• Journal of Information Processing Systems
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• 제14권6호
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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.

• Journal of Information Processing Systems
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• 제18권6호
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• pp.719-728
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• 2022

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

• 한국습지학회지
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• 제13권3호
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• pp.547-565
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• 2011

수위예측을 위해 개념적, 물리적 모형들을 포함한 다양한 유형의 기법들이 사용되고 있다. 그럼에도 불구하고 이러한 기법들 중 수위예측을 위해 단일의 우수한 모형을 선정하는 것은 매우 어려운 일이다. 최근에는 수문학적 과정의 복잡성으로 인해 기존 물리적 기반의 강우-유출 모형이 가지고 있는 단점들을 극복하고자 자료 지향형 수위예측 모형이 널리 도입되고 있다. 본 연구의 목적은 이러한 자료 지향형 모형 중 뉴로-퍼지와 회귀분석 모형의 수위예측에 대한 성능을 비교하는 것이다. 제안된 두 모형을 한강수계의 왕숙천에 대해 적용하였다. 제안된 두 모형의 성능을 평가하기 위해 평균제곱근오차, Nash-Suttcliffe 효율계수, 평균절대오차, 수정 결정계수와 같이 4개의 통계지표들을 사용하였다. 모의결과 뉴로-퍼지 수위예측 모형이 다중선형회귀 수위예측 모형보다 좀 더 나은 예측 결과를 나타내는 것을 확인할 수 있었다. 본 연구결과는 향후 중소하천에서 충분한 선행시간을 확보한 정확도 높은 홍수정보시스템의 구축에 활용할 수 있을 것으로 판단된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.841-854
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• 2020

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users' devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall.

• 전자공학회논문지SC
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• 제43권5호
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• pp.60-67
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• 2006

우주 비행체 전자장비의 신뢰도를 예측하고 최적화하기 위해 탑재장치 내 부품의 온도 예측이 필수적으로 요구된다. 본 논문에서는 전자장비 부품의 온도 예측방법에 관해 기술하고 있다. 본 예측 방법은 PCB 기판의 열전도도를 등방성모델로 설정하여 등가 열전도도를 계산하고 열력 모델을 이용하여 열 저항 행렬을 생성하였으며, 중첩의 원리를 이용하여 각 부품들의 온도를 예측하였다. 또한 본 논문의 온도 예측방법을 이용하여 전장품 소자의 열해석 결과와 상용 프로그램을 이용한 온도 계산 결과를 비교 분석하였다.

• 한국자동차공학회논문집
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• 제9권5호
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• pp.148-156
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• 2001

Computational Fluid Dynamics (CFD) analysis was carried out to investigate exhaust gas flow and acoustic characteristics in the exhaust system of a passenger car. Transient 3-dimensional flow field in the front and rear mufflers was simulated by CFD and far-field sound pressure was modeled by a simple monopole source method. Engine performance simulation was also performed to obtain the boundary condition of instantaneous fluid flow variation at the inlet of the exhaust system. Detailed exhaust gas flow characteristics such as velocity and pressure distribution inside the mufflers were presented and the pulsating pressure amplitude was compared at several positions in the exhaust system to deduce sound pressure level. The present method of the acoustic analysis coupled with CFD techniques would be very effective for the prediction of sound noise from vehicle exhaust systems although the effects of the inlet boundary condition and heat transfer on the accuracy of the prediction have to be validated through further studies.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.22-24
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• 2006

This paper presents the prediction method of small current interruption Performance for EHV gas circuit breakers. The FVFLIC method is used for the gas flow analysis and the FEM for the electric field analysis. Then, the dielectric withstanding voltage is evaluated by the empirical formulation or Streamer theory. By comparing the calculated dielectric strength with the test result. it is found that both methods show good prediction capability for the small current interruption performance. Especially, when both methods predict the same interrupting performance, the prediction is in accordance with the experimental result.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2004년도 춘계 학술발표회 제16권1호
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• pp.532-535
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• 2004

This paper presents a nonlinear finite element analysis procedure for the prediction of shear strength of reinforced concrete deep beams. A computer program, named RCAHESTC(Reinforced Concrete Analysis in Higher Evaluation System Technology), for the analysis of reinforced concrete structures was used. Material nonlinearity is taken into account by comprising tensile. compressive and shear models of cracked concrete and a model of reinforcing steel. The smeared crack approach is incorporated. The proposed numerical method for the prediction of shear strength of reinforced concrete deep beams is verified by comparison with the reliable experimental results.

• 한국도로학회논문집
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• 제18권4호
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• pp.93-102
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• 2016

PURPOSES : There are many recently constructed roundabouts in Jeollabuk-do province. This study analyzed how roundabouts reduce the risk of accidents and improve safety in the province. METHODS : This study analyzed safety improvement at roundabouts by using an accident prediction model that uses an Empirical Bayes method based on negative binomial distribution. RESULTS : The results of our analysis model showed that the total number of accidents decreased from 130 to 51. Roundabouts also decreased casualties; the number of casualties decreased from 7 to 0 and the seriously wounded from 87 to 16. The effectiveness of accident reduction as analyzed by the accident prediction model with the Empirical Bayes method was 60%. CONCLUSIONS : The construction of roundabouts can bring about a reduction in the number of accidents and casualties, and make intersections safer.

• 전기학회논문지
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• 제56권1호
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• pp.28-34
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• 2007

Recently, with the rapid growth of industry, environmental condition became worse. In addition to outdoor insulators in seashore are polluted due to salty wind. Also this pollution causes the flashover and failure of electric equipments. Especially the salt contaminant is one of the most representative pollutants, and known as the main source of the accident by contamination. As well known, the pollution has a close relation with meteorological factors such as wind velocity, wind direction, temperature, relative humidity, precipitation and so on. In this paper we have statistically analyzed the correlation between the pollution and the meteorological factors. The multiple regression analysis was used for the statistical analysis; daily measured equivalent salt deposit density(dependent variable) and the weather condition data(independent variable) were used. Also we have developed an expert program to predict the pollution deposit. A new prediction system using this program called SPPP(salt pollution prediction program) has been used to model accurately the relationship between ESDD with the meteorological factors.