• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2468-2487
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• 2015

In web searching, trustable and precise results are greatly affected by the inherent uncertainty in the input queries. Queries submitted to search engines are by nature ambiguous and constitute a significant proportion of the instances given to web search engines. Ambiguous queries pose real challenges for the web search engines due to versatility of information. Temporal based approaches whereas somehow reduce the uncertainty in queries but still lack to provide results according to users aspirations. Web search science has created an interest for the researchers to incorporate contextual information for resolving the uncertainty in search results. In this paper, we propose an Adaptive Disambiguation Approach (ADA) of hybrid nature that makes use of both the temporal and contextual information to improve user experience. The proposed hybrid approach presents the search results to the users based on their location and temporal information. A Java based prototype of the systems is developed and evaluated using standard dataset to determine its efficacy in terms of precision, accuracy, recall, and F1-measure. Supported by experimental results, ADA demonstrates better results along all the axes as compared to temporal based approaches.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.368-373
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• 2007

In recent year s there have been increasing interests in real-time object tracking with image information. This dissertation presents a real-time object tracking method through the object recognition based on neural networks that have robust characteristics under various illuminations. This dissertation proposes a global search and a local search method to track the object in real-time. The global search recognizes a target object among the candidate objects through the entire image search, and the local search recognizes and track only the target object through the block search. This dissertation uses the object color and feature information to achieve fast object recognition. The experiment result shows the usefulness of the proposed method is verified.

• The Journal of Information Systems
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• v.25 no.1
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• pp.159-182
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• 2016

Purpose This paper aims to develop a novel tabu search algorithm for solving the sequencing problems with precedence constraints. Due to constraints, the traditional meta heuristic methods can generate infeasible solutions during search procedure, which must be carefully dealt with. On the contrary, the candidate order based tabu search (COTS) is based on a novel neighborhood structure that guarantees the feasibility of solutions, and can dealt with a wide range of sequencing problems in flexible manner. Design/methodology/approach Candidate order scheme is a strategy for constructing a feasible sequence by iteratively appending an item at a time, and it has been successfully applied to genetic algorithm. The primary benefit of the candidate order scheme is that it can effectively deal with the additional constraints of sequencing problems and always generates the feasible solutions. In this paper, the candidate order scheme is used to design the neighborhood structure, tabu list and diversification operation of tabu search. Findings The COTS has been applied to the single machine job sequencing problems, and we can see that COTS can find the good solutions whether additional constraints exist or not. Especially, the experiment results reveal that the COTS is a promising approach for solving the sequencing problems with precedence constraints. In addition, the operations of COTS are intuitive and easy to understand, and it is expected that this paper will provide useful insights into the sequencing problems to the practitioners.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.1
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• pp.62-68
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• 1999

The attitude of a vehicle can be precisely determined using GPS carrier phase measurements from more than two antennas attached to a vehicle and an efficient integer ambiguity resolution technique. Many methods utilizing the known baseline length as a constraint of independent elements of integer ambiguities are proposed to resolve integer ambiguity at real time. Three-dimensional search space is reduced to two-dimensional search space with this constraint. Thus the true integer ambiguity can be easily determined with less computational burden and fewer number of measurements. But there are still strong requirements for the real time integer ambiguity resolution, which uses single epoch measurement of long baseline. In this paper, a new constraint from the geometry of multiple baselines is derived. With this new constraint, two-dimensional search space is further reduced to one-dimensional search space. It makes possible to determine integer ambiguity with single epoch measurement. The proposed method is applied to real data to show its effectiveness.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.866-870
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• 2010

This paper presents a application of Harmony Search (HS) algorithm for optimal placement of distributed generations(DGs) in distribution systems. In optimization procedure, the HS algorithm denotes the searching ability for the global optimal solution with simple coding of the iteration procedure, and shows the fast convergence characteristics for getting solutions. The HS algorithm is tested on 9 buses and 69 buses distribution systems, and the results prove its effectiveness to determine appropriate placement points of DGs and reducing amount of active power without the occurrence of any mis-determination in selection of its capacity.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.215-221
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• 2003

Generally, we want to be searched the newest as well as some appropriate personalized information from the internet resources. However, it is a complex and repeated procedure to search some appropriate information. Moreover, because the user's interests are changed as time goes, the real time modeling of a user's interests should be necessary. In this paper, I propose PREA system that can search and filter documents that users are interested from the World Wide Web. And then it constructs the user's interest model by a modified spreading neural network. Based on this network, PREA can easily produce some queries to search web documents, and it ranks them. The conventional spreading neural network does not have a visualization function, so that the users could not know how to be configured his or her interest model by the network. To solve this problem, PREA gives a visualization function being shown how to be made his interest user model to many users.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.233-238
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• 2003

Living creatures maintain their variety through speciation, which helps them to have more fitness for an environment. So evolutionary algorithm based on biological evolution must maintain variety in order to adapt to its environment. In this paper, we utilize the concept of speciation. Each individual of population creates their offsprings using mutation, and next generation consists of them. Each individual explores search space determined by mutation. Useful search space is extended by differentiation, then population explorers whole search space very effectively. If evolvable hardware evolves through mutation, it is useful way to explorer search space because of less varying inner structure. We verify the effectiveness of the proposed method by applying it to two optimization problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.206-211
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• 2011

In this paper, the evolutionary algorithm was specifically formulated for optimization with continuous parameter space. The proposal was motivated by the fact that the genetic algorithms have been most intensively reported for parameter identification problems with continuous search space. The difference of primary characteristics between genetic algorithms and the proposed algorithm, discrete or continuous individual representation has made different areas to which the algorithms should be applied. Results obtained by optimization of some well-known test functions indicate that the proposed algorithm is superior to genetic algorithms in all the performance, computation time and memory usage for continuous search space problems.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.11a
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• pp.375-389
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• 2000

Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule- inference. network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search costs for searching sequentially and searching by means of search priorities.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.290-293
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• 1996

This paper proposes a scheme of initial cluster center selection in FCM algorithm using the genetic algorithms. The FCM algorithm often fails in the search for global optimum because it is local search techniques that search for the optimum by using hill-climbing procedures. To solve this problem, we search for a hypersphere encircling each clusters whose parameters are estimated by the genetic algorithms. Then instead of a randomized initialization for fuzzy partition matrix in FCM algorithm, we initialize each cluster center by the center of a searched hypersphere. Our experimental results show that the proposed initializing scheme has higher probabilities of finding the global or near global optimal solutions than the traditional FCM algorithm.