• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.59-67
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• 2011

This paper proposes a model to analyze the fault factors of air-to-ground weapon delivery utilizing software fault localization methods. In the previous study, to figure out the factors to affect the accuracy of air-to-ground weapon delivery, the FBEL (Factor-based Error Localization) method had been proposed and the fault factors were analyzed based on the method. But in the study, the correlation between weapon delivery accuracy and the fault factors could not be revealed because the firing accuracy among several factors was fixed. In this paper we propose a more precise fault analysis model driven through a study of the correlation among the fault factors of weapon delivery, and a method to estimate the possibility of faults with the limited number of test cases utilizing the model. The effectiveness of proposed method is verified through the simulation utilizing real delivery data. and weapons delivery testing in the evaluation of which element affecting the accuracy of analysis that was available to be used successfully.

• Convergence Security Journal
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• v.12 no.3
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• pp.19-26
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• 2012

In this study, software products developed in the course of testing, software managers in the process of testing software and tools for effective learning effects perspective has been studied using the NHPP software. The log type hazard function applied to distribution was based on finite failure NHPP. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model could be confirmed. This paper, a failure data analysis of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and $R^2$(coefficient of determination).

• Structural Engineering and Mechanics
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• v.60 no.4
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• pp.655-665
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• 2016

This paper aims to develop models to accurately predict the behavior of fresh concrete exposed to vibration using artificial neural networks (ANNs) model and regression model (RM). For this purpose, behavior of a full scale precast concrete mold was investigated experimentally and numerically. Experiment was performed under vibration with the use of a computer-based data acquisition system. Transducers were used to measure time-dependent lateral displacements at some points on mold while both mold is empty and full of fresh concrete. Modeling of empty and full mold was made using both ANNs and RM. For the modeling of ANNs: Experimental data were divided randomly into two parts. One of them was used for training of the ANNs and the remaining part was used for testing the ANNs. For the modeling of RM: Sinusoidal regression model equation was determined and the predicted data was compared with measured data. Finally, both models were compared with each other. The comparisons of both models show that the measured and testing results are compatible. Regression analysis is a traditional method that can be used for modeling with simple methods. However, this study also showed that ANN modeling can be used as an alternative method for behavior of fresh concrete exposed to vibration in precast concrete structures.

• Environmental Engineering Research
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• v.25 no.3
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• pp.418-425
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• 2020

In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R² values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.

• Computers and Concrete
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• v.27 no.4
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• pp.333-341
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• 2021

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.

• Geomechanics and Engineering
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• v.36 no.3
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• pp.259-276
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• 2024

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q_t), average wall friction (f_s), and effective vertical stress (σ_vo), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S_u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost - AOA is the most accurate model for predicting the pile set-up component.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.12-22
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• 2007

This paper presents to predict the powertrain structure-borne noise which is primary resource of interior noise. As the first step, it is built up a hybrid powertrain model which is based on the real powertrain which is verified with static and dynamic properties. The methods for verifying are modal analysis and running vibration testing which are experimentally implemented. Based on the Hybrid powertrain component model, an initial predictive assembly model is simulated. As the second step, the characteristic transfer functions are measured that are dynamic stiffness of rubber mounts and vibro-acoustic transfer function based on the acoustic reciprocity. Several techniques utilizing special experimental devices have been proposed for this research. Finally, the structure-borne noise by powertrain will be predict and verify with dynamic simulation and experiment.

• Journal of Technology Innovation
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• v.10 no.2
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• pp.91-105
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• 2002

This paper extends the empirical analysis on R&D based growth model so that the nonstationary panel unit root testing methods can be used to distinguish the exogenous growth model and R&D based growth model for the 1981-1999 period with fourteen OECD economies including Korea. Our results show that first, using U.S. and Group mean as benchmarking, the stochastic R&D productivity convergence to benchmarking is not supported in our data set. Second, the empirical results for stochastic nonconvergence to the U.S. or group mean also are robustness to panel unit root methods. We, therefore, find strong support for the implications for R&D based growth model.

• Proceedings of the ESK Conference
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• 1997.10a
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• pp.313-319
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• 1997

There is currently a focus on usability of interactive computer software. Previous research in software ergonomics has indicated the importance of evaluating the usability of software user interfaces. Software developers, interface designers or human foctors engineers often confront the task of comparative evaluation among systems, versions or interface designs. This study presents a structured model for comparative evaluation of user interface designs using usability criteria and measures. The proposed model consists of twomain phases : the prescreening phase ad the evaluation phase. The first phase involves expert judgment-based approach with qualitative criteria. The prescreening phase uses absolute measurement analytic hierarchy process to filter possible altermative interfaces to a reasonable subset. The second phase involves user-based approach such as usability testing, with quantitative criteria. The objective of the evaluation phase is to evaluate a subset of altermatives using objective measures. A set of criteria and measures for evaluating the usability of computer software designs is presented. The proposed model provides practitioners with a structured approach to select the best interface based on usability criteria and measures.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.127-138
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• 2003

Although the research on electronic commerce is plentiful, there is little empirical research related to Web-Based Shopping Systems (WBSS). This is especially so in global electronic commerce circumstances. WBSS are the fastest growing segment of digital economies and are perceived as driving forces of electronic commerce in terms of global markets and digital business. Using WBSS, organizations have a new chance of their business evolving successfully as global marketers. This paper develops a unified model to assess the diffusion of WBSS. Factors that impact WBSS diffusion are identified and analyzed as the basis for empirical testing. A set of propositions is developed that allows operationalization of the model. The ultimate goal is to provide the new research insights for the academic circles and the practical guidelines for organizations wishing to undertake WBSS.