• Korean Journal of Veterinary Service
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• v.40 no.1
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• pp.47-52
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• 2017

The main goal of this survey was to assess the current sanitation status and safety standards of meat processed products purchased at instant meat sales and processing operators. Analyses were carried out from April to September in 2016 in Gwangju area, Republic of Korea. A total number of 150 samples including seasoned meats, ground meat products, meat extract products, heated seasoned meats, sausages and hams from 35 butcher shops was collected. The number of inappropriate cases was revealed eleven cases (7.3%) in total viable count of bacteria (TVC), total coliform counts (TCC) and Listeria monocytogenes. The reported data indicate that more systemic and technical guidance is needed to monitor instant meat sales and processing operators in order to guarantee safety of meat processed products.

• Spatial Information Research
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• v.18 no.1
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• pp.77-87
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• 2010

Spatio-temporal join operators are essential to the management of spatio-temporal data such as moving objects. For example, the join operators are parts of processing to analyze movement of objects and search similar patterns of moving objects. Various studies on spatio-temporal join queries in outdoor space have been done. Recently with advance of indoor positioning techniques, location based services are required in indoor space as well as outdoor space. Nevertheless there is no one about processing of spatio-temporal join query in indoor space. In this paper, we introduce continuous spatio-temporal self-join queries in indoor space and propose a method of processing of the join queries over stream data of moving objects. The continuous spatio-temporal self-join query is to update the joined result set satisfying spatio-temporal predicates continuously. We assume that positions of moving objects are represented by symbols such as a room or corridor. This paper proposes a data structure, called Candidate Pairs Buffer, to filter and maintain massive stream data efficiently and we also investigate performance of proposed method in experimental study.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.590-599
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• 2006

Disordered streams cause two issues in processing sliding windows: i) how to place input tuples into a buffer in an increasing order efficiently and ii) how to determine a time point to process the windows from input tuples in the buffer. To address these issues, we propose a structure and method of operators for processing sliding windows. We first present a structure of the operators using an index to handle input tuples efficiently. Then, we propose a method to determine the time point to process the windows, which is called a mean-based estimation. In the proposed method, users can describe parameters required for estimation in a query specification, which provides a way for users to control the properties of query results such as the accuracy or the response time according to application requirements. Our experimental results show that the mean-based estimation provides better adaptivity and stability than the one used in the existing method.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05a
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• pp.133-138
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• 1997

For a long time, a number of alarm processing techniques have been researched to reduce the number of actuated alarms for operators to deal with effectively during the abnormal as well as the normal conditions. However, the strategy that the only systems with a few clear technologies should be used as a part of an alarm annunciation system has been adopted considering the effectiveness and the reliability in actual alarm processing systems. Therefore, alarm processing systems have difficult knowledge-base maintenance problems and limited expansion or enhancement defects. To solve these shortcomings, the integrated knowledge-base which can express the general information related to all the alarm processing techniques is proposed and its management tool, Knowledge Input Tool for Alarm (KIT-A) which can handle the data of the knowledge-base efficiently is developed. Since the integrated knowledge-base with KIT-A can manipulate all the alarm information without the modification of alarm processing system itself, it is expected to considerably advance the overall capability of maintenance and enhancement of the alarm processing systems.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.590-598
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• 2017

Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We propose an approach to fire detection using an image processing technique. In this paper, we propose a fire detection method using multichannel information and gray level co-occurrence matrix (GLCM) image features. Multi-channels consist of RGB, YCbCr, and HSV color spaces. The flame color and smoke texture information are used to detect the flames and smoke, respectively. The experimental results show that the proposed method performs better than the previous method in terms of accuracy of fire detection.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.360-368
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• 1999

We consider the problem of scheduling n jobs with sequence-dependent processing times on a set of parallel-identical machines. The processing time of each job consists of a pure processing time and a sequence-dependent setup time. The objective is to maximize the total remaining machine available time which can be used for other tasks. For the problem, a hybrid genetic algorithm is proposed. The algorithm combines a genetic algorithm for global search and a heuristic for local optimization to improve the speed of evolution convergence. The genetic operators are developed such that parallel machines can be handled in an efficient and effective way. For local optimization, the adjacent pairwise interchange method is used. The proposed hybrid genetic algorithm is compared with two heuristics, the nearest setup time method and the maximum penalty method. Computational results for a series of randomly generated problems demonstrate that the proposed algorithm outperforms the two heuristics.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.166-169
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• 1997

For parallel processing, the compiler partitions a loaded program into a set of tasks and makes a schedule for the tasks that will minimize parallel processing time for the loaded program. Building an optimal schedule for a given set of partitioned tasks of a program has known to be NP-complete. In this paper we introduce a GA(Genetic Algorithm)-based scheduling method in which a chromosome consists of two parts of a string which decide the number and order of tasks on each processor. An additional computation is used for feasibility constraint in the chromosome. By granularity theory, a partitioned program is categorized into coarse-grain or fine-grain types. There exist good heuristic algorithms for coarse-grain type partitioning. We suggested another GA adaptive to the coarse-grain type partitioning. The infeasibility of chromosome is overcome by the encoding and operators. The number of processors are decided while the GA find the minimum parallel processing time.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.72-79
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• 1995

The purpose of this paper is to develop an automatic measuring system based on the digital image processing which can be applied to the in-process measurment of the characteristics of the thin thickness. The derivative operators is used for edge detection in gray level image. This concept can be easily illustrated with the aid of object shows an image of a simple light object on a dark background, the gray level profile along a horizontal scan line of the image, and the first and second derivatives of the profile. The first derivative of an edge modeled in this manner is 0 in all regions of constant gray level, and assumes a constant value during a gray level transition. The experimental results indicate that the developed automatic inspection system can be applied in real situation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.981-984
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• 1993

During the past decade, several specific hardwares for fast fuzzy inference have been developed. Most of them are dedicated to a specific inference method and thus cannot support other inference methods. In this paper, we present a hardware architecture called KAFA(KAist Fuzzy Accelerator) which provides various fuzzy inference methods and fuzzy set operators. The architecture has SIMD structure, which consists of two parts; system control/interface unit(Main Controller) and arithmetic units(FPEs). Using the parallel processing technology, the KAFA has the high performance for fuzzy information processing. The speed of the KAFA holds promise for the development of the new fuzzy application systems.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.184-200
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• 2020

Similarity searching is a basic issue in information processing because of the large size of formal contexts and their complicated derivation operators. Recently, some researchers have focused on knowledge reduction methods by using granular computing. In this process, suitable information granules are vital to characterizing the quantities of attributes and objects. To address this problem, a novel approach to obtain an entropy-weighted concept lattice with inclusion degree and similarity distance (ECLisd) has been proposed. The approach aims to compute the combined weights by merging the inclusion degree and entropy degree between two concepts. In addition, another method is utilized to measure the hierarchical distance by considering the different degrees of importance of each attribute. Finally, the rationality of the ECLisd is validated via a comparative analysis.