• Geomechanics and Engineering
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• v.27 no.6
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• pp.551-560
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• 2021

Due to the low strength and high compressibility characteristics, the loess deposits tunnels are prone to large deformations and collapse. An accurate stability evaluation for loess deposits is of considerable significance in deformation control and safety work during tunnel construction. 37 groups of representative data based on real loess deposits cases were adopted to establish the stability evaluation model for the tunnel project in Yan'an, China. Physical and mechanical indices, including water content, cohesion, internal friction angle, elastic modulus, and poisson ratio are selected as index system on the stability level of loess. The data set is randomly divided into 80% as the training set and 20% as the test set. Firstly, principal component analysis (PCA) is used to convert the five index system to three linearly independent principal components X1, X2 and X3. Then, the principal components were used as input vectors for probabilistic neural network (PNN) to map the nonlinear relationship between the index system and stability level of loess. Furthermore, Leave-One-Out cross validation was applied for the training set to find the suitable smoothing factor. At last, the established model with the target smoothing factor 0.04 was applied for the test set, and a 100% prediction accuracy rate was obtained. This intelligent classification method for loess deposits can be easily conducted, which has wide potential applications in evaluating loess deposits.

• Journal of Hospice and Palliative Care
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• v.25 no.3
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• pp.110-120
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• 2022

Purpose: The purpose of this study was to confirm the factor structure of the McGill Quality of Life Questionnaire-Revised (MQOL-R) in the context of Korean culture and to verify its reliability and validity. Methods: The participants comprised terminal cancer patients aged 25 or older, and data from 164 participants were analyzed. The study was conducted in the following order: translation, expert review, reverse translation, preliminary investigation and interviews, and completion of the final version. Confirmatory factor analysis was applied to evaluate the validity of the instrument, and the Beck Depression Inventory, Korean version (K-BDI) was applied to confirm the criterion validity of the MQOL-R Korean version. The Cronbach's alpha coefficient, representing internal consistency, was measured to evaluate reliability. Results: Cronbach's alpha for all 14 questions was 0.862. The model fit indices for confirmatory factor analysis were within the acceptance criteria. The factor loadings of all four factors were over 0.50, and convergent validity and discriminant validity were confirmed. Regarding criterion validity, a negative correlation was found between the four factors of MQOL-R Korean version and the K-BDI. Conclusion: The MQOL-R Korean version, the reliability and validity of which were verified in this study, is a 15-item tool consisting of 14 items dealing with four physical, psychological, existential, and social factors and a single item evaluating the overall quality of life. The MQOL-R Korean version is an instrument that can more concisely and effectively measure the quality of life of patients with life-threatening diseases.

• Journal of Nutrition and Health
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• v.55 no.6
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• pp.601-616
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• 2022

In the era of the fourth industrial revolution technology, the inclusion of personalized nutrition for healthcare (PNH), when establishing a healthcare platform to prevent chronic diseases such as obesity, diabetes, cerebrovascular and cardiovascular disease, pulmonary disease, and inflammatory diseases, enhances the national competitiveness of global healthcare markets. Furthermore, since the government experienced COVID-19 and the population dead cross in 2020, as well as numerous health problems due to an increasing super-aged Korean society, there is an urgent need to secure, develop, and utilize PNH-related technologies. Three conditions are essential for the development of PNH technologies. These include the establishment of causality between obesity genome (genotype) and prevalence (phenotype) in Koreans, validation of clinical intervention research, and securing PNH-utilization technology (i.e., algorithm development, artificial intelligence-based platform, direct-to-customer [DTC]-based PNH, etc.). Therefore, a national control tower is required to establish appropriate PNH infrastructure (basic and clinical research, cultivation of PNH-related experts, etc.). The post-corona era will be aggressive in sharing data knowledge and developing related technologies, and Korea needs to actively participate in the large-scale global healthcare markets. This review provides the importance of scientific evidence based on a huge dataset, which is the primary prerequisite for the DTC obesity gene-based PNH technologies to be competitive in the healthcare market. Furthermore, based on comparing domestic and internationally approved DTC obese genes and the current status of Korean obesity genome-based PNH research, we intend to provide a direction to PNH planners (individuals and industries) for establishing scientific PNH guidelines for the prevention of obesity.

• Wind and Structures
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• v.36 no.4
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• pp.221-236
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• 2023

The lateral component of turbulence and the vortices shed in the wake of a structure result in introducing dynamic wind load in the acrosswind direction and the resulting level of motion is typically larger than the corresponding alongwind motion for a dynamically sensitive structure. The underlying source mechanisms of the acrosswind load may be classified into motion-induced, buffeting, and Strouhal components. This study proposes a frequency domain framework to decompose the overall load into these components based on output-only measurements from wind tunnel experiments or full-scale measurements. First, the total acrosswind load is identified based on measured acceleration response by solving the inverse problem using the Kalman filter technique. The decomposition of the combined load is then performed by modeling each load component in terms of a Bayesian filtering scheme. More specifically, the decomposition and the estimation of the model parameters are accomplished using the unscented Kalman filter in the frequency domain. An aeroelastic wind tunnel experiment involving a tall circular cylinder was carried out for the validation of the proposed framework. The contribution of each load component to the acrosswind response is assessed by re-analyzing the system with the decomposed components. Through comparison of the measured and the re-analyzed response, it is demonstrated that the proposed framework effectively decomposes the total acrosswind load into components and sheds light on the overall underlying mechanism of the acrosswind load and attendant structural response. The delineation of these load components and their subsequent modeling and control may become increasingly important as tall slender buildings of the prismatic cross-section that are highly sensitive to the acrosswind load effects are increasingly being built in major metropolises.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.333-340
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• 2023

Efficient prediction of corporate bankruptcy is an important part of making appropriate lending decisions for financial institutions and reducing loan default rates. In many studies, classification models using artificial intelligence technology have been used. In the financial industry, even if the performance of the new predictive models is excellent, it should be accompanied by an intuitive explanation of the basis on which the result was determined. Recently, the US, EU, and South Korea have commonly presented the right to request explanations of algorithms, so transparency in the use of AI in the financial sector must be secured. In this paper, an artificial intelligence-based interpretable classification prediction model was proposed using corporate bankruptcy data that was open to the outside world. First, data preprocessing, 5-fold cross-validation, etc. were performed, and classification performance was compared through optimization of 10 supervised learning classification models such as logistic regression, SVM, XGBoost, and LightGBM. As a result, LightGBM was confirmed as the best performance model, and SHAP, an explainable artificial intelligence technique, was applied to provide a post-explanation of the bankruptcy prediction process.

• Journal of Ocean Engineering and Technology
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• v.36 no.6
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• pp.380-389
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• 2022

In the Haeundae area of Busan, South Korea, damage has continued to occur recently from building wind from caused by dense skyscrapers. Five wind observation stations were installed near LCT residential towers in Haeundae to analyze the effect of building winds during typhoon Omais. The impact of building wind was analyzed through relative and absolute evaluations. At an intersection located southeast of LCT (L-2), the strongest wind speed was measured during the monitoring. The maximum average wind speed for one minute was observed to be 38.93 m/s, which is about three times stronger than at an ocean observation buoy (12.7 m/s) at the same time. It is expected that 3 to 4 times stronger wind can be induced under certain conditions compared to the surrounding areas due to the building wind effect. In a Beaufort wind scale analysis, the wind speed at an ocean observatory was mostly distributed at Beaufort number 4, and the maximum was 8. At L-2, more than 50% of the wind speed exceeded Beaufort number 4, and numbers up to 12 were observed. However, since actual measurement has a limitation in analyzing the entire range, cross-validation with computational fluid dynamics simulation data is required to understand the characteristics of building winds.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.179-186
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• 2022

This study aims to develop a predictive model to suggest a used sales price to sellers and buyers when trading used tablet PCs. For model development, we analyzed the real used tablet PC transaction data and additionally collected detailed product information. We developed several predictive models and selected the best predictive model among them. Specifically, we considered a multiple linear regression model using the used sales price as a dependent variable and other variables in the integrated data as independent variables, a multiple linear regression model including interactions, and the models from stepwise variable selection in each model. The model with the best predictive performance was finally selected through cross-validation. Through this study, we can predict the sales price of used tablet PCs and suggest appropriate used sales prices to sellers and buyers.

• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.41-49
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• 2023

Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children. Methods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen. Results: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model's ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother's height, television, the child's age, province, mother's education, birth weight, and childbirth size were the most important predictors of stunting status. Conclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

• Physical Therapy Rehabilitation Science
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• v.11 no.4
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• pp.414-420
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• 2022

Objective: In this study, the test-retest reliability and validity were presented to evaluate the usability of isokinetic rehabilitation equipment for the knee joint. Design: Cross-sectional design, reliability & validity study. Methods: Thirty healthy adults participated in the study. A CSMI dynamometer was used as a standardized measuring device to present the validity of the equipment. It was measured based on the dominant leg. The average peak torque value was selected as the measurement variable. After the measurement, a questionnaire was conducted on safety, satisfaction, and performance through the usability evaluation questionnaire. Results: The knee joint isokinetic rehabilitation equipment showed high reliability with Intraclass Correlations Coefficients (ICC) =0.883~0.956. In order to check the validity of the equipment, the 95% confidence interval of the mean difference limit was confirmed by the Bland & Altman plot. As a result, all three angular velocities showed a smaller confidence interval in the flexion than in extension. There were less than 10 plots that were not included in 2 Standard Deviation (SD) between all measurements. As a result of the usability evaluation questionnaire, the average of the safety domain(4.9±0.4), satisfaction domain(4.1±0.8), performance domain(4.3±0.8). Conclusions: If the product is improved by supplementing the items identified in the usability evaluation process, it is judged that it can be used as a useful device in various knee joint rehabilitation fields.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1431-1445
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• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.