• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.1046-1051
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• 1994

It is known that SOFM has the property of effectively creating topographically the organized map of various features on input signals, SOFM can effectively be applied to the recognition of Korean phonemes. However, is isn't guaranteed that the network is sufficiently learned in SOFM algorithm. In order to solve this problem, we propose the learning algorithm combined with the conventional K-means clustering algorithm in fine-tuning stage. To evaluate the proposed algorithm, we performed speaker dependent recognition experiment using six phoneme classes. Comparing the performances of the Kohonen's algorithm with a proposed algorithm, we prove that the proposed algorithm is better than the conventional SOFM algorithm.

• 한국환경교육학회지:환경교육
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• 제23권2호
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• pp.97-112
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• 2010

The study aims to comprehend the effect of the wetland ecology education on the elementary school students' changes of recognition about wetland through the wetland ecology map program. In this study, the literary research, the experimental research and the survey methods were operated. Through the literary research, the environmental factors were extracted, and the writing item of ecology map was reconstructed based on the literary research, so the experimental research was operated with the wetland ecology map program. Through four areas of test items such as the information and knowledge, values and attitudes, development and conservation, behavior and participation, and the analysis of children's study results, the effect of the wetland ecology map program on changes of recognition about wetland was verified quantitatively and qualitatively. Wetland ecology map program would be able to be an educational approach which can achieve the 'personalization of environment' setting up predictable environmental improvement goals and satisfying the needs of spatial information of the appropriate regions from the holistic perspective that students themselves plan and participate beyond a one-time experience program. Production of ecological map through continuous monitoring is expected to improve the possibility of subjective environmental actions by operating self-directed learning. Based on the conclusion of this study, we would suggest the following. For wetland ecology map program to be supplemented and utilized, the basic education of wetland should be organized in regular school curriculum, ecology map program including various teaching learning methods be prepared actively, and in future studies, studies of ecosystem-wide wetland ecology map program including animals like birds and fish are necessary.

• International journal of advanced smart convergence
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• 제8권4호
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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.

• 생태와환경
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• 제42권3호
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• pp.303-316
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• 2009

Benthic macroinvertebrate communities collected from eight different streams in South Korea were analyzed to compare community and biological indices across different levels of water pollution. The Self-Organizing Map (SOM) was utilized to provide overview on association of the proposed indices. The sample sites were accordingly clustered according to the gradient of pollution on the SOM. While the general trends of the indices were commonly observable according to different levels of pollution, the detailed differences among the indices were also illustrated on the SOM. The conventional diversity and evenness indices tended to be high even though the water quality state was poor representing relatively weak gradient at polluted sites, while the index presenting the saprobic degree such as family biotic index showed the stronger gradient at the polluted area and was robust to present the gradient. Our results also confirmed the general characterization of two indices: The Shannon index is more strengthened by the number of species occurring at the sample sites, while the Simpson index is more influenced by the degree of evenness among the species. The patterning based on the SOM was efficient in comparatively characterizing the proposed indices to present ecological states and water quality.

• 한국컴퓨터정보학회논문지
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• 제11권6호
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• pp.221-226
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• 2006

Ad Hoc 네트워크는 유선 네트워크와는 달리 이동성이 존재하는 노드들이 무선환경에서 상호 연결되어 네트워크를 구성한다. 이러한 형태는 라우팅과 서비스 운영환경에서 노드들의 단절 현상이 발생한다. 각 노드들의 위치 정보를 파악하면 이러한 현상을 회피하거나 복구할 수 있고, 일반적으로는 GPS를 이용하여 위치 정보를 파악하지만 구축과 관리가 복잡하고 고비용을 필요로 한다. 본 논문에서는 GPS를 사용하지 않고 전체 노드들이 가지고 있는 정보만을 이용하여, 이동 노드들의 전체적인 위치 정보를 나타내는 맵을 구성하는 방식을 제안하고 구현한다. 제안한 방식은 기존 시스템을 소프트웨어적으로 보완하므로 구축과 운영이 간단하고 구축비용을 절감할 수 있다.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 제11회 음성통신 및 신호처리 워크샵 논문집 (SCAS 11권 1호)
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• pp.407-410
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• 1994

In this paper, we describe the new unsuperivised learning algorithm, SASOM. It can solve the defects of the conventional SOM that the state of network can't converge to the minimum point. The proposed algorithm uses the object function which can evaluate the state of network in learning and adjusts the learning rate flexibly according to the evaluation of the object function. We implement the simulated annealing which is applied to the conventional network using the object function and the learning rate. Finally, the proposed algorithm can make the state of network converged to the global minimum. Using the two-dimensional input vectors with uniform distribution, we graphically compared the ordering ability of SOM with that of SASOM. We carried out the recognitioin on the new algorithm for all Korean phonemes and some continuous speech.

• 한국전기전자재료학회논문지
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• 제17권9호
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• pp.1006-1012
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• 2004

In this paper, neural networks is studied to apply as a PD source classification in XLPE power cable specimen. Two learning schemes are used to classification; BP(Back propagation algorithm), SOM(self organized map - kohonen network). As a PD source, using treeing discharge sources in the specimen, three defected models are made. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And a]so these distribution characteristics are applied to classify PD sources by two scheme of the neural networks. In conclusion, recognition efficiency of BP is superior to SOM.

• 통합자연과학논문집
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• 제3권1호
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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.

• 전자공학회논문지C
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• 제34C권1호
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.458-460
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• 2003

This paper proposes new algorithms for cloudy area detection by GHA (Generalized Hebbian Algorithm) and SOFM (Self-Organized Feature Map). SOFM and GHA are unsupervised neural networks and are used for pattern classification and shape detection of satellite image. Proposed algorithm is based on block based image processing that size is 16${\times}$16. Results of proposed algorithm shows good performance of cloudy area detection except blur cloudy area.