• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.180-188
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• 1995

A modified Kohonen's self-organizing feature map (SOFM) algorithm which has binary reinforcement function and a constant learning rate is proposed. In contrast to the time-varing adaptaion gain of the original Kohonen's SOFM algorithm, the proposed algorithm uses a constant adaptation gain, and adds a binary reinforcement function in order to compensate for the lowered learning ability of SOFM due to the constant learning rate. Since the proposed algorithm does not have the complicated multiplication, it's digital hardware implementation is much easier than that of the original SOFM.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.6-42
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• 2002

Kohonen's self organizing feature map (SOFM) converts arbitrary dimensional patterns into one or two dimensional arrays of nodes. Among the many competitive learning algorithms, SOFM proposed by Kohonen is considered to be powerful in the sense that it not only clusters the input pattern adaptively but also organize the output node topologically. SOFM is usually used for a preprocessor or cluster. It can perform dimensional reduction of input patterns and obtain a topology-preserving map that preserves neighborhood relations of the input patterns. The traditional SOFM algorithm[1] is a competitive learning neural network that maps inputs to discrete points that are called nodes on a lattice...

• Journal of Integrative Natural Science
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• v.3 no.1
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• pp.38-42
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• 2010

인간은 정보전달을 위하여 언어 이외에 동작, 표정과 같은 비언어적인 수단을 이용한다. 이러한 비언어적인 수단을 정확히 분석 할 수 있다면 인간과 컴퓨터간의 자연스럽고 지적인 인터페이스를 구축할 수 있게 된다. 본 논문은 별도의 센서를 부착하지 않은 단일 카메라 환경에서 손 형상을 입력정보로 사용하여 손 영역만을 분할한 후 자기 조직화 특징 지도(SOFM: Self Organized Feature Map) 신경망 알고리즘을 이용하여 손 형상을 인식함으로서 수화인식을 위한 보다 안정적이며 강인한 인식 시스템을 구현하고자 한다. 제안 방법으로는 피부색 정보를 이용하여 배경으로부터 손 영역만을 추출한 후 추출된 손 영역의 형상을 인식한다(전처리과정으로 모델이미지의 사이즈와 압축 및 컬러에 대한 정보를 정규화 시켰다). 또한 인식 효율을 높이기 위해 SOFM 신경망 알고리즘을 적용함으로서 보다 안정적으로 손 형상을 인식할 수 있게 되었으며, 손 형상 인식률에 대한 안전성과 정확성을 향상시킬 수 있었다. 그리고 인식된 손 형상의 의미를 텍스트로 보여줌으로서 사용자의 의사를 정확하게 전달할 수 있다.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.473-479
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• 2006

A global path planning algorithm using modified self-organizing feature map(SOFM) which is a method among a number of neural network is presented. The SOFM uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 2-dimensional mesh, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.1
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• pp.59-64
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• 1997

By using a selective learnable self-organizing feature map(SOFM) a more practical and generalized mehtod is proposed in which the effective nonlinear shape restoration is possible regardless of the existence of the distortion modelss. Nonlinear mapping relation is extracted from the distorted imate by using the proposed selective learning SOFGM which has the special property of effectively creating spatially organized internal representations and nonlinear relations of various input signals. For the exact extraction of the mapping relations between the distorted image and the original one, we define a disparity index as a proximal nmeasure of the present state to the final idealy trained state of the SOFM, and we used this index to adjust the training of the mapping relations form the weights of the SOFM. Simulations are conducted on various kinds of distorted images with or without distortion models, and the results show that the proposed method is very efficeint very efficient and practical in nonlinear shape restorations.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1380-1384
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• 2010

현재 환경부에서는 수질오염총량관리제를 위하여 각 단위유역의 말단지점에서 8일 간격으로 수질 및 유량을 측정하고 있으며, 이 자료들을 공개하고 있다. 이러한 양질의 자료의 활용성을 제고하기 위해서는 무엇보다도 자료의 분석을 위한 다양한 기법이 개발되고 제안되어야 한다. 따라서 본 연구에서는 수질 및 유량자료를 동시에 적용하여 두 자료 사이의 관계를 조사하고 특성을 파악하기 위하여 자기조직화 특성지도(Self-Organizing Feature Map: SOFM) 이론을 적용하였다. 시행착오법에 의해 적정한 SOFM 구조를 결정하였으며, 그 결과 $4{\times}4$ 구조의 육각형 배열을 갖는 구조를 이용하였다. SOFM에 의해 분류된 3개의 패턴 중 패턴-1은 유량자료의 크기에 의해 분류되었고, 패턴-2와 패턴-3은 BOD 농도의 크기에 따라 분류된 것으로 파악되었다. 따라서 SOFM의 적용에 의한 자료의 분류를 수행하고, 그 분류기준을 파악할 경우 SOFM의 자료 분석 도구로서의 활용성이 더욱 높아질 것으로 판단된다.

• 한국정보통신설비학회:학술대회논문집
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• 2005.08a
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• pp.345-348
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• 2005

본 논문은 무선 센서 네트워크에서 SOFM을 이용하여 센서 노드를 배치하는 방법을 제안한다. 제안한 방식은 특정 공간에서 센서 노드의 밀도가 일정하도록 SOFM을 이용하여 센서 노드를 배치시킨다. 시뮬레이션으로 최적의 위치를 탐색하고, 그 위치에 무선 센서 노드를 설치하여 제안한 방식의 성능을 검토하였다.

• Journal of KIISE:Software and Applications
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• v.29 no.7
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• pp.425-435
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• 2002

This paper introduces a macroblock-based rate control algorithm using the neural classifier based in Self Organization feature Maps (SOFM). In contrast to the conventional rate control methods based on the mathematical rate distortion (RD) model and the feedback regression, proposed method can actively adapt to the rapid-varying image characteristics by establishing the global model for bitrate control and by using the SOFM based neural classifier to manage that model. Proposed rate control algorithm has 0.2 dB ~ 0.6 dB better performances than MPEG-4 macroblock-based rate control algorithm by evaluating with the encoded Peak Signal to Noise Ratios while maintaining similar overall computational complexity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.430-435
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• 2007

In this paper, we propose an intelligent deployment of sensor network for reliable communication. The proposed method determines optimal transmission range based on the wireless channel characteristics, and searches the optimal number of sensor nodes, and optimal locations with SOFM. We calculate PRR against a distance uses the log-normal path loss model, and decide the communication range of sensor node from PRR. In order to verify the effectiveness of the proposed method, we performed simulations on the searching for intelligent deployment and checking for link condition of sensor network.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.