• Title/Summary/Keyword: Prediction of variables

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Damage Effects Modeling by Chlorine Leaks of Chemical Plants (화학공장의 염소 누출에 의한 피해 영향 모델링)

  • Jeong, Gyeong-Sam;Baik, Eun-Sun
    • Fire Science and Engineering
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    • v.32 no.3
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    • pp.76-87
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    • 2018
  • This study describes the damage effects modeling for a quantitative prediction about the hazardous distances from pressurized chlorine saturated liquid tank, which has two-phase leakage. The heavy gas, chlorine is an accidental substance that is used as a raw material and intermediate in chemical plants. Based on the evaluation method for damage prediction and accident effects assessment models, the operating conditions were set as the standard conditions to reveal the optimal variables on an accident due to the leakage of a liquid chlorine storage vessel. A model of the atmospheric diffusion model, ALOHA (V5.4.4) developed by USEPA and NOAA, which is used for a risk assessment of Off-site Risk Assessment (ORA), was used. The Yeosu National Industrial Complex is designated as a model site, which manufactures and handles large quantities of chemical substances. Weather-related variables and process variables for each scenario need to be modelled to derive the characteristics of leakage accidents. The estimated levels of concern (LOC) were calculated based on the Gaussian diffusion model. As a result of ALOHA modeling, the hazardous distance due to chlorine diffusion increased with increasing air temperature and the wind speed decreased and the atmospheric stability was stabilized.

A Study on Extraction of Defect Causal Variables for Defect Management in Financial Information System (금융정보시스템의 장애관리를 위한 장애요인변수 추출에 관한 연구)

  • Kang, Tae-Hong;Rhew, Sung-Yul
    • KIPS Transactions on Software and Data Engineering
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    • v.2 no.6
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    • pp.369-376
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    • 2013
  • Finance Information System is critical national infrastructure. Therefore it is important to select variables of defect causal factor for the system defect management effectively. We research and analyze detected errors in A Company's Finance Information System for three years. In the result of research and analysis, we have selected 9 variables of defect factor: the trading volume, the fluctuation of KOSDAQ index, and the number of public announcements, etc. Then we have assumed that these variables affect real system errors and analyzed correlation between the hypothesis and the detected system errors. After analyzing, we have extracted the trading volume, the number of orders and fills, changing tasks, and the fluctuations of NASDAQ index as valid variables of defect factor. These variables are proposed for failure prediction model as the variables to manage defects in the finance information system afterward.

A Study on the Prediction of Fall Factors for the Elderly Living in the City (도시 생활 노인의 낙상요인 예측에 관한 연구)

  • Lee, Hyun-Ju;Lee, Tae-Yong;Tae, Ki-Sik
    • Journal of rehabilitation welfare engineering & assistive technology
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    • v.12 no.1
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    • pp.46-52
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    • 2018
  • The purpose of this study was to investigate the factors affecting falls in 107 elderly living in the city aged 65 or older by evaluating general characteristics, chronic disease status, medical variables related to falls, balance-related confidence, physical ability and depression. Also, the correlations between the significant differences in variables were identified, and the prediction power was determined by deriving the variables with high influence to induce the fall. In the faller group, urinary incontinence, foot pain, lower extremity weakness, number of chronic disease and medication use were significantly higher than those of the nonfaller group. Also, statistically significant differences were evaluated in ABC (Activities-specific Balance Confidence) score, BBS (Berg Balance Scale) score, SGDS (Short Geriatric Depression Scale), FRT (Functional Reach Test) value. The main correlated factor for fall was ABC score, the lower the ABC score, fall risk is increased which is a significant negative impact. When the evaluation is performed by combining those scales, the hit ratio to classify whether faller or nonfaller is increased to 70.01% which is quite higher value.

Time series models for predicting the trend of voice phishing: seasonality and exogenous variables approaches (보이스피싱 발생 추이 예측을 위한 시계열 모형 연구: 계절성과 외생변수 활용)

  • Da-Yeon Kang;Seung-Yeon Lee;Eunju Hwang
    • Convergence Security Journal
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    • v.24 no.2
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    • pp.151-160
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    • 2024
  • In recent years with high interest rates and inflations, which worsen people's lives, voice phishing crimes also increase along with damage. Voice phishing that becomes more evolved by technology developments causes serious financial and mental damage to victims. This work aims to study time series models for its accurate prediction. ARIMA, SARIMA and SARIMAX models are compared. As exogenous variables, the amount of damages and the numbers of arrests and criminals are adopted. Forecasting performances are evaluated. Prediction intervals are constructed along with empirical coverages, which justify the superiority of the model. Finally, the numbers of voice phishing up to December 2024 are predicted, through which we expect the establishment of future prevention strategies for voice phishing.

Prediction Models for the Severity of Traffic Accidents on Expressway On- and Off-Ramps (유입·유출특성을 고려한 고속도로 연결로의 교통사고 심각도 예측모형)

  • Yun, Il-Soo;Park, Sung-Ho;Yoon, Jung-Eun;Choi, Jin-Hyung;Han, Eum
    • International Journal of Highway Engineering
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    • v.14 no.5
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    • pp.101-111
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    • 2012
  • PURPOSES: Because expressway ramps are very complex segments where diverse roadway design elements dynamically change within relatively short length, drivers on ramps are required to drive their cars carefully for safety. Especially, ramps on expressways are designed to guarantee driving at high speed so that the risk and severity of traffic accidents on expressway ramps may be higher and more deadly than other facilities on expressways. Safe deceleration maneuvers are required on off-ramps, whereas safe acceleration maneuvers are necessary on onramps. This difference in required maneuvers may contribute to dissimilar patterns and severity of traffic accidents by ramp types. Therefore, this study was aimed at developing prediction models of the severity of traffic accidents on expressway on- and off-ramps separately in order to consider dissimilar patterns and severity of traffic accidents according to types of ramps. METHODS: Four-year-long traffic accident data between 2007 and 2010 were utilized to distinguish contributing design elements in conjunction with AADT and ramp length. The prediction models were built using the negative binomial regression model consisting of the severity of traffic accident as a dependent variable and contributing design elements as in independent variables. RESULTS: The developed regression models were evaluated using the traffic accident data of the ramps which was not used in building the models by comparing actual and estimated severity of traffic accidents. Conclusively, the average prediction error rates of on-ramps and offramps were 30.5% and 30.8% respectively. CONCLUSIONS: The prediction models for the severity of traffic accidents on expressway on- and off-ramps will be useful in enhancing the safety on expressway ramps as well as developing design guidelines for expressway ramps.

Pre-processing and Bias Correction for AMSU-A Radiance Data Based on Statistical Methods (통계적 방법에 근거한 AMSU-A 복사자료의 전처리 및 편향보정)

  • Lee, Sihye;Kim, Sangil;Chun, Hyoung-Wook;Kim, Ju-Hye;Kang, Jeon-Ho
    • Atmosphere
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    • v.24 no.4
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    • pp.491-502
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    • 2014
  • As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Package for Observation Processing (KPOP), we have developed the modules for Advanced Microwave Sounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP system calculates the airmass bias correction coefficients via the method of multiple linear regression in which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and 10~1 hPa are respectively used for dependent and independent variables. Among the four airmass predictors, the multicollinearity has been shown by the Variance Inflation Factor (VIF) that quantifies the severity of multicollinearity in a least square regression. To resolve the multicollinearity, we adopted simple linear regression and Principal Component Regression (PCR) to calculate the airmass bias correction coefficients and compared the results with those from the multiple linear regression. The analysis shows that the order of performances is multiple linear, principal component, and simple linear regressions. For bias correction for the AMSU-A channel 4 which is the most sensitive to the lower troposphere, the multiple linear regression with all four airmass predictors is superior to the simple linear regression with one airmass predictor of 850~300 hPa. The results of PCR with 95% accumulated variances accounted for eigenvalues showed the similar results of the multiple linear regression.

Typhoon Path and Prediction Model Development for Building Damage Ratio Using Multiple Regression Analysis (태풍타입별 피해 분석 및 다중회귀분석을 활용한 태풍피해예측모델 개발 연구)

  • Yang, Seong-Pil;Son, Kiyoung;Lee, Kyoung-Hun;Kim, Ji-Myong
    • Journal of the Korea Institute of Building Construction
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    • v.16 no.5
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    • pp.437-445
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    • 2016
  • Since typhoon is a critical meteorological disaster, some advanced countries have developed typhoon damage prediction models. However, although South Korea is vulnerable to typhoons, there is still shortage of study in typhoon damage prediction model reflecting the vulnerability of domestic building and features of disaster. Moreover, many studies have been only focused on the characteristics and typhoon and regional characteristics without various influencing factors. Therefore, the objective of this study is to analyze typhoon damage by path and develop to prediction model for building damage ratio by using multiple regression analysis. This study classifies the building damages by typhoon paths to identify influencing factors then the correlation analysis is conducted between building damage ratio and their factors. In addition, a multiple regression analysis is applied to develop a typhoon damage prediction model. Four categories; typhoon information, geography, construction environment, and socio-economy, are used as the independent variables. The results of this study will be used as fundamental material for the typhoon damage prediction model development of South Korea.

Prediction models for phosphorus excretion of pigs

  • Jeonghyeon Son;Beob Gyun Kim
    • Animal Bioscience
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    • v.37 no.10
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    • pp.1781-1787
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    • 2024
  • Objective: The present study aimed to measure fecal and urinary phosphorus (P) excretion from pigs and to develop prediction models for P excretion of pigs. Methods: A total of 96 values for P excretions were obtained from pigs of 15 to 93 kg body weight (BW) fed 12 diets in four experiments and were used to develop the prediction models. All experimental diets contained exogenous phytase at 500 phytase units per kg. Body weight of pigs and dietary P concentrations were used as independent variables in the prediction models. Results: The BW, feed intake, and P intake were positively correlated with total (fecal plus urinary) P excretions (r = 0.80, 0.91, and 0.94, respectively; p<0.001). The models for estimating P excretion were: fecal P excretion (g/d) = -0.654-0.000618×BW2+0.273×BW×dietary P concentration (R2 = 0.83; p<0.001); urinary P excretion (g/d) = 0.045+0.00781×BW×dietary P concentration (R2 = 0.15; p<0.001); total P excretion (g/d) = -0.598-0.000613×BW2+0.280×BW×dietary P concentration (R2 = 0.86; p<0.001) where the BW of pigs and dietary P concentration are expressed as kg and % (as-fed basis), respectively. Based on the developed prediction models, the estimated annual fecal, urinary, and total P excretion for a market pig was 1.24, 0.09, and 1.33 kg/yr, respectively. Conclusion: The P excretions in market pigs can be estimated using BW of pigs and dietary P concentration. In the present model, a market pig excretes 1.24 kg of fecal P and 0.09 kg of urinary P per year.

Development and Evaluation of the High Resolution Limited Area Ensemble Prediction System in the Korea Meteorological Administration (기상청 고해상도 국지 앙상블 예측 시스템 구축 및 성능 검증)

  • Kim, SeHyun;Kim, Hyun Mee;Kay, Jun Kyung;Lee, Seung-Woo
    • Atmosphere
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    • v.25 no.1
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    • pp.67-83
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    • 2015
  • Predicting the location and intensity of precipitation still remains a main issue in numerical weather prediction (NWP). Resolution is a very important component of precipitation forecasts in NWP. Compared with a lower resolution model, a higher resolution model can predict small scale (i.e., storm scale) precipitation and depict convection structures more precisely. In addition, an ensemble technique can be used to improve the precipitation forecast because it can estimate uncertainties associated with forecasts. Therefore, NWP using both a higher resolution model and ensemble technique is expected to represent inherent uncertainties of convective scale motion better and lead to improved forecasts. In this study, the limited area ensemble prediction system for the convective-scale (i.e., high resolution) operational Unified Model (UM) in Korea Meteorological Administration (KMA) was developed and evaluated for the ensemble forecasts during August 2012. The model domain covers the limited area over the Korean Peninsula. The high resolution limited area ensemble prediction system developed showed good skill in predicting precipitation, wind, and temperature at the surface as well as meteorological variables at 500 and 850 hPa. To investigate which combination of horizontal resolution and ensemble member is most skillful, the system was run with three different horizontal resolutions (1.5, 2, and 3 km) and ensemble members (8, 12, and 16), and the forecasts from the experiments were evaluated. To assess the quantitative precipitation forecast (QPF) skill of the system, the precipitation forecasts for two heavy rainfall cases during the study period were analyzed using the Fractions Skill Score (FSS) and Probability Matching (PM) method. The PM method was effective in representing the intensity of precipitation and the FSS was effective in verifying the precipitation forecast for the high resolution limited area ensemble prediction system in KMA.

Selecting Stock by Value Investing based on Machine Learning: Focusing on Intrinsic Value (머신러닝 기반 가치투자를 통한 주식 종목 선정 연구: 내재가치를 중심으로)

  • Kim, Youn Seung;Yoo, Dong Hee
    • The Journal of Information Systems
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    • v.32 no.1
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    • pp.179-199
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    • 2023
  • Purpose This study builds a prediction model to find stocks that can reach intrinsic value among KOSPI and KOSDAQ-listed companies to improve the stability and profitability of the stock investment. And investment simulations are conducted to verify whether stock investment performance is improved by comparing the prediction model, random stock selection, and the market indexes. Design/methodology/approach Value investment theory and machine learning techniques are applied to build the model. Various experiments find conditions such as the algorithm with the best predictive performance, learning period, and intrinsic value-reaching period. This study selects stocks through the prediction model learned with inventive variables, does not limit the holding period after buying to reach the intrinsic value of the stocks, and targets all KOSPI and KOSDAQ companies. The stock and financial data are collected for 21 years (2001-2021). Findings As a result of the experiment, using the random forest technique, the prediction model's performance was the best with one year of learning period and within one year of the intrinsic value reaching period. As a result of the investment simulation, the cumulative return of the prediction model was up to 1.68 times higher than the random stock selection and 17 times higher than the KOSPI index. The usefulness of the prediction model was confirmed in that the number of intrinsic values reaching the predicted stock was up to 70% higher than the random selection.