• 한국CDE학회논문집
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• 제10권1호
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• pp.49-60
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• 2005

Existing CAD systems have configured geometry data and it is necessary to extend the configured geometry into a knowledge-based system. An intelligent CAD system emerged to provide such a knowledge-based system. However the intelligent CAD system has a limited product model to represent various knowledge models. This paper presents a model, called extended intelligent CAD model, which can extend the product model of the intelligent CAD system into further detailed knowledge model. The extended intelligent CAD model includes a whole design process knowledge and an efficiency of the model has been verified via a knowledge based wiper design system. The model can improve the functionality and efficiency of the existing CAD systems.

• 전력전자학회논문지
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• 제21권1호
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• pp.65-71
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• 2016

This study investigates a high-accuracy alternate QD model to estimate the characteristics of induction motor under light loads. To demonstrate the usefulness of the alternate QD model, a maximum torque per amp (MTPA) control based on the alternate model is shown to outperform MTPA control based on the standard QD model. The experimental study conducted in this work exhibits that the MTPA control based on the alternate QD model tracks torque commands between 20 Nm and 30 Nm with 5% error, whereas the MTPA control based on the standard QD model generates torques lower by over 23% compared with the aforementioned torque commands. This result indicates that the alternate QD model is a highly accurate model for induction motors under light loads.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
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• pp.1621-1624
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• 2003

As the entities participating Grid become larger, performance requirement for the LDAP-based GIS(Grid Information Service) goes beyond that provided by a stand-alone single LDAP server. This entails the exploration of distributed LDAP systems. This paper presents the performance evaluation respectively for a distribution-based and a replication-based LDAP model. The analysis is based on an analytic performance model for each distributed system which is obtained by applying the M/M/1 queuing model. The performance evaluation made to these analytic models reveals that the distribution-based and the replication-based model show a significant tradeoff in their performance with respect to the system size as well as the amount of system load.

• 한국멀티미디어학회논문지
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• 제10권12호
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• pp.1752-1761
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• 2007

Location-based services are a recent concept that integrates a mobile device's location with other information in order to provide added value to a user. Although Location-based Services provide users with comfortable information, it is a complex task to manage and share heterogeneous and numerous data in decentralized environments. In this paper, we propose the Semantic LBS Model as one of the solution to resolve the problem. The Semantic LBS Model is a LBS middleware model that includes an ontology-based data model for LBS POI information and its processing mechanism based on Semantic Web technologies. Our model enables POI information to be described and retrieved over various domain-specific ontologies based on our proposed POIDL ontology. This mechanism provide rich expressiveness, interoperability, flexibility in describing and using information about POls, and it can enhance POI retrieval services.

• 한국정밀공학회지
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• 제15권2호
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• pp.107-113
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• 1998

Grinding operation has difficulty in satisfying the qualitative knowledge based on the skilful expert as well as the quantitative data for all user. Design of grinding database based on the frame-based model is more effective method for utilizing the empirical and qualitative knowledge. In this paper. basic strategy to develop the grinding database by taking frame-based model, which is strongly dependent upon experience and intuition, is described. Grinding database based on the frame based model for designing the interaction and inference among the slots is accomplised by the object-oriented paradigm system.

• 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.

• 수산경영론집
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• 제47권4호
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• pp.1-13
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• 2016

Fish mortality is the most important success factor in aquaculture management. To analyze the effect of mortality considering biological and economic condition is a important problem in land-based aquaculture. This study is aimed to analyze the effect of mortality for duration of cultivation in land-based aquaculture. This study builds the mathematical model that finds the value of decision variable to minimize cost that sums up the water pool usage cost, sorting cost, fingerling cost and feeding cost under critical standing corp constraint. The proposed mathematical model involves many aspects, both biological and economical: (1) number of fingerlings (2) timing and number of batch splitting event, based on (3) fish growth rate, (4) mortality, and (5) several farming expense. Numerical simulation model presented here in. The objective of numerical simulation is to provide for decision makers to analyse and comprehend the proposed model. When extensive biological and cost data become available, the proposed model can be widely applied to yield more accurate results.

• 음성과학
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• 제11권2호
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• pp.247-257
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• 2004

There has been many research efforts to overcome the problems of speech recognition in the noisy conditions. Among them, the model-based compensation methods such as the parallel model combination (PMC) and vector Taylor series (VTS) have been found to perform efficiently compared with the previous speech enhancement methods or the feature-based approaches. In this paper, a data-driven model compensation approach that adapts the HMM(hidden Markv model) parameters for the noisy speech recognition is proposed. Instead of assuming some statistical approximations as in the conventional model-based methods such as the PMC, the statistics necessary for the HMM parameter adaptation is directly estimated by using the Baum-Welch algorithm. The proposed method has shown improved results compared with the PMC for the noisy speech recognition.

• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.470-479
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• 2016

The model based method for energy saving of the separately excited DC motor drive system is proposed in the paper. The accurate power loss model is necessary for this method. Therefore, the adaptive tabu search algorithm is applied to identify the parameters in the power loss model. The field current values for minimum power losses at any load torques and speeds are calculated by the proposed method. The rule based controller is used to control the field current and speed of the motor. The experimental results confirm that the model based method can successfully provide the energy saving for separately excited DC motor drive. The maximum value of the energy saving is 48.61% compared with the conventional drive method.

• 로봇학회논문지
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• 제4권1호
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• pp.56-61
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• 2009

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.