• 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.

• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.2
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• pp.112-122
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• 2012

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

• Geomechanics and Engineering
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• v.28 no.3
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• pp.313-324
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• 2022

Rock properties are important in the design of mines and civil engineering excavations to prevent the imminent failure of slopes and collapse of underground excavations. However, the time, cost, and expertise required to perform experiments to determine those properties are high. Therefore, empirical models have been developed for estimating the mechanical properties of rock that are difficult to determine experimentally from properties that are less difficult to measure. However, the inherent variability in rock properties makes the accurate performance of the empirical models unrealistic and therefore necessitate the use of soft computing models. In this study, Gaussian process regression (GPR), artificial neural network (ANN) and response surface method (RSM) have been proposed to predict the static and dynamic rock properties from the P-wave and rock density. The outcome of the study showed that GPR produced more accurate results than the ANN and RSM models. GPR gave the correlation coefficient of above 99% for all the three properties predicted and RMSE of less than 5. The detailed sensitivity analysis is also conducted using the RSM and the P-wave velocity is found to be the most influencing parameter in the rock mechanical properties predictions. The proposed models can give reasonable predictions of important mechanical properties of sedimentary rock.

• Steel and Composite Structures
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• v.42 no.1
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• pp.123-137
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• 2022

The fundamental period (FP) is one of the most critical parameters for the seismic design of structures. In the reinforced concrete (RC) infilled frame, the infill walls significantly affect the FP because they change the stiffness and mass of the structure. Although several formulas have been proposed for estimating the FP of the RC infilled frame, they are often associated with high bias and variance. In this study, an efficient soft computing model, namely the group method of data handling (GMDH), is proposed to predict the FP of regular RC infilled frames. For this purpose, 4026 data sets are obtained from the open literature, and the quality of the database is examined and evaluated in detail. Based on the cleaning database, several GMDH models are constructed and the best prediction model, which considers the height of the building, the span length, the opening percentage, and the infill wall stiffness as the input variables for predicting the FP of regular RC infilled frames, is chosen. The performance of the proposed GMDH model is further underscored through comparison of its FP predictions with those of existing design codes and empirical models. The accuracy of the proposed GMDH model is proven to be superior to others. Finally, explicit formulas and a graphical user-friendly interface (GUI) tool are developed to apply the GMDH model for practical use. They can provide a rapid prediction and design for the FP of regular RC infilled frames.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2334-2347
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• 2023

Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

• Geomechanics and Engineering
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• v.38 no.4
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• pp.439-453
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• 2024

The research findings of two nonlinear machine learning and soft computing models- the Cuckoo optimization algorithm (COA) and the Teaching-learning-based optimization (TLBO) in combination with artificial neural network (ANN)-are presented in this article. Detailed finite element modeling (FEM) of a shallow footing on two layers of cohesionless soil provided the data sets. The models are trained and tested using the FEM outputs. Additionally, various statistical indices are used to compare and evaluate the predicted and calculated models, and the most precise model is then introduced. The most precise model is recommended to estimate the solution after the model assessment process. When the anticipated findings are compared to the FEM data, there is an excellent agreement, which indicates that the TLBO-MLP solutions in this research are reliable (R²=0.9816 for training and 0.99366 for testing). Additionally, the optimized COA-MLP network with a swarm size of 500 was observed to have R² and RMSE values of (0.9613 and 0.11459) and (0.98017 and 0.09717) for both the normalized training and testing datasets, respectively. Moreover, a straightforward formula for the soft computing model is provided, and an excellent consensus is attained, indicating a high level of dependability for the suggested model.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.79-88
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• 2007

As the mobile computers such as Personal Digital Assistants (PDAs) or Smart Phone has been widespread in our daily life, the demand of an extensive data input will gradually be increased. Consequently, many alternative soft keyboards have been proposed to satisfy this requirement. However none of them can provide an absolute solutions for that, the segregated guideline for keyboard arrangement and usage still cause much confusion to the user. Therefore, an integrated guideline is seriously required. In order to come up with the integrated guideline for Korean Soft keyboard GUI, the questionnaire and the experimental test compared with five soft keyboards were conducted in this study. As a result this paper presents five guidelines for improving an input speed of soft keyboard on the mobile computing; following familiarity for effective screen use, using a single key rather than grouping key, considering position of prediction word, considering prompt feedback, finally, considering optimized keyboard size.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.537-547
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• 2001

The paper proposes the fuzzy logic global approximate optimization strategies in optimal sizing of automotive A-pillar trim with rib structures for occupant head protection. Two different strategies referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the inherent nonlinearity in analysis model should be accommodated over the entire design space and the training data is not sufficiently provided. The objective of structural design is to determine the dimensions of rib in A-pillar, minimizing the equivalent head injury criterion HIC(d). The paper describes the head-form modeling and head impact simulation using LS-DYNA3D, and the approximation procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and subsequently presents their generalization capabilities in terms of number of fuzzy rules and training data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.3
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• pp.166-175
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• 2024

In this paper, the Window-Masking Method and HAT (Hardware Attached Top) CPU SoM (System on Module) are used to improve the performance and reduce the weight of the MANET (Mobile Ad-hoc Network) network synchronization system using time division redundancy. We propose converting it into a RISC-V based soft-core MCU and mounting it on an FPGA, a hardware accelerator. It was also verified through experiment. In terms of performance, by applying the proposed technique, the synchronization acquisition range is from -50dBm to +10dBm to -60dBm to +10dBm, the lowest input level for synchronization is increased by 20% from -50dBm to -60dBm, and the detection delay (Latency) is 220ns. Reduced by 43% to 125ns. In terms of weight reduction, computing resources (48%), size (33%), and weight (27%) were reduced by an average of 36% by replacing with soft-core MCU.

• Journal of Engineering Education Research
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• v.19 no.5
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• pp.35-47
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• 2016

This study analyzed outcomes of CAC(Computing Accreditation Committee) program individually applied in the field of computing engineering since 2007, and draw improvements. Literature review through academic journals, survey research and the FGI(Focus Group Interview) were used to accomplish objectives of the study. In addition, the survey research and FGI were done for professors. For the survey research, nationally 20 out of 44 universities which operates the CAC program were investigated, and sample universities were considered by region. FGI was done to analyze the performance and problems of CAC in more detail for 6 experts. Results of the study were follows as; first, CAC program was activated through the Seoul Accord activation support business by government. Second, BSM(Basic Science and Math), engineering major and engineering design education have been strengthened compared with before and after of CAC introduction in the computing engineering field. Third, soft skills needed for students in the college of engineering have been organized in the professional general curriculum, and professors aware of improvement of ability of the students for the skills. The degree of satisfaction for the CAC program has been examined as normal level, but improvement of educational system and the overall quality enhancement of computing engineering education were affected by CAC program. Nonetheless of positive results of CAC program, incentive system for certification program graduates, the expansion of the autonomy of the department, reduction in the amount of self-evaluation report, and support of administrative human resources were suggested for taking root successfully of CAC program.