• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1737-1744
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• 2004

In the compression methods widely used today, the image compression by VQ is the most popular and shows a good data compression ratio. Almost all the methods by VQ use the LBG algorithm that reads the entire image several times and moves code vectors into optimal position in each step. This complexity of algorithm requires considerable amount of time to execute. To overcome this time consuming constraint, we propose an enhanced self-organizing neural network for color images. VQ is an image coding technique that shows high data compression ratio. In this study, we improved the competitive learning method by employing three methods for the generation of codebook. The results demonstrated that compression ratio by the proposed method was improved to a greater degree compared to the SOM in neural networks.

• Electrical & Electronic Materials
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• v.9 no.8
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• pp.825-830
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• 1996

In this study, Characteristics of Ultrasonic Transducer fabricated with porous piezoelectric resonator, 3-D underwater object restoration using the self made ultrasonic transducer and modified SCL(Simple Competitive Learning) neural network are investigated. The self-made transducer was satisfied the required condition of ultrasonic transducer in water, and the modified SCL neural network using the acquired object data 16*16 low resolution image was used for object restoration of $32{\times}32$ high resolution image. The experimental results have shown that the ultrasonic transducer fabricated with porous piezoelectric resonator could be applied for SONAR system.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.23-25
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• 1993

This paper presents a design technique of self-organizing fuzzy controller using a learning method of fuzzy inference rule by a gradient method for load frequency control of power system. The membership functions in antecedent part and in consequent part of fuzzy inference rules are tuned by the gradient method. The related simulation results show that the proposed fuzzy controller are more powerful than the conventional ones for reduction of undershoot and deviation of load frequency in steady-state, and for minimization of settling time.

• Journal of the Korean earth science society
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• v.27 no.7
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• pp.705-714
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• 2006

The purpose of this study was to explore the elementary students' perceptions of Earth systems and environmental issues. A survey was conducted to determine the students' perceptions on the following aveas: (1) the concepts of certainty and tangibility, (2) self-reported knowledge level, (3) perceived danger level of selected eight Earth systems and environmental issues, and (4) their primary information source on these issues. Results indicated that ozone hole, acid rain, El $Ni\widetilde{n}o$, and global warming were identified by the students as uncertain and intangible issues. Perceived certainty and perceived tangibility were highly positively correlated with self-reported knowledge compared to other relationships. The results also showed that learning from school was the most frequent information source for environmental issues. The second most frequently used source of information was television among several mass media sources. It is hoped that this study contributes to understanding the elementary school students' perceptions toward the selected Earth systems and environmental issues.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.103.1-103
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• 2002

In variable structure control algorithms, The control law used to realized the desired sliding mode dynamics is discontinuous on the switching manifold. However, due to imperfections in switching, such as time delays, the system trajectory chatters instead of sliding along the switching manifold. This chattering is undesirable because it may excite unmodeled high frequency dynamics in the physical system. In this paper, to overcome this drawback a self-organizing fuzzy sliding mode control algorithm using gradient descent method is proposed. The proposed method has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of disturbance, parameter variations and uncertainties ill the sliding mode. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum system. The results show that both alleviation of chattering and performance are achieved.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.6
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• pp.1156-1169
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• 1989

본 연구에서는 용접도중 발생할 수 있는 용접질 저항요인을 전극분리현상을 측정하여 파악하고 용접 열입력에 해당하는 용접전류를 학습제어방식(self-learning control)에 의하여 컴퓨터와 주변기기(interface)를 통해 조절함으로서 요구되는 균일한 용접질이 항상 보장되도록 하였다. 여기서 학습제어방식을 태택한 이유는 제어하고자 하는 대상의 동적 모델(dynamic model)이 없어도 제어기 이득의 선정이 비교적 자유롭고 용접 제어장치가 자체적으로 감지(monitoring)한 신호로 판단하여 제어동작을 취함으로서 용접시 축적되는 정보(data)가 용접기에 일종의 지능을 부여할 수 있어서 진보된 개념의 용접제어장치 개발의 가능성을 검토해 보기 위함이다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.316-318
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• 2001

본 논문에서는 자기조직화 형상지도(Self-organizing Feature Maps)를 사용하여 움직이는 물체에 대해 움직임의 특성을 자동으로 분석하였다. Kohonen Network는 자기조직을 형성하는 unsupervised learning 알고리즘으로서, 이 논문에서는 생태계에서의 데이터를 Patternizing하고, Clustering 하는데 사용한다. 본 논문에서 Kohonen 신경망의 학습에 사용한 데이터는 CCD 카메라로 물고기의 움직임을 추적한 좌표 데이터이며, diazinon 0.1 ppm을 처리한 물고기 점 데이터와 처리하지 않은 점 데이터를 각각 낮.밤 약 10시간동안 수집하여, \circled1처리전 낮 데이터 \circled2처리전 밤 데이터 \circled3처리전 낮 데이터 \circled4처리후 밤 데이터 각각 4개의 group으로 분류한 후, Kohonen Network을 사용하여 물고기의 행동 차이를 분석하였다.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.609-611
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• 1997

In this paper, we propose the self-tuning of PID controller using diagonal recurrent neural networks. The characteristic of the proposed structure is on-line adaptive learning scheme in spite of variations of feedback, signals. Control performance is compared with that of neural network based PID controller which was proposed by Iwasa. Computer simulation results show that the proposed controller is effective in controlling of unknown nonlinear plants.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.163-171
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• 2006

This paper introduces the research progress on the artificial brain in the Telerobotics and Control Laboratory at KAIST. This series of studies is based on the assumption that it will be possible to develop an artificial intelligence by copying the mechanisms of the animal brain. Two important brain mechanisms are considered: spike-timing dependent plasticity and dopaminergic plasticity. Each mechanism is implemented in two coding paradigms: spike-codes and rate-codes. Spike-timing dependent plasticity is essential for self-organization in the brain. Dopamine neurons deliver reward signals and modify the synaptic efficacies in order to maximize the predicted reward. This paper addresses how artificial intelligence can emerge by the synergy between self-organization and reinforcement learning. For implementation issues, the rate codes of the brain mechanisms are developed to calculate the neuron dynamics efficiently.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.12
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• pp.35-44
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• 1994

In this paper, we propose a new modification method of the look-up table in self-organizing fuzzy control using look-up table. This method has the property that look-up table is modified to have fast response property. Its principle is that the controller forces the trajectory to go into the fast respose region which the error change amount is larger than the error at initial time whenever the reference or disturbance change. Also we introduce the variable learning speed coefficient which is proportional to distance from switching curve. And to demonstrate the applicability of the proposed method, we had simulation study for some examples and esecuted pole balance experiments with inverted pendulum.