• Computers and Concrete
• /
• v.17 no.2
• /
• pp.173-188
• /
• 2016

Generally, thermal stress induced by hydration heat causes cracking in mass concrete structures, requiring a thorough control during the construction. The prediction of the thermal stress is currently undertaken by means of numerical analysis despite its lack of reliability due to the properties of concrete varying over time. In this paper, a method for the prediction of thermal stress in concrete structures by adjusting thermal stress measured by a thermal stress device according to the degree of restraint is proposed to improve the prediction accuracy. The ratio of stress in concrete structures to stress under complete restraint is used as the degree of restraint. To consider the history of the degree of restraint, incremental stress is predicted by comparing the degree of restraint and the incremental stress obtained by the thermal stress device. Furthermore, the thermal stresses of wall and foundation predicted by the proposed method are compared to those obtained by numerical analysis. The thermal stresses obtained by the proposed method are similar to those obtained by the analysis for structures with internally as well as externally strong restraint. It is therefore concluded that the prediction of thermal stress for concrete structures with various boundary conditions using the proposed method is suggested to be accurate.

• 한국전산유체공학회:학술대회논문집
• /
• 2004.10a
• /
• pp.47-51
• /
• 2004

Accurate Prediction of a supersonic missile base drag continues to defy even well-rounded CFD codes. In an effort to address the accuracy and predictability of the base drags, the influence of grid system and competitive turbulence models on the base drag is analyzed. Characteristics of some turbulence models is reviewed through incompressible turbulent flow over a flat plate, and performance for the base drag prediction of several turbulence models such as Baldwin-Lomax(B-L), Spalart-Allmaras(S-A), $\kappa-\epsilon$, $\kappa-\omega$ model is assessed. When compressibility correction is injected into the S-A model, prediction accuracy of the base drag is enhanced. The NSWC wind tunnel test data are utilized for comparison of CFD and semi-empirical codes on the accuracy of base drag predictability: they are about equal, but CFD tends to perform better. It is also found that, as angle of attack of a missile with control (ins increases, even the best CFD analysis tool we have lacks the accuracy needed for the base drag prediction.

• Journal of Ocean Engineering and Technology
• /
• v.22 no.6
• /
• pp.95-99
• /
• 2008

The block assembly of ship consists of a certain type of heat processes such as cutting, bending welding residual stress relaxation and fairing. The residual deformation due to welding is inevitable at each assembly stage. The geometric inaccuracy caused by the welding deformation tends to preclude the introduction of automation and mechanization and needs the additional man-hours for the adjusting work at the following assembly stage. To overcome this problem, a distortion control method should be applied. For this purpose, it is necessary to develop an accurate prediction method which can explicitly account for the influence of various factors on the welding deformation. The validity of the prediction method must be also clarified through experiments. This paper proposes a simplified analysis method to predict the welding deformation of panel block structure. For this purpose, a simple prediction model for fillet welding deformations has been derived based on numerical and experimental results through the regression analysis. On the basis of these results, the simplified analysis method has been applied to some examples to show its validity.

• Journal of Power Electronics
• /
• v.17 no.5
• /
• pp.1288-1297
• /
• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.6_2
• /
• pp.623-629
• /
• 2012

During the construction of crossing engineering one of the important measures to ensure the safety of subway operation is the implementation of deformation surveying to the existing subway tunnel. Guangzhou new subway line 2 engineering which crosses the existing tunnel is taken as the background. How to achieve intelligent and automatic deformation surveying forecast during the subway tunnel construction process is studied. Because large amount of surveying data exists in the subway construction, deformation analysis is difficult and prediction has low accuracy, a subway intelligent deformation prediction model based on the PBIL and support vector machine is proposed. The PBIL algorithm is used to optimize the exact key parameters combination of support vector machine though probability analysis and thereby the predictive ability of the model deformation is greatly improved. Through applications on the Guangzhou subway across deformation surveying deformation engineering the prediction method's predictive ability has high accuracy and the method has high practicality. It can support effective solution to the implementation of the comprehensive and accurate surveying and early warning under subway operation conditions with the environmental interference and complex deformation.

• Journal of Internet Computing and Services
• /
• v.8 no.6
• /
• pp.95-102
• /
• 2007

Recommender System based on the Collaborative Filtering has a problem of trust of the prediction accuracy because of its problem of sparsity. If the sparsity of a preference value is large, it causes a problem on a process of a choice of neighbors and also lowers the prediction accuracy. In this article, a change of MAE based on the sparsity is studied, groups are classified by sparsity and then, the significant difference among MAEs of classified groups is analyzed. To improve the accuracy of prediction among groups by the problem of sparsity, We studied the improvement of an accurate prediction for recommending system through reducing sparsity by sorting sparsity items, and replacing the average preference among them that has a lot of respondents with the preference evaluation value.

• Environmental Engineering Research
• /
• v.22 no.2
• /
• pp.175-185
• /
• 2017

As the energy and environmental problems are increasingly severe, researches about carbon dioxide emissions has aroused widespread concern. The accurate prediction of carbon dioxide emissions is essential for carbon emissions controlling. In this paper, we analyze the relationship between carbon dioxide emissions and influencing factors in a comprehensive way through correlation analysis and regression analysis, achieving the effective screening of key factors from 16 preliminary selected factors including GDP, total population, total energy consumption, power generation, steel production coal consumption, private owned automobile quantity, etc. Then fruit fly algorithm is used to optimize the parameters of least squares support vector machine. And the optimized model is used for prediction, overcoming the blindness of parameter selection in least squares support vector machine and maximizing the training speed and global searching ability accordingly. The results show that the prediction accuracy of carbon dioxide emissions is improved effectively. Besides, we conclude economic and environmental policy implications on the basis of analysis and calculation.

• Journal of the Society of Naval Architects of Korea
• /
• v.45 no.2
• /
• pp.175-185
• /
• 2008

One of the concerns that arise during navigation in ice-covered waters is the magnitude of ice loads encountered by ships. However, the accurate estimation of ice loads still remains as a rather difficult task in the design of icebreaking vessels. This paper focuses on the development of simple ice load prediction formulas for the icebreaking cargo vessels. The maximum ice loads are expected from unbroken ice sheet and these loads are most likely to be concentrated at the bow area. Published ice load data for icebreaking vessels, from the model tests and also from full-scale sea trials, are collected and then several ice load prediction formulas are compared with these data. Finally, based on collected data, a semi-empirical ice load prediction formula is recommended for the icebreaking cargo vessels.

• Proceedings of the Korean Information Science Society Conference
• /
• 2005.07b
• /
• pp.226-228
• /
• 2005

Predicting the cellular location of an unknown protein gives valuable information for inferring the possible function of the protein. For more accurate Prediction system, we need a good feature extraction method that transforms the raw sequence data into the numerical feature vector, minimizing information loss. In this paper we propose new methods of extracting underlying features only from the sequence data by computing pairwise sequence alignment scores. In addition, we use composition based features to improve prediction accuracy. To construct an SVM ensemble from separately trained SVM classifiers, we propose specificity based weighted majority voting . The overall prediction accuracy evaluated by the 5-fold cross-validation reached $88.53\%$ for the eukaryotic animal data set. By comparing the prediction accuracy of various feature extraction methods, we could get the biological insight on the location of targeting information. Our numerical experiments confirm that our new feature extraction methods are very useful forpredicting subcellular localization of proteins.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.52 no.1
• /
• pp.13-18
• /
• 2010

In this study the validities of runoff prediction methods are reviewed around ESP (Ensemble Streamflow Prediction) techniques. The improvements of runoff predictions on Yongdam river basin are evaluated by the comparison of different prediction methods including ESP incorporated with qualitative meteorological outlooks provided by meteorological agency as well as the runoff forecasting based on the analysis of the historical rainfall scenarios. As a result it is assessed that runoff predictions with ESP may give rise to more accurate results than the ordinary historical average runoffs. In deed the latter gave the mean of yearly absolute error as to be 60.86 MCM while the errors of the former ones amounted to 44.12 MCM (ESP) and 42.83 MCM (ESP incorporated with qualitative meteorological outlooks) respectively. In addition it is confirmed that ESP incorporated with qualitative meteorological outlooks could improve the accuracy of the results more and more. Especially the degree of improvement of ESP with meteorological outlooks shows rising by 10.8% in flood season and 8% in drought season. Therefore the methods of runoff predictions with ESP can be further used as the basic forecasting information tool for the purpose of the effective watershed management.