• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.945-953
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• 2007

In this paper, we propose a smoothing and thus noise-reducing method of data sequences for stock price prediction with hidden Markov models, HMMs. The suggested method just uses simple moving average. A proper average size is obtained from forecasting experiments with stock prices of bank sector of Korean Exchange. Forecasting method with HMM and moving average smoothing is compared with a conventional method.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 추계학술대회 논문집
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• pp.485-486
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• 2006

• 한국철도학회:학술대회논문집
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• 한국철도학회 1998년도 창립기념 춘계학술대회 논문집
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• pp.211-218
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• 1998

The demand prediction becomes an essential mean to utilize efficiently finite traffic facilities and to provide the optimized schedules for transportation system. The demand prediction is one of the critical complex management schemes for distibuting resources of transportation service by means of computer system. The construction of a prediction model is based on data granulization, followed by processing the raw input data and evaluating the predicted output values. A large number of economic-social parameters are also to be implemented in conventional prediction models which are only based on a sequence of past data. The proposed prediction models are classified by static and dynamic characteristics and its performances are evaluated utilizing computer simulation.

• Water Engineering Research
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• 제4권2호
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• pp.59-68
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• 2003

The applicabilities and validities of two methodologies fur the prediction of THM (trihalomethane) formation in a water pipeline system were proposed and discussed. One is the multiple regression technique and the other is an artificial neural network technique. There are many factors which influence water quality, especially THMs formations in water pipeline systems. In this study, the prediction models of THM formation in water pipeline systems are developed based on the independent variables proposed by American Water Works Association(AWWA). Multiple linear/nonlinear regression models are estimated and three layer feed-forward artificial neural networks have been used to predict the THM formation in a water pipeline system. Input parameters of the models consist of organic compounds measured in water pipeline systems such as TOC, DOC and UV254. Also, the reaction time to each measuring site along pipeline is used as input parameter calculated by a hydraulic analysis. Using these variables as model parameters, four models are developed. And the predicted results from the four developed models are compared statistically to the measured THMs data set. It is shown that the artificial neural network approaches are much superior to the conventional regression approaches and that the developed models by neural network can be used more efficiently and reproduce more accurately the THMs formation in water pipeline systems, than the conventional regression methods proposed by AWWA.

• Geomechanics and Engineering
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• 재33권6호
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• pp.567-581
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• 2023

The prediction and achievement of a proper rock fragmentation size is the main challenge of blasting operations in surface mines. This is because an optimum size distribution can optimize the overall mine/plant economics. To this end, this study attempts to develop four improved artificial intelligence models to predict rock fragmentation through cascaded forward neural network (CFNN) and radial basis function neural network (RBFNN) models. In this regards, the CFNN was trained by the Levenberg-Marquardt algorithm (LMA) and Conjugate gradient backpropagation (CGP). Further, the RBFNN was optimized by the Dragonfly Algorithm (DA) and teaching-learning-based optimization (TLBO). For developing the models, the database required was collected from the Midouk copper mine, Iran. After modeling, the statistical functions were computed to check the accuracy of the models, and the root mean square errors (RMSEs) of CFNN-LMA, CFNN-CGP, RBFNN-DA, and RBFNN-TLBO were obtained as 1.0656, 1.9698, 2.2235, and 1.6216, respectively. Accordingly, CFNN-LMA, with the lowest RMSE, was determined as the model with the best prediction results among the four examined in this study.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 A
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• pp.103-104
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• 2006

Since the first proposition of IEC61850 object model at 1993, many questions about making a seamless model have been issued. the reason which they have worry about is that the functions of the equipment are supposed to be changed properly and new equipment and scheme are need to be introduced according to user's application. To handle those issues, TC57 which is a IEC committee for power control and communication has continuously updated the object model. Nowadays along with the new object model involving power quality, distribution resource and wind power, the committee has a plan to announce the revision of IEC61850-7-4. In the study, authors will present the prediction and diagnosis object models for transformer. Transformer models for protection and control have already been dealt with in the international standard but the models for prediction and diagnosis have never mentioned until now. Designing the prediction and diagnosis functions with the existing IEC61850-7-4, it'll be shown what is a proper object model for prediction and diagnosis.

• 한국의류학회지
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• 제34권9호
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• pp.1567-1580
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• 2010

This study investigates the effects of visual texture on color emotion and establishes prediction models for color emotion by both physical color properties and visual texture characteristics. A variety of fabrics including silk, cotton, and flax were colored by digital textile printing according to chromatic hue and tone combinations that are evaluated in terms of color emotion. Subjective visual texture ratings are also obtained for gray-colored same fabrics to those used in color emotion tests. As a result, fabric clusters by visual texture factors showed significant differences in color emotion factors that are primarily affected by physical color properties. Finally prediction models for color emotion factors by both physical color properties and visual texture clusters were established, which has a potential to be used to explain color emotion according to the visual texture characteristics of fabrics.

• 한국인터넷방송통신학회논문지
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• 제19권3호
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• pp.127-133
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• 2019

소프트웨어 결함 예측 연구들의 대부분은 라벨 데이터를 훈련 데이터로 사용하는 감독형 모델에 관한 연구들이다. 감독형 모델은 높은 예측 성능을 지니지만 대부분 개발 집단들은 충분한 라벨 데이터를 보유하고 있지 않다. 언라벨 데이터만 훈련에 사용하는 비감독형 모델은 모델 구축이 어렵고 성능이 떨어진다. 훈련 데이터로 라벨 데이터와 언라벨 데이터를 모두 사용하는 세미 감독형 모델은 이들의 문제점을 해결한다. Self-training은 세미 감독형 기법들 중 여러 가정과 제약조건들이 가장 적은 기법이다. 본 논문은 Self-training 알고리즘들을 이용해 여러 모델들을 구현하였으며, Accuracy와 AUC를 이용하여 그들을 평가한 결과 YATSI 모델이 가장 좋은 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1193-1215
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• 2021

The measurement of the key product quality index plays an important role in improving the production efficiency and ensuring the safety of the enterprise. Since the actual working conditions and parameters will inevitably change to some extent with time, such as drift of working point, wear of equipment and temperature change, etc., these will lead to the degradation of the quality variable prediction model. To deal with this problem, the selective integrated moving windows based principal component regression (SIMV-PCR) is proposed in this study. In the algorithm of traditional moving window, only the latest local process information is used, and the global process information will not be enough. In order to make full use of the process information contained in the past windows, a set of local models with differences are selected through hypothesis testing theory. The significance levels of both T - test and χ² - test are used to judge whether there is identity between two local models. Then the models are integrated by Bayesian quality estimation to improve the accuracy of quality variable prediction. The effectiveness of the proposed adaptive soft measurement method is verified by a numerical example and a practical industrial process.

• Computers and Concrete
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• 제28권5호
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• pp.441-450
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• 2021

This study presents the first analysis system and fast-access cloud database for shrinkage and creep of concrete in the world, named "shrinkage and creep database in Taiwan", SCDT. SCDT not only has the most comprehensive experimental data, including NU, JSCE, Europe, and TW databases, but provides a design tool for researchers and engineers. It can further facilitate the development of prediction models for localized concrete. Users can obtain the shrinkage and creep curves based on their selected prediction models in SCDT. Comparisons of the predicted results of selected models and test results in the chosen database can be generated in seconds. One example of the development of basic creep prediction model in Taiwan based on model B4 by using SCDT to reflect concrete characteristics in Taiwan is also presented in this study. Users anywhere in the world can easily access SCDT to browse and upload data, receive predictive results, or develop predictive models.