• Title, Summary, Keyword: pattern classification

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Development of Multi-DoFs Prosthetic Forearm based on EMG Pattern Recognition and Classification (근전도 패턴 인식 및 분류 기반 다자유도 전완 의수 개발)

  • Lee, Seulah;Choi, Yuna;Yang, Sedong;Hong, Geun Young;Choi, Youngjin
    • The Journal of Korea Robotics Society
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    • v.14 no.3
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    • pp.228-235
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    • 2019
  • This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

The Study on Pattern Differentiations of Primary Headache in Korean Medicine according to the International Classification of Headache Disorders (ICHD 분류에 따른 원발 두통의 한의학적 변증 연구)

  • Lee, Jeong So;Park, Mi Sun;Kim, Yeong Mok
    • Journal of Physiology & Pathology in Korean Medicine
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    • v.31 no.4
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    • pp.201-212
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    • 2017
  • This study draws pattern differentiations of headache disorders on the ground of modern clinical applications and Korean medical literature. Categorization and symptoms of headache disorders are based on International Classification of Headache Disorders 3rd edition(beta version). And clinical papers are searched in China Academic Journals(CAJ) of China National Knowledge Infrastructure(CNKI). In the aspect of eight principle pattern identification, primary headache occurs due to lots of yang qi and has more inner pattern rather than exterior pattern, heat pattern rather than cold pattern, excess pattern rather than deficiency pattern. And primary headache is related with liver in the aspect of visceral pattern identification and blood stasis, wind and phlegm are relevant mechanisms. Migraine without aura is associated with ascendant hyperactivity of liver yang, phlegm turbidity, sunken spleen qi, wind-heat, blood deficiency or yin deficiency. Migraine with aura is mainly related with wind and it's major mechanisms are ascendant hyperactivity of liver yang, liver fire, yin deficiency of liver and kidney, blood deficiency or liver depression and qi stagnation. High repetition rate of tension-type headache can be identified as heat pattern or excess pattern. And trigeminal autonomic cephalalgias can also be accepted as heat pattern or excess pattern when the occurrence frequency is high and is relevant to combined pattern with excess pattern of external contraction and deficiency pattern of internal damage based on facial symptoms by external contraction and nervous and anxious status by liver deficiency. This study can be expected to be Korean medical basis of clinical practice guidelines on headache by proposing pattern identifications corresponding to the western classifications of headache disorders.

The Implementation of Pattern Classifier or Karyotype Classification (핵형 분류를 위한 패턴 분류기 구현)

  • Eom, S.H.;Nam, K.G.;Chang, Y.H.;Lee, K.S.;Chang, H.H.;Kim, G.S.;Jun, G.R.
    • Proceedings of the KOSOMBE Conference
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    • v.1997 no.11
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    • pp.133-136
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    • 1997
  • The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room or improving the accuracy of chromosome classification. In this paper, We propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of multi-step multi-layer neural network(MMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted three morphological features parameters such as centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.). This Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results show that the chromosome classification error is reduced much more than that of the other classification methods.

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TEMPORAL CLASSIFICATION METHOD FOR FORECASTING LOAD PATTERNS FROM AMR DATA

  • Lee, Heon-Gyu;Shin, Jin-Ho;Ryu, Keun-Ho
    • Proceedings of the KSRS Conference
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    • pp.594-597
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    • 2007
  • We present in this paper a novel mid and long term power load prediction method using temporal pattern mining from AMR (Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

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A Design of Index/XML Sequence Relation Information System for Product Abstraction and Classification (산출물 추출 및 분류를 위한 Index/XML순서관계 시스템 설계)

  • Sun Su-Kyun
    • The KIPS Transactions:PartD
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    • v.12D no.1
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    • pp.111-120
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    • 2005
  • Software development creates many product that class components, Class Diagram, form, object, and design pattern. So this Paper suggests Index/XML Sequence Relation information system for product abstraction and classification, the system of design product Sequence Relation abstraction which can store, reuse design patterns in the meta modeling database with pattern Relation information. This is Index/XML Sequence Relation system which can easily change various relation information of product for product abstraction and classification. This system designed to extract and classify design pattern efficiently and then functional indexing, sequence base indexing for standard pattern, code indexing to change pattern into code and grouping by Index-ID code, and its role information can apply by structural extraction and design pattern indexing process. and it has managed various products, class item, diagram, forms, components and design pattern.

ECG Pattern Classification Using Back-Propagation Neural Network (역전달 신경회로망을 이용한 심전도 패턴분류)

  • Lee, Je-Suk;Kwon, Hyuk-Je;Lee, Jung-Whan;Lee, Myoung-Ho
    • Proceedings of the KOSOMBE Conference
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    • v.1992 no.11
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    • pp.47-50
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    • 1992
  • This paper describes pattern classification algorithm of ECG using back-propagation neural network. We presents new feature extractor using second order approximating function as the input signals of neural network. We use 9 significant parameters which were extracted by feature extractor. 5 most characterized ECG signal pattern is classified accurately by neural network. We use AHA database to evaluate the performance ol the proposed pattern classification algorithm.

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Pattern Classification Method using SOFM and Multilayer Neural Network (SOFM과 다층신경회로망을 이용한 패턴 분류 방식)

  • 박진성;공휘식;이현관;김주웅;엄기환
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.296-300
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    • 2002
  • We proposed a method of a pattern classification using unsupervised teaming rules, SOFM, and supervised teaming rules, Multilayer neural network. Establish result that classify and get input pattern using SOFM by initial weighting vector of Multilayer neural network and target value. Got superior Performance as result that do simulation about face image to confirm usefulness of way that propose.

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A Pattern Classification Method using Closest Decision Method in k Nearest Neighbor Prototypes (k 근방 원형상에서 최근접 결정법을 이용한 패턴식별법)

  • Kim, Eung-Kyeu;Lee, Soo-Jong
    • Proceedings of the IEEK Conference
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    • pp.833-834
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    • 2008
  • In this paper, a pattern classification method using closest decision method based on the mean of norm in the closet prototype from an input pattern and its k nearest neighbor prototypes is presented to do accurate classification in arbitrary distributed patterns when the number of patterns is very low. Also this method can be used to classify input pattern precisely when the number patterns is very low because this method considers the weight by the difference of variance in prototypes around the discrimination boundary.

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Karyotype Classification of Chromosome Using the Hierarchical Neu (계층형 신경회로망을 이용한 염색체 핵형 분류)

  • Chang, Yong-Hoon;Lee, Young-Jin;Lee, Kwon-Soon
    • Proceedings of the KIEE Conference
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    • pp.555-559
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    • 1998
  • The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis have been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, We proposed an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of two-step multi-layer neural network(TMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted four morphological features parameters such as centromeric index (C.I.), relative length ratio(R.L.), relative area ratio(R.A.) and chromosome length(C.L.). These Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results shown that the chromosome classification error was reduced much more than that of the other classification methods.

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Construction of Design Pattern Retrieval System using Pattern Information (패턴 정보를 이용한 설계패턴 검색 시스템 구축)

  • 김귀정;송영재
    • The KIPS Transactions:PartD
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    • v.8D no.1
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    • pp.88-98
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    • 2001
  • in this paper, we imlemented design pattern retrieval system for efficient managemant and reusability of design patterns. Pattern is conssisted of property information and meta information id used for similarity measurement on classification and retrieval of patterns.Meta information od used for UML modeling of patterns. We classified design patterns with the empirical scope in addition to Gamma's basic classification. also we used E-SARM for retrieval represented UML diagram with pattern meta information, and simulated the environment so as to obtain best result on applying to retrieval of design pattern. This system is able ro resister new patterns through pattern viewer and manages these patterns with property informaiton and meta information. Thus this system supports efficient management of patterns, UML modeling, priority pattern retrieval, higher reusability and reduces pattern selection cost.

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