed modulation scheme and the reduction of capacitance are verified by simulation results using PLECS.

关键词： Modular multilevel converters (MMCs) Renewable energy Multilevel converters Level-shifted carrier (LSC) Low-voltage systems Modulation sorting algorithm sorting algorithm multistage converter Renewable energy Low voltage capacitance Modularity METAL MATRIX COMPOSITES Mitocin-C Capacitors topology

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Imbalanced Data Classification algorithm Based on Boosting and Cascade Model

Imbalanced Data Classification Algorithm Based on Boosting a...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Xiaolong Zhang Chao Cheng School of Computer Science and Technology Wuhan University of Science and Technology

ISBN: (纸本)9781467317139

Traditional classification algorithms are difficult in dealing with imbalance data. This paper proposes a classification algorithm called CascadeBoost, which combines with the advantages of boosting algorithm and cascade model that can learn imbalance data. Cascade model allows the pre-training data to be balanced by gradually reducing the number of the major class;and then the most rich information samples based on the weight distribution can be gradually selected using boosting algorithm. The experimental results show that the proposed method obtains better performance compared to other methods.

关键词： Imbalance Data Boosting algorithm Cascade Model SVM AUC Boost sorting algorithm cascades data classification

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Operational Risk Evaluation Of Cable Plugs Using An Automated Multisensor Classification System

Operational Risk Evaluation Of Cable Plugs Using An Automate...

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VDE High Voltage Technology 2018;ETG-Symposium

作者： Bjoern Boettcher Ali Sinai Matthias Menge Ronald Plath Thomas Graef Thomas Huecker

ISBN: (纸本)9783800748075

The integrity of high-voltage (HV) insulation can be evaluated by measuring partial discharge (PD) activity. PD detection and monitoring is usually performed based on a single physical sensor and is susceptible for noise interference. In order to increase the evaluation reliability, an automated multi sensor expert system for PDs at cable terminations of medium-voltage gas-insulated switchgears is presented. The objective of this work is to combine data from different sensors that capture various physical quantities, i.e. sound and electrical wave to improve reliability. The introduced system includes an intelligent classification algorithm, which classifies the detected signals from an acoustic sensor and a high frequency current transformer (HFCT). The system utilizes three redundant classification algorithms, which work independently and thus make the evaluation result even more reliable. For the classification features the Fourier transformations of the PD apparent charge Q, the voltage gradient DeltaU/Deltaphi and the voltage change DeltaU between pulses are used. An L2 distance classifier examines the data of corresponding PD defect classes in respect to certain probabilities.

关键词： Risk Assessment Fourier Transform multi sensor sorting algorithm Cable terminations Taxonomy noise disturbance acoustic transducers Palladium Physically Handicapped Proportional derivative Professional Development Descriptions, Job Automation Legumes

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Effective Insect Recognition Using a Stacked Autoencoder with Maximum Correntropy Criterion

Effective Insect Recognition Using a Stacked Autoencoder wit...

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International Joint Conference on Neural Networks

作者： Yu Qi Goktug T. Cinar Vinicius M. A. Souza Gustavo E. A. P. A. Batista Yueming Wang Jose C. Principe Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China

ISBN: (纸本)9781479919611

Throughout the history, insects had been intimately connected to humanity, in both positive and negative ways. Insects play an important part in crop pollination, on the other hand, some of them spread diseases that kill millions of people every year. Effective control of harmful insects while having little impact to beneficial insects and environment is extremely important. Recently, an intelligent trap that uses laser sensors was presented to control the population of target insects. The device could record and analyze sensor signals when an insect passes through the trap and make quick decisions whether to catch it or not. The effectiveness of the trap relies on the correct choice of classification algorithm to perform the insect detection. In this paper, we propose to use a deep neural network with maximum correntropy criterion (MCC) for reliable classification of insects in real-time. Experimental results show that, deep networks are effective for learning stable features from brief insect passage signals. By replacing the mean square error cost with MCC, the robustness of autoencoders against noise is improved significantly and robust features could be learned. The method is tested on five species of insects and a total of 5325 passages. High classification accuracy of 92.1 % is achieved. Compared with previously applied methods, better classification performance is obtained using only 10% of the computation time. Therefore, our method is efficient and reliable for online insect detection.

关键词： Effective Insect Recognition Stacked Autoencoder Maximum Correntropy Criterion Insecta Traps pest insects Laser sensors sorting algorithm Humanities beneficial insect MCC Genes computational time PASSAGE

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