• 제어로봇시스템학회논문지
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• 제18권7호
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• pp.644-649
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• 2012

This paper proposes the gait phase classifier using backpropagation neural networks method which uses the angle of lower body's joints and ground reaction force as input signals. The classification of a gait phase is useful to understand the gait characteristics of pathologic gait and to control the gait rehabilitation systems. The classifier categorizes a gait cycle as 7 phases which are commonly used to classify the sub-phases of the gait in the literature. We verify the efficiency of the proposed method through experiments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.754-756
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• 2004

The ultrasonic piezo motor is a new type motor that has an excellent performance and many useful features that electromagnetic motors do not have. But, it suffers from severe system non-linearities and parameter variations especially during speed control. Therefore, it is difficult to accomplish satisfactory control performance by using the conventional PID controller. In this paper, to achieve the precise control, we analyzed response time & change with a driving time, and proposed PD controller combined with neural network. The backpropagation algorithm is used to train a given trajectory. The effectiveness of the used method is confirmed by experiments. The effectiveness of the used method is confirmed by experiments using the ultrasonic motor made in Korea.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.107-112
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• 1998

In this paper, we propose a new paradigm (NEW_BP) to be capable of overcoming limitations of the traditional backpropagation(OLD_BP). NEW_BP is based on the method of conjugate gradients with the normalized direction vectors and computes step size through the linear search which may be characterized by order statistics and golden section. Simulation results showed that NEW_BP was definitely superior to both the stochastic OLD_BP and the deterministic OLD_BP in terms of accuracy and rate of convergence and might sumount the problem of local minima. Furthermore, they confirmed us that stagnant phenomenon of training in OLD_BP resulted from the limitations of its algorithm in itself and that unessential approaches would never cured it of this phenomenon.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권4호
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• pp.236-241
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• 2007

This paper presents an adaptive controller using chaotic dynamic neural networks(CDNN) for nonlinear dynamic system. A new dynamic backpropagation learning method of the proposed chaotic dynamic neural networks is developed for efficient learning, and this learning method includes the convergence for improving the stability of chaotic neural networks. The proposed CDNN is applied to the system identification of chaotic system and the adaptive controller. The simulation results show good performances in the identification of Lorenz equation and the adaptive control of nonlinear system, since the CDNN has the fast learning characteristics and the robust adaptability to nonlinear dynamic system.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 추계학술대회 논문집
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• pp.488-492
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• 1993

This paper proposes a new approach to the optimization method of a blending process with neural network. The method is based on the error backpropagation learning algorithm for neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a system solver. A fuzzy membership function is used in parallel with the neural network to minimize the difference between measurement value and input value of neural network. As a result, we can guarantee the reliability and stability of blending process by the help of neural network and fuzzy membership function.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.1221-1224
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• 2004

In this study, we developed a method classifying slice shot during golf practice using backpropagation algorithm. The 144 data based on the backpropagation model(11 inputs, 2 outputs) was used as a learning set and the model was verified based on the extra 50 data in the process to predict a slice shot in golf swing. The results showed 100％ separating rate of learning set and 91.5％ separating rate of verified set. The developed method can be potentially beneficial for the predicting of slice shot in an indoor golf excercise setting without applying any additional equipment.

• Journal of information and communication convergence engineering
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• 제21권3호
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• pp.233-238
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• 2023

This study presents a method for identifying partial discharge defects in an eco-friendly gas insulated system using a backpropagation algorithm. Four partial discharge (PD) electrode systems, namely, a free-moving particle, protrusion on the conductor, protrusion on the enclosure, and voids, were designed to simulate PD defects that can occur during the operation of eco-friendly gas-insulated switchgear. The PD signals were measured using an ultrahigh-frequency sensor as a nonconventional method based on IEC 62478. To identify the types of PD defects, the PD parameters of single PD pulses in the time and frequency domains and the phase-resolved partial discharge patterns were extracted, and a back-propagation algorithm in the artificial neural network was designed using a virtual instrument based on LabVIEW. The backpropagation algorithm proposed in this paper has an accuracy rate of over 90% for identifying the types of PD defects, and the result is expected to be used as a reference database for asset management and maintenance work for eco-friendly gas-insulated power equipment.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.1021-1024
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. In this paper, the vision system of the sensing methods of drill flank wear on the basis of image processing is used to detect the wear pattern by non-contact and direct method and get the reliable wear information about drill. In image processing of acquired image, median filter is applied for noise removal. The vision flank wear area of the drill was measured. Backpropagation neural networks (BPns) were used for no-line detection of drill wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, thrust and torque signals. The output was the drill wear state which was either usable or failure. Drilling experiments with various spindle rotational speed and feed rates were carried out. The learning process was peformed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.

• 지구물리와물리탐사
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• 제3권3호
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• pp.83-87
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• 2000

송신원의 파워 증가가 제한되고 인공잡음이 존재하는 지역에서 양질의 탄성파 자료를 획득하기 위하여 근/원기준점(reference)을 이용한 탄성파 잡음예측필터를 개발하였다. 잡음예측필터에 사용된 방법은 backpropagation 알고리즘을 이용한 3층의 인공신경망(neural network)으로서, 훈련자료(training data) 및 검증자료(testing data)에 훈련된 잡음예측필터를 적용시 신호대잡음비(signal-to-noise ration)를 약 3배 정도 증가시켰다. 그러나, 일반적으로 전기, 전자탐사 자료의 질을 향상하기 위해 사용되는 스케일링(scaling)기법으로는 전혀 탄성파의 잡음을 제거할 수 없었다.

• 대한전자공학회논문지SP
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• 제39권3호
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• pp.88-98
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• 2002

MLP(multilayer perceptron)는 다른 패턴인식 방법에 비해 몇 가지 유리한 이점을 지니고 있어 화자증명 시스템의 화자학습 및 인식 방법으로서 사용이 기대된다. 그러나 MLP의 학습은 학습에 이용되는 EBP(error backpropagation) 알고리즘의 저속 때문에 상당한 시간을 소요한다. 이 점은 화자증명 시스템에서 높은 화자인식률을 달성하기 위해서는 많은 배경화자가 필요하다는 점과 맞물려 시스템에 화자를 등록하기 위해 많은 시간이 걸린다는 문제를 낳는다. 화자증명 시스템은 화자 등록후 곧바로 증명 서비스를 제공해야 하기 때문에 이 문제를 해결해야 한다. 본 논문에서는 이 문제를 해결하기 위해 EBP의 학습속도를 개선하는 방법과, 기존의 화자증명 방법에서 화자군집 방법을 도입한 배경화자 축소방법을 사용하여 MLP 기반 화자증명 시스템에서 화자등록에 필요한 시간의 단축을 시도한다.