• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.5
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• pp.25-32
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• 2011

In this paper, we propose a subspace search-based detector (SSD) with low-complexity to achieve near optimal performance for multiple-input multiple-output systems. As an effective solution to reduce the prohibitive computational complexity of the optimal maximum likelihood detector, a partial candidate symbol vector is generated through a partitioned search space but not the entire search space. In addition, based on a partial candidate symbol vector, an ensemble candidate symbol vector generation considering the whole search space is introduced to produce a near optimal solution. As a result, the proposed SSD achieves near-maximum-likelihood performance while having a significantly reduced computational complexity.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.12-18
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• 2017

This paper presents a Dijkstra's-search-based sphere decoding (SD) algorithm with limited complexity for the symbol detection in MIMO communication systems. The Dijkstra search-based SD is efficient to achieve a near-optimal error rate in the MIMO symbol detection, but has a critical problem in that its complexity is variable and can correspond to that of the exhaustive search in the worst case. The proposed algorithm limits the computations while achieving a near-optimal error rate. Simulation results show that the error rate is near optimal even with the limited complexity.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.811-814
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• 2003

In this paper, we propose a fast block-matching motion estimation method using the cross pattern and the hexagonal pattern. For the block-matching motion estimation method, full search finds the best motion estimation, but it requires huge search time because it has to check every search point within the search window. The proposed method makes use of the fact that most of motion vectors lie near the center of block. The proposed method first uses the cross pattern to search near the center of block, and then uses the hexagonal pattern to search larger motion vectors. Experimental results show that our method is better than recently proposed search algorithms in terms of mean-square error performance and required search time.

• Journal of Welding and Joining
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• v.28 no.6
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• pp.76-83
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• 2010

The purpose of this study is to propose a method to decide near-optimal settings of the constant current fuzzy control parameters using a controlled random search. This method tries to find the near-optimal settings of the constant current fuzzy control parameters through experiments. It has an advantage of being able to carry out searches in the search domain which includes some irregular points. The method suggested in this study was used to determine the fuzzy control parameters by which the desired welding current were formed during inverter DC resistance spot welding. The output variable was the ITAE (integral of time multiplied by the absolute error). This output variable was determined according to the input variables, which are the GE, GDE, and GDU. This study described how to obtained near-optimal welding current condition over a wide search space conducting a relatively small number of experiments.

• Journal of Smart Tourism
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• v.3 no.3
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• pp.29-40
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• 2023

This research aims to understand the role of online reviews in the local search context by examining the effects of reviews on the representation of tourism businesses on local search platforms (LSPs). By simulating tourists' local searches for restaurants on three LSPs, namely Google, Bing, and Yelp, this study examines how different ranking results are generated across the platforms and how online reviews contribute to the differences. The findings suggest that online reviews are incorporated into LSPs as ranking factors and, thus, affect tourists' decision-making by influencing the information search results in the local search context. As one of the earliest studies on local search, this study discusses how the existing knowledge about the role of online reviews in tourists' decision-making needs to be reevaluated in mobile and more dynamic environments, and offers practical implications for tourism businesses' search engine marketing.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.2
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• pp.157-167
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• 2005

This paper presents a new perturbation technique for developing efficient iterated local search procedures for the asymmetric traveling salesman problem(ATSP). This perturbation technique uses global information on ATSP instances to speed-up computation and to improve the quality of the tours found by heuristic method. The main idea is to escape from a local optima by introducing perturbations on the out-of-kilter arcs in the problem instance. For a local search heuristic, we use the Kwon which finds optimum or near-optimum solutions by applying the out-of-kilter algorithm to the ATSP. The performance of our algorithm has been tested and compared with known method perturbing on randomly chosen arcs. A number of experiments has been executed both on the well-known TSPLIB instances for which the optimal tour length is known, and on randomly generated Instances. for 27 TSPLIB instances, the presented algorithm has found optimal tours on all instances. And it has effectively found tours near AP lower bound on randomly generated instances.

• IE interfaces
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• v.18 no.4
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• pp.504-511
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• 2005

The NP-hard channel assignment problem becomes more and more important to use channels as efficiently as possible because there is a rapidly growing demand and the number of usable channel is very limited. The hybrid genetic and local search (HGLS) method in this paper is a hybrid method of genetic algorithm with no interference channel assignment (NICA) in clustering stage for diversified search and local search in tuning stage when the step of search is near convergence for minimizing blocking calls. The new representation of solution is also proposed for effective search and computation for channel assignment.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.135-138
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• 1996

In solving the Travel Salesman Problem(TSP), we easily reach local optimal solutions with the existing methods such as TWO-OPT, THREE-OPT, and Lin-Kernighen. Tabu search, as a meta heuristic, is a good mechanism to get an optimal or a near optimal solution escaping from the local optimal. By utilizing AI concepts, tabu search continues to search for improved solutions. In this study, we focus on developing a new neighborhood structure that maintains the feasibility of the tours created by exchange operations in TSP. Intelligent methods are discussed, which keeps feasible tour routes even after exchanging several edges continuously. An extended tabu search model, performing cycle detection and diversification with memory structure, is applied to TSP. The model uses effectively the information gathered during the search process. Finally, the results of tabu search and simulated annealing are compared based on the TSP problems in the prior literatures.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.33-38
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• 2020

Elasticsearch is an open source search and analytics engine that can search petabytes of data in near real time. It is designed as a distributed system horizontally scalable and highly available. It provides RESTful APIs, thereby making it programming-language agnostic. Full text search of multilingual text requires language-specific analyzers and field mappings appropriate for indexing and searching multilingual text. Additionally, a language detector can be used in conjunction with the analyzers to improve the multilingual text search. Elasticsearch provides more than 40 language analysis plugins that can process text and extract language-specific tokens and language detector plugins that can determine the language of the given text. This study investigates three different approaches to index and search Chinese, Japanese, and Korean (CJK) text (single analyzer, multi-fields, and language detector-based), and identifies the advantages of the language detector-based approach compared to the other two.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.2
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• pp.121-128
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• 2013

There have been various research efforts for pedestrian recognition in embedded imaging systems. However, many suffer from their heavy computational complexities. SVM classification method has been widely used for pedestrian recognition. The reduction of candidate region is crucial for low-complexity scheme. In this paper, We propose a real time HOG based pedestrian detection system on GPU which images are captured by a pair of cameras. To speed up humans on road detection, the proposed method reduces a number of detection windows with disparity-search and near-search algorithm and uses the GPU and the NVIDIA CUDA framework. This method can be achieved speedups of 20% or more compared to the recent GPU implementations. The effectiveness of our algorithm is demonstrated in terms of the processing time and the detection performance.