• Journal of information and communication convergence engineering
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• 제6권4호
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• pp.454-458
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• 2008

It can improve human being's life quality that all people can have more convenient medical service under pervasive computing environment. For a pervasive health care application for diabetes patient, we've implemented a health care system, which is composed of three parts. Various sensors monitor both outer and inner environment of human such as temperature, blood pressure, pulse, and glycemic index, etc. These sensors form zigbee based sensor network. And medical information server accumulates sensing values and performs back-end processing. To simply transfer these sensing values to a medical team is a low level's medical service. So, we've designed a new service model based on back propagation neural network for more improved medical service. Our experiments show that a proposed healthcare system can give high level's medical service because it can recognize human's context more concretely.

• Journal of Computing Science and Engineering
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• 제9권2호
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• pp.98-107
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• 2015

Hierarchical constraints and constraint satisfaction were analyzed in order to solve the problem of conflict detection in collaborative design. The constraints were divided into two sets: one set consisted of known constraints and the other of unknown constraints. The constraints of the two sets were detected with corresponding methods. The set of the known constraints was detected using an interval propagation algorithm, a back propagation (BP) neural network was proposed to detect the set with the unknown constraints. An immune algorithm (IA) was utilized to optimize the weights and the thresholds of the BP neural network, and the steps were designed for the optimization process. The results of the simulation indicated that the BP neural network that was optimized by IA has a better performance in terms of convergent speed and global searching ability than a genetic algorithm. The constraints were described using the eXtensible Markup Language (XML) for computers to be able to automatically recognize and establish the constraint network. The implementation of the conflict detection system was designed based on constraint satisfaction. A wind planetary gear train is taken as an example of collaborative design with a conflict detection system.

• 전력전자학회논문지
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• 제3권3호
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• pp.214-221
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• 1998

직접 구동용 브러시 없는 직류전동기(BRUSHLESS direct drive motor : BLDD motor)의 강인한 위치제어를 위해 신경망을 사용하여 접근하는 새로운 제어방식이 소개된다. 전향 신경망이 추가된 선형 2차 제어기는 AC서보의 객체지향 방법을 사용함으로서 대략적으로 선형화 되어지는 강인한 BLDD 모터 시스템을 얻기 위해 사용된다. 구동 상태의 온-라인 위상에서 학습되는 이 신경망은 전향신호와 오차 역 전파법(Back-Propagation Method)에 의해 구성된다. 총 노드의 수가 8개이기 때문에 이 시스템은 일반적인 마이크로 프로세서에 의해 쉽게 실현될 수 있다. 일반적인 작동중, 입출력 응답은 표본화되어지고 가중치는 매개변수 또는 부하 토크의 능한 변이를 적용하기 위해 각 표본주기에서 오차 역 전파법에 의해 학습된다. 그리고, 상태공간에서 시스템 분석은 상태 궤환 이득을 얻기 위해 체계적으로 실행했다. 또한, 강인성은 전반적인 시스템응답에 영향력을 주지 않고 얻어진다.

• 대한조선학회논문집
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• 제34권2호
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• pp.56-63
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• 1997

본 논문에서는 신경망을 이용한 자동조타장치의 개발에 관한 연구결과를 소개한다. 본 연구에서는 먼저 신경망이론에 사용되는 대표적인 방법인 Back-Propagation 알고리즘의 원리를 설명하고 이를 이용하여 선박의 조종모델을 신경망으로 재구성하는 방법을 제시하였다. 신경망이론을 사용하여 선박운동모델을 System Identification 하는 경우의 문제점을 간단한 조종모델을 이용하여 수치적으로 검증하고 보다 복잡한 모델로 적용하는 경우에 대한 토의를 하였다. 본 논문에서 개발된 신경망이론들은 비선형성을 내포하고 있는 선박운동을 재구성하는데 효과적으로 사용될 수 있을 것으로 기대된다.

• Journal of Electrical Engineering and Technology
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• 제9권4호
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• pp.1375-1384
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• 2014

Flashover of power transmission line insulators is a major threat to the reliable operation of power system. This paper deals with the flashover prediction of polymeric insulators used in power transmission line applications using the novel condition monitoring technique developed by PD signal time-frequency map and neural network technique. Laboratory experiments on polymeric insulators were carried out as per IEC 60507 under AC voltage, at different humidity and contamination levels using NaCl as a contaminant. Partial discharge signals were acquired using advanced ultra wide band detection system. Salient features from the Time-Frequency map and PRPD pattern at different pollution levels were extracted. The flashover prediction of polymeric insulators was automated using artificial neural network (ANN) with back propagation algorithm (BPA). From the results, it can be speculated that PD signal feature extraction along with back propagation classification is a well suited technique to predict flashover of polymeric insulators.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1804-1807
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• 1997

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learnign architecture. It is proposed a learning controller consisting of two neural networks-fuzzy based on independent reasoning and a connecton net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 비파괴검사학회지
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• 제28권3호
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.65-68
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• 1996

Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. The TMS32OC31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the, network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time, control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• 대한기계학회논문집A
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• 제21권5호
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• pp.727-734
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• 1997

In practice, the design of forging processes is performed based on an experience-oriented technology, that is designer's experience and expensive trial and errors. Using the finite element simulation and the artificial neural network, we propose an optimal die geometry satisfying the design conditions of final product. A three-layer neural network is used and the back propagation algorithm is employed to train the network. An optimal die geometry that satisfied the same between inner extruded rib and outer extruded one is determined by applying the ability of function approximation of neural network. The neural networks may reduce the number of finite element simulation for determine the optimal die geometry of forging products and further they are usefully applied to physical modelling for the forging design.

• 한국정보통신학회논문지
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• 제14권5호
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• pp.1078-1084
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• 2010

본 논문에서는 오차역전파알고리즘에 의한 신경회로망을 사용하여 이산푸리에변환에 의한 진폭성분과 위상 성분을 복원하는 음성강조 시스템을 제안한다. 먼저, 신경회로망이 잡음이 부가된 음성신호의 이산푸리에변환의 진폭성분과 위상성분을 사용하여 학습된 후, 제안한 시스템은 백색잡음에 의하여 열화된 잡음이 부가된 음성 신호를 강조한다. 백색잡음에 의하여 열화된 음성신호는 이산푸리에변환에 의한 진폭성분과 위상성분을 입력으로 하는 신경회로망을 사용하여 제안된 시스템에 의하여 강조되는 것을 실험결과로 증명한다. 제안한 시스템은 스펙트럼 왜곡율의 평가법을 사용하여 백색잡음에 의하여 열화된 음성신호에 대하여 효과적인 것을 실험으로 확인한다.