• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2056-2069
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• 2014

In order to implement interactive and personalized Web services properly, it is necessary to understand the tangible and intangible responses of the users and to recognize their emotional states. Recently, some studies have attempted to build emotional state estimation models based on facial expressions. Most of these studies have applied multiple regression analysis (MRA), artificial neural network (ANN), and support vector regression (SVR) as the prediction algorithm, but the prediction accuracies have been relatively low. In order to improve the prediction performance of the emotion prediction model, we propose a novel SVR model that is optimized using a genetic algorithm (GA). Our proposed algorithm-GASVR-is designed to optimize the kernel parameters and the feature subsets of SVRs in order to predict the levels of two aspects-valence and arousal-of the emotions of the users. In order to validate the usefulness of GASVR, we collected a real-world data set of facial responses and emotional states via a survey. We applied GASVR and other algorithms including MRA, ANN, and conventional SVR to the data set. Finally, we found that GASVR outperformed all of the comparative algorithms in the prediction of the valence and arousal levels.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.89-89
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• 2021

부영양화된 호수나 유속이 느린 하천에서 발생하는 녹조의 과도한 발생은 하천 생태계 훼손, 동식물의 건강, 담수의 오염 등 환경 사회 경제적으로 큰 피해를 준다. 현재 수질 측정망은 정해진 지점에서 Chlorophyll-a(Chl-a), Phycocyanin(PC)을 대표농도로 산정하고 조류경보에 활용하고 있으나, 일주일에 한번씩 샘플링을 통해 Chl-a 및 PC를 측정하여 시공간적인 신뢰성의 문제가 제기될 수 있다. 본 연구에서는 기존 점단위 조류 모니터링의 한계점을 개선하기 위해 초분광영상 자료를 머신러닝 기법에 적용하여 Chl-a 및 PC 산정 알고리즘을 개발하였다. 이를 위해 Chl-a와 PC의 최대 흡수, 반사 파장대, 주요 물 흡수 파장대 자료를 조합하여 9개의 파장비를 구축하였으며, 기존 연구에서 활용한 머신러닝 기법인 Partial Least Square, Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Network를 검토하여 최적 모델을 선정하였다. 학습된 머신러닝의 성능을 R², NSE, RMSE 목적함수를 이용해 평가하였으며, 그 결과 ANN이 각각 PC 0.801, 0.755, 11.774 mg/m³, Chl-a 0.733, 0.622, 8.736 mg/m³로 가장 우수한 성능을 보였다. 최적화 된 ANN 모델을 백제보 상류 2016-2017년 항공 초분광영상에 적용하여 시공간에 따른 조류 분포변화를 평가하고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.329-330
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• 2021

벌크화물운송은 해상운송시장에서 가장 큰 규모이고 철강 및 에너지 산업을 뒷받침 하는 중요한 시장이다. 또한 운임의 변동성이 가장 큰 시장으로 상당한 수익을 기대할 수 있는 반면에 파산에 이르는 큰 손실이 발생할 수 있기때문에 시장 참여자들은 합리적이고 과학적인 예측을 기반하여 의사결정을 해야 한다. 그러나 해운시장에서는 과학적 의사결정보다는 경험기반의 의사결정에 의존하기 때문에 시황변동성에 취약하다. 본 논문은 벌크운임예측에 신호 분해 방법인 EMD와 인공신경망을 결합한 하이브리드 모델을 적용하여 과학적 예측방법을 제시하고자 한다. 본 논문은 학문적으로 해운시장 운임예측연구에서 거의 시도되지 않았던 시계열분해법과 기계학습기법을 결합한 하이브리드 모델을 제시하였다는데 의미가 있으며 실무적으로는 해운시장에서 빈번이 일어나는 의사결정의 질이 제고되는데 기여할 것으로 기대된다.

• Computers and Concrete
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• v.32 no.2
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• pp.217-232
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• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• Analytical Science and Technology
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• v.24 no.6
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• pp.533-543
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• 2011

The hERG (human ether-a-go-go related gene) ion channel is a main factor for cardiac repolarization, and the blockade of this channel could induce arrhythmia and sudden death. Therefore, potential hERG ion channel inhibitors are now a primary concern in the drug discovery process, and lots of efforts are focused on the minimizing the cardiotoxic side effect. In this study, $IC_{50}$ data of 202 organic compounds in HEK (human embryonic kidney) cell from literatures were used to develop predictive 2D-QSAR model. Multiple linear regression (MLR), Support Vector Machine (SVM), and artificial neural network (ANN) were utilized to predict inhibition concentration of hERG ion channel as machine learning methods. Population based-forward selection method with cross-validation procedure was combined with each learning method and used to select best subset descriptors for each learning algorithm. The best model was ANN model based on 14 descriptors ($R^2_{CV}$=0.617, RMSECV=0.762, MAECV=0.583) and the MLR model could describe the structural characteristics of inhibitors and interaction with hERG receptors. The validation of QSAR models was evaluated through the 5-fold cross-validation and Y-scrambling test.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.163-168
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• 2005

It is one of the most important things to determinate optimized cutting conditions which satisfy productivity and cost simultaneously in production and CAPP systems. These days many researchers have figured out the optimizing way for solutions of multi-object function to find the approach methods using algorithm such as genetic algorithm or tabu search, etc., instead of mathematical methods. The main creation of objective function is proposed by empirical method but which is difficult to set it up and to analysis. In this paper, an optimization method of cutting condition is shown using the ANN and GA for the multi-objective function in high speed machining.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2006.05a
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• pp.429-433
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• 2006

High Temperature deformation behavior of newly developed beta-gamma TiAl alloy was investigated in this study. The optimum processing condition was investigated with the aid of Dynamic Materials Model (DMM). Processing maps representing the efficiency of power dissipation for microstructural evolution and instability were constructed utilizing the results of hot compression test at temperatures ranging from $1000^{\circ}C$ to $1200^{\circ}C$ and strain rate ranging from $10^{-4}/s$ to $10^2/s$. The Artificial Neural Network (ANN) simulation was adopted to consider the deformation heating. With the help of processing map and microstructural analysis, the optimum processing condition was presented and the role of $\beta$ phase was also discussed in this study.

• Transactions of Materials Processing
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• v.24 no.5
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• pp.332-339
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• 2015

In the current paper, a cold forging sequence was developed to manufacture a precisely cold forged H/Lower, which is used as the air back unit in commercial automobiles. The preform shape of the H/Lower influences the dimensional accuracy and stiffness of the final product. The shape factor (SF) ratio and shape of the tools are considered as the design parameters to achieve adequate backward extrusion height and maintain appropriate thickness variations. The optimal conditions of the design parameters were determined by using an artificial neural network (ANN). To experimentally verify the optimal preform and tool shapes, the experiments of the backward extrusion of the H/Lower were executed. The process design methodology proposed in the current paper, can provide a more systematic and economically feasible means for designing the preform and tool shapes for cold forging.