• 한국정보시스템학회지:정보시스템연구
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• 제14권2호
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• pp.277-292
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• 2005

This paper uses a data mining approach to develop bankruptcy prediction models suitable for traditional (off-line) companies and electronic (on-line) companies. It observes the differences in the composition prediction models between these two types of companies and provides interpretation of bankruptcy classifications. The bankruptcy prediction models revealed the major variables in predicting bankruptcy to be 'cash flow to total assets' and 'gross value-added to net sales' for traditional off-line companies while 'cash flow to liabilities','gross value-added to net sales', and 'current ratio' for electronic companies. The accuracy rates of final prediction models for traditional off-line and electronic companies were found to be $84.7\%\;and\;82.4\%$, respectively. When the model for traditional off-line companies was applied for electronic companies, prediction accuracy dropped significantly in the case of bankruptcy classification (from $70.4\%\;to\;45.2\%$) at the level of a blind guess ($41.30\%$). Therefore, the need for different models for traditional off-line and electronic companies is justified.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2004년도 제5회 압연심포지엄 신 시장 개척을 위한 압연기술
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• pp.368-377
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• 2004

A mathematical model was developed for the prediction of the average temperature and RDT(RM Delivery temperature) in a roughing mill. The model consisted of three parts as follows (1) The intermediate numerical model calculated the deformation and heat transfer phenomena in the rolling: region by steady state FEM and the heat transfer phenomena in the interpass region by unsteady state FEM (2) The Off-line prediction model was derived from non-linear regression analysis based on the results of intermediate numerical model considering the various rolling conditions, (3) Using the heat flux in rolling region, temperature profile along thickness direction was calculated. For validation of the presented model, the rolling force per pass and RDT measued in on-line process was compared with those of model and the results showed close agreement with the existing data. In order to demonstrate the effectiveness of the proposed model, the various rolling conditions was tested.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.3-6
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• 2002

This paper presents the cutting simulation system for prediction and regulation of cutting force in CNC machining. The cutting simulation system includes geometric model, cutting force model, and off-line fred rate scheduling model. ME Z-map(Moving Edge node Z-map) is constructed for cutting configuration calculation. The cutting force models using cutting-condition-independent coefficients are developed for flat-end milling and ball-end milling. The off-line feed rate scheduling model is derived from the developed cutting force model. The scheduled feed rates are automatically added to a given set of NC code, which regulates the maximum resultant cutting force to the reference force preset by an operator. The cutting simulation system can be used as an effective tool for improvement of productivity in CNC machining.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2004년도 제5회 압연심포지엄 신 시장 개척을 위한 압연기술
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• pp.329-335
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• 2004

An, FE-based, on-line model is presented for the rapid and precise prediction of roll thermal profile in hot strip rolling. The validity of the model is demonstrated through comparison with FE-based off-line model which was verified by measurements. Also demonstrated is its capability of reflecting the effect of diverse process variables.

• Journal of Mechanical Science and Technology
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• 제15권9호
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• pp.1292-1301
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• 2001

The mean-line method using empirical models is the most practical method of predicting off-design performance. To gain insight into the empirical models, the influence of empirical models on the performance prediction results is investigated. We found that, in the two-zone model, the secondary flow mass fraction has a considerable effect at high mass flow-rates on the performance prediction curves. In the TEIS model, the first element changes the slope of the performance curves as well as the stable operating range. The second element makes the performance curves move up and down as it increases or decreases. It is also discovered that the slip factor affects pressure ratio, but it has little effect on efficiency. Finally, this study reveals that the skin friction coefficient has significant effect on both the pressure ratio curve and the efficiency curve. These results show the limitations of the present empirical models, and more resonable empirical models are reeded.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.310-316
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• 2004

In this paper, we developed a Windows XP version off-line programming system which can simulate a track vehicle model in 3D graphics space. The track vehicle was adopted as an objective model. The interface between users and the off-line program system in the Windows XP's graphic user interface environment was also studied. The developing language is Microsoft Visual C＋＋. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D Graphics.

• 스마트미디어저널
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• 제13권8호
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• pp.58-66
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• 2024

질소산화물(NOx)은 대기 오염의 주요 원인 물질로, 오존과 초미세먼지의 형성을 유발하여 건강에 해로운 영향을 준다. 석탄화력 발전소에서는 NOx 등 다양한 유해 물질이 발생하고 있고 그에 대한 정확한 예측은 매우 중요하다. 지금까지는 오프라인 학습법에 기반한 연구가 주류를 이루었고, 또한 초기 데이터가 부족한 상황을 고려한 연구는 존재하지 않았다. 본 연구는 온라인 학습 방법을 활용하여 화력발전소의 NOx 배출량을 제안한다. 온라인 학습법은 새로운 관측치가 발생할 때마다 모델을 학습하여 초기 데이터가 부족한 상황에서도 높은 예측 정확도를 보이는 모델이다. 오픈 데이터를 사용하여 훈련 데이터가 적은 상황을 가정해 실험을 진행하였으며, 오프라인 방법론과 비교한 결과 본 연구에서 적용한 온라인 학습법이 가장 우수한 예측 성능을 보였다.

• Journal of Power Electronics
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• 제13권4호
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• 대한전기학회논문지:전력기술부문A
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• 제49권3호
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• pp.107-117
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• 2000

This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models. The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptrons, radial basis function networks, and neuro-fuzzy models without the structure learning.

• 분석과학
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• 제37권1호
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• pp.12-24
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• 2024

In this study, laser-induced breakdown spectroscopy (LIBS) was used to estimate the degree of rice polishing. As-threshed rice seeds were dehusked and polished for different times, and the resulting grains were analyzed using LIBS. Various atomic, ionic, and molecular emissions were identified in the LIBS spectra. Their correlation with the amount of polished-off matter was investigated. Na I and Rb I emission line intensities showed linear sensitivity in the widest range of polished-off-matter amount. Thus, univariate models based on those lines were developed to predict the weight percent of polished-off matter and showed 3-5 % accuracy performances. Partial least squares-regression (PLS-R) was also applied to develop a multivariate model using Si I, Mg I, Ca I, Na I, K I, and Rb I emission lines. It outperformed the univariate models in prediction accuracy (2 %). Our results suggest that LIBS can be a reliable tool for authenticating the degree of rice polishing, which is closed related to nutrition, shelf life, appearance, and commercial value of rice products.