• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.285-307
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• 2009

This paper introduces DICE, a Domain-Independent text Classification Engine. DICE is robust, efficient, and domain-independent in terms of software and architecture. Each module of the system is clearly modularized and encapsulated for extensibility. The clear modular architecture allows for simple and continuous verification and facilitates changes in multiple cycles, even after its major development period is complete. Those who want to make use of DICE can easily implement their ideas on this test bed and optimize it for a particular domain by simply adjusting the configuration file. Unlike other publically available tool kits or development environments targeted at general purpose classification models, DICE specializes in text classification with a number of useful functions specific to it. This paper focuses on the ways to locate the optimal states of a practical text classification framework by using various adaptation methods provided by the system such as feature selection, lemmatization, and classification models.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.1-16
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• 1991

This paper deals with some critical activities of CAPP system such as generation of part description database, part feature recognition, process and operation selection, and sequencing method for turning operation of symmetric rotational parts. The part description database is generated by data conversion module from CAD data, and the part feature is recognized by using both pattern primitives and feature recognition rules. Machining processes and operations are selected based on machining surface features and its sequence is determined by rules acquired from process planning expert. AutoCAD is employed as CAD system and computer program is developed by using Turbo-C on IBM PC/AT compatible system.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1169-1172
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• 2006

The prices of chemical products are fluctuated by several factors. The chemical companies can't predict and be ready to all of these changes, so they are exposed to the risk of a profit fluctuation. But they can reduce this risk by making a well-diversified product portfolio. This problem can be thought as the optimization of the product portfolio. We assume that the profits come from the 'spread' between a naphtha and a chemical product. We calculate a mean and a variation of each spread and develop an automatic module to calculate the optimal portion of each product. The theory is based on the Markowitz portfolio management. It maximizes the expected return while minimizing the volatility. At last we draw an investment selection curve to compare each alternative and to demonstrate the superiority. And we suggest that an investment selection curve can be a decision-making tool.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.302-305
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• 2005

To comply to demand for a development requirement of aircraft design part, the expert system builds up standard knowledge-base based on presently maintained expert knowledge and experience in aircraft structure material selection. It also builds up database based on aircraft design open data, and standard calculation module used for present design and analysis method. This system is developed using Visual Basic language. The expert system standardize aircraft structure material selection and can be applied to all type of elementary stage of aircraft structure design. It is working on Windows, which has a friendly interface and is convenient for debugging, maintenance and transplanting. Explanation of the structure and the function of the system was given in this paper.

• Journal of Biomedical Engineering Research
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• v.17 no.4
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• pp.491-498
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• 1996

The development of a compact two-wavelength Nd:YAG laser for dental and ENT applications is presented. The Nd:YAG resonator generates either 1.06$\mu$m or 1.32$\mu$m. The wavelength selection is made at the control panel. The Nd:YAG laser parameters at 1.06$\mu$m are ; the maximum pulse duration of 150$\mu$s, repetition rates of I-100Hz, and the maximum average power of 25W. At 1.32$\mu$m, the pulse duration is the same where the repetition rates and the maximum average power are I-30Hz and lOW respectively. High voltage power supply consists of a simmer module and two identical high voltage DC converters. In order to make a complete medical laser system, an optical fiber delivery unit, foot pedal and water spray handpiece are also developed. The wavelength selection is reliable since no movement of optical or mechanical components is required. The high voltage power supply is compact, easy to be maintained and applicable for other laser systems due to its modular design.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.720-723
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• 1995

As a part of the configuration design expert system of machine tools, inference mechanisms are constructed in this paper. In addition to procedural inference, the method of multivariable inference is considered as an efficient approach to deal with the cases of highly coupled condition. We propose a generalized multivariable inference procedure. The procedure is applied to the type selection module of the configuration design expert system of machine tools in order to demonstrate the efficiency and validity.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.5
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• pp.161-166
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• 1998

As a part of the configuration design expert system of machine tools, inference mechanisms are constructed in this paper. In addition to procedural inference, the method of multivariable inference is considered as an efficient approach to deal with the cases of highly coupled condition. We propose a generalized multi-variable inference procedure. The procedure is applied to the type selection module of the configuration design expert system of machine tools in order to demonstrate the efficiency and validity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1058-1062
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• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.

• Korean Journal of Materials Research
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• v.33 no.4
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• pp.164-174
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• 2023

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material's compositional features. The compositional features were generated using the python module of 'Pymatgen' and 'Matminer'. Pearson's correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R² score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R² score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.4
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• pp.251-260
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• 2020

A growing interest has emerged in recycling used automobile batteries into energy storage systems (ESSs) to prevent their harmful effects to the environment from improper disposal and to recycle such resources. To transform used batteries into ESSs, composing battery modules with similar performance by grading them is crucial. Imbalance among battery modules degrades the performance of an entire system. Thus, the selection of modules with similar performance and remaining life is the first prerequisite in the reuse of used batteries. In this study, we develop an instrument to measure the impedance spectrum of a battery module to predict the useful remaining life of the used battery. The developed hardware and software are used to apply the AC perturbation to the used battery module and measure its impedance spectrum. The developed instrument can measure the impedance spectrum of the battery module from 0.1 Hz to 1 kHz and calculate the equivalent circuit parameters through curve fitting. The performance of the developed instrument is verified by comparing the measured impedance spectra with those obtained by a commercial equipment.