• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.103-108
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• 2005

Loading/Unloading task in the real industry is performed by crane, but most of the loading/unloading task with the weight of 5kg∼30kg is done by human workers and this kind of work causes industrial disaster of workers. Therefore it is necessary to develop low cost loading/unloading manipulator system to prevent this kind of industrial accidents. This paper is concerned with the design and fabrication of 2 axis pneumatic manipulators using on/off solenoid valves and accurate position control without respect to the external load and low damping in the pneumatic rotary actuator. To overcome the change of external load, switching of control parameter using LVQNN (Learning Vector Quantization Neural Network) is newly applied, which estimates the external loads in the pneumatic cylinder. As an underlying controller, a state feedback controller using position, velocity and acceleration is applied to the switching control system. The effectiveness of the proposed control algorithms are demonstrated through experiments of pneumatic cylinder with various loads.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.15-25
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• 2002

This paper proposes a vehicle license plate recognition system, which has relatively a simple structure and is highly tolerant of noise, by using the DCT(Discrete Cosine Transform) coefficients extracted from the character region of a license plate and the LVQ(Learning Vector Quantization) neural network. The image of a license plate is taken from a captured vehicle image based on RGB color information, and the character region is derived by the histogram of the license plate and the relative position of individual characters in the plate. The feature vector obtained by the DCT of extracted character region is utilized as an input to the LVQ neural classifier fur the recognition process. In the experiment, 109 vehicle images captured under various types of circumstances were tested with the proposed method, and the relatively high extraction rate of license plates and recognition rate were achieved.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.37
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• pp.41-51
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• 1996

This paper evaluates the clustering performance of a neural network and a statistical method. Algorithms which are used in this paper are the GLVQ(Generalized Learning vector Quantization) for a neural method and the k-means algorithm fer a statistical clustering method. For comparison of two methods, we calculate the Rand's c statistics. As a result, the mean of c value obtained with the GLVQ is higher than that obtained with the k-means algorithm, while standard deviation of c value is lower. Experimental data sets were the Fisher's IRIS data and patterns extracted from handwritten numerals.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.1
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• pp.31-36
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• 2000

To enhance the fault diagnosis ability for the dissolved gas analysis(DGA) of the power transformer, this paper proposes a learning vector quantization(LVQ) for the incipient fault recognition. LVQ is suitable expecially for pattern recognition such as fault diagnosis of power transformer using DGA because it improves the performance of Kohonen neural network by placing emphasis on the classification around the decision boundary. The capabilities of the proposed diagnosis system for the transformer DGA decision support have been extensively verified through the practical test data collected from Korea Electrical Power Corporation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.31-34
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• 2000

코호넨 자기조직 신경망을 사용하면 클러스터링뿐만 아니라 그 데이터가 할당된 클러스터의 대표값(Centroid)과의 거리 차이(Quantization Error)를 알아볼 수 있다 이를 이용하면 어떤 데이터가 정상적인 분포를 따르는지 정상적인 분포에서 벗어나는 비정상적인 데이터인지 알 수 있고, 유닉스 시스템 사용자의 명령어 사용 패턴에 적용하여 어떤 사용자의 명령어 사용 패턴이 정상적인 것인지 비정상적인 것인지 알 수 있다. 본 논문에서는 유닉스 시스템 사용자 8명의 명령어 패턴을 클러스터링한 후 Quantization Error를 이용하여 비정상 패턴을 탐지하는 오프라인에서의 비정상 행위를 탐지하는 시스템을 구현하였다. 그리고 통계적인 학습 방법을 적용한 비정상 패턴 탐지와의 비교를 통하여 두 가지 비정상 패턴 탐지 결과가 동일함을 확인하였다.

• 유체기계공업학회:학술대회논문집
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• 1999.12a
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• pp.317-323
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• 1999

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal vibration diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised teaming algorithm is used to improve the quality of the classifier decision regions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.742-745
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• 2009

This paper proposed indoor location recognition method based on RSSI (received signal strength indication) using the LVQ (Learning Vector Quantization) network. The LVQ inputs are the RSSI values measured by the fixed reference nodes and the output are the spatial sections. In order to verify the effectiveness of the proposed method, we performed experiments, and then compared to the conventional triangularity measurement method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.662-663
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• 2020

최근 MPEG 에서는 인공신경망 모델을 다양한 딥러닝 프레임워크에서 상호운용 가능한 포맷으로 압축 표현할 수 있는 NNR(Compression of Neural Network for Multimedia Content Description and Analysis) 표준화를 진행하고 있다. 본 논문에서는 MPEG-NNR 에서 CNN 모델을 압축하기 위한 지역 비선형 양자화(Local Non-linear Quantization: LNQ) 기법을 제시한다. 제안하는 LNQ 는 균일 양자화된 CNN 모델의 각 계층의 가중치 행렬 블록 단위로 추가적인 비선형 양자화를 적용한다. 또한, 제안된 LNQ 는 가지치기(pruning)된 모델의 경우 블록내의 영(zero) 값의 가중치들은 그대로 전송하고 영이 아닌 가중치만을 이진 군집화를 적용한다. 제안 기법은 음성 분류를 위한 CNN 모델(DCASE Task)의 압축 실험에서 기존 균일 양자화를 대비 동일한 분류 성능에서 약 1.78 배 압축 성능 향상이 있음을 확인하였다.

• Journal of Biosystems Engineering
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• v.32 no.3
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• pp.160-165
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• 2007

This study was conducted to evaluate beef using a color machine vision system. The machine vision system has an advantage to measure larger area than a colorimeter and also could measure other quality factors like distribution of fats. However, the machine vision measurement is affected by system components. To measure the beef color with the machine vision system, the effect of color balancing control was tested and calibration model was developed. Neural network for color calibration which learned reference color patches showed a high correlation with colorimeter in L*a*b* coordinates and had an adaptability at various measurement environments. The trained network showed a very high correlation with the colorimeter when measuring beef color.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.211-218
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• 1994

Stochastic computation employs random pulse streams to represent numbers. In this paper, we study a new method to implement the number system which uses the ratio of the numbers of ones and zeros in the pulse streams. In this number system. if P is the probability that a pulse is one in a pulse stream then the number X represented by the pulse stream is defined as P/(1-P). We propose circuits to implement the basic operations such as addition multiplication and sigmoid function with this number system and examine the error characteristics of such operations in stochastic computation. We also propose a neuron model and derive a learning algorithm based on backpropagation for the 3-layered feedforward neural networks. We apply this learning algorithm to a digit recognition problem. To analyze the results, we discuss the errors due to the variance of the random pulse streams and the quantization noise of finite length register.