• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1310-1312
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• 1996

In this study, the algorithm for detection of evoked potentials is proposed. The observed evoked potentials are first preprocessed by blind identification so as to eliminate the ongoing EEG Bile noise. Then, statistic characteristics of the peak components i.e latency and amplitude are detected from prefiltered responses by latency-corrected averaging method. The performance of blind identification is compared with those of adaptive fillers as to deterministic and stochastic EPs, is assessed in terms of NMSE, distortion index, correlation coefficient with original EPs. The estimated deterministic and stochastic EPs restored with peak components are compared and assessed. The results show the superiority of this proposed algorithm using blind identification in detecting deterministic and stochastic EPs.

• 로봇학회논문지
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• 제17권1호
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• pp.58-67
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• 2022

In this paper, we propose an approach resolving inaccuracy of the low-cost redundant manipulator workspace with low encoder and low stiffness. When the manipulators are manufactured with low-cost encoders and low-cost links, the robots can run into workspace inaccuracy issues. Furthermore, trajectory generation based on conventional forward/inverse kinematics without taking into account inaccuracy issues will introduce the risk of end-effector fluctuations. Hence, we propose an optimization for the trajectory generation method based on the DDPG (Deep Deterministic Policy Gradient) algorithm for the low-cost redundant manipulators reaching the target position in Euclidean space. We designed the DDPG algorithm minimizing the distance along with the jacobian condition number. The training environment is selected with an error rate of randomly generated joint spaces in a simulator that implemented real-world physics, the test environment is a real robotic experiment and demonstrated our approach.

• ETRI Journal
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• 제45권4호
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• pp.627-635
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• 2023

In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0-1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility.

• 한국산업정보학회논문지
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• 제7권3호
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• pp.62-67
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• 2002

본 논문에서는 기존의 결정론적 볼츠만 머신의 학습알고리즘을 수정하여 양자화결합을 갖는 결정론적 볼츠만 머신에도 적용할 수 있는 알고리즘을 제안하였다. 제안한 알고리즘을 2-입력 XOR 문제와 3-입력 패리티 문제에 적용하여 성능을 분석하였다. 그 결과 하중이 대폭적으로 양자화된 네트워크에 대해서도 학습이 가능하다는 것과 은닉층 뉴런의 수를 증가시키면 한정된 하중값의 범위로 유지할 수 있는 것을 보여준다. 또한 1회에 갱신하는 하중의 갯수를 제어함으로써 학습계수를 제어하는 효과가 얻어지는 것을 확인하였다.

• Geomechanics and Engineering
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• 제10권6호
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• pp.709-726
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• 2016

Corrugated-core sandwich panels are prevalent for many applications in industries. The researches performed with the aim of optimization of such structures in the literature have considered a deterministic approach. However, it is believed that deterministic optimum points may lead to high-risk designs instead of optimum ones. In this paper, an effort has been made to provide a reliable and robust design of corrugated-core sandwich structures through stochastic and probabilistic multi-objective optimization approach. The optimization is performed using a coupling between genetic algorithm (GA), Monte Carlo simulation (MCS) and finite element method (FEM). To this aim, Prob. Design module in ANSYS is employed and using a coupling between optimization codes in MATLAB and ANSYS, a connection has been made between numerical results and optimization process. Results in both cases of deterministic and probabilistic multi-objective optimizations are illustrated and compared together to gain a better understanding of the best sandwich panel design by taking into account reliability and robustness. Comparison of results with a similar deterministic optimization study demonstrated better reliability and robustness of optimum point of this 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.

• 전력전자학회논문지
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• 제11권2호
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• pp.159-163
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• 2006

선형 서보 응용분야에서 속도제어기를 정밀하게 조정하기 위해서는 부하 및 가동자의 질량을 항상 정확하게 알고 있어야 한다. 본 논문에서는 선형 영구자석 동기전동기의 가동부 질량을 추정하기 위하여 상수추정 알고리즘으로 최소자승법을 적용한 질량 추정방법을 제안하였다. 먼저 최소자승법을 적용하기 위한 기계적인 동전 시스템에 대한 DARMA(deterministic autoregressive moving average)모델을 유도하고, 유도된 DARMA모델에 최소자승법을 적용한 시뮬레이션 덴 실험 결과를 제시하여 제안한 방법으로 질량을 정밀하게 추정할 수 있음을 보였다.

• 전자공학회논문지A
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• 제29A권8호
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• pp.99-109
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• 1992

First, the coupling faults of semiconductor memory are classified in detail. The chained coupling fault is introduced and defined, which results from sequential influencing of the coupling effects among memory cells, and its mapping relation is described. The linked coupling fault and its order are defined. Second, the deterministic “Algorithm GA” is proposed, which detects stuack-at faults, transition faults, address decoder faults, unlinked 2-coupling faults, and unlinked chained coupling faults. The time complexity and the fault coverage are improved in this algorithm. Third, it is proved that the march test of an address sequence can detect 97.796% of the linked 2-coupling faults with order 2. The deterministic “Algorithm NA” proposed can detect to the limit. The time complexity and the fault coverage are improved in this algorithm.

• 한국인터넷방송통신학회논문지
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• 제15권2호
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• pp.255-262
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• 2015

경제급전 최적화 문제를 해결하는 결정론적인 알고리즘에 존재하지 않아 지금까지는 비결정론적인 휴리스틱 알고리즘들이 제안되고 있다. 본 논문은 균형과 교환 방법을 도입하여 경제급전의 최적화 문제를 풀 수 있는 알고리즘을 제안하였다. 제안된 알고리즘은 초기치에 대해 성인걸음수와 아기걸음 수별로 발전량을 감소시켜 ${\Sigma}P_i=P_d$로 균형을 맞추고, 이 때 최소 발전비용을 가진 방법을 선택한다. 다음으로 선택된 방법에 대해 성인걸음-아기걸음 교환과 거인걸음 교환 방법으로 최적화한 값을 구하여 최소값 방법을 선택한다. 마지막으로 선택된 방법에 대해 $P_i{\pm}{\beta}$, (${\beta}=0.1,0.01,0.001,0.0001$)의 교환을 수행하였다. 경제급전 문제의 시험사례로 빈번히 활용되고 있는 3개 데이터에 대해 제안된 알고리즘을 적용한 결과 2개 데이터에서는 성능을 향상시켰으며, 1개 데이터는 기존의 최적해와 동일한 결과를 얻었다. 제안된 알고리즘은 항상 동일한 결과를 얻을 수 있고, 모든 데이터에 적합하므로 경제급전 최적화 알고리즘으로 실제 적용이 가능하다.