• Parasites, Hosts and Diseases
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• v.58 no.2
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• pp.153-159
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• 2020

The chigger mite Leptotrombidium sialkotense is one of the 6 main vectors of scrub typhus in China. Before present study, L. sialkotense was found in some parts of Hunan province, China with a narrow geographical distribution. During field investigation 2016-2017, we found L. sialkotense in Jingha, southern Yunnan, China. Of 15 small mammal host species, L. sialkotense were collected from 6 species of the hosts. Rattus brunneusculus was a dominant host of L. sialkotense, from which 98.3% of the mites were collected. The chigger mite showed a relatively high infestation prevalence (P_M =11.7%) and mean abundance (MA=0.5) in comparison with the rest 5 host species. These results reveal a certain host specificity of L. sialkotense to a rat R. brunneusculus. The mite L. sialkotense showed an aggregated distribution on the host (P<0.05). A positive correlation observed between L. sialkotense and the body length of hosts. There was a positive interspecific association between L. sialkotense and 2 other dominant vectors, L. deliense and L. scutellare.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.2
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• pp.21-23
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• 1981

It is known that two-person zero-sum game with vector payoff can be reduced to a multiple objective linear programming. However, in this case, solutions for the game nay not be one, but many, In many cases in reality, one may need only one solution rather than all solutions. This paper develops a method to find a practical solution for the game by linear programming.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.1-7
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• 2004

We demonstrate in this paper that the level crossing technique can be applied to such a system that not only the state vector is two-dimensional but Its two components are heterogeneous. As an example system, we use the GI-G/c/K queue whose state vector consists of the number of customers in the system and the total unfinished work.

• Journal of the Korean Operations Research and Management Science Society
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• v.10 no.2
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• pp.28-37
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• 1985

An obvious of ESACF approach for model identification by Tsay and Tiao(1984) is that the user may be confused by the elements which are in triangular but marginally larger two standard deviation values. To avoid this drawback, the joint limiting distribution of the vector whose elements are in triangular of ESACF arrays is verified and the statistics to test the nulity of the vector suggested. We illustrate this approach with three examples.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.608-613
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• 2007

Prediction of corporate bankruptcies has long been an important topic and has been studied extensively in the finance and management literature because it is an essential basis for the risk management of financial institutions. Recently, support vector machines (SVMs) are becoming popular as a tool for bankruptcy prediction because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don't require huge training samples and have little possibility of overfitting. However. in order to Use SVM, a user should determine several factors such as the parameters ofa kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM In this study, we propose a novel hybrid SVM classifier with simultaneous optimization of feature subsets, instance subsets, and kernel parameters. This study introduces genetic algorithms (GAs) to optimize the feature selection, instance selection, and kernel parameters simultaneously. Our study applies the proposed model to the real-world case for bankruptcy prediction. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.

• Journal of Distribution Research
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• v.10 no.3
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• pp.1-14
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• 2005

It is important to control performance and a Sustainable Collaboration (SC) for the successful Supply Chain Management (SCM). This research developed a control model which analyzed SCM performances based on a Balanced Scorecard (ESC) and an SC using Support Vector Machine (SVM). 108 specialists of an SCM completed the questionnaires. We analyzed experimental data set using SVM. This research compared the forecasting accuracy of an SCMSC through four types of SVM kernels: (1) linear, (2) polynomial (3) Radial Basis Function (REF), and (4) sigmoid kernel (linear > RBF > Sigmoid > Polynomial). Then, this study compares the prediction performance of SVM linear kernel with Artificial Neural Network. (ANN). The research findings show that using SVM linear kernel to forecast an SCMSC is the most outstanding. Thus SVM linear kernel provides a promising alternative to an SC control level. A company which pursues an SCM can use the information of an SC in the SVM model.

• Journal of Plant Biotechnology
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• v.6 no.4
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• pp.221-227
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• 2004

Protocol was established for production of virus free healthy seeds using meristem ($0.3-0.5\;\cal{mm}$ in size) culture and field management under net house condition in tomato. The isolated meristem was found well established in MS liquid medium containing $0.1\;\cal{mg}\;1^{-1}\;of\;GA_3$. For shoot and root development either from primary meristem or from nodal segment of meristem derived plants, semisolid MS medium having $0.5\;\cal{mg}\;1^{-1}$ of IBA was found most effective. The elimination of the studied viruses (ToMV, CMV, ToLCV) in meristem-derived plants was confirmed by DAS-ELISA test. For field management of the virus eradicated meristem-derived plants, use of net house was found very effective measures to check viral vector visit and eventually infection. The meristem-derived plants were vigor and high yielder than the native seed derived plants and produced healthy seeds. Due to stop vector visit, no viral symptoms were observed in both $R_1\;and\;R_2$ plants cultivated in net house condition. Starting of viral infestation was observed in $R_2$ generation when they were planted in open house condition without control of vector visit. Therefore, for management of viral diseases, use of virus free meristem derived plantlets and their subsequent cultivation in soil under net house condition without using any vector killing insecticide can be recommended for producing healthy seeds in tomato. The developed protocol for environmentally healthy tomato seed production in Bangladesh may be used in the countries having similar tropical like environment conducive for viral vector visit.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.107-113
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• 2016

In this paper, by managing the biometric data is changed with the passage of time, a systematic and scientifically propose a framework to increase the bio-vector generation efficiency of the smart health care. Increasing the development of human life as a medicine and has emerged smart health care according to this. Organic and efficient health management becomes possible to generate a vector when the biological domain to the wireless communication infrastructure based on the measurement of the health status and to take action in accordance with the change of the physical condition. In this paper, we propose a framework to create a bio-vector that contains information about the current state of health of the person. In the proposed framework, Bio vectors may be generated by collecting the biometric data such as blood pressure, pulse, body weight. Biometric data is the raw data from the bio-vector. The scope of the primary data can be set to active. As the collecting biometric data from multiple items of the bio-recognition vectors may increase. The resulting bio-vector is used as a measure to determine the current health of the person. Bio-vector generating the proposed framework, it can aid in the efficiency and systemic health of healthcare for the individual.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1987.10a
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• pp.35-35
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• 1987

Consider a job shop that is modelled as an open queueing network of the Jackson(l957) type. All work stations in the shop have the same number of parallel servers. Two problems are studied : the loading of stations and the assignment of servers, which are represented by loading and assingment vectors, respectively. Ma jorization and arrangement orderings are established to order, respectively, the loading and the assignment vectors. It is shown that reducing the loading vector under ma jorizat ion or increasing the assignment vector under arrangement ordering will reduce the congestion in the shop in terms of reducing the total number of jobs(in the sense of likelihood ratio ordering), the maximum queue length(in the sense of stochastic ordering), and the queue-length vector( in the sense of stochastic majorization). The results can be used to supprot production planning in certain job shops, and to aid the desing of storage capacity. (OPEN QUEUEING NETWORK; WJORIZATION; ARRANGEMENT ORDERINC; LIKELIHOOD RATIO ORDERINC; STOCHASTIC ORDERING)

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.2
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• pp.17-33
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• 2016

Clustering is one of the leading methods to analyze big data and is used in many different fields. This study deals with Clustering Validity Index (CVI) to verify the effectiveness of clustering results. We compare the performance of CVIs with business applications of various field. In this study, the used CVIs for comparing performance are DU, CH, DB, SVDU, SVCH, and SVDB. The first three CVIs are well-known ones in the existing research and the last three CVIs are based on support vector data description. It has been verified with outstanding performance and qualified as the application ability of CVIs based on support vector data description.