• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.347-350
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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 is 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. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1287-1292
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• 2005

This paper presents an effective pattern classification model by designing an artificial neural network based pattern classifiers for face recognition. First, a RGB image inputted from a frame grabber is converted into a HSV image which is similar to the human beings' vision system. Then, the coarse facial region is extracted using the hue(H) and saturation(S) components except intensity(V) component which is sensitive to the environmental illumination. Next, the fine facial region extraction process is performed by matching with the edge and gray based templates. To make a light-invariant and qualified facial image, histogram equalization and intensity compensation processing using illumination plane are performed. The finally extracted and enhanced facial images are used for training the pattern classification models. The proposed H-ART2 model which has the hierarchical ART2 layers and F-LVQ model which is optimized by fuzzy membership make it possible to classify facial patterns by optimizing relations of clusters and searching clustered reference patterns effectively. Experimental results show that the proposed face recognition system is as good as the SVM model which is famous for face recognition field in recognition rate and even better in classification speed. Moreover high recognition rate could be acquired by combining the proposed neural classification models.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.640-643
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• 2003

A Walsh function based associative memory is capable of storing m patterns in a single pattern storage space with Walsh encoding of each pattern. Furthermore, each stored pattern can be matched against the stored patterns extremely fast using algorithmic parallel processing. As such, this special type of memory is ideal for real-time processing of large scale information. However this incredible efficiency generates large amount of crosstalk between stored patterns that incurs mis-recognition. This crosstalk is a function of the set of different sequencies [number of zero crossings] of the Walsh function associated with each pattern to be stored. This sequency set is thus optimized in this paper to minimize mis-recognition, as well as to maximize memory saying. In this paper, this Walsh memory has been applied to the problem of face recognition, where PCA is applied to dimensionality reduction. The maximum Walsh spectral component and genetic algorithm (GA) are applied to determine the optimal Walsh function set to be associated with the data to be stored. The experimental results indicate that the proposed methods provide a novel and robust technology to achieve an error-free, real-time, and memory-saving recognition of large scale patterns.

• 제어로봇시스템학회논문지
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• 제11권12호
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• pp.991-995
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• 2005

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 health, environment and habitat monitoring, military, 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 modern teaming 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 haying the capability of simple processing and wireless communication. The proposed system is able to perform classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to hand gesture recognition system.

• 한국정밀공학회지
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• 제16권12호
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• pp.119-125
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• 1999

This paper gives an overview of the various approaches to designing statistical pattern recognition scheme based on Bayes discrimination rule and the artificial neural networks for rotating machine condition classification. Concerning to Bayes discrimination rule, this paper contains the linear discrimination rule applied to classification into several multivariate normal distributions with common covariance matrices, the quadratic discrimination rule under different covariance matrices. Also we discribes k-nearest neighbor method to directly estimate a posterior probability of each class. Five features are extracted in time domain vibration signals. Employing these five features, statistical pattern classifier and neural networks have been established to detect defects on rotating machine. Four different cases of rotation machine were observed. The effects of k number and neural networks structures on monitoring performance have also been investigated. For the comparison of diagnosis performance of these two method, their recognition success rates are calculated form the test data. The result of experiment which classifies the rotating machine conditions using each method presents that the neural networks shows the highest recognition rate.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

• 대한전자공학회논문지
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• 제13권1호
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• pp.23-30
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• 1976

이 논문에서는 위상회전에 의한 오목구조의 짐출로서 필기체 한글을 인식하는 한 방법을 검토한다. 문자 Pattern를 오목구조적인 기본 Segment로 분해하여 집합으로 분류하고, 그들 집함에 대한 각 Segment의 폐상태와 위상특징을 logic으로 표현한다. 다음 그들 logic pattern의 위상회전으로서 오목구조의 topological성질과 위상특징을 검출하여 문자를 결정한다. 이 방법은 필기체의 변화와 문자의 대소, 경사 띤 위치 변위에 대한 식별의 유연성을 가지며, 인식율이 높다. In this paper, a method is proposed for the recognition of hand-written Hangeul. This is peiformed by extraction of the concave structural segments by phase rotation. Character patterns can be decomposed into the fundamental concave structural segments which are also categorized into segment sects, and the closure and phase features of each segment in set is represented by logics. By rotating the logic pattern, the topological and phase features of segment are extracted for the reliable recognition of the character. It is also evaluated that this method applies to a wide variety of shape, position and declination of the character.

• 한국지능시스템학회논문지
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• 제19권1호
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• pp.54-61
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• 2009

본 논문에서는 비전 패턴인식 알고리즘인 시공간적 계층 메모리 학습 알고리즘을 이용한 새로운 근전도 패턴인식 방법을 제시한다. 효율적인 근전도 신호의 학습과 분류를 위하여 단순화된 2 레벨의 공간적 집합, 시간적 집합, 그리고 관리 맵퍼를 이용한 수정된 시공간적 계층 메모리 학습 알고리즘을 제안한다. 인식 성능을 향상시키기 위해서 관리 맵퍼 학습뿐만 아니라 시간적 집합 학습에도 카테고리 정보를 사용한다. 실험을 통하여 열 가지 손동작이 성공적으로 인식됨을 검증한다.

• 조명전기설비학회논문지
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• 제17권1호
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• pp.31-36
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• 2003

본 논문은 GIS내 파티클에 의해 발생한 부분방전 신호에 대한 정량적 분석 및 상태에 따른 패턴인식에 관하여 설명하였다. GIS내 파티클의 상태를 4가지로 모의하여 각각의 경우에 부분방전 신호를 계측한 후 Ф-Q-N분포로 나타내었고, 다시 Ф-Q분포, Ф-Qm분포, Ф-N분포, Ф-N분포로 나타내었다. 각각의 분포는 통계적 연산자에 의해 정량화 하여 분석하였고, 또한 연산자들을 패턴인식을 위한 입력데이터로 이용하여 수행하였다. 그 결과 파티클의 상태에 따른 분포 형태는 파티클의 상태에 따라 서로 다른 특성을 나타냈었으며, 또한 뉴럴 네트웍을 이용한 패턴 인식의 결과는 약92〔%〕였으며 연산자들의 입력 데이터가 많을수록 더 정확한 결과가 나타났다.