• Computers and Concrete
• /
• 제19권6호
• /
• pp.651-658
• /
• 2017

High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.

• 소성∙가공
• /
• 제29권2호
• /
• pp.76-88
• /
• 2020

The need for advanced high strength steel (AHSS) forming technology is increasing as interest in light weight and safe automobiles increases. Multipoint dieless forming (MDF) is a novel sheet metal forming technology that can create any desired longitudinal and transverse curvature in sheet metal. However, since the springback phenomenon becomes larger with high strength metal such as AHSS, predicting the required MDF to produce the exact desired curvature in two directions is more difficult. In this study, a prediction model using artificial neural network (ANN) was developed to predict the springback that occurs during AHSS forming through MDF. In order to verify the validity of model, a fit test was performed and the results were compared with the conventional regression model. The data required for training was obtained through simulation, then further random sample data was created to verify the prediction performance. The predicted results were compared with the simulation results. As a result of this comparison, it was found that the prediction of our ANN based model was more accurate than regression analysis. If a sufficient amount of data is used in training, the ANN model can play a major role in reducing the forming cost of high-strength steels.

• International Journal of Naval Architecture and Ocean Engineering
• /
• 제8권1호
• /
• pp.1-12
• /
• 2016

One of the most important factors in sailing yacht design is accurate velocity prediction. Velocity prediction programs (VPP's) are widely used to predict velocity of sailing yachts. VPP's, which are primarily based on experimental data and experience of long years, however suffer limitations when applied in realistic conditions. Thus, in the present study, a high fidelity velocity prediction method using computational fluid dynamics (CFD) was proposed. Using the developed method, velocity and attitude of a 30 feet sloop yacht, which was developed by Korea Research Institute of Ship and Ocean (KRISO) and termed KORDY30, were predicted in upwind sailing condition.

• Structural Monitoring and Maintenance
• /
• 제10권2호
• /
• pp.175-190
• /
• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2000년도 춘계학술대회 논문집
• /
• pp.529-536
• /
• 2000

This paper describes the analysis of aerodynamics and the prediction of airflow induced noise around simplified pantograph. First, computational fluid dynamics (CFD) is conducted far several model to evaluate linear/nonlinear flow field characteristics due to high speed flow and the CFD results support the computational aeroacoustics. The accurate prediction of the aeroacoustic analysis is necessary for designers to control and reduce the airflow induced noise. We adopt the acoustic analogy based on Ffowcs Williams- Hawkings (FW-H) equation and predict aeroacoustic noise.

• 한국정보통신설비학회:학술대회논문집
• /
• 한국정보통신설비학회 2008년도 정보통신설비 학술대회
• /
• pp.186-189
• /
• 2008

In order to estimate cell images, high-performance electron microscopes are used nowadays. In this paper, we propose a new simple, fast and efficient method for real-time automatic focusing in electron microscopes. The proposed algorithm is based on the prediction-error variance, and demonstrates its feasibility by using extensive experiments. This method is fast, easy to implement, accurate, and not demanding on computation time.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2021년도 학술발표회
• /
• pp.183-183
• /
• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• 대한기계학회논문집B
• /
• 제26권8호
• /
• pp.1088-1094
• /
• 2002

A channel flow with a high Reynolds number but coarse grids is numerically studied to investigate the prediction possibility of its turbulence which is three-dimensional and time-dependent. In the present paper, a Reynolds-Averaged Navier-Stokes (RANS) model, a Large Eddy Simulation (LES) and a Navier-Stokes equation with no model are tested with a new approach of hybrid RANS/LES, which reduces to RANS model in the boundary layers and at separation, and to Smagorinsky-like LES downstream of separation, and then compared with each other. It is found that the simulations of hybrid RANS/LES method sustain turbulence like those of LES and with no model, and the results are stable and fairly accurate. This indicates strongly that gradual improvements could lead to a simple, stable, and accurate approach to predict turbulence phenomena of wall-bounded flow.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1999년도 하계학술대회 논문집 C
• /
• pp.1088-1090
• /
• 1999

The radio noise produced by corona discharge in high voltage transmission tines is one of the most important line design considerations. Therefore it is necessary to pre-evaluate radio noise for transmission line designers using Prediction formulas or field test results. In this Paper, more accurate and useful formulas for Predicting radio noise during fair and foul weathers in AC transmission lines were proposed through comparison with the existing formulas. Also it was verified by comparing with the long-term measured data from operating lines that the Proposed formulas are very accurate. The Proposed prediction formulas are developed by the applications of nonlinear least square optimization method to radio noise database collected from lines throughout the world.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2002년도 추계학술대회논문집
• /
• pp.541-546
• /
• 2002

Railway noise is one of the main causes of environmental impact. Whenever a new railway line is planned or a housing project near an existing railway is proposed, an estimate of the relevant levels is usually required. For this, it is necessary to quantify those parameters that affect the railway noise. Therefore we investigated the noise and vibration level which 107 high speed trains generated passing through the block of test railway track between Chunan and Chungwon. This paper presents the status and characteristics of the high speed railway noise and an accurate prediction of the high speed railway noise.