• Journal of Information Technology Services
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• v.6 no.3
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• pp.87-110
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• 2007

Recently, researches on IT Service Management (ITSM) for improving information system operation service and information system outsourcing cost estimation model are proliferating. This study suggests a new cost model of IT operation service and optimizing method based upon the characteristics of operation service as a long-term and continuous business service for both user's and service provider's point of view. This study explains the cost optimization model of IT operation service by improving service request management process, such as adequate reception and control, proper valuation, process management using project management methodology, effective organization and time management of service personnel. Especially in this study, service ability improvement effect and fixed operation cost reduction effect are defined to prove the proposed new cost model.

• Journal of applied mathematics & informatics
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• v.9 no.1
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• pp.185-195
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• 2002

Previous research has shown that the flow of patients around departments of geriatric medicine and ex-patients in the community may be-modelled by the application of a mixed-exponential distribution. In this proper we considered a ave-compartment model using a continuous-time Markov process to describe the flow of patients. Using a M/ph/c queuing model, we present a way of optimizing the number of beds in order to maintain an acceptable delay probability a sufficiently low level. Finally, we constructed a Java computer simulation, using data from St George's Hospital, London.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.12
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• pp.68-74
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• 1991

This paper proposes a HMM model based on fuzzy, as a method on the speech recognition of speaker-independent. In this recognition method, multi-observation sequences which give proper probabilities by fuzzy rule according to order of short distance from VQ codebook are obtained. Thereafter, the HMM model using this multi-observation sequences is generated, and in case of recognition, a word that has the most highest probability is selected as a recognized word. The vocabularies for recognition experiment are 146 DDD are names, and the feature parameter is 10S0thT LPC cepstrum coefficients. Besides the speech recognition experiments of proposed model, for comparison with it, we perform the experiments by DP, MSVQ and general HMM under same condition and data. Through the experiment results, it is proved that HMM model using fuzzy proposed in this paper is superior to DP method, MSVQ and general HMM model in recognition rate and computational time.

• KIEE International Transactions on Electrophysics and Applications
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• v.5C no.6
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• pp.223-227
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• 2005

Insulation failure of traction motor stator coil depends on the continuous stress imposed on it and knowing its insulation condition is an issue of significance for proper safety operation. In this paper, application of the NN (Neural Network) as a scheme of the off-line PD (partial discharge) diagnosis method that occurs at the stator coil of a traction motor was studied. For PD data acquisition, three defective models were made; internal void discharge model, slot discharge model and surface discharge model. PD data for recognition were acquired from a PD detector. Statistical distributions and parameters were calculated to perform recognition between model discharge sources. These statistical distribution parameters are applied to classify PD sources by the NN with a good recognition rate on the discharge sources.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1073-1081
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• 2017

Inductive dynamic drives (IDD) are ultra rapid actuators where the moving part (disc) is subjected to impulse force. This paper presents the second model of inductive dynamic drive - a mechanical model where analytic- numerical approach was applied. The magnetic pressure, which was determined on the basis of the results obtained in the electrodynamic model, becomes the input data for mechanical model. Research with application of the mechanical model is necessary in order to determine the proper disc oscillation frequency and to obtain the stress state control for drive elements to be designed. Also, the selection of drive parameters to keep the disc deformation insignificant (while oscillating) is a condition under which these models do not need to be coupled together.

• IE interfaces
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• v.11 no.2
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• pp.125-137
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• 1998

A new vehicle design approach coupled with an ergonomic human model was proposed in the study. The seating package layout of a vehicle is very important to the driving comfort, and it has been one of the primary ergonomic research areas since the past 30 years. The diverse and interrelated design factors of seating package layout in the limited workspace make designers often neglect many parameters related with drivers which differ in their anthropometric characteristics. It is due to the lack of the proper tools by which the designer can easily apply several ergonomic design guidelines to the vehicle design. In this study. an iterative package layout procedure was developed, and the effectiveness of an ergonomic human model was examined in this procedure. A discomfort function was developed for the quantitative evaluation of the driving posture. This study clearly demonstrates that the package layout using an ergonomic human model is very helpful to improve the usability and driving comfort of the drivers or passengers.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.564-569
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• 2013

An accurate railway environmental noise prediction model is required to make the proper solution of the railway noise problems. In this paper, an engineering model for predicting the noise of conventional passenger cars is presented considering the acoustic source strength in octave-band frequencies and the propagation over grounds with varying surface properties. Since the formation of a train can be variable, the source strength of each locomotive and passenger car was estimated by measuring the pass-by noise and analysing the wheel-rail rolling noise. Some validation cases show on the average small differences between the predictions of the present model and the measurement results.

• Genomics & Informatics
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• v.16 no.4
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• pp.32.1-32.7
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• 2018

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.41-51
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• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.6
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• pp.1-9
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• 2022

This study introduces the calculation model of ionization composition and electrical conductivity for metallic plasma for practical application to modeling and simulation of modern electrical detonators. The present model includes the correction for non-ideality of dense plasma conditions which are expected in electrical explosion of bridge in detonators. The computational results for copper plasma show favorable agreement with experimental data for a wide range of plasma temperature and high density conditions and the model is proper for detonator modeling with good prediction accuracy.