• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.162-169
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• 1999

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital Signal Processors. Digital signal processors DSPs. are micro-processors that are particularly developed for variables. Digital version of most advanced control algorithms can be defined as sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a biable computatinal tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for implementation of real-time control of robot system by the simulation and experiment.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.309-314
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.821-832
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• 1998

This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. Fuzzy neuron, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an adaptive learning method by which a membership function and fuzzy rules were adapted for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water supplied to the city of Kwangju. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supplied (b) the mean temperature, and (c)the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.35% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.723-732
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• 2001

In this paper, the neurofuzzy controller of optically guided AGV is proposed to improve the path-tracking performance A differential steered AGV has front-side and rear-side optical sensors, which can identify the guiding path. Due to the discontinuity of measured data in optical sensors, optically guided AGVs break away easily from the guiding path and path-tracking performance is being degraded. Whenever the On/Off signals in the optical sensors are generated discontinuously, the motion errors can be measured and updated. After sensing, the variation of motion errors can be estimated continuously by the dead reckoning method according to left/right wheel angular velocity. We define the estimated contour error as the sum of the measured contour in the sensing error and the estimated variation of contour error after sensing. The neurofuzzy system consists of incorporating fuzzy controller and neural network. The center and width of fuzzy membership functions are adaptively adjusted by back-propagation learning to minimize th estimated contour error. The proposed control system can be compared with the traditional fuzzy control and decision system in their network structure and learning ability. The proposed control strategy is experience through simulated model to check the performance.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.247-262
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• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.

• 한국체육학회지인문사회과학편
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• v.58 no.6
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• pp.449-459
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• 2019

The purpose of this study is to identify how postural mechanics affects postural control on balance and stability by using frequency analysis technique from the kinematic data acquired during the one leg standing posture. For this purpose, the experimental group consisted of two groups, the normal group (n=6) and the national Gymnastics group (n=6). Displacement data of CoP were analyzed by frequency analysis of rambling (RM) and trembling (TR) by FFT signal processing. As a results, there was a significant difference in evaluating the stabilization index between the two groups with the eyes open and closed one leg stnading (p <.05). The cause of the difference was found to be the output of the maximum amplitude of RM (f1) and TR (f2) (p <.05). In particular, in the low frequency RM of 8-9 Hz, which is a natural frequency of signal wave involved in postural feedback feedback, the main frequency appeared to be performs the exercise mechanism of stable brain posture control. And in the high frequency TM of 120-135 Hz, it is considered that the adaptation of the reflective muscle response is minimized to minimize posture shaking. In conclusion, this study provides evidence for the intrinsic main frequencies according to the postural control ability which affects the CNS in one leg standing.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2001.11a
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• pp.43-43
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• 2001

원료의 저급화와 공정의 폐쇄화가 급속히 진행되고 있는 현재의 국내 제지산업 현황을 고려할 때 고품질의 제품을 저렴하게 생산하기 위해서는 화학첨가제의 효과적 활용기술 확립과 이를 통한 초지공정 wet end의 성공적 제어기술이 요청된다. 특히 환경보전과 환 경관련 규제에 부응하기 위하여 초지공정의 무방류화가 점차 가속화되고 있는 현 시점에 서 wet end의 효율적 관리를 위해서는 그 동안 개방형 wet end에서 적용되던 개념의 공 정관리의 한계를 극복할 수 있는 기술 개발이 필요하다. 공정의 폐쇄화에 따른 습부화학적 문제를 해결하기 위해서는 백수의 재활용에 따른 지료 화학적 특성을 평가하고 고분자전해질의 거동을 분석해야 한다. 보류향상제 혹은 탈수촉 진제로서 첨가된 고분자전해질은 그 기능을 수행하기 위해서는 섬유에 흡착이 이루어져 야 하므로 백수로 제조된 지료 내에서 고분자전해질의 흡착 특성을 파악하는 것은 습부에 서 일어나는 현상들에 대한 이해 및 효율적인 공정제어를 위한 첫 단계라 할 수 있다. 본 연구에서는 실험실적으로 제조된 백수를 이용하여 조성된 지료 내에서 양이온성 구 아 검의 흡착현상을 분석하였다. 본 연구에서는 폐쇄화의 기준으로 폐쇄화 정도(Level of C Closure ; LC)에 따라 실험을 수행하였다. 여기에서 LC란 총 사용된 물의 양에 대한 지료조성 시 사용되는 백수의 양을 백분율로 나타낸 것이다. 양이온성 구아 검의 흡착을 평가하기 위해 PhenoVsulfuric acid spectrophotometric method를 이용하여 펄프 슬 러리 상등액에 존재하는 미흡착된 양이온성 구아 검의 함량을 측정하였고. 양이온성 구아 검 이 섬유상에 흡착하였을 때 나타나는 섬유의 S-potential 변화와 펄프 상등액의 양이 온 요구량 변화를 평가하였으며 이들의 상관관계를 분석하였다.축력으로 변형시키는데 비해 도침은 단순히 압축 압력만을 종이에 가하는 것이 다르다고 볼 수 있는데, 라 이너지와 백상지가 같은 조건하에서 왜 이러한 큰 차이를 보이는 이유를 아직 알수 없다.해 동일한 공정 데이터들올 이용하여 보편적으로 사용하는 통계기법 중의 하나인 주성분회귀분석을 실시하였다. 주성분 분석은 여러 개의 반응변수에 대하여 얻어진 다변량 자료의 다차원적인 변 수들을 축소, 요약하는 차원의 단순화와 더불어 서로 상관되어있는 반응변수들 상호간 의 복잡한 구조를 분석하는 기법이다. 본 발표에서는 공정 자료를 활용하여 인공신경망 과 주성분분석을 통해 공정 트러블의 발생에 영향 하는 인자들을 보다 현실적으로 추 정하고, 그 대책을 모색함으로써 이를 최소화할 수 있는 방안을 소개하고자 한다.금 빛 용사 둥과 같은 표면처리를 할 경우임의 소재 표면에 도금 및 용 사에 용이한 재료를 오버레이용접시킨 후 표면처리를 함으로써 보다 고품질의 표면층을 얻기위한 시도가 이루어지고 있다. 따라서 국내, 외의 오버레이 용접기술의 적용현황 및 대표적인 적용사례, 오버레이 용접기술 및 용접재료의 개발현황 둥을 중심으로 살펴봄으로서 아직 국내에서는 널리 알려지지 않은 본 기 술의 활용을 넓이고자 한다. within minimum time from beginning of the shutdown.및 12.36%, $101{\sim}200$일의 경우 12.78% 및 12.44%, 201일 이상의 경우 13.17% 및 11.30%로 201일 이상의 유기의 경우에만 대조구와 삭제 구간에 유의적인(p<0.05) 차이를 나타내었다.는 담수(淡水)에서 10%o의 해수(海水)로 이주된지 14일(日) 이후에 신장(腎臟)에서 수축된 것으로 나타났다. 30%o의 해수(海水)에 적응(適應)된 틸라피아의 평균 신사구체(腎絲球體)의 면적은 담수

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.331-338
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• 2022

Successor representation (SR) is a model of human reinforcement learning (RL) mimicking the underlying mechanism of hippocampal cells constructing cognitive maps. SR utilizes these learned features to adaptively respond to the frequent reward changes. In this paper, we evaluated the performance of SR under the context where changes in latent variables of environments trigger the reward structure changes. For a benchmark test, we adopted SR-Dyna, an integration of SR into goal-driven Dyna RL algorithm in the 2-stage Markov Decision Task (MDT) in which we can intentionally manipulate the latent variables - state transition uncertainty and goal-condition. To precisely investigate the characteristics of SR, we conducted the experiments while controlling each latent variable that affects the changes in reward structure. Evaluation results showed that SR-Dyna could learn to respond to the reward changes in relation to the changes in latent variables, but could not learn rapidly in that situation. This brings about the necessity to build more robust RL models that can rapidly learn to respond to the frequent changes in the environment in which latent variables and reward structure change at the same time.