• Title, Summary, Keyword: pattern recognition

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Pattern Recognition of Human Grasping Operations Based on EEG

  • Zhang Xiao Dong;Choi Hyouk-Ryeol
    • International Journal of Control, Automation, and Systems
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    • v.4 no.5
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    • pp.592-600
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    • 2006
  • The pattern recognition of the complicated grasping operation based on electroencephalography (simply named as EEG) is very helpful on realtime control of the robotic hand. In the paper, a new spectral feature analysis method based on Band Pass Filter (simply named as BPF) and Power Spectral Analysis (simply named as PSA) is presented for discriminating the complicated grasping operations. By analyzing the spectral features of grasping operations with the use of the two-channel EEG measurement system and the pattern recognition of the BP neural network, the degree of recognition by the traditional spectral feature method based on FFT and the new spectral features method based on BPF and PSA could be compared. The results show that the proposed method provides highly improved performance than the traditional one because the new method has two obvious advantages such as high recognition capability and the fast learning speed.

A Study on the Optimization of PD Pattern Recognition using Genetic Algorithm (유전알고리즘을 이용한 부분방전 패턴인식 최적화 연구)

  • Kim, Seong-Il;Lee, Sang-Hwa;Koo, Ja-Yoon
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.1
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    • pp.126-131
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    • 2009
  • This study was carried out for the reliability of PD(Partial Discharge) pattern recognition. For the pattern recognition, the database for PD was established by use of self-designed insulation defects which occur and were mostly critical in GIS(Gas Insulated Switchgear). The acquired database was analyzed to distinguish patterns by means of PRPD(Phase Resolved Partial Discharge) method and stored to the form with to unite the average amplitude of PD pulse and the number of PD pulse as the input data of neural network. In order to prove the performance of genetic algorithm combined with neural network, the neural networks with trial-and-error method and the neural network with genetic algorithm were trained by same training data and compared to the results of their pattern recognition rate. As a result, the recognition success rate of defects was 93.2% and the neural network train process by use of trial-and-error method was very time consuming. The recognition success rate of defects, on the other hand, was 100% by applying the genetic algorithm at neural network and it took a relatively short time to find the best solution of parameters for optimization. Especially, it could be possible that the scrupulous parameters were obtained by genetic algorithm.

Relation between food pattern and self-recognition of major oral disease on the Korean adults (한국성인의 식사패턴과 본인이 인지한 양대 구강병과의 관련성 연구)

  • Choi, Jeong-Hee;Lee, Sung-Lym
    • Journal of Korean society of Dental Hygiene
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    • v.10 no.2
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    • pp.335-344
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    • 2010
  • Objectives : Targeting Korean adults, the food pattern are grasped. And, its correlation with oral disease is analyzed. In order to offer basic data to developing the nutritional policy and nutritional program for the future prevention from oral disease, a research was conducted by utilizing the Korean National Health and Nutrition Examination Survey 2005(the 3rd term). Methods : The subjects in this study were 6,526 adults in more than fully 19 years among 9,047 persons who participated in the food intake survey out of those who completed the health interview survey. The statistical analysis was analyzed by using SPSS 12.0 program. Results : 1. As a result of Group Analyzing was indicated to dangerous-type food pattern and protection-type food pattern. 2. As a result of analyzing the answers for having dental caries in the annually personal recognition was indicated to be high in the dangerous-type food pattern, and had not the statistically significant difference. 3. As a result of analyzing the answers for having periodontal disease in the annually personal recognition was indicated to be high in the dangerous-type food pattern, and had the statistically significant difference(p<0.05). 4. As a result of analyzing the food pattern factors that have influence upon both major oral illnesses in the annually personal recognition, the person, who has the dangerous-type food pattern, had high risk level of the periodontal disease in the annually personal recognition. Conclusions : In the above results, as a result of surveying and analyzing importance of the food pattern in the incidence of both major oral illnesses, it is considered that there will be necessity of continuing to research into developing the nutritional policy and nutritional program in order to prevent oral illness in the future.

A Study on the Extraction of Feature Variables for the Pattern Recognition of Welding Flaws (용접결함의 형상인식을 위한 특징변수 추출에 관한 연구)

  • Kim, Jae-Yeol;Roh, Byung-Ok;You, Sin;Kim, Chang-Hyun;Ko, Myung-Soo
    • Journal of the Korean Society for Precision Engineering
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    • v.19 no.11
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    • pp.103-111
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    • 2002
  • In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

A Study on the EMG Pattern Recognition Using SOM-TVC Method Robust to System Noise (시스템잡음에 강건한 SOM-TVC 기법을 이용한 근전도 패턴 인식에 관한 연구)

  • Kim In-Soo;Lee Jin;Kim Sung-Hwan
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.54 no.6
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    • pp.417-422
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    • 2005
  • This paper presents an EMG pattern classification method to identify motion commands for the control of the artificial arm by SOM-TVC(self organizing map - tracking Voronoi cell) based on neural network with a feature parameter. The eigenvalue is extracted as a feature parameter from the EMG signals and Voronoi cells is used to define each pattern boundary in the pattern recognition space. And a TVC algorithm is designed to track the movement of the Voronoi cell varying as the condition of additive noise. Results are presented to support the efficiency of the proposed SOM-TVC algorithm for EMG pattern recognition and compared with the conventional EDM and BPNN methods.

Discrimination of Plant Transient by Using the Fuzzy Pattern Recognition (퍼지 패턴인식법을 이용한 발전소 과도상태 판별)

  • Kim Jong-Seog;Lee Dong-ju
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.14 no.1
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    • pp.37-43
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    • 2005
  • Plant pipe has a fatigue life which is induced by repeated stress come from the variation of temperature and pressure. To avoid the fatigue crack of plant pipe which is produced by long term repeated stress, plant operator has to limit the mont of operating transient. This paper introduced the study result about discrimination methodology of plant transient by using the fuzzy pattern recognition. As result of applying the fuzzy pattern recognition to actual plant operation data, it is confirmed that fuzzy pattern recognition methodology can be useful for the comparison of similarity for the transients of similar output but has different time pattern.

Driving Pattern Recognition Algorithm using Neural Network for Vehicle Driving Control (차량 주행제어를 위한 신경회로망을 사용한 주행패턴 인식 알고리즘)

  • Jeon, Soon-Il;Cho, Sung-Tae;Park, Jin-Ho;Park, Yeong-Il;Lee, Jang-Moo
    • Proceedings of the KSME Conference
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    • pp.505-510
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    • 2000
  • Vehicle performances such as fuel consumption and catalyst-out emissions are affected by a driving pattern, which is defined as a driving cycle with the grade in this study. We developed an algorithm to recognize a current driving pattern by using a neural network. And this algorithm can be used in adapting the driving control strategy to the recognized driving pattern. First, we classified the general driving patterns into 6 representative driving patterns, which are composed of 3 urban driving patterns, 2 suburban driving patterns and 1 expressway driving pattern. A total of 24 parameters such as average cycle velocity, positive acceleration kinetic energy, relative duration spent at stop, average acceleration and average grade are chosen to characterize the driving patterns. Second, we used a neural network (especially the Hamming network) to decide which representative driving pattern is closest to the current driving pattern by comparing the inner products between them. And before calculating inner product, each element of the current and representative driving patterns is transformed into 1 and -1 array as to 4 levels. In the end, we simulated the driving pattern recognition algorithm in a temporary pattern composed of 6 representative driving patterns and, verified the reliable recognition performance.

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Pattern Recognition Methods for Emotion Recognition with speech signal

  • Park Chang-Hyun;Sim Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.2
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    • pp.150-154
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    • 2006
  • In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition are determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section.

Numerical Reconstruction and Pattern Recognition using Integral Imaging

  • Yeom, Seo-Kwon
    • 한국정보디스플레이학회:학술대회논문집
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    • pp.1131-1134
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    • 2008
  • In this invited paper, numerical reconstruction and pattern recognition using integral imaging are overviewed. The computational integral imaging method reconstructs three-dimensional information at arbitrary depth-levels. Photon-counting nonlinear matched filtering combined with the computational reconstruction provides promising results for the application of low-light level recognition.

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Novel Method for Face Recognition using Laplacian of Gaussian Mask with Local Contour Pattern

  • Jeon, Tae-jun;Jang, Kyeong-uk;Lee, Seung-ho
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.11
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    • pp.5605-5623
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    • 2016
  • We propose a face recognition method that utilizes the LCP face descriptor. The proposed method applies a LoG mask to extract a face contour response, and employs the LCP algorithm to produce a binary pattern representation that ensures high recognition performance even under the changes in illumination, noise, and aging. The proposed LCP algorithm produces excellent noise reduction and efficiency in removing unnecessary information from the face by extracting a face contour response using the LoG mask, whose behavior is similar to the human eye. Majority of reported algorithms search for face contour response information. On the other hand, our proposed LCP algorithm produces results expressing major facial information by applying the threshold to the search area with only 8 bits. However, the LCP algorithm produces results that express major facial information with only 8-bits by applying a threshold value to the search area. Therefore, compared to previous approaches, the LCP algorithm maintains a consistent accuracy under varying circumstances, and produces a high face recognition rate with a relatively small feature vector. The test results indicate that the LCP algorithm produces a higher facial recognition rate than the rate of human visual's recognition capability, and outperforms the existing methods.