• Journal of the Korea Society for Simulation
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• v.25 no.4
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• pp.77-84
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• 2016

This paper proposes a methodology for creating a function based reliability prediction model. Although, there are various works for reliability prediction, one of the features of their research is that the research is based on hardware-centered reliability prediction. Reliability is often defined as the probability that a device will perform its intended function, under operating condition, for a specified period of time, there is a profound irony about reliability prediction problem. In this paper, we proposed four-phase modeling procedure for function-centered reliability prediction. The proposed modeling procedure consists of four models; 1) structure block model, 2) function block model, 3) device model, and 4) reliability prediction model. We performed function-centered reliability prediction for electronic ballast using the proposed modeling procedure and MIL-HDBK-217F which is the military handbook for reliability prediction of electronic equipment.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.3
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• pp.209-214
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• 2015

The purpose of this study was to examine (1) the influence of the word-prediction function on the sentence generation speed and (2) the necessity, convenience, and satisfaction of the word-prediction function of the AAC keyboard. A total of 10 adults with cerebral palsy participated and the word-prediction function of the Korean high-tech AAC device called "MyTalkie Smart" keyboard was used for this study. Participants were required to generate sentence as voice outputs using a word-prediction function and letters direct-input function respectively, then they were required to evaluate the necessity, convenience, and satisfaction using a five-point Likert scale. Other user requirements were examined using a free feedback. The results of this study presented that the sentence generation speeds were faster when participants used a word-prediction function than using a letters direct-input function. However, there was no statistically significant difference between these two input methods, and it might be due to the lack of time to practice the new device. Participants showed positive responses for the necessity, convenience, and satisfaction of the word-prediction function.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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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.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.343-351
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• 2008

By supervised learning with p predictors, we frequently obtain a prediction function of the form $y\;=\;f(x_1,...,x_p)$. When $p\;{\geq}\;3$, it is not easy to understand the inner structure of f, except for the case the function is formulated as additive. In this study, we propose to use p simple graphs for visual understanding of complex prediction functions produced by several supervised learning engines such as LOESS, neural networks, support vector machines and random forests.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.5
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• pp.637-653
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• 1999

In this study well-known burned rate expressions of Weibe function and double Wiebe function have been adopted for the combustion analysis of large two stroke marine diesel engine. A cycle simulation program was also developed to predict the performance and pressure waves in pipes using validated burned rate function,. Levenberg-Marquardt iteration method was applied to cali-brate the shape coefficients included in double Wiebe function for the performance prediction of two-stroke marine diesel engine. As a result the performance prediction using double Wiebe func-tion is well correlated withexperimental dta with the accuracy of 5% and pressure waves in intake and transport pipe are well predicted. From the results of this study it can be confirmed that the shape coefficients of burned rate function should be modified using the numerical method suggested for the accurated prediction and double Wiebe function is more suitable than Wiebe func-tion for combustion analysis of large two stroke marine engine.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.315-315
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• 2000

In this paper, we perform the time series prediction using the SVM(Support Vector Machine). We make use of two different loss functions and two different kernel functions; i) Quadratic and $\varepsilon$-insensitive loss function are used; ii) GRBF(Gaussian Radial Basis Function) and ERBF(Exponential Radial Basis Function) are used. Mackey-Glass time series are used for prediction. For both cases, we compare the results by the SVM to those by ANN(Artificial Neural Network) and show the better performance by SVM than that by ANN.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.137-146
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• 2023

The dropout of university freshmen is a very important issue in the financial problems of universities. Moreover, the dropout rate is one of the important indicators among the external evaluation items of universities. Therefore, universities need to predict dropout students in advance and apply various dropout prevention programs targeting them. This paper proposes a method to predict such dropout students in advance. This paper is about a method for predicting dropout students. It proposes a method to select dropouts by applying logistic regression using a shift sigmoid classification function using only quantitative data from the first semester of the first year, which most universities have. It is based on logistic regression and can select the number of prediction subjects and prediction accuracy by using the shift sigmoid function as an classification function. As a result of the experiment, when the proposed algorithm was applied, the number of predicted dropout subjects varied from 100% to 20% compared to the actual number of dropout subjects, and it was found to have a prediction accuracy of 75% to 98%.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.168-175
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• 1999

In this paper, a modified RBF(Radial Basis Function) network structure is suggested for the prediction of a time-series with non-linear, non-stationary characteristics. Coventional RBF network predicting time series by using past outputs sense the trajectory of the time series and react when there exists strong relation between input and hidden activation function's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden activation functions are modified to react to the increments of input variable and multiplied by increment(or dectement) for prediction. When the suggested structure is applied to prediction of Macyey-Glass chaotic time series, Lorenz equation, and Rossler equation, improved performances are obtained.

• Journal of Broadcast Engineering
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• v.17 no.5
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• pp.756-764
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• 2012

In current HEVC, prediction pixels are finally calculated by linear-function interpolation on two reference pixels. It is hard to expect good performance on the case of occurring large difference between two reference pixels. This paper decides more accurate prediction pixel values than current HEVC using linear function. While existing prediction process only uses two reference pixels, proposed method uses DCT-IF. DCT-IF analyses frequency characteristics of more than two reference pixels in frequency domain. And proposed method calculates prediction value adaptively by using linear-function, DCT-IF and cubic-function to decide more accurate interpolation value than to only use linear function. Cubic-function has a steep slope than linear-function. So, using cubic-function is utilized on edge in prediction unit. The complexity of encoder and decoder in HM6.0 has increased 3% and 1%, respectively. BD-rate has decreased 0.4% in luma signal Y, 0.3% in chroma signal U and 0.3% in chroma signal V in average. Through this experiment, proposed adaptive intra prediction method using DCT-IF and cubic-function shows increased performance than HM6.0.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.561-568
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• 1994

A man model can be used as an effective tool to design ergonomically sound products and workplaces, and subsequently evaluate them properly. For a man model to be truly useful, it must be integrated with a posture prediction model which should be capable of representing the human arm reach posture in the context of equipments and workspaces. Since the human movement possesses redundant degrees of freedom, accurate representation or prediction of human movement was known to be a difficult problem. To solve this redundancy problem, a psychophysical cost function was suggested in this study which defines a cost value for each joint movement angle. The psychophysical cost function developed integrates the psychophysical discomfort of joints and the joint range availability concept which has been used for redundant arm manipulation in robotics to predict the arm reach posture. To properly predict an arm reach posture, an arm reach posture prediction model was then developed in which a posture configuration that provides the minimum total cost is chosen. The predictivity of the psychophysical cost function was compared with that of the biomechanical cost function which is based on the minimization of joint torque. Here, the human body is regarded as a two-dimensional multi-link system which consists of four links ; trunk, upper arm, lower arm and hand. Real reach postures were photographed from the subjects and were compared to the postures predicted by the model. Results showed that the postures predicted by the psychophysical cost function closely simulated human reach postures and the predictivity was more accurate than that by the biomechanical cost function.