• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.312-320
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• 2021

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.

• Nuclear Engineering and Technology
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• v.55 no.8
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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.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1011-1014
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• 1999

In this paper, a new algorithm has been presented that helps to preserve diversity as well as to enhance the convergence speed of the evolutionary programming. This algorithm is based on the cell partitioning of search region for preserving the diversity. Until now, the greater part of researches is not concerned about preserving the diversity of individuals in a population but improving convergence speed. Although these evolutions are started from multi-point search at the early phase, but at the end those search points are swarming about a one-point, the strong candidate. These evolutions vary from the original idea in some points such as multi-point search. In most case we want to find the only one point of the best solution not several points in the vicinity of that. That is why the cell partitioning of search region has been used. By restricting the search area of each individual, the diversity of individual in solution space is preserved and the convergence speed is enhanced. The efficiency of the proposed algorithm has been verified through benchmark test functions.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.3
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• pp.5-33
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• 2018

In this paper, we analyze a user's usage behavior, identify and track search intention and interest field based on the National Science and Technology Standard Classification, and use it to personalize science and technology information. In other words, we sought to satisfy both efficiency and satisfaction in searching for information that users want by improving scientific information search performance. We developed the personalization service of science and technology information and evaluated the suitability and usefulness of personalized information by comparing the search performance between expert experimental group and control group. As a result, the personalization service proposed in this study showed better search performance than comparative service and proved to provide higher usability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4357-4378
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• 2017

In cooperative multi-relay networks, the relay nodes which are selected are very important to the system performance. How to choose the best cooperative relay nodes is an optimization problem. In this paper, multi-relay selection schemes which consider either single objective or multi-objective are proposed based on evolutionary algorithms. Firstly, the single objective optimization problems of multi-relay selection considering signal to noise ratio (SNR) or power efficiency maximization are solved based on the quantum bee colony optimization (QBCO). Then the multi-objective optimization problems of multi-relay selection considering SNR maximization and power consumption minimization (two contradictive objectives) or SNR maximization and power efficiency maximization (also two contradictive objectives) are solved based on non-dominated sorting quantum bee colony optimization (NSQBCO), which can obtain the Pareto front solutions considering two contradictive objectives simultaneously. Simulation results show that QBCO based multi-relay selection schemes have the ability to search global optimal solution compared with other multi-relay selection schemes in literature, while NSQBCO based multi-relay selection schemes can obtain the same Pareto front solutions as exhaustive search when the number of relays is not very large. When the number of relays is very large, exhaustive search cannot be used due to complexity but NSQBCO based multi-relay selection schemes can still be used to solve the problems. All simulation results demonstrate the effectiveness of the proposed schemes.

• Journal of the Korean Home Economics Association
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• v.40 no.12
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• pp.199-216
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• 2002

The purpose of this research is to study the efficiency of the Internet market, not provided to consumers by the existing traditional market. This research examines whether consumers properly understand the efficiency of the Internet market. The result from consumers perception on the efficiencies in the two market are as follows. First, consumers perceived the traditional market as having more product alternatives compared to the Internet market. Second, consumers perceived that the Internet market was more efficient in price dispersion and price change Third, the Internet market was considered more efficient in searching and travel time, and in the search cost. Finally, the traditional market was considered as a better provider of the information about product function, feature, reality, usage and service compared to the Internet market. On the contrary, the Internet market turned out to be more effective in providing product information, price information and trading information compared to the traditional market. Therefore consumers perceived the traditional market of having more information.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.617-620
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• 2000

In this paper, we show an application of constraint logic programming to the operation scheduling on machines in a job shop. Constraint logic programming is a new genre of programming technique combining the declarative aspect of logic programming with the efficiency of constraint manipulation and solving mechanisms. Due to the latter feature, combinatorial search problems like scheduling may be resolved efficiently. In this study, the jobs that consist of a set of related operations are supposed to be constrained by precedence and resource availability. We also explore how the constraint solving mechanisms can be defined over a scheduling domain. Thus the scheduling approach presented here has two benefits: the flexibility that can be expected from an artificial intelligence tool by simplifying greatly the problem; and the efficiency that stems from the capability of constraint logic programming to manipulate constraints to prune the search space in an a priori manner.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.1007-1008
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• 2006

This paper presents a optimal path search of mobile robot using energy efficiency. Proposed method has two goals that determining the velocity of mobile robot and finding the optimal path. We model the relationship of motors' speed and power consumption with quadratic polynomials. In order to verify the effectiveness of the proposed method, we performed simulations on the energy consumption of different paths.

• Journal of Korea Multimedia Society
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• v.2 no.3
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• pp.231-242
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• 1999

Generally, either caption-based search method or content-based search methods is used to retrieve video information. However, each search method has its limitations. Caption-based search is apt to lose consistency as for user's subjects, and content-based search is hard to extract general means. To enhance efficiency and correctness as for complementing each other, we propose the Integrated Video Data Model(IVDM) which integrates the two search methods, to device the model, we analyze video data and construct the structure of video information hierarchically. IVDM supports caption-based search as assigning meta-data by analyzing thematic-unit in the higher level, and also supports content-based search as extracting feature data by analyzing the content of video data in the lower level. We design Object-Oriented database schema of news video, based-on the IVDM. And we provide 4-type of queries and query processing algorithm to retrieve news video information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1083-1088
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• 2007

In this paper, We investigate the search patterns that are applied to the character based word search and make the search algorithm. We used to various hangout coded set that are KS X 1001 hangeul coded set and unicode 3.0 for the character based word search algorithm. In each case, We study of efficiency of algorithms that are related to hangeul coded set.