• Corrosion Science and Technology
• /
• v.3 no.3
• /
• pp.87-97
• /
• 2004

This paper presents a new approach with meso scale structure models to express mechanical property, such as stress - strain relationships, of concrete. This approach is successful to represent both uniaxial tension and uniaxial compression stress - strain relationship, which is in macro scale. The meso scale approach is also applied to predict degraded mechanical properties of frost-damaged concrete. The degradation of mechanical properties with frost-damaged concrete was carefully observed. Strength and stiffness in both tension and compression decrease with freezing and thawing cycles (FTC), while stress-free crack opening in tension softening increases. First attempt shows that the numerical simulation can express the experimentally observed degradation by introducing changes in the meso scale structure in concrete, which are assumed based on observed damages in the concrete subjected to FTC. At the end applicability of the meso scale approach to prediction of the degradation by combined effects of salt attack and FTC is discussed. It is shown that clarification of effects of frost damage in concrete on corrosion progress and on crack development in the damaged cover concrete due to corrosion is one of the issues for which the meso scale approach is useful.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.434-442
• /
• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• International Journal of Highway Engineering
• /
• v.7 no.4 s.26
• /
• pp.1-8
• /
• 2005

Permanent Deformation is one of the most important load-related pavement distresses in asphalt pavements. The Korean Pavement Design Guide currently being developed adopted the mechanistic-empirical approach and needed the pavement distress prediction models. This study intends to develop the model for prediction of permanent deformation in the asphalt layer and estimate the pavement performance. The objectives of this paper are to figure out the factors affecting the permanent deformation and then develop the permanent deformation prediction model for asphalt mixtures. The repeated triaxial load test was Performed on the 19mm dense graded asphalt mixture with variation of temperature and air void. Results from the laboratory tests showed that temperature and air void in asphalt mixtures have significantly influenced on the factors in prediction model. The permanent deformation prediction model for 19m dense grade asphalt mixtures has been developed using the multiple regression approach and validated the proposed permanent deformation prediction model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2007.11a
• /
• pp.283-286
• /
• 2007

This paper proposes a genetic algorithm (GA) approach to instance selection in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in complex problem solving. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for instance selection in CBR.

• The KSFM Journal of Fluid Machinery
• /
• v.16 no.2
• /
• pp.42-47
• /
• 2013

Ship stability prediction is very complex in reality. In this paper, risk based approach is applied to predict the probability of a certified ship, which is effected by the forces of sea especially the wave loading. Safety assessment and risk analysis process are also applied for the probabilistic prediction of ship stability. The survival probability of ships encountering with different waves at sea is calculated by the existed statistics data and risk based models. Finally, ship capsizing probability is calculated according to single degree of freedom(SDF) rolling differential equation and basin erosion theory of nonlinear dynamics. Calculation results show that the survival probabilities of ship excited by the forces of the seas, especially in the beam seas status, can be predicted by the risk based method.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.20 no.6
• /
• pp.1810-1818
• /
• 1996

A new finite elemetn approach is introduced for direct prediction of bland shapes and strain distributions from desired final shapes in sheet metal forming. The approach deals with the geometric compatibility of finite elements, plastic deformation theory, minimization of plastic work with constraints, and a proper initial guess. The algorithm developed is applied to cylindrical cup drawing, square cup drawing, and fron fender forming to confirm its validity by demonstratin reasonable accurate numerical results of each problems. Rapid calculation with this algorithm enables easy determination of various process variables for design of sheet metal forming process.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.16 no.5
• /
• pp.675-682
• /
• 2013

Reliability analysis and prediction of next failure time is critical to sustain weapon systems, concerning scheduled maintenance, spare parts replacement and maintenance interventions, etc. Since 1981, many methodology derived from various probabilistic and statistical theories has been suggested to do that activity. Nowadays, many A.I. tools have been used to support these predictions. Support Vector Regression(SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVM and SVR with combining time series to predict the next failure time based on historical failure data. A numerical case using failure data from the military equipment is presented to demonstrate the performance of the proposed approach. Finally, the proposed approach is proved meaningful to predict next failure point and to estimate instantaneous failure rate and MTBF.

• Journal of the Korean Chemical Society
• /
• v.58 no.6
• /
• pp.543-552
• /
• 2014

A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}=0.603$ and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

• Computers and Concrete
• /
• v.20 no.1
• /
• pp.49-56
• /
• 2017

A clear correlation exists between the compressive strength and elastic modulus of concrete. Unfortunately, determining the static elastic modulus requires destructive methods and determining the dynamic elastic modulus is greatly complicated by the shape and size of the specimens. This paper reports on a novel approach to the prediction of compressive strength in concrete cylinders using numerical calculations in conjunction with the impact-echo method. This non-destructive technique involves obtaining the speeds of P-waves and S-waves using correction factors through numerical calculation based on frequencies measured using the impact-echo method. This approach makes it possible to calculate the dynamic elastic modulus with relative ease, thereby enabling the prediction of compressive strength. Experiment results demonstrate the speed, convenience, and efficacy of the proposed method.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.5
• /
• pp.1419-1425
• /
• 2000

When we predict of software reliability, we can't know the testing stopping time and how many faults be residues in software the (the maximum value of data) during these software testing process, therefore we assume the maximum value and the training result can be inaccuracy. In this paper, we present neural network approach for software reliability prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data.