• Speech Sciences
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• v.15 no.2
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• pp.55-65
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• 2008

Recently, the multi-model based speech recognizer has been used quite successfully for noisy speech recognition. For the selection of the reference HMM (hidden Markov model) which best matches the noise type and SNR (signal to noise ratio) of the input testing speech, the estimation of the SNR value using the VAD (voice activity detection) algorithm and the classification of the noise type based on the GMM (Gaussian mixture model) have been done separately in the multi-model framework. As the SNR estimation process is vulnerable to errors, we propose an efficient method which can classify simultaneously the SNR values and noise types. The KL (Kullback-Leibler) distance between the single Gaussian distributions for the noise signal during the training and testing is utilized for the classification. The recognition experiments have been done on the Aurora 2 database showing the usefulness of the model compensation method in the multi-model based speech recognizer. We could also see that further performance improvement was achievable by combining the probability density function of the MCT (multi-condition training) with that of the reference HMM compensated by the D-JA (data-driven Jacobian adaptation) in the multi-model based speech recognizer.

• Management Science and Financial Engineering
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• v.7 no.1
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• pp.27-39
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• 2001

The multi-projective model considers attributes and the relationships among attributes called projections. The critical features of the multi-projective model are the way of relating attributes in the description of the system, the way of reasoning incomplete projections, and the determination of connected patterns between projection. In order to get a full picture of the system, we build a set of projections. The multi-projective model can be thought of as projections of a multi-dimensional reality onto simplified “model space”. The multi-projective database modeling approach used in this paper unified the ideas and terminology of various database models. Most importantly, the multi-projective modeling is presented as a tool of database design in the relational and other database models.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2669-2671
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• 2001

In this paper, we propose design a Multi-Fuzzy Inference model structure. In order to determine structure of the proposed Multi-Fuzzy Inference model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy are identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy model and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.370-370
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• 2000

In this paper, we design a Multi-Fuzzy model by means of HCM clustering and genetic algorithms for a nonlinear system. In order to determine structure of the proposed Multi-Fuzzy model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy ate identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy mode] and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Earthquakes and Structures
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• v.7 no.3
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• pp.333-347
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• 2014

Multi-scale model can take both computational efficiency and accuracy into consideration when it is used to conduct elasto-plastic seismic response analysis for complex steel bridges. This paper proposed a method based on pushover analysis of member sharing the same section pattern to verify the accuracy of multi-scale model. A deck-through type steel arch bridge with a span length of 200m was employed for seismic response analysis using multi-scale model and fiber model respectively, the validity and necessity of elasto-plastic seismic analysis for steel bridge by multi-scale model was then verified. The results show that the convergence of load-displacement curves obtained from pushover analysis for members having the same section pattern can be used as a proof of the accuracy of multi-scale model. It is noted that the computational precision of multi-scale model can be guaranteed when length of shell element segment is 1.40 times longer than the width of section where was in compression status. Fiber model can only be used for the predictions of the global deformations and the approximate positions of plastic areas on steel structures. However, it cannot give exact prediction on the distribution of plastic areas and the degree of the plasticity.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.2
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• pp.15-22
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• 2000

Shock and vibration characteristics of a tractor-trailer type vehicle system with air suspension and air coupler running on a single bump road are investigated. The vehicle system is modelled and solved to two types of models, i.e. rigid-multi-body and flexible-multi-body model, by ADAMS and NASTRAN software. And the shock impulse is given by a single bump model on the road. When the analysis results of the rigid-multi-body model is compared with those of the flexible-multi-body model, it is revealed that the vibration and accelerations of the latter model are more repetitive and larger than the former.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.6
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• pp.579-592
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• 2010

This paper presents a multi-phase model update approach for system identification of prestressed concrete (PSC) girders under various prestress forces. First, a multi-phase model update approach designed on the basis of eigenvalue sensitivity concept is newly proposed. Next, the proposed multi-phase approach is evaluated from controlled experiments on a lab-scale PSC girder for which forced vibration tests are performed for a series of prestress forces. On the PSC girder, a few natural frequencies and mode shapes are experimentally measured for the various prestress forces. The corresponding modal parameters are numerically calculated from a three-dimensional finite element (FE) model which is established for the target PSC girder. Eigenvalue sensitivities are analyzed for potential model-updating parameters of the FE model. Then, structural subsystems are identified phase-by-phase using the proposed model update procedure. Based on model update results, the relationship between prestress forces and model-updating parameters is analyzed to evaluate the influence of prestress forces on structural subsystems.

• Journal of the Ergonomics Society of Korea
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• v.32 no.2
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• pp.159-165
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• 2013

Objective: The aim of this study was to predict human performance time in finding objects on multi-display monitors using ACT-R cognitive architecture. Background: Display monitors are one of the representative interfaces for interaction between people and the system. Nowadays, the use of multi-display monitors is increasing so that it is necessary to research about the interaction between users and the system on multi-display monitors. Method: A cognitive model using ACT-R cognitive architecture was developed for the model-based evaluation on multi-display monitors. To develop the cognitive model, first, an experiment was performed to extract the latency about the where system of ACT-R. Then, a menu selection experiment was performed to develop a human performance model to find objects on multi-display monitors. The validation of the cognitive model was also carried out between the developed ACT-R model and empirical data. Results: As a result, no significant difference on performance time was found between the model and empirical data. Conclusion: The ACT-R cognitive architecture could be extended to model human behavior in the search of objects on multi-display monitors.. Application: This model can help predicting performance time for the model-based usability evaluation in the area of multi-display work environments.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.269-272
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• 2001

In this paper, we presents a systematic methodology for minimizing tardiness and maximizing resource utilization in a multi-plant supply chain. A methodology is represented to a multi-objective mathematical program model. The model offers flexible and efficient multi-plant planning and scheduling. Also, We develope a realistic and flexible planning model using the genetic algorithm to solve the model.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.1
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• pp.21-28
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• 2005

Web servers or web application servers, in general, adopt multi-thread model for efficient handling of many user requests. However, the multi -thread model always does not show the better performance than multi -process model. Sometimes, in a certain specific case, it can show worse performance than multi -process model. In this paper, to trace the cause of the decreased performance of multi -thread model, we experiment and analyze the performance of the multi-thread model by using two approaches. At first, we compare the performance of the multi-process model and multi-thread model for various application environments. Second, we observe the effects of variations of web server's dynamic directives, which are used to increase the flexibility of the web server for various system environments. For the experiments, we integrated a web client simulator, which was written by us, with the Apache 2.0 web server. This paper shows and analyze the results of the experiments.