• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1574-1580
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• 2020

Traditional pose estimation methods include using special devices or images through image processing. The disadvantage of using a device is that the environment in which the device can be used is limited and costly. The use of cameras and image processing has the advantage of reducing environmental constraints and costs, but the performance is lower. CNN(Convolutional Neural Networks) were studied for pose estimation just using only camera without these disadvantage. Various techniques were proposed to increase cognitive performance. In this paper, the effect of the skip connection on the network was experimented by using various skip connections on the joint recognition of the hand. Experiments have confirmed that the presence of additional skip connections other than the basic skip connections has a better effect on performance, but the network with downward skip connections is the best performance.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.91-97
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• 2022

Analyzing the psychological and pedagogical literature, we showed the interest of researchers in the problem posed. The concept of competence is considered, which is interpreted as giving the key to solving a wide range of educational and life tasks. Research competence implies the ability to cooperate, enter into contacts, readiness for changes, for self-determination and is an integral quality of the individual, expressed in the readiness and ability to independently search for solutions to new problems and creative transformation of reality based on a set of personal and meaningful knowledge, skills, methods of activity and value attitudes.The article offers conditions that certify the improvement of forms and methods of training students in the formation of research competence of future specialists. The use of innovative technologies contributes to improving the level of training of future specialists: students are better prepared for classes, take an active part in the assimilation of program material in laboratory classes. It is noted that this creates a subject-subject relationship between the student and the teacher, and changes the attitude of students to classes. In the process of such organization of educational activities, students are convinced of the need for knowledge and its effectiveness, learn to compare, generalize, classify, establish cause-and-effect relationships, express opinions, defend their point of view, they ensure success in their studies, and develop research competence. It is proved that in order to apply the latest technologies, the teacher himself must know them well, that is, constantly improve himself, master new methods, techniques, ideas, which will help him create new pedagogical technologies and implement them in the educational process.

• Geomechanics and Engineering
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• v.10 no.3
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• pp.269-284
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• 2016

Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods were developed by analyzing liquefaction case histories from which the liquefaction boundary (limit state) separating two categories (the occurrence or non-occurrence of liquefaction) is determined. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model using conventional modeling techniques that take into consideration all the independent variables, such as the seismic and soil properties. In this study, a modification of the Multivariate Adaptive Regression Splines (MARS) approach based on Logistic Regression (LR) LR_MARS is used to evaluate seismic liquefaction potential based on actual field records. Three different LR_MARS models were used to analyze three different field liquefaction databases and the results are compared with the neural network approaches. The developed spline functions and the limit state functions obtained reveal that the LR_MARS models can capture and describe the intrinsic, complex relationship between seismic parameters, soil parameters, and the liquefaction potential without having to make any assumptions about the underlying relationship between the various variables. Considering its computational efficiency, simplicity of interpretation, predictive accuracy, its data-driven and adaptive nature and its ability to map the interaction between variables, the use of LR_MARS model in assessing seismic liquefaction potential is promising.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.870-875
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• 2013

After introducing diagnosis equipment power failure prevention activities for distribution system have become more active. To do facility diagnosis and maintenance work more efficiently we need to evaluate reliability for the system and should determine the priority line with appropriate criteria. Thus, to calculate risk factor for the power distribution line that are composed of many component facilities its historical failure events for the last 5 years are collected and analysed. The failure statics show that more than 60% of various failures are related to environment factors randomly and about 20% of the failures are refer to the aging. As a strategic evaluation system reflecting these environmental influence is needed, a system on the basis of the probabilistic approach related statical variables in terms of failure rate and failure probability of electrical components is proposed. The figures for the evaluation are derived from the field failure DB. With adopting Bayesian rule we can calculate easily about conditional probability query. The proposed evaluation system is demonstrated with model system and the calculated indices representing the properties of the model line are discussed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.487-496
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• 2020

This study introduces how GISs (Geographic Information Systems) are used to assess spatial disparities in urban green spaces in the Chicago. Green spaces provide us with a variety of benefits, namely environmental, economic, and physical benefits. This study seeks to explore socioeconomic relationships between green spaces and their surrounding communities and to evaluate spatial disparities from a variety of perspectives, such as health-related, socioeconomic, and physical environment factors. To achieve this goal, this study used spatial statistics, such as optimized hotspot analysis, network analysis, and space-time cluster analysis, which enable conclusions to be drawn from the geographic data. In particular, 12 variables within the three factors are used to assess spatial disparities in the benefits of the use of green spaces. Finally, the variables are standardized to rank the community areas and identify where the most vulnerable community areas or parks are. To evaluate the benefits given to the community areas, this study used the z- and composite scores, which are compared in the three different combinations. After identifying the most vulnerable community area, crime data is used to spatially understand when and where crimes occur near the parks selected. This work contributes to the work of urban planners who need to spatially evaluate community areas in considering the benefits of the uses of green spaces.

• Journal of the Economic Geographical Society of Korea
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• v.24 no.4
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• pp.386-399
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• 2021

This study targets the rapidly growing electric vehicle industry. Currently, China is the world's largest electric vehicle producer, and several global and local companies are adapting to the transition environment of the electric vehicle industry and carrying out various activities. To analyze the causes and environmental characteristics of this transition, we analyzed the activities of the Chinese government, automobile companies, and companies in forward and backward linked industries, which are major actors in the production network. As a result, with full support from the Chinese government, functional changes of existing actors and an increase in the entry of new actors resulted in a transition to an electric vehicle industry space accompanied by new values.

• Korean Journal of Health Education and Promotion
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• v.24 no.3
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• pp.73-86
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• 2007

Objective: The purposes of this study are to understand the characteristics of the participants in a community walking campaign and to analyze the factors related to their participation based on the Transtheoretical Model (TTM). Methods: The study composed of the description of participant characteristics and comparison of them with non-participant characteristics in a walking campaign in K province. The data were collected through a survey of 2,590 participants and 258 non-participants from the same community. The survey instrument included questions about stages of walking and exercise, knowledge and attitude toward walking, and environmental condition for walking. Results: A majority of the walking campaign participants were in the action(24.8%) and maintenance(43.6%) stages of walking and exercise behavior. The non-participant group was split between maintenance(51.6%) and precontemplation (30.6%) stages. Among the participants, effective campaign promotion channels differed by age group while motivation for participation and participation patterns were associated with both age and gender. Favorable physical environment was a significant factor of participating in walking campaign(OR=1.396, CI=1.149-1.696). Although the campaign participants scored higher than the non-participants in most attitude toward walking questions, differences in knowledge scores between two groups were less significant. Conclusion: In conclusion, further social marketing to increase the awareness and to increase the concern of population in the community is needed based on the survey result. Transtheoretical model seems appropriate to apply to the evaluation and the planning the program of the behavior change in the community. Also, more organized and sustainable support in need to maintain the good habit of walking for the participants in walking campaign.

• The Journal of Korean Academic Society of Nursing Education
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• v.9 no.2
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• pp.253-263
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• 2003

Rheumatoid arthritis patients not only suffer from the physical damage, but they are afflicted severely mental and psychologic after effects. Their depression and low self-esteem eventually yields serious mental damages, which makes difficult for them to recover. The states of mental health of arthritis patients are diversified depending on the characters and surrounding circumstances, although they may have suffered from the similar condition. Therefore this research was conbucted to the factors that can give positive influences to the patients. In recent time, social support for the arthritis patients has become an important factor that can positively influence their mental health. In other words, social support can act as an important environmental system for arthritis patients to recover their damaged mental health. In order to fulfill this purpose, 118 patients were examined to identify the relationship between the variables. The summary of the result obtained from the research is as follow: 1. The structural aspect of social support for arthritis patients showed the most of them had various social support network size and their highest support system were 'family', 'relative' and 'friends'. The functional aspect of social support for arthritis patients showed moderate degree and their highest sub component was 'approval'. 2. High correlation was found between duration of relationship, similarity, frequency of meeting and functional support. There was no relationship between depression, self-esteem and social support. 3. Therefore this research has suggested that nurses who care rheumatoid arthritis patient consider the above condition to develop self-help group.

• IMMUNE NETWORK
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• v.14 no.5
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• pp.255-259
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• 2014

Physical activity could be considered one of the factors that affect the immune system status and function. To find the relation between exercise and cytokines, we examined the possible effects of an 8-week endurance training program on the serum levels of cytokines, including tumour necrosis factor-alpha (TNF-${\alpha}$) and interferon-gamma (IFN-${\gamma}$) in sedentary men. A total of 30 healthy young male volunteers were randomly divided into an endurance training group and a control group. The training group followed a specific exercise protocol (running on a treadmill for 15~30 min at 50~70% maximal heart rate) for 8 weeks and the control group did not participate in any exercise program. Venous blood samples were collected from both the groups 24 h before and 24 h and 48 h after the exercise. Repeated ANOVA was used for statistical purposes. The serum levels of TNF-${\alpha}$ and IFN-${\gamma}$ were determined by ELISA. Significant (p<0.05) and non-significant (p>0.05) decreases were observed in the serum levels of IFN-${\gamma}$ and TNF-${\alpha}$, respectively, after the 8-week endurance training program. Our findings indicated that an 8-week endurance exercise may affect the serum levels of some inflammatory cytokines, suggesting the beneficial role of this training protocol in elderly population and people with certain conditions (inflammation of the vertebrae or other inflammatory diseases).

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.