• Title/Summary/Keyword: pattern classification

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Construction of A Nonlinear Classification Algorithm Using Quadratic Functions (2차 하수를 이용한 비 선형 패턴인식 알고리즘 구축)

  • 김락상
    • Journal of the Korean Operations Research and Management Science Society
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    • v.25 no.4
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    • pp.55-65
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    • 2000
  • This paper presents a linear programming based algorithm for pattern classification. Pattern classification is being considered to be critical in the area of artificial intelligence and business applications. Previous methods employing linear programming have been aimed at two-group discrimination with one or more linear discriminant functions. Therefore, there are some limitations in applying available linear programming formulations directly to general multi-class classification problems. The algorithm proposed in this manuscript is based on quadratic or polynomial discriminant functions, which allow more flexibility in covering the class regions in the N-dimensional space. The proposed algorithm is compared with other competitive methods of pattern classification in experimental results and is shown to be competitive enough for a general purpose classifier.

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Development of An Operation Monitoring System for Intelligent Dust Collector By Using Multivariate Gaussian Function (Multivariate Gaussian Function을 이용한 지능형 집진기 운전상황 모니터링 시스템 개발)

  • Han, Yun-Jong;Kim, Sung-Ho
    • Proceedings of the KIEE Conference
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    • 2006.10c
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    • pp.470-472
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    • 2006
  • Sensor networks are the results of convergence of very important technologies such as wireless communication and micro electromechanical systems. In recent years, sensor networks found a wide applicability in various fields such as environment and health, industry scene system monitoring, etc. A very important step for these many applications is pattern classification and recognition of data collected by sensors installed or deployed in different ways. But, pattern classification and recognition are sometimes difficult to perform. Systematic approach to pattern classification based on modem learning techniques like Multivariate Gaussian mixture models, can greatly simplify the process of developing and implementing real-time classification models. This paper proposes a new recognition system which is hierarchically composed of many sensor nodes having the capability of simple processing and wireless communication. The proposed system is able to perform context classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to intelligent dust collecting system.

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A Study on Gender Classification Based on Diagonal Local Binary Patterns (대각선형 지역적 이진패턴을 이용한 성별 분류 방법에 대한 연구)

  • Choi, Young-Kyu;Lee, Young-Moo
    • Journal of the Semiconductor & Display Technology
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    • v.8 no.3
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    • pp.39-44
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    • 2009
  • Local Binary Pattern (LBP) is becoming a popular tool for various machine vision applications such as face recognition, classification and background subtraction. In this paper, we propose a new extension of LBP, called the Diagonal LBP (DLBP), to handle the image-based gender classification problem arise in interactive display systems. Instead of comparing neighbor pixels with the center pixel, DLBP generates codes by comparing a neighbor pixel with the diagonal pixel (the neighbor pixel in the opposite side). It can reduce by half the code length of LBP and consequently, can improve the computation complexity. The Support Vector Machine is utilized as the gender classifier, and the texture profile based on DLBP is adopted as the feature vector. Experimental results revealed that our approach based on the diagonal LPB is very efficient and can be utilized in various real-time pattern classification applications.

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Texture Classification Using Rotation Invariant Local Directional Pattern (Rotation Invariant Local Directional Pattern을 이용한 텍스처 분류 방법)

  • Lee, Tae Hwan;Chae, Ok Sam
    • Convergence Security Journal
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    • v.17 no.3
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    • pp.21-29
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    • 2017
  • Accurate encoding of local patterns is a very important factor in texture classification. However, LBP based methods w idely studied have fundamental problems that are vulnerable to noise. Recently, LDP method using edge response and dire ction information was proposed in facial expression recognition. LDP is more robust to noise than LBP and can accommod ate more information in it's pattern code, but it has drawbacks that it is sensitive to rotation transforms that are critical to texture classification. In this paper, we propose a new local pattern coding method called Rotation Invariant Local Direc tional Pattern, which combines rotation-invariant transform to LDP. To prove the texture classification performance of the proposed method in this paper, texture classification was performed on the widely used UIUC and CUReT datasets. As a result, the proposed RILDP method showed better performance than the existing methods.

Approximate Pattern Classification with Rough set (Rough 집합을 이용한 근사 패턴 분류)

  • 최성혜;정환묵
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.11a
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    • pp.248-251
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    • 1997
  • In this paper, We propose the concept of approximate Classification in the field of two group discriminan analysis. In our approach, an attribute space is divided into three subspaces. Two subspaces are for given two group and one subspace is for a boundary area between the two groups. We propose Approximate Pattern Classification with Rough set. We also propose learning procedures of neural networks for approximate classification. We propose two weighting methods which lead to possibility analysis and necessity analysis. We illustrate the proposed methods by numerical examples.

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Classification of Welding Defects in Austenitic Stainless Steel by Neural Pattern Recognition of Ultrasonic Signal (초음파신호의 신경망 형상인식법을 이용한 오스테나이트 스테인레스강의 용접부결함 분류에 관한 연구)

  • Lee, Gang-Yong;Kim, Jun-Seop
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.20 no.4
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    • pp.1309-1319
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    • 1996
  • The research for the classification of the natural defects in welding zone is performd using the neuro-pattern recognition technology. The signal pattern recognition package including the user's defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection, The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian calssifier are compared and discussed. The neuro-pattern recognition technique is applied to the classificaiton of such natural defects as root crack, incomplete penetration, lack of fusion, slag inclusion, porosity, etc. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the natural welding defects.

Brain activation pattern and functional connectivity network during classification on the living organisms

  • Byeon, Jung-Ho;Lee, Jun-Ki;Kwon, Yong-Ju
    • Journal of The Korean Association For Science Education
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    • v.29 no.7
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    • pp.751-758
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    • 2009
  • The purpose of this study was to investigate brain activation pattern and functional connectivity network during classification on the biological phenomena. Twenty six right-handed healthy science teachers volunteered to be in the present study. To investigate participants' brain activities during the tasks, 3.0T fMRI system with the block experimental-design was used to measure BOLD signals of their brain. According to the analyzed data, superior, middle and inferior frontal gyrus, superior and inferior parietal lobule, fusiform gyrus, lingual gyrus, and bilateral cerebellum were significantly activated during participants' carrying-out classification. The network model was consisting of six nodes (ROIs) and its fourteen connections. These results suggested the notion that the activation and connections of these regions mean that classification is consist of two sub-network systems (top-down and bottom-up related) and it functioning reciprocally. These results enable the examination of the scientific classification process from the cognitive neuroscience perspective, and may be used as basic materials for developing a teaching-learning program for scientific classification such as brain-based science education curriculum in the science classrooms.

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.

Classficiation of Bupleuri Radix according to Geographical Origins using Near Infrared Spectroscopy (NIRS) Combined with Supervised Pattern Recognition

  • Lee, Dong Young;Kang, Kyo Bin;Kim, Jina;Kim, Hyo Jin;Sung, Sang Hyun
    • Natural Product Sciences
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    • v.24 no.3
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    • pp.164-170
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    • 2018
  • Rapid geographical classification of Bupleuri Radix is important in quality control. In this study, near infrared spectroscopy (NIRS) combined with supervised pattern recognition was attempted to classify Bupleuri Radix according to geographical origins. Three supervised pattern recognitions methods, partial least square discriminant analysis (PLS-DA), quadratic discriminant analysis (QDA) and radial basis function support vector machine (RBF-SVM), were performed to establish the classification models. The QDA and RBF-SVM models were performed based on principal component analysis (PCA). The number of principal components (PCs) was optimized by cross-validation in the model. The results showed that the performance of the QDA model is the optimum among the three models. The optimized QDA model was obtained when 7 PCs were used; the classification rates of the QDA model in the training and test sets are 97.8% and 95.2% respectively. The overall results showed that NIRS combined with supervised pattern recognition could be applied to classify Bupleuri Radix according to geographical origin.

A Study on the Digital Signal Processing for the Pattern fiecognition of Weld Flaws (용접결함의 패턴인식을 위한 디지털 신호처리에 관한 연구)

  • 김재열;송찬일;김병현
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1995.10a
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    • pp.393-396
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    • 1995
  • In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

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