• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.2
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• pp.86-90
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• 2001

This paper presents a multi output converter system that controls, simultaneously, the separate buck converter and boost converter with the different specification by one digital controller using fuzzy algorithm. As two separate converters are regulated by only one DSP, it is possible to achieve the simple digital control circuit for regulating multi output DC-DC converter. Inference procedure of fuzzy controller is included. The control characteristics of each PWM DC-DC converter is validated by experimental results.

• Proceedings of the KIEE Conference
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• 2002.06a
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• pp.60-63
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• 2002

This paper presents a control approach for designing a self tuning fuzzy controller for SVC system, A SVC constructed by a Fixed Capacitor and a Thyristor Controlled Reactor is designed and implemented to improve the damping of a synchronous generator, as well as controlling the system voltage, The proposed parameter self tuning algorithm of fuzzy controller is based on the steepest decent method using two direction vectors which make error between inference values of fuzzy controller and output values of the specially selected PSS reduce steepestly, The related simulation results show that the proposed fuzzy controller is more powerful than the conventional ones.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1073-1075
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• 1996

This paper presents a neuro fuzzy system(NFS) for implementing fuzzy inference system with a monotonic membership function. The modeling and control of a discrete nonlinear system using a NFS is described. The membership function parameters of a identifier and controller are adjusted by back-propagation algorithm. These identifier and controller is constructed to proposed NFS. A on-line identification and control are accomplished by this NFS. A controller is gived information of the system, that is variation of the system output according to that of the control input by a identifier. A controller makes control input in order to control discrete-time nonlinear system. A Simulation is presented to demonstrate the efficiency of a suggested method.

• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.522-533
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• 1999

This paper proposes speaker recognition algorithm which includes both the pitch parameter and the fuzzy inference. This study proposes a pitch detection method PVPF(peak and valley pitch detection fuction) by means of comparing spectra which utilizes the transform characteristics between time and frequency. In this paper, makes reference pattern using membership function and performs vocal tract recognition of common character using fuzzy pattern matching in order to include time variation width for non-linear utterance time.

• International Journal of Safety
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• v.10 no.1
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• pp.22-31
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• 2011

Seismic response control method of the bridge structures with semi-active control device, i.e., magneto-rheological (MR) damper, is studied in this paper. Design of various kinds of clipped optimal controller and fuzzy controller are suggested as a semi-active control algorithm. For determining the control force of MR damper, clipped optimal control method adopts bi-state approach, but the fuzzy control method continuously quantifies input currents through fuzzy inference mechanism to finely modulate the damper force. To investigate the performances of the suggested control techniques, numerical simulations of a multi-span continuous bridge system subjected to various earthquakes are performed, and their performances are compared with each other. From the comparison of results, it is shown that the fuzzy control system can provide well-balanced control force between girder and pier in the view point of structural safety and stability and be quite effective in reducing both girder and pier displacements over the existing control method.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.807-815
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• 2008

A simple efficient method of spotting and recognizing hand gestures in video is presented using a network of hidden Markov models and dynamic programming search algorithm. The description starts from designing a set of isolated trajectory models which are stochastic and robust enough to characterize highly variable patterns like human motion, handwriting, and speech. Those models are interconnected to form a single big network termed a spotting network or a spotter that models a continuous stream of gestures and non-gestures as well. The inference over the model is based on dynamic programming. The proposed model is highly efficient and can readily be extended to a variety of recurrent pattern recognition tasks. The test result without any engineering has shown the potential for practical application. At the end of the paper we add some related experimental result that has been obtained using a different model - dynamic Bayesian network - which is also a type of stochastic model.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.22.1-22
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• 2001

In this paper we present Multiple-Channel Active Noise Control[ANC] system by employing Independent Component Analysis[ICA] and Adaptive Network Fuzzy Inference System[ANFIS]. ICA is widely used in signal processing and communication and it use prewhiting and appropriate choice of non-linearities, ICA can separate mixed signal. ANFIS controller is trained with the hybrid learning algorithm to optimize its parameters for adaptively canceling noise. This new method which minimizes a statistical dependency of mutual information(MI) in mixed low frequency noise signal and there is no need to secondary path modeling. The proposed implementations achieve more powerful and stable noise reduction than Filtered-X LMS algorithms which is needed for LTI assumption and precise secondary error

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.5
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• pp.667-673
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• 2019

We present an orienteering problem with unknown stochastic reward(OPUSR) model for persistent monitoring tasks with unknown event probabilities at each point of interest. Prior studies on orienteering problem for persistent monitoring task assume that rewards and event probabilities are known as a prior. In this paper, we propose a stochastic reward model with unknown event statistics and a path re-planning algorithm based on Bayesian reward inference. Experiments demonstrate the efficiency of our method.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.993-996
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• 2004

사람들 사이에는 DNA 서열의 변이로 인한 유전적 차이가 있으며, 가끔 이러한 차이가 유전 질병의 원인이 되기도 한다. 일반적으로 DNA에서 가장 잘 알려진 변이가 바로 SNP(Single Nucleotide Polymorphism : 스닙)이다. SNP는 보통 블록단위로 유전되어지며 한쪽 부모로부터 유전되어진 SNP 블록을 SNP 하플로타입이라고 부른다. 생물학 실험을 통하여 추출되어진 결과물은 부모로부터 유전되어진 대립 유전자가 혼합되어진 지노타입(genotype)의 정보이다. 지노타입은 직관적으로 정확한 SNP 하플로타입을 추정하기가 힘들고, 생물학 실험을 통하여 하플로타입(haplotype)을 분석하는데 많은 비용이 들기때문에, 이를 컴퓨터 계산을 통하여 추론하는 연구가 Clark[1]에 의해서 제안되어진 이후 활발하게 진행되고 있다. 본 논문에서는 하플로타입을 효과적으로 추론하기 위해 유전자 알고리즘을 이용한 새로운 방법을 설명하고, 실험 결과를 기존의 연구 결과와 비교 분석한다.

• Computers and Concrete
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• v.24 no.3
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• pp.271-282
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• 2019

Concrete undergoes a series of thermo-based physio-chemical changes once exposed to elevated temperatures. Such changes adversely alter the composition of concrete and oftentimes lead to fire-induced explosive spalling. Spalling is a multidimensional, complex and most of all sophisticated phenomenon with the potential to cause significant damage to fire-exposed concrete structures. Despite past and recent research efforts, we continue to be short of a systematic methodology that is able of accurately assessing the tendency of concrete to spall under fire conditions. In order to bridge this knowledge gap, this study explores integrating novel artificial intelligence (AI) techniques; namely, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA), together with traditional statistical analysis (multilinear regression (MLR)), to arrive at state-of-the-art procedures to predict occurrence of fire-induced spalling. Through a comprehensive datadriven examination of actual fire tests, this study demonstrates that AI techniques provide attractive tools capable of predicting fire-induced spalling phenomenon with high precision.