• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.845-852
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• 2003

Forecasting exchange rate movements is a challenging task since exchange rates impact world economy and determine value of international investments. In particular, Korean-U.S. exchange rate behavior is very important because of strong Korean and U.S. trading relationship. Neural networks models have been used for short-term prediction of exchange rate movements. Least squares support vector machine (LS-SVM) is used widely in real-world regression tasks. This paper describes the use of LS-SVM for short-term and long-term prediction of Korean-U.S. exchange rate.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.562-564
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• 2004

The containment leakage rate test performed on the nuclear power plants consists of following phases : pressurizing the containment, stabilizing the atmosphere, conducting a Type A test, conducting a verification test, depressurizing the containment. It takes more than 48 hours from the pressurization to the depressurization and the prediction of the results will help to prepare the next test phase. In this paper, to predict the leakage rate, the prediction methods based on the least square method are evaluated according to the input variables and the measurement period.

• Journal of ILASS-Korea
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• v.25 no.4
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• pp.188-195
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• 2020

To meet stringent emission regulations of automotive engines, fuel injection control techniques have advanced based on reliable and fast computing prediction models. This study aims to develop a reliable lightweight prediction model of fuel injection rates using a small number of input parameters and based on simple fluid dynamic theories. The prediction model uses the geometry of the injector nozzle, needle motion data, injection conditions and the fuel properties. A commercial diesel injector and US No. 2 diesel were used as the test injector and fuel, respectively. The needle motion data were measured using X-ray phase-contrast imaging technique under various fuel injection pressures and injection pulse durations. The actual injector rate profiles were measured using an injection rate meter for the validation of the model prediction results. In the case of long injection durations with the steady-state operation, the model prediction results showed over 99 % consistency with the measurement results. However, in the case of short injection cases with the transient operation, the prediction model overestimated the injection rate that needs to be further improved.

• Journal of the Korea Safety Management & Science
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• v.10 no.3
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• pp.81-87
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• 2008

The reliability prediction and evaluation for general electronic components are required to guarantee in quality and in efficiency. Although many methodologies for predicting the reliability of electronic components have been developed, their reliability might be subjective according to a particular set of circumstances, and therefore it is not easy to quantify their reliability. In this study reliability prediction of electronic components, that is the interface card, which is used in the CNC(Computerized Numerical Controller) of machine tools, was carried out using PRISM reliability prediction specification. Reliability performances such as MTBF(Mean Time Between Failure), failure rate and reliability were obtained, and the variation of failure rate for electronic components according to temperature change was predicted. The results obtained from this study are useful information to consider a counter plan for weak components before they are used.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.323-326
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• 2016

High Efficiency Video Coding (HEVC) adopts intra transform skip mode, in which a residual block is directly quantized in the pixel domain without transforming the block into the frequency domain. Intra transform skip mode provides a significant coding gain for screen content. However, when intra-prediction errors are not transformed, the errors are often correlated along the intra-prediction direction. This paper introduces a residual differential pulse code modulation (DPCM) method for the intra-predicted and transform-skipped blocks to remove redundancy. The proposed method performs pixel-by-pixel residual prediction along the intra-prediction direction to reduce the dynamic range of intra-prediction errors. Experimental results show that the transform skip mode's Bjøntegaard delta rate (BD-rate) is improved by 12.8% for vertically intra-predicted blocks. Overall, the proposed method shows an average 1.2% reduction in BD-rate, relative to HEVC, with negligible computational complexity.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1119-1126
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• 2002

In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending un the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is mort powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower.

• Journal of the Korean Society for Railway
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• v.8 no.2
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• pp.170-175
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• 2005

This study presents a prediction of a failure rate in a safety required system that consists of a embedded control system, requiring a satisfaction of a quantitative safety requirement. International Standards are employed to achieve a regular procedures in the whole life cycle of a system, for the purpose of a prediction and a evaluation of a fault that might be able to be happened in a system. This International Standards uses SIL (Safety Integrity Level) to evaluate a safety level of a system. SIL is divided into 4 levels, from level 1 to level 4, and each level has functional failure rate and dangerous failure rate of a system. In this paper we describe the conventional method to predict the dangerous failure rate and propose a method using hazard analysis to predict the dangerous failure rate. The conventional method and the technique using hazard analysis to predict the dangerous failure rate are made a comparison through the control modules of the interlocking system in KTX. The proposed method verify better effectiveness for the prediction of the dangerous failure rate than that of the conventional method.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.1-6
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• 2021

This research examines the stock market index determinants and the prediction using the FFT curve fitting of the Jakarta Stock Exchange (JKSE) Composite Index during the COVID-19 pandemic. This paper has used daily data of Jakarta Stock Exchange (JKSE) Composite Index, interest rate, and exchange rate from 15 October 2019 to 15 September 2020, and a total of 224 observations, retrieved from Indonesia Stock Exchange (IDX), Indonesia Statistics Central Bureau and Observation & Research of Taxation. The study covers descriptive statistics, multicollinearity test, hypothesis tests, determination test, and prediction using FFT curve fitting. The results unveil four fresh and robust evidence. Partially, the interest rate has affected positively and significantly the stock market index. Partially, the exchange rate has affected negatively and significantly the stock market index. The F-test result, interest rate, and exchange rate have significantly affected the stock market index (JKSE) simultaneously. Furthermore, the FFT curve fitting has predicted that the stock market fluctuates and increases over time. The results have shown a strong influence of the independent variables and the dependent variable. The value of Adjusted R-Square is 0.719, which means that the independent variables have simultaneously impacted the dependent variable for 71.9%; other factors have influenced the remaining 28.1%.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1508-1520
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• 2018

In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

• Geomechanics and Engineering
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• v.29 no.5
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• pp.523-533
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• 2022

Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R²≥0.79; whereas, a one-step prediction is R²≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R² and UPD is proposed. When UPD increases R² decreases exponentially; In particular, UPD at R²=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R² value targeted by an operator. Therefore, a calculation process for the equation between the R² and UPD is addressed.