• Journal of Korean Institute of Industrial Engineers
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• v.38 no.1
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• pp.25-30
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• 2012

Desirability approaches have been proposed to find an optimum of multiple response problem. The existing desirability approaches use either of mean or min of individual desirability in aggregation of multiple responses. However, in order to find an optimum having high mean and low dispersion among individual desirability, the dispersion needs to be simultaneously considered with its mean. This study proposes bias and variance (BV) method which aggregates bias (ideal target-mean) and variance of individual desirability in multiple response optimization. The proposed BV method was applied to an example to evaluate its usefulness by comparing with existing methods. Evaluation results showed that the solution of BV method was a fairly good compared with DS (Derringer and Suich, 1980) and KL (Kim and Lin, 2000) methods. The BV method can be utilized to multiple response surface problems when decision makers want to find an optimum having high mean and low variance among responses.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.720-723
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• 1997

This paper presents a cooperative control method for multiple robots. This method is based on local sensors. The proposed method integrates all information obtained by local perception through a set of sensors and generates commands without logical conflicts in designing control logic. To control multiple robots effectively, a global control strategy is proposed. These methods are constructed by using AND/OR logic and transition firing sequences in Petri nets. To evaluate these methods, the object-searching task is introduced. This task is to search an object like a box by two robots and consists of two sub-tasks, i.e., a wall tracking task and a robot tracking task. Simulation results for the object-searching task and the wall tracking task are presented to show the effectiveness of the method.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.971-984
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• 2015

Imputation procedures fill-in missing values, thereby enabling complete data analyses. Fully efficient fractional imputation (FEFI) and multiple imputation (MI) create multiple versions of the missing observations, thereby reflecting uncertainty about their true values. Methods have been described for hypothesis testing with multiple imputation. Fractional imputation assigns weights to the observed data to compensate for missing values. The focus of this article is the development of tests of independence using FEFI for partially classified two-way contingency tables. Wald and deviance tests of independence under FEFI are proposed. Simulations are used to compare type I error rates and Power. The partially observed marginal information is useful for estimating the joint distribution of cell probabilities, but it is not useful for testing association. FEFI compares favorably to other methods in simulations.

• Journal of the Korean Society for information Management
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• v.19 no.4
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• pp.35-51
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• 2002

Inductive learning and classification techniques have been employed in various research and applications that organize textual data to solve the problem of information access. In this study, we develop hybrid model combination methods which incorporate the concepts and techniques for multiple modeling algorithms to improve the accuracy of text classification, and conduct experiments to evaluate the performances of proposed schemes. Boosted stacking, one of the extended stacking schemes proposed in this study yields higher accuracy relative to the conventional model combination methods and single classifiers.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1149-1154
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• 1991

For a robotic workcell, which consists of multiple robots, several interconnection methods are presented in terms of the processor based architecture. Since few attempts have been made to formulate and analyze multiple robot system(MRS), we turn the knowledge of multiple processor system(MPS) or multiple computer system(MCS) to good account. The performance evaluation is achieved through queueing analysis, the aim being to compare their response time, utilization, probability of service failure under different workload. To verify the validity of the proposed analysis methods, a computer simulation is performed. The results together with comments presented here give some useful guidelines for the selection of an appropriate interconnection method.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.451-459
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• 2008

Accurate load modeling is essential for power system static and dynamic analysis. By the nature of the problem of parameter estimation for power system load modeling using actual measurements, multiple local optimal solutions may exist and local methods can be trapped in a local optimal solution giving possibly poor performance. In this paper, Trust-Tech, a novel methodology for global optimization, is applied to tackle the multiple local optimal solutions issue in measurement-based power system load modeling. Multiple sets of parameter values of a composite load model are obtained using Trust-Tech in a deterministic manner. Numerical studies indicate that Trust-Tech along with conventional local methods can be successfully applied to power system load model parameter estimation in measurement-based approaches.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.243-246
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields Classifying a group into subgroups is one of the most important subjects in data mining Tree-based methods, known as decision trees, provide an efficient way to finding classification models. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variables should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present several methods for measuring the node impurity, which are applicable to data sets with multiple target variables. For illustrations, numerical examples are given with discussion.

• International Journal of Industrial Entomology and Biomaterials
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• v.5 no.1
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• pp.37-43
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• 2002

Eighteen new bivoltine silkworm (Bombyx mori L.) hybrids developed at Andhra Pradesh State Sericul-ture Research and Development Institute, Hindupur are evaluated for 10 economic traits by following two multiple trait index methods, i.e., Subordinate Function and Evaluation Index for their economic merit. The hybrid genotype, APS6${\times}$APS11 with highest Subordinate function value of 8.2432 and highest average Evaluation Index of 61.67 ranked first. This hybrid is adjudicated as most promising hybrid and recommended for commercial use. Further, applicability of Subordinate Function Index Method is tested and recommended for application of multiple trait evaluation similar to Evaluation Index Method as the results obtained are comparable. Further, both these methods can be applied for confirmation of results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2166-2180
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• 2016

We investigate the opposite of artificial noise (AN)-assisted communication in multiple-input-multiple-output (MIMO) wiretap channels for the multiuser case by taking the side of the eavesdropper. We first define a framework for an AN-assisted multiuser multiple-input-multiple-output (MU-MIMO) system, for which eavesdropping methods are proposed with and without knowledge of legitimate users' channel state information (CSI). The proposed method without CSI is based on a modified joint approximate diagonalization of eigen-matrices algorithm, which eliminates permutation indetermination and phase ambiguity, as well as the minimum description length algorithm, which blindly estimates the number of secret data sources. Simulation results show that both proposed methods can intercept information effectively. In addition, the proposed method without legitimate users' CSI performs well in terms of robustness and computational complexity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.582-588
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• 2008

Microgrids are new distribution level power networks that consist of various electronically-interlaced generators and sensitive loads. The important control object of Microgrids is to supply reliable and high-quality power even during the faults or loss of mains(islanding) cases. This paper presents power control methods to coordinate multiple distributed generators(DGs) against abnormal cases such as islanding and load power variations. Using speed-droop and voltage-droop characteristics, multiple distributed generators can share the load power based on locally measured signals without any communications between them. This paper adopts the droop controllers for multiple DG control and improved them by considering the generation speed of distribution level generators. Dynamic response of the proposed control scheme has been investigated under severe operation cases such as islanding and abrupt load changes through PSCAD/EMTDC simulations.