• Korean Management Science Review
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• v.13 no.1
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• pp.13-27
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• 1996

This paper considers the sequencing problem in mixed model assembly lines with hybrid workstation types and sequence-dependent setup times. Computation time is often a critical factor in choosing a method of determining the sequence. We develop a mathematical formulation of the problem to minimize the overall length of a line, and present a tabu search technique which can provide a near optimal solution in real time. The proposed technique is compared with a genetic algorithm and a branch-and-bound method. Experimental results are reported to demonstrate the efficiency of the technique.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.12
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• pp.61-70
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• 2005

In this paper, we propose an efficient global placement algorithm which is an enhanced version of Hybrid Placer$^{[25]}$, a standard cell placement tool, which uses a middle-down approach. Combining techniques used in the well-known partitioner hMETIS and the RBLS(Relaxation Based Local Search) in Hybrid Placer improves the quality of global placements. Partitioning techniques of hMETIS is applied in a top-down manner and RBLS is used in each level of the top-down hierarchy to improve the global placement. The proposed new approach resolves the problem that Hybrid Placer seriously depends on initial placements and it speeds up without deteriorating the placement quality. Experimental results prove that solutions generated by the proposed method on the MCNC benchmarks are comparable to those by FengShui which is a well known placement tool. Compared to the results of the original Hybrid Placer, new method is 5 times faster on average and shows improvement on bigger circuits.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.260-267
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• 2017

Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.

• Proceedings of the Botanical Society of Korea Conference
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• 1998.07a
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• pp.117.2-118
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• 1998

• Journal of the Korean Society for Library and Information Science
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• v.49 no.4
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• pp.203-220
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• 2015

This study was to suggest the creation of hybrid bibliographic records in complicated bibliographic environment applying both AACR2 and RDA for uniform display and correct search. The literature reviews and case studies were used as the research methods. In case studies, Kent State University, University of Houston as the libraries, and Backstage Library Works as algorithm converting to hybrid records were surveyed. For creating the hybrid records, it was needed to differentiate hybrid records and born RDA records, to keep the data that appeared in 250 tag without spelling out edition data, to maintain 260 tag instead of converting 260 tag to 264 tag, and last to design mapping table for converting GMD to CMC. This study would suggest uniform display and search through hybridization of bibliographic records of foreign resources.

• Microbiology and Biotechnology Letters
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• v.47 no.2
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• pp.278-288
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• 2019

In the present investigation, we attempted to design a protocol to develop a hybrid protein with better bioremediation capacity. Using in silico approaches, a Hybrid Open Reading Frame (Hybrid ORF) is developed targeting the genes of microorganisms known for degradation of organophosphates. Out of 21 genes identified through BLAST search, 8 structurally similar genes (opdA, opd, opaA, pte RO, pdeA, parC, mpd and phnE) involved in biodegradation were screened. Gene conservational analysis categorizes these organophosphates degrading 8 genes into 4 super families i.e., Metallo-dependent hydrolases, Lactamase B, MPP and TM_PBP2 superfamily. Hybrid protein structure was modeled using multi-template homology modeling (3S07_A; 99%, 1P9E_A; 98%, 2ZO9_B; 33%, 2DXL_A; 33%) by $Schr{\ddot{o}}dinger$ software suit version 10.4.018. Structural verification of protein models was done using Ramachandran plot, it was showing 96.0% residue in the favored region, which was verified using RAMPAGE. The phosphotriesterase protein was showing the highest structural similarity with hybrid protein having raw score 984. The 5 binding sites of hybrid protein were identified through binding site prediction. The docking study shows that hybrid protein potentially interacts with 10 different organophosphates. The study results indicate that the hybrid protein designed has the capability of degrading a wide range of organophosphate compounds.

• Korean Journal of Construction Engineering and Management
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• v.21 no.1
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• pp.50-60
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• 2020

Estimates of project costs in the early stages of a construction project have a significant impact on the operator's decision-making in important matters, such as the site's decision or the construction period. However, it is difficult to carry out the initial stage with confidence because information such as design books and specifications is not available. In previous studies, case-based reasoning was used to predict initial construction costs, and genetic algorithms were used to calculate the weight of the inquiry phase among them. However, some say that it is difficult to perform better than the current year because existing genetic algorithms are calculated in random numbers. To overcome these limitations, correlation numbers using correlation analysis rather than random numbers are reflected in the genetic algorithm by method of local search, and weights are calculated using a hybrid genetic algorithm that combines local search and genetic algorithms. A case-based reasoning model was developed using the weights calculated and validated with the data. As a result, it was found that the hybrid GA-CBR applied with local search performed better than the existing GA-CBR.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.4
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• pp.745-750
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• 2009

The goal of this study is to examine on reasoning and search for construction of diagnosis ontology as a knowledge base of diagnosis expert system in oriental medicine. Expert system is a field of artificial intelligence. It is a system to acquire information with diverse reasoning methods after putting expert's knowledge in computer systematically. A typical model of expert system consists of knowledge base and reasoning & explanatory structure offering conclusion with the knowledge. To apply ontology as knowledge base to expert system practically, consideration on reasoning and search should be together. Therefore, this study compared and examined reasoning, search with diagnosis process in oriental medicine. Reasoning is divided into Rule-based reasoning and Case-based reasoning. The former is divided into Forward chaining and Backward chaining. Because of characteristics of diagnosis, sometimes Forward chaining or backward chaining are required. Therefore, there are a lot of cases that Hybrid chaining is effective. Case-based reasoning is a method to settle a problem in the present by comparing with the past cases. Therefore, it is suitable to diagnosis fields with abundant cases. Search is sorted into Breadth-first search, Depth-first search and Best-first search, which have respectively merits and demerits. To construct diagnosis ontology to be applied to practical expert system, reasoning and search to reflect diagnosis process and characteristics should be considered.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05b
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• pp.35-38
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• 2002

In this paper, for digital x-ray conversion receptor development studied by hybrid technology of based on CdZnTe. For this study, First searched fabricate method of CdZnTe x-ray receptor. Second, search the phosphor material & fabricate method for scintillator layer. Fabricated sample is analyzed with physical & electric measurement. This result is showed good SNR ratio hybrid thechnology with direct method & indirect method. In this paper offer the method can reduce the dark-current in the hybrid X-ray detector.