• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.851-867
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• 1999

Numerous proposals for extending the relational data model as well as conceptual and object-oriented data models have been suggested. However, there has been relatively less research in the area of defining segmented storage structure and data migration strategies for temporal data. This paper presents the segmented storage structure in order to increment search performance and the two data migration strategies for segmented storage structure. this paper presents the two data migration strategies : the migration strategy by Time granularity, the migration strategy by LST-GET. In the migration strategy by Time Granularity, the dividing time point to assign the entity versions to the past segment, the current segment, and future segment is defined and the searching and moving process for data validity at a granularity level are described. In the migration strategy by LST-GET, we describe the process how to compute the value of dividing criterion. searching and moving processes are described for migration on the future segment and the current segment and entity versions 새 assign on each segment are defined. We simulate the search performance of the segmented storage structure in order to compare it with conventional storage structure in relational database system. And extensive simulation studies are performed in order to compare the search performance of the migration strategies with the segmented storage structure.

• Journal of the Korean Society for information Management
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• v.30 no.3
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• pp.111-131
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• 2013

The aim of this study is to reveal a necessity of and a strategy for the integrated use and the single search across the visual arts resources. For this purpose, at first, analysis was made on the current situation of Korean visual art resource management and retrieval systems. Secondly, the single search methods and its related technological foundation in foreign art resource institutions were categorized and analysed. As a result, this study suggested foundation for creating a sustainable environment for collaboration and single search that enhance access to and use of art resources.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.199-206
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• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.149-151
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• 2014

주의(attention)를 어떻게 할당하는가는 정신물리학뿐만 아니라, 컴퓨터 시각과정을 모델링하는데 중요한 주제 중 하나이다. 기존 연구는 saliency와 같은 활성화 값에 의해서 주의탐사 순위가 결정된다. 본 논문에서는 주의탐사과정을 병렬처리를 통한 활성화 값 추출과정과 순차적 처리를 통한 공간적 전략과정으로 구분하였다. 단서패러다임에 기초한 계산모형을 이용하여, 실제 인간의 수행결과를 AUC와 Levenshtein 척도를 이용하여 비교하였다. Fixation point 비교에서는 인간과 활성화를 기반으로 한 계산모형의 수행은 높은 상관성을 가지고 있었다. 주의궤적 혹은 scanpath 분석에서는 활성화기반 전략보다는 공간적 전략 모형이 더 높은 유사성을 보였다. 이는 주의탐사과정이 병렬처리과정을 통해 얻어진 saliency에 의해서만 결정되는 것이 아니라, 목표물간의 근접성 등의 공간적 전략을 통해 순차적 (sequential) 경로가 생성됨을 의미한다.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.1-9
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• 2023

This paper presents an optimal path planning strategy for aerial searching and surveying of a user-designated area using multiple Unmanned Aerial Vehicles (UAVs). The method is designed to deal with a single unseparated polygonal area, regardless of polygonal convexity. By defining the search area into a set of grids, the algorithm enables UAVs to completely search without leaving unsearched space. The presented strategy consists of two main algorithmic steps: cellular decomposition and path planning stages. The cellular decomposition method divides the area to designate a conflict-free subsearch-space to an individual UAV, while accounting the assigned flight velocity, take-off and landing positions. Then, the path planning strategy forms paths based on every point located in end of each grid row. The first waypoint is chosen as the closest point from the vehicle-starting position, and it recursively updates the nearest endpoint set to generate the shortest path. The path planning policy produces four path candidates by alternating the starting point (left or right edge), and the travel direction (vertical or horizontal). The optimal-selection policy is enforced to maximize the search efficiency, which is time dependent; the policy imposes the total path-length and turning number criteria per candidate. The results demonstrate that the proposed cellular decomposition method improves the search-time efficiency. In addition, the candidate selection enhances the algorithmic efficacy toward further mission time-duration reduction. The method shows robustness against both convex and non-convex shaped search area.

• 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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.5
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• pp.173-180
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• 2014

In this research, we aim to analyze how different external knowledge search strategies of small and medium enterprises affect technological innovation. In particular, since there has been little comparison between Korean small and medium firms, we investigated the differences of the two groups by employing the "Korean Innovation Survey 2010." As a result, it was found that "external search breadth", which refers to expanding the spectrum of external knowledge search due to lack of resources and capabilities inside small firms, spurred technological innovation. On the other hand, "external search depth", which implies seeking a long-term and close relationship with the subjects that provide outside knowledge for medium firms, catalyzed technological innovation. These results emphasize that we need to separately analyze technological innovation of small and medium firms, which was, in most previous studies, viewed as one group, i.e. SMEs. In addition, the results can be, from the perspective of a firm's growth, interpreted as follows. That is, it is more effective to formulate a "diversity" pursuing strategy in the "small firm" stage where little time has passed since its establishment, whilst it is more useful to apply a "focus" strategy on sophisticated knowledge in the "medium firm" stage where a firm has grown to some extent.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.495-502
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• 2013

Motion estimation (ME) that limits the performance of image quality and encoding speed has been developed to reduce temporal redundancy in video sequences and plays an important role in digital video compression. But it is computational demanding part of the encoder. Multi-view video is obtained by capturing one three-dimensional scene with many cameras at different positions. ME for Multi-view video requires high computational complexity. To reduce computational complexity and maintain the image quality, a fast motion estimation method is proposed in this paper. The proposed method uses a hierarchical search strategy. This strategy method consists of modified diamond search patten, multi gird diamond search pattern, and raster search pattern. These search patterns place search points symmetrically and evenly that can cover the overall search area not to fall into the local minimum or exploits the characteristics of the distribution of motion vectors to place the search points. Experiment results show that the speedup improvement of the proposed method over TZ search method (JMVC) can be up to 1.2 ~3 times faster while maintaining similar video quality and bit rates.

• The Journal of Information Technology and Database
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• v.6 no.1
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• pp.27-46
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• 1999

This study was intended to find the use patterns of internet search engines of librarians and to measure the relationship between internet use frequency and the use behavior of internet search engines. The results showed that librarians use Web search engines for academic information retrieval and are satisfied with the search results. The major problems when librarians use search engines were that search engines retrieve many non-relevant documents. As a result of hypotheses test, the relationship between internet frequency and the preference of search engines was not significantly different. On the other hand, the hypotheses that internet frequency affects satisfaction of search results, recognition of importance of search engines, and the need of retraining of librarians for internet information retrieval were shown to be significant.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.319-326
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• 2009

PSO(Particle Swarm Optimization) is an optimization algorithm in which simple particles search an optimal solution using shared information acquired through their own experiences. PSO applications are so numerous and diverse. Lots of researches have been made mainly on the parameter settings, topology, particle's movement in order to achieve fast convergence to proper regions of search space for optimization. In standard PSO, since each particle uses only information of its and best neighbor, swarm does not explore diverse regions and intended to premature to local optima. In this paper, we propose a new particle's movement strategy in order to explore diverse regions of search space. The strategy is that each particle moves according to relative weights of several better neighbors. The strategy of exploring diverse regions is effective and produces less local optimizations and accelerating of the optimization speed and higher success rates than standard PSO. Also, in order to raise success rates, we propose a strategy for checking whether swarm falls into local optimum. The new PSO algorithm with these two strategies shows the improvement in the search speed and success rate in the test of benchmark functions.