• Therapeutic Science for Rehabilitation
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• v.12 no.4
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• pp.9-22
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• 2023

The Lifestyle-DEPER (Decision, Execution, Personal Factors, Environment, Resources) model explains lifestyle formation. Lifestyles are shaped through the decision, execution, and habituation stages. Factors influencing the establishment of a lifestyle are categorized as environmental, resource, and personal. The environment encompasses our surroundings and social, physical, cultural, and virtual environments. Resources refer to what individuals possess, such as health, time, economic, and social resources. Personal factors include competencies, needs, and values. At the lifestyle establishment stage, each of these factors influences a different stage. These collective processes are referred to as events, encompassing both personal and social events. Health-related lifestyle factors include physical activity, nutrition, social relationships, and occupational participation. These are the goals of lifestyle intervention. The intervention strategy based on the Lifestyle-DEPER model, called KEEP (Knowledge, Evaluation, Experience, Plan), is a comprehensive approach to promoting a healthy lifestyle by considering lifestyle formation stages and their influencing factors. This study introduces the Lifestyle-DEPER model and presents a lifestyle intervention strategy (KEEP) to promote health. Further research is required to validate the practicality of the model after applying interventions based on the lifestyle construction model.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.315-324
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• 2022

The experimental data obtained in a wave flume were analyzed using machine learning techniques to establish a model that predicts the input wave height of the wavemaker based on the waves that have experienced wave shoaling and to verify the performance of the established model. For this purpose, artificial neural network (NN), the most representative machine learning technique, and Gaussian process regression (GPR), one of the non-parametric regression analysis methods, were applied respectively. Then, the predictive performance of the two models was compared. The analysis was performed independently for the case of using all the data at once and for the case by classifying the data with a criterion related to the occurrence of wave breaking. When the data were not classified, the error between the input wave height at the wavemaker and the measured value was relatively large for both the NN and GPR models. On the other hand, if the data were divided into non-breaking and breaking conditions, the accuracy of predicting the input wave height was greatly improved. Among the two models, the overall performance of the GPR model was better than that of the NN model.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• Convergence Security Journal
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• v.12 no.1
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• pp.79-87
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• 2012

Future Internet has been designing and developing in the world because of overcoming limits of current Internet and accepting new requirements. Therefore it is totally different from architectures of current Internet and it is based on Clean-Slate. Future Internet already has been studying with enormous investment by advanced countries such as USA, EU etc. Technical characteristics of Future Internet can be categorized into Infra techniques, Architectures and Service techniques. Especially, our country is in a superior position in Infra techniques and Service techniques. We can have competitiveness to develop trust communication in Future Internet because we have advantages of various Services such as mobile communication, Machine to Machine and Sensor Networks. This paper aims to analysis reference model of trust communication in Future Internet. To achieve this, we studied analysis of security techniques in four Future Internet researches of NSF.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.89-96
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• 2020

Linked Data allows structured data to be published in a standard way that datasets from various domains can be interlinked. With the rapid evolution of Linked Open Data(LOD), researchers are exploiting it to solve particular problems such as semantic similarity assessment. In this paper, we propose a method, on top of the basic concept of Linked Data Semantic Distance (LDSD), for calculating the Linked Data semantic distance between resources that can be used in the LOD-based recommender system. The semantic distance measurement model proposed in this paper is based on a similarity measurement that combines the LOD-based semantic distance and a new link weight using TF-IDF, which is well known in the field of information retrieval. In order to verify the effectiveness of this paper's approach, performance was evaluated in the context of an LOD-based recommendation system using mixed data of DBpedia and MovieLens. Experimental results show that the proposed method shows higher accuracy compared to other similar methods. In addition, it contributed to the improvement of the accuracy of the recommender system by expanding the range of semantic distance calculation.

• Convergence Security Journal
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• v.15 no.3_1
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• pp.107-114
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• 2015

Since 2002, information security management system has been implemented (ISMS) certification scheme whilst providing telecommunications services to enhance the level of enterprise information security was ongoing and Prevent accidents and avoid spread of infringement, such as rapid response and there is a lot of it came true. However, this system is the protection of the country or the investment company, as part of the actual information on how management affects the performance came from or how measures are still lacking for. In this study, the companies have their own privacy ISMS certification measures the level of activity continued to improve information security performance measures and methodology are presented. The government is also based on the validity of the certification system to ensure the overall implementation of the ISMS itself is this a step increase effective information security system is to be certified in advance to prevent security incidents and to improve business performance to help.

• Journal of the Korea Convergence Society
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• v.11 no.1
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• pp.267-277
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• 2020

The purpose of this study is to analyze the needs of experts in childhood education field on what is the primary core competency of the beginning childhood teachers to cope with the future society. For this, the survey was conducted for 252 of teachers in charge, assistant directors, and directors who are currently working at the daycare centers and kindergartens in Busan and Gyeongsangnamdo. In order to derive the requirement and priority on the core competencies required to beginning childhood teachers, 3-step analysis method of paired t-Test, Borich Needs Model Analysis, and the Locus for Focus was used. As a result, seven sub-competencies of five competencies of cooperation, communication, self-improvement and development, vocational ethics, and emotional intelligence was identified as the core competencies required by priority to the beginning childhood teachers in order to cope with the future society. These results may provide the basic data for designing the competence and field-based curriculum in the teachers' training schools for pre-service childhood teachers.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.77-88
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• 2018

This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.