• Industrial Engineering and Management Systems
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• v.3 no.2
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• pp.100-115
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• 2004

The network representation and branch and bound algorithm with efficient lower and upper bounding procedures are developed to determine a global optimal production schedule on a machine that minimizes sequence-dependent setup cost and earliness/tardiness penalties. Lower bounds are obtained based on heuristic and Lagrangian relaxation. Priority dispatching rule with local improvement procedure is used to derive an initial upper bound. Two dominance criteria are incorporated in a branch and bound procedure to reduce the search space and enhance computational efficiency. The computational results indicate that the proposed procedure could optimally solve the problem with up to 40 jobs in a reasonable time using a personal computer.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.313-326
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• 2017

The systematic management of plant risk is crucial for enhancing the safety of nuclear power plants and for designing new nuclear power plants. Accident sequence precursor (ASP) analysis may be able to provide risk significance of operational experience by using probabilistic risk assessment to evaluate an operational event quantitatively in terms of its impact on core damage. In this study, an ASP methodology for two operation mode, full power and low power/shutdown operation, has been developed and applied to significant accident precursors that may occur during the operation of nuclear power plants. Two operational events, loss of feedwater and steam generator tube rupture, are identified as ASPs. Therefore, the ASP methodology developed in this study may contribute to identifying plant risk significance as well as to enhancing the safety of nuclear power plants by applying this methodology systematically.

• Journal of the Korean Society of Safety
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• v.12 no.1
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• pp.60-70
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• 1997

Since, human error is a major factor contributing to disaster during start-up of operation in Boiler plant. it is necessary to develop an automation system which prevent mode of operation that are known to be dangerous and to bring a loss of energy. The aim of this study is to suggest a model for the computer-aided synthesis of operation sequence as a part of automation system for start-up operation. Synthesis is accomplished by the formation of a hierarchical network of goals which decide sequence of operations. Hierarchical Network is formed by using method which upper goals ( i.e. upper operation al situation) are classified by the function of themselves into lower goals (i.e. lower operational situation). Knowledge for deciding operation sequence is generated by putting In order the knowledge of hazard operation and energy management.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.800-807
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• 2017

The Pattern Sequence-based Forecasting (PSF) is an approach to forecast the behavior of time series based on similar pattern sequences. The innovation of PSF method is to convert the load time series into a label sequence by clustering technique in order to lighten computational burden. However, it brings about a new problem in determining the number of clusters and it is subject to insufficient similar days occasionally. In this paper we proposed an adaption of the PSF method, which introduces a new clustering index to determine the number of clusters and imposes a threshold to solve the problem caused by insufficient similar days. Our experiments showed that the proposed method reduced the mean absolute percentage error (MAPE) about 15%, compared to the PSF method.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.2
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• pp.18-24
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• 1988

We consider sensitivity analysis sequencing problems, in which sequence of a finite set of expansion projects is sought to meet a deterministic demand projection in minimum discounted cost. In particular, by characterizing the underlying network structure, we find analytically the sensitivity range for a project cost such that the optimal sequencing policy remains unchanged for any value in the range. A numerical example is presented.

• Journal of Plant Biotechnology
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• v.43 no.2
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• pp.181-188
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• 2016

In this study, we developed 15 novel polymorphic simple sequence repeat (SSR) markers by SSR-enriched genomic library construction from Codonopsis lanceolata. We obtained a total of 226 non-redundant contig sequences from the assembly process and designed primer sets. These markers were applied to 53 accessions representing the cultivated C. lanceolata in South Korea. Fifteen markers were sufficiently polymorphic, and were used to analyze the genetic relationships between the cultivated C. lanceolata. One hundred three alleles of the 15 SSR markers ranged from 3 to 19 alleles at each locus, with an average of 6.87. By cluster analysis, we detected clear genetic differences in most of the accessions, with genetic distance varying from 0.73 to 0.93. Phylogenic analysis indicated that the accessions that were collected from the same area were distributed evenly in the phylogenetic tree. These results indicate that there is no correlative genetic relationship between geographic areas. These markers will be useful in differentiating C. lanceolata genetic resources and in selecting suitable lines for a systemic breeding program.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.6
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• pp.773-785
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• 2014

For decades, modeling of pilots has been intensively studied due to its advantages in reducing costs for training and enhancing safety of pilots. In particular, research for modeling of pilots' non-kinetic behaviors which refer to the decisions made by pilots is beneficial as the expertise of pilots can be inherent in the models. With the recent growth in the amount of combat logs accumulated, employing statistical learning methods for the modeling becomes possible. However, the combat logs consist of heterogeneous data that are not only continuous or discrete but also sequence independent or dependent, making it difficult to directly applying the learning methods without modifications. Therefore, in this paper, we present a kernel function named hybrid sequence kernel which addresses the problem by using multiple kernel learning methods. Based on the empirical experiments by using combat logs obtained from a simulator, the proposed kernel showed satisfactory results.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.111-119
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• 2016

In recent years, the opinion analysis is one of the key research fronts of any domain. Opinion target extraction is an essential process of opinion analysis. Target is usually referred to noun or noun phrase in an entity which is deliberated by the opinion holder. Extraction of opinion target facilitates the opinion analysis more precisely and in addition helps to identify the opinion polarity i.e. users can perceive opinion in detail of a target including all its features. One of the most commonly employed algorithms is a sequence labeling algorithm also called Conditional Random Fields. In present article, recent opinion target extraction approaches are reviewed based on sequence labeling algorithm and it features combinations by analyzing and comparing these approaches. The good selection of features combinations will in some way give a good or better accuracy result. Features combinations are an essential process that can be used to identify and remove unneeded, irrelevant and redundant attributes from data that do not contribute to the accuracy of a predictive model or may in fact decrease the accuracy of the model. Hence, in general this review eventually leads to the contribution for the opinion analysis approach and assist researcher for the opinion target extraction in particular.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.99-102
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• 2004

Recently according to developing of bioinformatics techniques, there are a lot of researches about large amount of biological data. And a variety of files and databases are being used to manage these data efficiently. However, because of the deficiency of standardization there are a lot of problems to manage the data and transform one into the other among heterogeneous formats. We are interested in integrating. saving, and managing gene and protein sequence data generated through sequencing. Accordingly, in this paper the goal of our research is to implement the system to manage sequence data and transform a sequence file format into other format. To satisfy these requirements, we adopt BSML (Bioinformatics Sequence Markup Language) as the standard to manage the bioinformatics data. And then we integrate and store the heterogeneous 리at file formats using BSML schema based DTD. And we developed the system to apply the characteristics of object-oriented database and to process XPath query, one of the efficient structural query. that saves and manages XML documents easily.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.3
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• pp.264-273
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• 2002

This paper focuses on disassembly sequencing, which is the problem of determining the optimum disassembly level and the corresponding disassembly sequence for a product at its end-of-life with the objective of maximizing the overall profit. In particular, sequence-dependent operation times, which frequently occur in practice due to tool-changeover, part reorientation, etc, are considered in the parallel disassembly environment. To represent the problem, a modified directed graph of assembly states is suggested as an extension of the existing extended process graph. Based on the directed graph, the problem is transformed into the shortest path problem and formulated as a linear programming model that can be solved straightforwardly with standard techniques. A case study on a photocopier was done and the results are reported.