• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권12호
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

• 산업경영시스템학회지
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• 제45권4호
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• pp.217-224
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• 2022

Due to the issue of the sustainability in transportation area, the number of electric vehicles has significantly increased. Most automakers have decided or planned to manufacture the electric vehicles rather than carbon fueled vehicles. However, there are still some problems to figure out for the electric vehicles such as long charging time, driving ranges, supply of charging stations. Since the speed of growing the number of electric vehicles is faster than that of the number of charging stations, there are lack of supplies of charging stations for electric vehicles and imbalances of the location of the charging stations. Thus, the location problem of charging stations is one of important issues for the electric vehicles. Studies have conducted to find the optimal locations for the charging stations. Most studies have formulated the problem with deterministic or hierarchical models. In this paper, we have investigated the fluctuations of locations and the capacity of charging stations. We proposed a mathematical model for the location problem of charging stations with the vehicle routing problem. Numerical examples provide the strategy for the location routing problems of the electric vehicles.

• Nuclear Engineering and Technology
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• 제55권8호
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• pp.3030-3038
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• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1939-1943
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• 1991

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the shortest collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal of this paper, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy[3] and a traveling salesman problem strategy(TSP)[23]. The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Neural Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is proposed to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm[21] and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm[5].

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.79-84
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• 1992

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the optimal collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy and a traveling salesman problem strategy (TSP). The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Hopfield Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is used to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm.

• 대한토목학회논문집
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• 제40권2호
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• pp.247-255
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• 2020

현재 도시재생은 대부분 영구적이고 고정적인 계획방식으로 진행되고 있으며, 종합계획 특성상 주민들의 의견을 즉각적으로 수용하기에는 한계가 있다. 본 연구에서는 일시적 활용의 전략을 기반으로 유휴공간의 활용요소를 도출하여 주민 인식조사 및 선호도 조사와 전문가 중요도 분석을 비교하여 일시적 활용 기법을 적용한 유휴공간 활용의 방향성을 제시하였다. 분석 결과, 거주민은 대상지 특성상 노후화된 물리적 환경을 먼저 개선하는 것을 선호하는 반면, 전문가는 빠르게 도시의 환경을 개선시킬 수 있는 일시적 활용의 특성을 담고 있는 활용방안을 중요도에 반영하여 그 순위의 차이가 있었다. 따라서 일시적 활용 전략의 중요성을 인식하고 두 집단의 우선순위 차이를 세분화하여 한시적으로 테스트(Test)를 진행함으로써 최적의 활용방안을 도출하여 종합계획에 반영하는 것이 바람직하다. 향후 일시적 활용 전략을 시행하고자 할 때 대상지 특성과 이용 패턴 분석을 통해 면밀한 사전조사와 이를 반영한 공간 활용 계획이 제시되어야 할 것이다.

• 디지털융복합연구
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• 제12권6호
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• pp.277-287
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• 2014

위치 기반 소셜 네트워크 서비스에서 사용자들은 체크인 데이터를 이용해 장소를 공유하고 커뮤니케이션을 한다. 체크인 데이터는 POI명, 카테고리, 장소 좌표/주소, 사용자 닉네임, 장소 평가 점수, 관련 글/사진/동영상 등으로 구성된다. 위치 기반 소셜 네트워크 서비스에서 이러한 체크인 데이터를 상황에 맞게 분석하면 다양한 맞춤형 서비스를 제공할 수 있다. 따라서, 본 논문에서는 사용자 체크인 데이터를 활용할 수 있는 LBSNS(Location-based Social Network Service) 기반 장소 추천 시스템을 개발하였다. LBSNS 기반 장소 추천 시스템은 체크인 데이터 중 장소 카테고리를 분석하여 가중치를 추출하고, 피어슨 상관계수를 이용해 사용자간의 유사도를 구한다. 그리고 협업적 필터링 알고리즘을 이용하여 추천 장소에 대한 선호도 점수를 구하고, 추천 대상 장소의 위치와 사용자의 현재 위치에 대해 유클라디안 알고리즘을 적용하여 거리 점수를 구한다. 마지막으로, 선호도 점수와 거리 점수에 대해 가중치 계수를 적용하여 상황에 맞게 장소를 추천한다. 또한, 본 논문에서는 실제 데이터를 이용한 실험을 통해 본 논문에서 제시한 시스템의 우수성도 입증하였다.

• 한국컴퓨터정보학회논문지
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• 제21권3호
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• pp.57-64
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• 2016

This paper deals with the PCB assembly time minimization problem that the PAP (pick-and-placement) machine pickup the K-weighted group of N-components, loading, and place into the PCB placement location. This problem considers the rotational turret velocity according to component weight group and moving velocity of distance in two component placement locations in PCB. This paper suggest heavy-weight component group first pick-and-place strategy that the feeder sequence fit to the placement location Hamiltonean cycle sequence. This algorithm applies the quadratic assignment problem (QAP) that considers feeder sequence and location sequence, and the linear assignment problem (LAP) that considers only feeder sequence. The proposed algorithm shorten the assembly time than iATMA for QAP, and same result as iATMA that shorten the assembly time than ATMA.

• 산업기술연구
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• 제27권A호
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• pp.141-147
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• 2007

In this paper, we define the multi-states exponential cell resident time mobility model for describing the mobility characteristics of mobile user and analyzed the total cost of the hybrid method using multi-states cell resident time. Generally, the mobile user has three states for its movement, such as staying, walking and driving. This multi-states cell resident time based hybrid method reflects the movement characteristics of mobile user and adapts the location update period according to the states of mobility speed. As a results of the performance analysis, we can get the optimum parameters of the hybrid method for multi-states based mobile users.

• 산업기술연구
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• 제14권
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• pp.49-61
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• 1994

This study deals with the automated storage/retrieval systems (AS/RS) where the rack is discrete and the random storage assignment rule is adopted. New storage policies named FNSC (first no-cost zone, second closest-open location) and FNSH (first no-cost zone, second a half side closest-open location) are proposed. Simulation results indicate that performance of FNSH is slightly lower than that of the SCL but it does not need much time to perform. The "stay" strategy of S/R machine is examined as a means of increasing the frequency of dual command.