• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.489-492
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• 2007

산업재산권중 하나인 상표에 대한 효율적인 이미지 검색은 상표도용 및 이로 인한 분쟁을 방지할 수 있다. 이를 위해서는 효율적인 내용기반 유사이미지 검색이 필요하다. 본 논문에서는 상표이미지검색에 있어 가시적인 특성(visual feature)을 그레이 히스토그램을 통해서 상표이미지의 특성값을 추출하여 이를 입력패턴으로 SOM(Self-Organizing Map)알고리즘을 적용한 내용기반 유사이미지 검색시스템을 제안한다.

• Journal of Technology Innovation
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• v.13 no.2
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• pp.223-237
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• 2005

This paper explains that tacit knowledge is a critical component for the success of government-led R&D project selection, where rapid and accurate decision making need to be made under lack of information circumstances. It also explores ways to fully exploit the tacit knowledge of experts participating in the Korean government's R&D project selection process. Some of these include: (1) strategic attention from the top officials, (2) forming self-organizing teams, (3) establishing a horizontal and risk-taking culture, (4) encouraging a sense of responsibility in creating and sharing tacit knowledge, and (5) providing a seamless monitoring system and training.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.660-665
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• 1993

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD ( an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very fast training and processing implementation required for real time control.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.499-502
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• 1996

In this paper, we propose a fuzzy iterative learning controller(FILC). It can control fully unknown dynamic plants through iterative learning. To design learning controllers based on the steepest descent method, it is one of the difficult problems to identify the change of plant output with respect to the change of control input(.part.e/.part.u). To solve this problem, we propose a method as follows: first, calculate .part.e/.part.u using a similarity measure and information in consecutive time steps, then adjust the fuzzy logic controller(FLC) using the sign of .part.e/.part..u. As learning process is iterated, the value of .part.e/.part.u is reinforced. Proposed FILC has the simple architecture compared with previous other controllers. Computer simulations for an inverted pendulum system were conducted to verify the performance of the proposed FILC.

• International Journal of Ocean System Engineering
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• v.2 no.1
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• pp.57-61
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• 2012

In this paper we use the Kohonen neural network based Self Organizing Map (SOM) algorithm for Urdu Character Recognition. Kohenen NN have more efficient in terms of performance as compare to other approaches. Classification is used to recognize hand written Urdu character. The number of possible unknown character is reducing by pre-classification with respect to subset of the total character set. So the proposed algorithm is attempt to group similar character. Members of pre-classified group are further analyzed using a statistical classifier for final recognition. A recognition rate of around 79.9% was achieved for the first choice and more than 98.5% for the top three choices. The result of this paper shows that the proposed Kohonen SOM algorithm yields promising output and feasible with other existing techniques.

• Journal of Information Technology Applications and Management
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• v.15 no.3
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• pp.43-59
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• 2008

This paper studies small and medium sized financial agency's management states that take advantage of the Korea Federation of Saving Bank's data. It also presents the management state and the decision-making model that monitors progressive insolvency by standardizing transfer path between relevant groups. With this in mind, we extracted explanatory variables for predictions of insolvency by using existing studies of document related insolvency. First of all, we designed a state model based on demarcated groups to take advantage of the self organizing map that groups in line with a neural network. Secondly, we developed a transition model by standardizing the transfer path between individual banks in a state model. Finally, we presented a decision-making model that integrated the state model and the transition model. This paper will provide groundwork for methods of insolvency prevention to businesses in order for them to have a smooth management system in the financial agencies.

• Research in Mathematical Education
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• v.12 no.2
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• pp.143-149
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• 2008

Before the 14th century, China had been thought as one of the countries with the most developed mathematics all along. But after the 16th century, Chinese mathematics increasingly walked up to the eclipse. The main reasons include the following points. First, the development of neoteric mathematics was closely associated with the social industrialization, but the lags in feudal China seriously blocked the development of the capitalistic seed, and China was still in the agricultural society then and couldn't step into the industrial society, which impeded the development of mathematics concerned with the industry and commerce. Second. the increasingly carrion feudalization was one of the essential reasons to block the development of Chinese neoteric mathematics. Finally, seeing about the developing logics of Chinese neoteric mathematics, we can find it was a scattered and experiential mathematical knowledge without strict and rational self-organizing structure system, which had the limitations existing in its interior mechanism.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.1180-1186
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• 2007

This paper contains that the current emergency response procedure through the railroad accident type at domestic and foreign, and the response program which is more systematic as a result of improving weaknesses. Because the response program at home is less sufficient than developed country's, we have made an investigation into system of classification in America, British, Germany, Japan for organizing separate emergency response code. we have researched the recognition response of five senses for minimizing the accident damage in a way of spreading the emergency response type analyzed through the accident procedure according to the feature of five senses by the first recognizer. Based on the first recognitions response of five senses, the scenario has been established according to each types and the criteria of accident has been researched through classifying the recognition of five senses response level, early response level, self-response level, outside support level.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1133-1138
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• 2000

This paper describes a signal recognition method for diagnosing the rotating machinery using wavelet-aided Self-Organizing Feature Map(SOFM). The SOFM specialized from neural network is a new and effective algorithm for interpreting large and complex data sets. It converts high-dimensional data items into simple order relationships with low dimension. Additionally the Learning Vector Quantization(LVQ) is used for reducing the error from SOFM. Multi-resolution and wavelet transform are used to extract salient features from the primary vibration signals. Since it decomposes the raw timebase signal into two respective parts in the time space and frequency domain, it does not lose either information unlike Fourier transform. This paper is focused on the development of advanced signal classifier in order to automatize vibration signal pattern recognition. This method is verified by the experiment and several abnormal vibrations such as unbalance and rubbing are classified with high flexibility and reliability by the proposed methods.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.171-175
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• 2000

Neural networks are in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.