• Proceedings of the KIEE Conference
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.264-267
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• 2002

This paper proposed a effective priority selection algorithm named weighted round-robin algorithm and show the implementation result of DMAC priority selection module using prosed weighted round-robin algorithm. I parameterize timing constraints of each functional module, which decide the effectiveness of system. Proposed weighted round-robin algorithm decide the most effective module for data transmission using parameterize timing constraints and update timing parameter of each module for next transmission module selection. I implement DMAC priority selection module using this weighted round-robin algorithm and can improve the timing effective for data transmission from memory to functional module or one functional module to another functional module.

• Proceedings of the IEEK Conference
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• 대한전자공학회 2004년도 하계종합학술대회 논문집(2)
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• pp.459-462
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• 2004

In this paper, we present a reducing power consumption of a scheduling for module selection under the time constraint. A a reducing power consumption of a scheduling for module selection under the time constraint execute scheduling and allocation for considering the switching activity. The focus scheduling of this phase adopt Force-Directed Scheduling for low power to existed Force-Directed Scheduling. and it constructs the module selection RT library by in account consideration the mutual correlation of parameters in which the power and the area and delay. when it is, in this paper we formulate the module selection method as a multi-objective optimization and propose a branch and bound approach to explore the large design space of module selection. Therefore, the optimal module selection method proposed to consider power, area, delay parameter at the same time. The comparison experiment analyzed a point of difference between the existed FDS algorithm and a new FDS_RPC algorithm.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• 제2권3호
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• pp.174-179
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• 2007

In this paper, we present a optimal module selection using genetic algorithm under the power, area, delay constraint. The proposed algorithm use the way of optimal module selection it will be able to minimize power consumption. In the comparison and experimental results, The proposed application algorithm reduce maximum power saving up to 26.9% comparing to previous non application algorithm, and reduce minimum power saving up to 9.0%. It also show the average power saving up to 15.525% and proved the power saving efficiency.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 한국에너지공학회 1998년도 추계 학술발표회 논문집
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• pp.3-8
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• 1998

The present work proposes the potential fuzzy framework, based on fuzzy set theory, for supporting decision-making problems, especially, selection problems of a best design in the area of nuclear energy system. The framework proposed is composed of the hierarchical structure module, the assignment module, the fuzzification module, and the defuzzification module. In the structure module, the relationship among decision objectives, decision criteria, decision sub-criteria, and decision alternatives is hierarchically structured. In the assignment module, linguistic or rank scoring approach can be used to assign subjective and/or vague values to the decision analyst's judgment on decision variables. In the fuzzification module, fuzzy numbers are assigned to these values of decision variables. Using fuzzy arithmetic operations, for each alternative, fuzzy preference index as a fuzzy synthesis measure is obtained. In the defuzzification module, using one of methods ranking fuzzy numbers, these indices are defuzzified to overall utility values as a cardinality measure determining final scores. According these values, alternatives of interest are ranked and an optimal alternative is chosen. To illustrate the applicability of the framework proposed to selection problem, as a case example, the best option choice of four design options under five decision criteria for primary containment wall thickening around large penetrations in an advanced nuclear energy system is studied.

• Journal of Advanced Marine Engineering and Technology
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• 제32권6호
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• pp.915-922
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• 2008

The gas separation technology using membrane is widely used to refine various gases in many industry fields and recently is being applying in $CO_2$ recovery technology. In the gas and chemical tanker. nitrogen generators for inerting, purging and padding are on board and most of them have membrane modules of hollow fiber type with long life and vibration resisting properties. Because a membrane module is a key component accounting for 50% of total manufacturing cost of nitrogen generator, adequate selection for it is an important problem. In this paper, the flow performance coefficient based on dimension and specification data of membrane module was relatively selected to compare nitrogen generating capacity of module and various performance tests about the selected PARKER ST6010 membrane module were conducted. As a result, the useful coefficient and basic data in selecting a membrane module were achieved.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제58권12호
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• Photonics industry news
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• 통권17호
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• pp.10-19
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• 2003

We have developed a single integrated wavelengh selection type - 8channel light source module. This module oscillates in 8 wavelengh at 200GHz(1.6nm) ITU channel intervals within the c-band (1530~1560nm), and can transmit at 1.25Gbps level.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제29권9A호
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• pp.1091-1100
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• 2004

In this paper, we present a reducing power conswnption of a scheduling for module selection under the time constraint. The proposed low power scheduling executes FDS_LP considering low power to exist the FDS scheduling by inputted the behavioral language. The proposed FDS_LP perfonns lower power consumption with dynamic power which is minimized the switching activity, based on force conception In the time step of module selection, an optimal RT(Register Transfer) library is composed by exploration of the parameters such as power, area, and delay. To find optimal parameters of RT library, an optimal module selection algorithm using Branch and Bound algorithm is also proposed. In the comparison and experimental results, The proposed FDS_LP algorithm reduce maximum power saving up to 23.9% comparing to previous FDS algorithm.

• Journal of the Korean Society for Precision Engineering
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• 제20권1호
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• pp.118-125
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• 2003

As consumer's desire becomes various, agility of mold manufacturing is the most important factor for competitive mold manufacturer. Decision making process is required to produce optimal result of CAM systems in using commercial CAM system to generate tool path. The paper proposes a methodology fur computer-assisted tool selection procedures for various cutting type of rough, semi-rough and finish cuts. The procedure provides assistance for machining tool selection by analyzing sliced CAD model section of die cavity and core. Information about machining time for the generated NC-code is used to aid the tool selection. The module is developed with commercial CAM API. This module will be used fur the optimization of tool selection and planning process.