• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권2호
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• pp.89-97
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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. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and 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 LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. 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 LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• 전자공학회논문지S
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• 제35S권4호
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• pp.67-75
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• 1998

In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

• 전자공학회논문지C
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• 제35C권3호
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• pp.79-89
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• 1998

The Error Back-Propagation(EBP) algorithm is widely applied to train a multi-layer perceptron, which is a neural network model frequently used to solve complex problems such as pattern recognition, adaptive control, and global optimization. However, the EBP is basically a gradient descent method, which may get stuck in a local minimum, leading to failure in finding the globally optimal solution. Moreover, a multi-layer perceptron suffers from locking a systematic determination of the network structure appropriate for a given problem. It is usually the case to determine the number of hidden nodes by trial and error. In this paper, we propose a new algorithm to efficiently train a multi-layer perceptron. OUr algorithm uses stochastic perturbation in the weight space to effectively escape from local minima in multi-layer perceptron learning. Stochastic perturbation probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the EGP learning gets stuck to it. Addition of new hidden nodes also can be viewed asa special case of stochastic perturbation. Using stochastic perturbation we can solve the local minima problem and the network structure design in a unified way. The results of our experiments with several benchmark test problems including theparity problem, the two-spirals problem, andthe credit-screening data show that our algorithm is very efficient.

• 한국인터넷방송통신학회논문지
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• 제11권4호
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• pp.245-249
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• 2011

본 논문에서 등가의 Wiener-Hopf 공식을 제안한다. 제안된 알고리듬은 입력신호들이 직교하는 경우 TDL 필터의 가중치 벡터와 오차를 동시에 가질 수 있게 된다. 등가의 Wiener-Hopf 방정식은 최소 평균 자승 오차 방식에 근여 이론적으로 분석이 되었다. 제안된 알고리듬의 성능 결과는 원래 Wiener-Hopf 방정식의 성능과 동일함을 확인할 수 있다. 결론적으로 제안된 방식은 격자 필터가 적용되는 경우 TDL 필터 계수를 가지게 된다. 게다가 새로운 비용함수가 제안되어 더욱 우수한 적응신호처리 분야에서의 발전을 보일 것으로 기대된다.

• 한국통신학회논문지
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• 제40권2호
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• pp.247-252
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• 2015

영확률을 성능기준으로 하는 적응 알고리듬은 충격성 잡음에 강인함을 나타내며 그 결정 궤환 알고리듬은 심각한 다경로 채널 왜곡을 효과적으로 보상하는 것으로 알려져 있다. 그러나 이러한 결정 궤환 영확률 알고리듬은 각 필터 구역에 대해 매 샘플시간마다 여러 합산 동작을 계산해야하는데 이것이 실제 구현에 장애가 되고 있다. 이 논문에서는 반복적 기울기 추정 방식을 가진 결정 궤환 영확률 알고리듬을 제안하며 이 알고리듬은 기존 계산량 O(N)을 샘플 사이즈 N에 무관한 상수량으로 줄일 수 있음을 보인다. 또한 초기상태와 안정상태의 가중치 갱신이 연속적인 과정으로 이루어져 결정 궤환에서 어떤 기울기 추정 오류 전파도 일으키지 않음을 보인다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 추계종합학술대회
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• pp.248-251
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• 1999

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-1, we may compute the updated estimate of this vector at i(oration n upon the arrival of new data. The RLS algorithm may be viewed as a special case of the Kalman filter. Indeed this special relationship between the RLS algorithm and the Kalman filter is considered. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. The resulting rate of convergence is therefore typically an order of magnitude faster than the simple LMS algorithm. This improvement in performance, however, Is achieved at the expensive of a large increase in computational complexity.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.394-399
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• 2002

In this paper, Multisource data classification methods based on Bayesian formula are considered. For this decision fusion scheme, the individual data sources are handled separately by statistical classification algorithms and then Bayesian fusion method is applied to integrate from the available data sources. This method includes the combination of each expert decisions where the weights of the individual experts represent the reliability of the sources. The reliability measure used in the statistical approach is common to all pixels in previous work. In this experiment, the weight factors have been assigned to have different value for all pixels in order to improve the integrated classification accuracies. Although most implementations of Bayesian classification approaches assume fixed a priori probabilities, we have used adaptive a priori probabilities by iteratively calculating the local a priori probabilities so as to maximize the posteriori probabilities. The effectiveness of the proposed method is at first demonstrated on simulations with artificial and evaluated in terms of real-world data sets. As a result, we have shown that Bayesian statistical fusion scheme performs well on multispectral data classification.

• 한국컴퓨터정보학회논문지
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• 제23권1호
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• pp.25-32
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• 2018

In this paper, we propose an edge detection algorithm for auto focus of infrared camera. We designed and implemented the edge detection of infrared image by using a spatial filter on FPGA. The infrared camera should be designed to minimize the image processing time and usage of hardware resource because these days surveillance systems should have the fast response and be low size, weight and power. we applied the $3{\times}3$ mask filter which has an advantage of minimizing the usage of memory and the propagation delay to process filtering. When we applied Laplacian filter to extract contour data from an image, not only edge components but also noise components of the image were extracted by the filter. These noise components make it difficult to determine the focus state. Also a bad pixel of infrared detector causes a problem in detecting the edge components. So we propose an adaptive edge detection filter that is a method to extract only edge components except noise components of an image by analyzing a variance of pixel data in $3{\times}3$ memory area. And we can detect the bad pixel and replace it with neighboring normal pixel value when we store a pixel in $3{\times}3$ memory area for filtering calculation. The experimental result proves that the proposed method is effective to implement the edge detection for auto focus in infrared camera.

• 로봇학회논문지
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• 제16권1호
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• pp.41-48
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• 2021

In this paper, we review papers which report to the all solid-state electroactive polymer-based tunable lens. Since electroactive polymer-based tunable lenses change their focal length by responding to electric stimuli, it can be minimized the size and weight of optical modules. Thus, it has been received attention in the robot, mobile device and display industry. The all solid-state electroactive polymer-based tunable lenses can be classified into two categories depending on the classification of materials: ionic electroactive polymer-based lenses and non-ionic electroactive polymer-based lenses. Most of the ionic electroactive polymer-based tunable lenses are fabricated with ionic polymer-metal composite. So, the ionic electroactive polymer-based tunable lenses can be operated under low electric voltage. But small force, slow recovery time and environmental limitation for operation has been pointed to the disadvantage of the lenses. The non-ionic electroactive polymer-based tunable lenses are classified again into two categories: dielectric polymer-based tunable lenses and polyvinylchloride gel-based tunable lenses. The advantage of the dielectric polymer-based tunable lenses is fast response to electric stimuli. But the essential flexible electrodes degrade performance of the lens. Polyvinylchloride gel-based tunable lens has reported impressive performance without flexible electrodes.

• Journal of Ecology and Environment
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• 제45권2호
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• pp.78-87
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• 2021

Background: To understand shade and wind effects on seedling traits of common reed (Phragmites australis), we conducted a mesocosm experiment manipulating day length (10 h daytime a day as open canopy conditions or 6 h daytime a day as partially closed canopy conditions) and wind speed (0 m/s as windless conditions or 4 m/s as windy conditions). Results: Most values of functional traits of leaf blades, culms, and biomass production of P. australis were higher under long day length. In particular, we found sole positive effects of long day length in several functional traits such as internode and leaf blade lengths and the values of above-ground dry weight (DW), rhizome DW, and total DW. Wind-induced effects on functional traits were different depending on functional traits. Wind contributed to relatively low values of chlorophyll contents, angles between leaf blades, mean culm height, and maximum culm height. In contrast, wind contributed to relatively high values of culm density and below-ground DW. Conclusions: Although wind appeared to inhibit the vertical growth of P. australis through physiological and morphological changes in leaf blades, it seemed that P. australis might compensate the inhibited vertical growth with increased horizontal growth such as more numerous culms, indicating a highly adaptive characteristic of P. australis in terms of phenotypic plasticity under windy environments.