• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1300-1304
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• 2003

The following paper deals with a control problem of a mobile robot in home network environment. The home network causes the mobile robot to communicate with sensors to get the sensor measurements and to be adapted to the environment changes. To get the improved performance of control of a mobile robot in spite of the change in home network environment, we use the fuzzy inference system with multiple reward reinforcement learning. The multiple reward reinforcement learning enables the mobile robot to consider the multiple control objectives and adapt itself to the change in home network environment. Multiple reward fuzzy Q-learning method is proposed for the multiple reward reinforcement learning. Multiple Q-values are considered and max-min optimization is applied to get the improved fuzzy rule. To show the effectiveness of the proposed method, some simulation results are given, which are performed in home network environment, i.e., LAN, wireless LAN, etc.

• ETRI Journal
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• 제39권1호
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• pp.13-20
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• 2017

A method is proposed for scheduling sensor accesses to the shared network in a networked control system. The proposed method determines the access order in which the sensors are granted medium access through minimization of the state estimation error covariance. Solving the problem by evaluating the error covariance for each possible ordered set of sensors is not practical for large systems. Therefore, a convex optimization problem is proposed, which yields approximate yet acceptable results. A state estimator is designed for the augmented system resulting from the incorporation of the optimally chosen communication sequence in the plant dynamics. A car suspension system simulation is conducted to test the proposed method. The results show promising improvement in the state estimation performance by reducing the estimation error norm compared to round-robin scheduling.

• 한국컴퓨터산업학회논문지
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• 제7권5호
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• pp.473-480
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• 2006

본 논문에서는 여러 개의 출력단을 갖는 논리회로에서 공통식을 찾는 방법을 제안하였다. 각각의 출력단위로 2개의 큐브로 구성된 몫을 찾고, 이 몫들 간의 쌍을 이용해서 부울 공통식을 찾는 방법을 보였다. 실험 결과로 2개의 큐브만을 이용한 공통식 산출만으로 전체 논리회로의 크기를 줄이는 데 효과가 있음을 SIS1.2 결과와 비교하여 보였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2003년도 추계학술발표논문집 (중)
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• pp.1137-1140
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• 2003

인터넷 mobility 기술의 발전으로 인해 Mobile IPv6 를 기반으로 하는 이동 네트워크(Mobile Network, NEMO)기술이 등장하였으며, MR(Mobile Router)와 HA(Home Agent)간의 bi-directional 터널을 통해 네트워크의 이동성을 지원한다. 그러나. 이동네트워크 안에 또 다른 이동네트워크가 존재하는 중첩된 이동네트워크에서는 bi-directional 터널이 중복되는 routing problem이 발생한다. 따라서 본 논문에서는 중첩된 이동네트워크가 계층적 구조를 가지는 것을 이용하여, 최상위 MR 이 지역 HA 역할을 수행하게 항으로써, 중첩된 이동네트워크내의 노드들을 위한 경로최적화와 마이크로 이동성을 동시에 지원할 수 잇도록 한다.

• Structural Engineering and Mechanics
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• 제63권4호
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• Advances in aircraft and spacecraft science
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• 제4권3호
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• pp.297-316
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• 2017

With rapid growth in the complexity of large scale engineering systems, the application of multidisciplinary analysis and design optimization (MDO) in the engineering design process has garnered much attention. MDO addresses the challenge of integrating several different disciplines into the design process. Primary challenges of MDO include computational expense and poor scalability. The introduction of a distributed, collaborative computational environment results in better utilization of available computational resources, reducing the time to solution, and enhancing scalability. SORCER, a Java-based network-centric computing platform, enables analyses and design studies in a distributed collaborative computing environment. Two different optimization algorithms widely used in multidisciplinary engineering design-VTDIRECT95 and QNSTOP-are implemented on a SORCER grid. VTDIRECT95, a Fortran 95 implementation of D. R. Jones' algorithm DIRECT, is a highly parallelizable derivative-free deterministic global optimization algorithm. QNSTOP is a parallel quasi-Newton algorithm for stochastic optimization problems. The purpose of integrating VTDIRECT95 and QNSTOP into the SORCER framework is to provide load balancing among computational resources, resulting in a dynamically scalable process. Further, the federated computing paradigm implemented by SORCER manages distributed services in real time, thereby significantly speeding up the design process. Part 1 covers SORCER and the algorithms, Part 2 presents results for aircraft panel design with curvilinear stiffeners.

• Advances in aircraft and spacecraft science
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• 제4권3호
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• pp.317-334
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• 2017

With rapid growth in the complexity of large scale engineering systems, the application of multidisciplinary analysis and design optimization (MDO) in the engineering design process has garnered much attention. MDO addresses the challenge of integrating several different disciplines into the design process. Primary challenges of MDO include computational expense and poor scalability. The introduction of a distributed, collaborative computational environment results in better utilization of available computational resources, reducing the time to solution, and enhancing scalability. SORCER, a Java-based network-centric computing platform, enables analyses and design studies in a distributed collaborative computing environment. Two different optimization algorithms widely used in multidisciplinary engineering design-VTDIRECT95 and QNSTOP-are implemented on a SORCER grid. VTDIRECT95, a Fortran 95 implementation of D. R. Jones' algorithm DIRECT, is a highly parallelizable derivative-free deterministic global optimization algorithm. QNSTOP is a parallel quasi-Newton algorithm for stochastic optimization problems. The purpose of integrating VTDIRECT95 and QNSTOP into the SORCER framework is to provide load balancing among computational resources, resulting in a dynamically scalable process. Further, the federated computing paradigm implemented by SORCER manages distributed services in real time, thereby significantly speeding up the design process. Part 1 covers SORCER and the algorithms, Part 2 presents results for aircraft panel design with curvilinear stiffeners.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.9-10
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• 2007

[1],[2]의 드래프트 문서에서는 Mobile Network 내부에 HA를 두고 이동하는 Mobile Node를 LMN(Local Mobile Node)라 정의하고 있다. NEMO Basic Support를 기반으로 Nested Mobile Network에서 LMN의 이동에 대한 패킷 전송 경로를 가정 할 때 이 경로는 일반적인 Nested NEMO의 경우 보다 상당히 복잡한 경로를 가지게 된다. 본 논문에서는 이러한 LMN이 이동할 경우 패킷 전송경로에 대해 분석하고, Nested NEMO에 MANET을 적용하여 LMN의 이동에 대한 경로 최적화 방안을 제안하고자 한다.

• 전자공학회논문지CI
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• 제39권4호
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• pp.10-18
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• 2002

통신망 설계는 다양한 설계 인자들이 고려되는 다목적 함수 문제이다. 특히 망의 구성 비용, 메시지 지연 그리고 신뢰도는 망의 최대 효율을 얻는데 중요한 설계 인자이다. 최근 들어 유전자 알고리즘은 조합최적화 문제, 통신망 설계문제와 같은 현실적 문제를 위한 최적화 기법으로 널리 활용되어 지고 있다. 본 논문은 망의 구성비용과 메시지 지연시간을 최소화 하는 통신망 설계를 위한 다목적 유전 알고리즘을 제시한다. 본 알고리즘은 다목적 함수의 최적화에서 일반적으로 어려운 목적 함수간의 최적화를 위해 파레토를 이용하였다. 부호화 방법으로 프뤼퍼 숫자와 클러스터링 문자를 사용했고, 적합도 배분방법으로 파레토 순위할당 제거방법과 생태적 적소형태(niche-formation)방법을 사용하였으며, 조기수렴을 방지위해 변형된 엘리트 기법을 사용했다. 시뮬레이션을 통해 제안하는 알고리즘이 망구성의 후보해를 효과적으로 찾음을 보여준다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.333-336
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• 1991

In this paper, Hopfield & Tank model-like artificial neural network structure is proposed, which can be used for the optimal path planning problems such as the unit commitment problems or the maintenance scheduling problems which have been solved by the dynamic programming method or the branch and bound method. To construct the structure of the neural network, an energy function is defined, of which the global minimum means the optimal path of the problem. To avoid falling into one of the local minima during the optimization process, the simulated annealing method is applied via making the slope of the sigmoid transfer functions steeper gradually while the process progresses. As a result, computer(IBM 386-AT 34MHz) simulations can finish the optimal unit commitment problem with 10 power units and 24 hour periods (1 hour factor) in 5 minites. Furthermore, if the full parallel neural network hardware is contructed, the optimization time will be reduced remarkably.