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检索条件"任意字段=5th International Conference on Pattern Recognition in Bioinformatics"

共 1408 条记录，以下是501-510 订阅

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Trip Activity Chain pattern recognition and Travel Trajectory Data Mining 5

Trip Activity Chain Pattern Recognition and Travel Trajector...

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5th international conference on Transportation Engineering, ICTE 2015

作者： Wang, Lei Zuo, Zhongyi Cao, Can Cao, Yi School of Traffic and Transportation Engineering Dalian Jiaotong University Dalian LIAONING China

ISBN: (数字)9780784479384

ISBN: (纸本)9780784479384

this study focuses on recognizing the travel modes and activity types in personal travel trajectory. Firstly the paper proposes a concept of trip-Activity chain pattern to describe the general form of travel trajectory, and analyzes the structure and features of this pattern and its sub-patterns: Trip sub-pattern and activity sub-pattern. then normalized Euclidean distance measurement method is adopted to decompose the travel trajectory into trip and activity parts. Finally we apply RBFNN to solve the pattern recognition problem, which obtains an accuracy of 88.5% of travel mode recognition and 74.4% of activity type recognition. © ASCE.

关键词： Travel trajectory Trip activity chain Travel analysis pattern recognition pattern recognition travel pattern recognition problem Travel patterns ACTIVITY TYPES Kinematic chain trajectory data trajectory

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pattern recognition in Load Profiles of Electric Drives in Manufacturing Plants 5

Pattern Recognition in Load Profiles of Electric Drives in M...

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5th international Electric Drives Production conference (EDPC)

作者： Reger, Arnim Oette, Cedric Aires, Ana Paula Steinhilper, Rolf Fraunhofer IPA Fraunhofer Projectgrp Processinnovat Bayreuth Germany Univ Bayreuth Chair Mfg & Remfg Technol Bayreuth Germany

ISBN: (纸本)9781467375122

With the introduction of energy management systems, an analysis of load profiles of manufacturing plants becomes increasingly important. Each manufacturing plant is characterized by a process and product specific power consumption. Often, electric drives are the main power consumers. In this paper methods for pattern recognition in load profiles of electric drives are presented on the example of a multiaxial lathe. A transfer of techniques used for speech recognition e.g. Hidden Markov Models, Fourier and Wavelet Transforms to manufacturing application is discussed. In combination with energy measurement systems, those techniques proved to be a good solution regarding energy efficiency calculations and derivation for key performance indicators. the investigated methods can also be applied to other process data with significant cost advantages, because a lot of process information can be extracted from a single sensor.

关键词： pattern recognition Load Profiles of Electric Drives Cross-Correlation Hidden Markov Models (HMM) Wavelet Transform Viterbi algorithm Energy Performance Indicator

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5th international Workshop on Computational Forensics, IWCF 2012 and 6th international Workshop on Computational Forensics, IWCF 2014

5th International Workshop on Computational Forensics, IWCF ...

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5th international Workshop on Computational Forensics, IWCF 2012 and 6th international Workshop on Computational Forensics, IWCF 2014

ISBN: (纸本)9783319201245

the proceedings contain 20 papers. the special focus in this conference is on Biometrics, Document Image Inspection and Applications. the topics include: Voice passphrase variability evaluation for speaker recognition;studies in individuality;robust 2d face recognition under different illuminations using binarized partial face features;comparison of multidirectional representations for multispectral palmprint recognition;efficient iris recognition system using relational measures;a study of identification performance of facial regions from CCTV images;inverse of low resolution line halftone images for document inspection;when document security brings new challenges to document analysis;stamp verification for automated document authentication;introducing and analysis of the windows 8 event log for forensic purposes;automatic creation of computer forensic test images;art forgery detection via craquelure pattern matching;forensics acquisition and analysis of instant messaging and VOIP applications and an integrated tool for forensic writer identification.

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Inverse Kinematics and Gesture pattern recognition using Hidden Markov Model on BeatMe! Project : Traditional Dance Digitalization 5

Inverse Kinematics and Gesture Pattern Recognition using Hid...

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5th international conference on Electrical Engineering and Informatics

作者： Ulfah, Zahrotul Aisyah Wuryandari, Aciek Ida Priyana, Yoga Inst Teknol Bandung Sch Elect Engn & Informat Bandung Indonesia

ISBN: (纸本)9781467373197

Indonesian traditional dance preservation efforts that nowadays increasingly eroded by foreign culture needs to be improved and adapted to technological improvement. To answer that, BeatMe! Project developed for fulfil the needs of entertainment and traditional dance learning media by integrate 3D motion capture, processing data and visualization. this paper will explain the processing data detail implementation which include big data summarizing as in summarizing XYZ coordinate of joint to be angle between two joints. In addition, this paper also explain detail implementation of HMM learning system for gesture pattern learning and recognizing. Environment used is Kinect, Visual Studio and MATLAB. Result show summarized data of discrete value between joints angle, learning curve as the learning process output that tends to rise and converge on a value within limits of 700.000 symbols, new gesture pattern recognition show a good performance in one degree joint because its position nearest the main torso, and not to good performance in two degree joint because of randomized value that happen when the body position isn't aligned with Kinect.

关键词： inverse kinematics Hidden Markov Model recognition Kinect gesture pattern

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Computational color imaging: 5th international Workshop, CCIW 2015 Saint Etienne, France, March 24–26, 2015 proceedings 5

Computational color imaging: 5th International Workshop, CCI...

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5th international Workshop on Computational Color Imaging, CCIW 2015

作者： Trémea, Alain Schettini, Raimondo Tominaga, Shoji University Jean Monnet Saint-Etienne France Dipartimento di Informatica Sistemistica e Comunicazione Universit degli Studi di Milano-Bicocca Milano Italy Chiba University Chiba Japan

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the Feature Extraction and pattern recognition of Partial Discharge Type Using Fnergy Percentage of Wavelet Packet Coefficients and Support Vector Machines 5

The Feature Extraction and Pattern Recognition of Partial Di...

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5th international conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT)

作者： Xu, Jia Niu, Haiqing Hu, Riliang South China Univ Technol Sch Elect Power Guangzhou 510640 Guangdong Peoples R China Southern Power Grid Dongguan Power Supply Bur Dongguan 523129 Peoples R China

ISBN: (纸本)9781467371063

Partial discharge is an important cause of the deterioration of the cable insulation condition, and also an important manifestation of the deteriorated insulation condition. Partial discharge detection for cable insulation is an important means to find the cable insulation defects and assess state of the cable insulation. Partial discharge signals from different sources have great harm to the power equipment, so pattern identification is an important purpose in this research. First, the four kinds of discharge waveforms are processed by dc-noising preprocessing, then discharge waveforms are decomposed by wavelet packet, and finally the energy percentage in different frequency bands can he obtained. Studies show that when the type of discharge signal is different, the wavelet packet coefficients in the distribution of each band is also different. So energy percentage of the wavelet packet coefficients can be used for discharge signal feature recognition. In this paper, SVM binary classification is first extended to multi-classification by M-ary algorithm, then the feature vectors composed by energy percentage of the wavelet packet coefficients arc inputted into SVM for recognition. the recognition results of test samples show that feature vectors composed by the percentage of energy of wavelet packet coefficients can well reflect the characteristics of the original signal, and the method based on SVM has better recognition performance.

关键词： partial discharge electric cable wavelet packet decomposition percentage of energy feature extraction support vector machine pattern recognition

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3-D reconstruction of historical documents using an X-Ray C-Arm CT system

3-D reconstruction of historical documents using an X-Ray C-...

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international conference on Image and Vision Computing New Zealand, IVCNZ

作者： Daniel Stromer Gisela Anton Vincent Christlein Andreas Maier Patrick Kugler Pattern Recognition Lab Erlangen Centre for Astroparticle Physics Friedrich-Alexander-University Erlangen-Nuremberg Germany Advanced Therapy Siemens Healthcare GmbH Forchheim Germany

ISBN: (纸本)9781509027491

there exist historical documents that can not be inspected without damaging them. We present a non-invasive method for the reconstruction of historical documents which have been written with iron gall ink. Iron gall ink has been used since the 5th century and is still in use as an indelible ink for writings and drawings. the fact, that this kind of ink is based on metallic particles leads to the assumption that modern X-Ray scanners could image the writings such that it can be differentiated from the paper mainly consisting of cellulose. Our work shows a method that is capable of imaging writings written with iron gall ink by using a modern X-Ray imaging system that is commonly used for medical applications. Even though the imaging system is not optimized for the given task, the writing can be identified. Furthermore, we present a method that is able to reconstruct the volume even if the phantom is not centered properly or misaligned. Applying this method makes it possible to image books, writings or drawings based on all metal-containing inks without page-turning or unrolling the paper.

关键词： Ink Image reconstruction Iron Writing Absorption X-ray imaging Imaging

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pattern recognition of EMG Signal During Load Lifting Using Artificial Neural Network (ANN) 5

Pattern Recognition of EMG Signal During Load Lifting Using ...

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5th IEEE international conference on Control System, Computing and Engineering (ICCSCE)

作者： Jali, M. H. Izzuddin, T. A. Bohari, Z. H. Jaafar, H. I. Nasir, M. N. M. Univ Tekn Malaysia Melaka Malacca Fac Elect Engn Melaka Malaysia

ISBN: (纸本)9781479982523

this paper describes pattern recognition of electromyography (EMG) signal during load lifting using Artificial Neural Network (ANN). EMG is a method to measure and record the muscle activity when individuals perform certain operation and actions. this research will classify the EMG signal based on force apply to the arm due to the gravity act on it during load lifting. Recognizing pattern based on EMG signal is not an easy task because of the nonlinearities behavior of the signal. It required a good classifier to distinguish each pattern. the motivation of this project is to help the person suffer with hemiparesis to perform daily activities as well as to improve the lifestyle. It is important for patients to realize the hopes of hemiparesis after experiencing their inability to do activity as a normal human. Recognizing EMG pattern is crucially important for rehabilitation control that enables the patients to lift the heavy load despite of their muscle weaknesses. therefore, a proper analysis of muscle behavior is necessary. the objectives of this research are to extract the important features of EMG signal using time domain analysis and to classify EMG signal based on load lifting using ANN. the experiment was performed to five subjects that were selected mainly based on criteria specified. the EMG signals are acquired at long head biceps brachii. then, the subjects were asked to lift the loads of 2kg, 5kg, and 7kg. It is expected an accurate classifier which can recognize the pattern precisely and could be further used for arm rehabilitation control.

关键词： Electromyography Artificial Neural Network pattern recognition Load Lifting Rehabilitation

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Automatic pattern recognition on archaeological ceramic by 2D and 3D image analysis : A feasibility study 5

Automatic pattern recognition on archaeological ceramic by 2...

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international conference on Image Processing theory Tools and Applications

作者： Teddy, Debroutelle Romain, Janvier Aladine, Chetouani Sylvie, Treuillet Matthieu, Exbrayat Lionel, Martin Sebastien, Jesset Univ Orleans Lab PRISME 12 Rue Blois F-45067 Orleans 2 France Univ Orleans LIFO F-45067 Orleans 2 France Serv Archeol Municipal Orleans F-45000 Orleans France

ISBN: (纸本)9781479986378

the ARCADIA project aims at developing automatic image analysis and machine learning methods to facilitate the interpretation and promotion of an archaeological ceramic heritage. It consists in implementing a 3D digitalization chain of the discovered shards to extract the decoration imprinted by the potters using a carved wood wheel. the characterization of the hollow pattern from a binary map given by the 3D image then allows the automatic classification of these patterns. We propose here a feasibility study by comparing the results of automatic classification from the manual inkings made by archaeologists and those from 3D digitalizations of the same corpus of shards.

关键词： Archaeological ceramic heritage carved wood wheel decoration 3D pattern recognition classification

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Symbol recognition using directional and spatial features 5

Symbol recognition using directional and spatial features

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5th international conference on Image Processing, theory, Tools and Applications 2015, IPTA 2015

作者： Pham, the-Anh Hoang, Nam Le, Hao Le, Hong Laboratoire d'Informatique 64 Avenue Jean Portalis Tours37200 France Hong Duc University Thanh Hoa city Viet Nam

ISBN: (纸本)9781479986354

this paper is interested in shape representation and recognition with a particular target to technical and line-drawing symbols. Specifically, two sorts of directional and spatial features are explored to construct a new descriptor for symbol matching and recognition. these features are rotation-, translation- and scale-invariant and can be extracted with a low cost of computation. the descriptor is constructed by vertical and horizontal binning of these features. the proposed approach works well for both types of object representation (i.e., contour and skeleton). Experimental results show the robustness of the proposed method on various datasets (e.g., technical symbols and logos) compared to other baseline systems in the literature. © 2015 IEEE.

关键词： pattern recognition

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