• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.423-426
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• 2005

In this paper, we proposed a PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kp, $\tau_{I}$, $\tau_{D}$. by the Ziegler-Nichols formula that uses the ultimate gain and ultimate period from a relay tuning experiment. We will get the error and the error rate of plant output corresponding to the initial value of parameter and fnd the new proportion gain(Kp) and the integral time ($\tau_{I}$) from fuzzy tuner by the error and error rate of plant oueut as a membership function of fuzzy theory. This fuzzy auto tuning algorithm for PID controller considerably reduced the overshoot and rise time as compared to any other PID controller tuning algorithms. And in real parametric uncertainty systems, it constitutes an appreciable improvement of performance. The significant property of this algorithm is shown by simulation

• 한국전자통신학회논문지
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• 제14권6호
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• pp.1187-1196
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• 2019

정보 보안은 클라우드 및 소셜 네트워킹 사이트의 출현으로 주요 과제가 되었다. 기존의 암호화 알고리즘은 디지털 영상의 큰 데이터 크기와 원시 픽셀 간에 높은 중복성으로 인해 영상 암호화에 적합하지 않을 수 있다. 본 논문에서는 Jeong 등이 제안한 컬러 영상의 암호화 방법을 C-MLCA와 Laplace 전개를 이용한 매개변수식 3차원 카오스 캣맵을 사용하여 일반화한다. 제안된 새로운 영상 암호시스템이 높은 보안성과 신뢰성을 제공한다는 것을 엄격한 실험을 통해 입증한다.

• Structural Engineering and Mechanics
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• 제25권2호
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• pp.239-268
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• 2007

This paper deals with optimal stacking sequence design of laminate composite structures. The stacking sequence optimisation of laminate composites is formulated as a combinatorial problem and is solved using Simulated Annealing (SA), an algorithm devised based on inspiration of physical process of annealing of solids. The combinatorial constraints are handled using a correction strategy. The SA algorithm is strengthened by embedding Tabu search in order to prevent recycling of recently visited solutions and the resulting algorithm is referred to as tabu embedded simulated Annealing (TSA) algorithm. Computational performance of the proposed TSA algorithm is enhanced through cache-fetch implementation. Numerical experiments have been conducted by considering rectangular composite panels and composite cylindrical shell with different ply numbers and orientations. Numerical studies indicate that the TSA algorithm is quite effective in providing practical designs for lay-up sequence optimisation of laminate composites. The effect of various neighbourhood search algorithms on the convergence characteristics of TSA algorithm is investigated. The sensitiveness of the proposed optimisation algorithm for various parameter settings in simulated annealing is explored through parametric studies. Later, the TSA algorithm is employed for multi-criteria optimisation of hybrid composite cylinders for simultaneously optimising cost as well as weight with constraint on buckling load. The two objectives are initially considered individually and later collectively to solve as a multi-criteria optimisation problem. Finally, the computational efficiency of the TSA based stacking sequence optimisation algorithm has been compared with the genetic algorithm and found to be superior in performance.

• 대한교통학회지
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• 제20권4호
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• pp.109-122
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• 2002

본 연구에서는 교차로의 비용 및 특성을 고려한 도로선형최적화 모형을 유전자 알고리즘(Genetic Algorithms)을 이용하여 개발하였다. 기존의 도로선형최적화 모형은 교차로 특성을 고려하지 못해서 실제 적용에 심대한 문제점을 내재하고 있다. 본 논문에서는 특정 도로선형에 교차로 건설의 필요가 있을 경우, 민감(Sensitive)하고 지배적인(Dominating) 교차로 비용 항목들 즉, 토공비용, 보상비, 포장비, 사고비용, 지체 및 연료소모비용 등의 산정이 시도되었다. 또한 비교적 우수한 도로선형 대안을 유전자 알고리즘을 이용한 탐색과정 중에서 비효율적으로 강제 퇴화시키는 단점 보완을 위한 교차로 국소 최적화 방법(Local Optimization of Intersections)이 개발되어 기존 모형을 보완하였다. 공간상의 도로선형은 매개변수적 묘사(Parametric Representation)를 통하여 구현하였으며 벡터운영(Vector Manipulation)을 통해 교차로비용 산정의 근간인 교차점과 다른 중요점들의 좌표를 찾을 수 있었다. 개발된 교차로 비용산정 모형이 보다 정밀하게 교차로 비용을 산정함이 증명되었으며 궁극적으로는 기존의 최적화 모형의 단점을 보완할 수 있음이 제시되었다. 또한, 새로이 제시된 교차로 국소 최적화 방법이 최적대안 탐색과정의 유연성을 증대하였으며, 결과적으로 효율적인 교차로의 유지에 기여함을 알 수 있었다. 제시된 교차로 국소 최적화 방법은 추후 단일노선이 아닌 도로망 최적화시의 기초를 제시함은 주목할 만 하다. 두개의 예제에서 도출된 최적노선 및 교차로 비용 등의 검토 결과, 도로상의 교차로 건설비용은 도로선형 최적화에 큰 영향을 미치는 실질적이며 민감한 비용 항목임이 검증되었으며 이는 도로선형최적화 모형이 교차로 비용을 반드시 검토 및 평가할 수 있어야 함을 반증한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.207-209
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• 2007

In this paper, we introduce a genetic optimization of fuzzy set-fuzzy model using successive tuning method to carry out the model identification of complex and nonlinear systems. To identity we use genetic alrogithrt1 (GA) sand C-Means clustering. GA is used for determination the number of input, the seleced input variables, the number of membership function, and the conclusion inference type. Information Granules (IG) with the aid of C-Means clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the, membership functions in the premise part and the initial values of polyminial functions in the consequence part of the fuzzy rules. The overall design arises as a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and estimate parameters of the fuzzy model we introduce the successive tuning method with variant generation-based evolution by means of GA. Numerical example is included to evaluate the performance of the proposed model.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.621-632
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• 2013

In this paper, we introduce advanced architectures of genetically-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNN and FRNN, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.136-141
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• 2009

This paper proposed a framework for handover method in continuously tracking a person of interest across cooperative pan-tilt-zoom (PTZ) cameras. The algorithm here is based on a robust non-parametric technique for climbing density gradients to find the peak of probability distributions called the mean shift algorithm. Most tracking algorithms use only one cue (such as color). The color features are not always discriminative enough for target localization because illumination or viewpoints tend to change. Moreover the background may be of a color similar to that of the target. In our proposed system, the continuous person tracking across cooperative PTZ cameras by mean shift tracking that using color and shape histogram to be feature distributions. Color and shape distributions of interested person are used to register the target person across cameras. For the first camera, we select interested person for tracking using skin color, cloth color and boundary of body. To handover tracking process between two cameras, the second camera receives color and shape cues of a target person from the first camera and using linear color calibration to help with handover process. Our experimental results demonstrate color and shape feature in mean shift algorithm is capable for continuously and accurately track the target person across cameras.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.327-332
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• 2005

We investigatea new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons thatare fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• 대한안전경영과학회지
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• 제7권5호
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• pp.155-174
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• 2005

Parametric life-cycle cost(LCC) models have been integrated with traditional design tools, and used in prior work to demonstrate the rapid solution of holistic, analytical tradeoffs between detailed design variations. During early designs stages there may be competing concepts with dramatic differences. Additionally, detailed information is scarce, and decisions must be models. for a diverse range of concepts, and the lack of detailed information make the integration make the integration of traditional LCC models impractical. This paper explores an approximate method for providing preliminary life-cycle cost. Learning algorithms trained using the known characteristics of existing products be approximated quickly during conceptual design without the overhead of defining new models. Artificial neural networks are trained to generalize on product attributes and life cycle cost date from pre-existing LCC studies. The Product attribute data to quickly obtain and LCC for a new and then an application is provided. In additions, the statistical method, called regression analysis, is suggested to predict the LCC. Tests have shown it is possible to predict the life cycle cost, and the comparison results between a learning LCC model and a regression analysis is also shown

• 한국음향학회지
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• 제34권4호
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• pp.310-315
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• 2015

본 논문에서는 비정체성(nonstationary) 잡음 환경을 위한 단일 채널 VAD(Voice Activity Detection) 알고리듬 제안하였다. VAD 판별을 위한 특징계수의 임계값은 과거 비음성 프레임들의 평균과 표준편차를 추산하여 적응적으로 갱신하였다. 특징계수로는 SPD-TE(Spectral Power Difference-Teager Energy)를 사용했는데, 이것은 WPD(Wavelet Packet Decomposition) 계수에 Teager 에너지를 적용한 것으로서 잡음에 강인한 것으로 보고된 바 있다. TIMIT 음성과 NOISEX-92 잡음을 사용하여 10 dB부터 -10 dB까지의 SNR에 대한 실험 결과, 제안된 알고리듬이 표준을 포함한 기존의 알고리듬과 비슷한 정확도를 보였다.