• International Journal of Contents
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• v.1 no.1
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• pp.50-58
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• 2005

Generally, multidimensional data such as image and spatial data require large amount of storage space. There is a limit to store and manage those large amounts of data in single workstation. If we manage the data on parallel computing environment which is being actively researched these days, we can get highly improved performance. In this paper, we propose a parallel multidimensional index structure that exploits the parallelism of the parallel computing environment. The proposed index structure is nP(processor)-nxmD(disk) architecture which is the hybrid type of nP-nD and 1P-nD. Its node structure in-creases fan-out and reduces the height of an index. Also, a range search algorithm that maximizes I/O parallelism is devised, and it is applied to k-nearest neighbor queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures.

• Structural Monitoring and Maintenance
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• v.1 no.4
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• pp.427-449
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• 2014

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.34-36
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• 2000

This paper proposes the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gain of input-output variables of fuzzy logic controller and weights of neural network using Tabu search. Neural network used to tune the output gain of FLC adaptively. We have weights of neural network learned using back propagation algorithm. We performed the nonlinear simulation on an single-machine infinite system to prove the efficiency of the proposed method. The proposed AFLC showed the better performance than PD controller in terms of the settling time and damping effect, for power system operation condition.

• Journal of applied mathematics & informatics
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• v.33 no.1_2
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• pp.157-172
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• 2015

We introduce a non-Euclidean metric for transportation systems with a defined minimum transportation cost (initial fare) and investigate the continuous single-facility Weber location problem based on this metric. The proposed algorithm uses the results for solving the Weber problem with Euclidean metric by Weiszfeld procedure as the initial point for a special local search procedure. The results of local search are then checked for optimality by calculating directional derivative of modified objective functions in finite number of directions. If the local search result is not optimal then algorithm solves constrained Weber problems with Euclidean metric to obtain the final result. An illustrative example is presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.667-671
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• 2002

It is very difficult to extract the words fitted for the purpose in spite of the great importance of efficient keyword extraction in information retrieval systems because there are many compound words. For example, AC machine is not able to search compound keywords from a single keyword. The DER structure solves this problem, but there remains a problem that it takes too much time to search keywords. Therefore a DERtable structure based on these methods is proposed in this dissertation to solve the above problems in which method tables are added to the existing DER structure and utilized to search keywords.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05a
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• pp.121-126
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• 2002

We proposed the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gains of input-output variables of fuzzy logic controller(FLC) and weights of neural network using Tabu search. Neural network was used for adaptively tuning the output gain of FLC. The weights of neural network was learned from the back propagation algorithm in real-time. To evaluate the usefulness of AFLC, we applied the proposed method to single-machine infinite system. AFLC showed the better control performance than PD controller and GAFLC[8] for. three-phase fault in nominal load which had used when tuning AFLC. To show the robustness of AFLC, we applied the proposed method to disturbances such as three-phase fault in heavy and light load. AFLC showed the better robustness than PD controller and GAFLC[8].

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.79-86
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• 2019

For decades, in-memory data structures have been designed for DRAM-based main memory that provides symmetric read/write performances and has no limited write endurance. However, such data structures provide sub-optimal performance for NVM as it has different characteristics to DRAM. With this motivation, we rethink a conventional red-black tree in terms of its efficacy under NVM settings. The original red-black tree constantly rebalances sub-trees so as to export fast access time over dataset, but it inevitably increases the write traffic, adversely affecting the performance for NVM with a long write latency and limited endurance. To resolve this problem, we present a variant of the red-black tree called a hierarchical balanced binary search tree. The proposed structure maintains multiple keys in a single node so as to amortize the rebalancing cost. The performance study reveals that the proposed hierarchical binary search tree effectively reduces the write traffic by effectively reaping the high capacity of NVM.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.4
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• pp.93-102
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• 1995

This paper introduces a general purpose Associative Processor(AP) which is very efficient for search-oriented applications. The proposed architecture consists of three main functional blocks: Content-Addressable Memory(CAM) arry, row logic, and control section. The proposed AP is a Single-Instruction, Multiple-Data(SIMD) device based on a CAM core and an array of high speed processors. As an application for the proposed hardware, we present a parallel algorithm to solve a global routing problem in the layout process utilizing the processing capabilities of a rudimentary logic and the selective matching and writing capability of CAMs, along with basic algorithms such a minimum(maximum) search, less(greater) than search and parallel arithmetic. We have focused on the simultaneous minimization of the desity of the channels and the wire length by sedking a less crowded channel with shorter wire distance. We present an efficient mapping technique of the problem into the CAM structure. Experimental results on difficult examples, on randomly generated data, and on benchmark problems from MCNC are included.

• Journal of KIISE
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• v.44 no.12
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• pp.1313-1318
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• 2017

Aggregating local features in a single vector is a fundamental problem in an image search. In this process, the image search process can be speeded up if binary features which are extracted almost two order of magnitude faster than gradient-based features are utilized. However, in order to utilize the binary features in an image search, it is necessary to study the techniques for clustering binary features to generate binary visual words. This investigation is necessary because traditional clustering techniques for gradient-based features are not compatible with binary features. To this end, this paper studies the techniques for clustering binary features for the purpose of generating binary visual words. Through experiments, we analyze the trade-off between the accuracy and computational efficiency of an image search using binary features, and we then compare the proposed techniques. This research is expected to be applied to mobile applications, real-time applications, and web scale applications that require a fast image search.

• Journal of Digital Convergence
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• v.16 no.2
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• pp.93-102
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• 2018

This study suggests external knowledge search as a way of creating knowledge and efforts for reaching out to the innovation and examines the relationship between innovation speed and the breadth and depth of external knowledge search. As a part of efforts to overcome the limitations of the routines of external knowledge search in the domestic market, the efforts of a firm to enter the foreign market have been examined with a moderate effect. Although previous research focused on exploration of external knowledge that is mostly effected in single country or similar industry, this study expands it by introducing the concept of market expansion and empirically inspect the interaction effect. 818 valid samples used in this study are based on the innovation survey published by STEPI and empirical results are as follows. First, the breadth and depth of external knowledge search have a positive impact on innovation speed. Second, export performance as the effect of expanding the market has a moderating effect on the speed of innovation. Third, younger companies prefer various networks while older companies prefer depth networks in order to increase the speed.