• ETRI Journal
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• 제42권3호
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• pp.366-375
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• 2020

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA-WNN. A comparative analysis of the experimental results shows that the performance of the IGSA-WNN-based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.

• 제어로봇시스템학회논문지
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• 제6권1호
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• pp.104-111
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• 2000

A fuzzy logic Run-by-Run(RbR) controller and an in -line wafer characteristics prediction scheme for the rapid thermal processing system have been developed for the study of process repeatability. The fuzzy logic RbR controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The fuzzy logic RbR controller combines the advantages of both fuzzy logic and feedback control. It has two components : fuzzy logic diagnostic system and model modification system. At first, a neural network model is constructed with the I/O data collected during the designed experiments. The wafer state after each run is assessed by the fuzzy logic diagnostic system with featuring step. The model modification system updates the existing neural network process model in case of process shift or drift, and then select a new recipe based on the updated model using genetic algorithm. After this procedure, wafer characteristics are predicted from the in-line wafer characteristics prediction model with principal component analysis. The fuzzy logic RbR controller has been applied to the control of Titanium SALICIDE process. After completing all of the above, it follows that: 1) the fuzzy logic RbR controller can compensate the process draft, and 2) the in-line wafer characteristics prediction scheme can reduce the measurement cost and time.

• 조명전기설비학회논문지
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• 제29권5호
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• pp.94-99
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• 2015

Prediction algorithm of the energy storage system in accordance with the load pattern can cause economic loss in case of a failure prediction. In addition, algorithm that uses TOU(Time of Use) based on the revelation by the power electric charge which covers most simply is an inefficient operation because it is only for the purpose of reducing the peak power. In this paper, we introduced a non-prediction algorithm with a conventional TOU in order to solve this problem operating the energy storage system economic and efficient.

• 산업경영시스템학회지
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• 제41권3호
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• pp.154-161
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• 2018

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

• Tribology and Lubricants
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• 제10권1호
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• pp.62-68
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• 1994

The analysis of statically indeterminate bearing-shaft system was investigated. The moment loads and misalignment angles which were induced in the ball bearings were determined, and the influence of span length of this system on the moment loads and fatigue lives was identified. The sliding and spinning speeds between balls and raceways which affected the performance of ball bearing evaluated. The equation to estimate the cage speed of ball bearing under moment loads was proposed. This equation had been verified by the test results of measuring of cage speed, which was useful to the prediction of ball bearing under moment loads.

• 한국농공학회논문집
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• 제55권4호
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• pp.1-11
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• 2013

Unsaturated hydraulic conductivity function is one of key parameters to solve the flow phenomena in problems of landslide. Prediction models for hydraulic conductivity function related to soil-water retention curve equations in many geotechnical applications have been still used instead of direct measurement of the hydraulic conductivity function since prediction models from soil-water retention curve equations are attractive for their fast and easy use and low cost. However, many researchers found that prediction models for the hydraulic conductivity function can not predict the hydraulic conductivity exactly in comparison with experimental outputs. This research introduced an inverse analysis to evaluate the hydraulic conductivity function corresponding to experimental output from the flow pump system. Optimisation process was carried out to obtain the hydraulic conductivity function. This research showed that the inverse analysis with flow pump system was suitable to assess the hydraulic conductivity in unsaturated soil, and the prediction models for the hydraulic conductivity were led to the significant discrepancy from actual experimental outputs.

• 산업경영시스템학회지
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• 제39권1호
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• pp.64-72
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• 2016

In this paper, we utilize a Gaussian process to predict the power consumption in the air-conditioning system. As the power consumption in the air-conditioning system takes a form of a time-series and the prediction of the power consumption becomes very important from the perspective of the efficient energy management, it is worth to investigate the time-series model for the prediction of the power consumption. To this end, we apply the Gaussian process to predict the power consumption, in which the Gaussian process provides a prior probability to every possible function and higher probabilities are given to functions that are more likely consistent with the empirical data. We also discuss how to estimate the hyper-parameters, which are parameters in the covariance function of the Gaussian process model. We estimated the hyper-parameters with two different methods (marginal likelihood and leave-one-out cross validation) and obtained a model that pertinently describes the data and the results are more or less independent of the estimation method of hyper-parameters. We validated the prediction results by the error analysis of the mean relative error and the mean absolute error. The mean relative error analysis showed that about 3.4% of the predicted value came from the error, and the mean absolute error analysis confirmed that the error in within the standard deviation of the predicted value. We also adopt the non-parametric Wilcoxon's sign-rank test to assess the fitness of the proposed model and found that the null hypothesis of uniformity was accepted under the significance level of 5%. These results can be applied to a more elaborate control of the power consumption in the air-conditioning system.

• 대기
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• 제17권1호
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• pp.27-44
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• 2007

The purpose of this study is to investigate predictability of the seasonal simulation by the METRI (Meteorological Research Institute) AGCM (Atmospheric General Circulation Model), which is a long-term prediction model for the METRI 3-month prediction system. We examine the performance skill of climate simulation and predictability by the analysis of variance of the METRI AGCM, focusing on the precipitation, 850 hPa temperature, and 500 hPa geopotential height. According to the result, the METRI AGCM shows systematic errors with seasonal march, and represents large errors over the equatorial region, compared to the observation. Also, the response of the METRI AGCM by the variation of the sea surface temperature is obvious for the wintertime and springtime. However, the METRI AGCM does not show the significant ENSO-related signal in autumn. In case of prediction over the east Asian region, errors between the prediction results and the observation are not quite large with the lead-time. However, in the predictability assessment using the analysis of variance method, longer lead-time makes the prediction better, and the predictability becomes better in the springtime.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2009년도 춘계학술대회 논문집 특별세미나,특별/일반세션
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• pp.595-600
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• 2009

Passengers in a vehicle feel uncomfortable due to squeal noise. Squeal noise, a kind of self-excited vibration, is generated by the friction force between the disc and the pad of the automobile. In this paper, modal analysis of wheel brake system was performed in order to prediction of squeal phenomenon. It was shown that the prediction of system instability is possible by FEM. Finite element model of that brake system was made. Some parts of a real brake was selected and modeled. The normal mode analysis method performs analyses of each brake system component. Experiment of modal analysis was performed for each brake components and experimental results were compared with analytical result from FEM. The complex eigenvalue analysis results compared with braking test. The analysis results show good correlation with braking test for the squeal frequency at an unstable mode.

• International Journal of Naval Architecture and Ocean Engineering
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• 제10권6호
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• pp.661-669
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• 2018

Engineering system problems consist of multi-objective optimisation and the performance analysis is generally time consuming. To optimise the system concerning its performance, many researchers perform the optimisation using an approximation model. The Response Surface Method (RSM) is usually used to predict the system performance in many research fields, but it shows prediction errors for highly nonlinear problems. To create an appropriate metamodel for marine systems, Lee (2015) compares the prediction accuracy of the approximation model, and multi-objective optimal design framework is proposed based on a confirmed approximation model. The proposed framework is composed of three parts: definition of geometry, generation of approximation model, and optimisation. The major objective of this paper is to confirm the applicability/usability of the proposed optimal design framework and evaluate the prediction accuracy based on sensitivity analysis. We have evaluated the proposed framework applicability in derrick structure optimisation considering its structural performance.