• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.703-706
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• 2004

공정기술의 발달과 컴퓨터 구조적인 발전에 의해서, 시스템의 동작속도가 기하급수적으로 증가하고 있다. 동작속도의 증가는 CMOS로 구현된 chip의 RC 특성에 의해서 timing variation 문제가 발생할 가능성이 높아지면서 테스트 비용이 전체 설계비용에서 차지하게 되는 비중이 급격하게 증가하고 있다. 따라서 온라인 테스트와 진화하드웨어 등이 테스트 비용감소를 위해서 연구되고 있다. 본 논문에서는 네트워크프로세서의 생존성을 위해서, 패킷엔진의 pipline의 각 stage사이의 clock slack borrowing을 이용해서 timing variation 문제를 자체적으로 해결할 수 있다는 것을 mixed-mode simulation을 통해서 통합 검증하였다. 또한 기존의 off-chip 진화하드웨어에 비해서 on-chip구현을 통해서 진화하드웨어의 성능향상과 메모리에 의해서 발생하는 overhead를 감소시키는 것이 가능함을 확인했다.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.732-735
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• 1999

A GA-based optimization technique is adopted in the paper to obtain optimal future control inputs for predictive control systems. For reliable future predictions of a process, we identify the underlying process with an NNARX model structure and investigate to reduce the volume of neural network based on the Lipschitz index and a criterion. Since most industrial processes are subject to their constraints, we deal with the input-output constraints by modifying some genetic operators and/or using a penalty strategy in the GAPC. Some computer simulations are given to show the effectiveness of the GAPC method compared with the adaptive GPC algorithm.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2052-2054
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• 2003

본 논문에서는 혼돈 비선형 시스템의 지능 제어를 위해 간접 적응 제어 기법에 기반한 웨이블릿 신경 회로망 제어기 설계 방법을 제안한다. 제어기 성능에 큰 영향을 미칠 수 있는 웨이블릿 신경 회로망 구조의 파라미터 동정은 본질적으로 강인하고 전역 최적해에 근사한 값을 결정할 수 있는 유전 알고리듬을 사용한다. 본 논문에서 제안한 제어 방법은 유전 알고리듬을 이용한 혼돈 비선형 시스템의 오프라인 동정 모델 및 기준 신호와 플랜트 출력으로 정의되는 제어 오차를 이용하여 원하는 제어 입력을 생성한다. 한편 본 논문에서 제안한 웨이블릿 신경 회로망 제어기를 대표적인 연속 시간 혼돈 비선형 시스템인 Duffing 시스템에 적용하여 설계된 제어기의 효율성 및 우수성을 검증하고자 한다.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2085-2087
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• 2003

A new interacting multiple model (IMM) method based on intelligent input estimation (IIE) is proposed for tracking a maneuvering target. In the proposed method, the acceleration level of each sub-filter is determined by IIE using the fuzzy system, which is optimized by the genetic algorithm (GA). The tracking performance of the proposed method is compared with those of the input estimation (IE) technique and the adaptive interacting multiple model (AIMM) method in computer simulations.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.89-93
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• 2000

구조 적응형 자기구성 지도는 일반적으로 자기구성 지도의 구조가 초기에 결정되어 학습이 끝날 때까지 변하지 않기 때문에 발생하는 문제를 해결하기 위해 지도의 구조를 학습 중에 적절하게 변경시킨다. 이때, 변화된 구조의 가중치를 어떻게 초기화시킬 것인가 하는 것이 중요한 문제이다. 이 논문에서는 기존의 비교사 학습방법에 LVQ 알고리즘을 이용한 교사 학습방법을 결합한 구조 적응형 자기구성 지도 모델에서 유전자 알고리즘을 이용하여 분화된 노드의 가중치를 결정하는 방법을 제안한다. 이 방법은 기존의 구조 적응형 자기구성 지도 알고리즘보다 빠르게 학습되었고, 인식률 면에서도 기존의 방법보다 높은 값을 나타내었으며, 자기구성 지도의 특성인 위상 보존도 잘 이루어졌다. 오프라인 필기 숫자 데이터로 실험한 결과, 제안한 방법이 유용함을 알 수 있었다.

• 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.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• Journal of Navigation and Port Research
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• v.32 no.5
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• pp.379-385
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• 2008

In this paper, we presents a scheme for the parameter estimation and optimal control scheme for apparatus of container crane system. For parameter estimation, first, we construct the open loop of the container crane system and estimate its parameters based on input-output data, a real-coded genetic algorithm(RCGA) and the model adjustment technique. The RCGA plays an important role in parameter estimation as an adaptive mechanism. For controller design, state feedback gain matrix is searched by another RCGA and the estimated model. The performance of the proposed methods are demonstrated through a set of simulation and experiments of the experimental apparatus.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.304-306
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• 2007

In this paper, we presents a scheme for the parameter estimation and optimal control scheme for apparatus of container crane system. For parameter estimation, first, we construct the open loop of the container crane system and estimate its parameters based on input-output data, a real-coded genetic algorithm(RCGA) and the model adjustment technique. The RCGA plays an important role in parameter estimation as an adaptive mechanism. For controller design, state feedback gain matrix is searched by another RCGA and the estimated model. The performance of the proposed methods are demonstrated through a set of simulation and experiments of the experimental apparatus.

• Advances in Computational Design
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• v.1 no.3
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• pp.235-251
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• 2016

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.