• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.6
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• pp.75-85
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• 2020

The purpose of this study was to investigate the citizens' perception of urban ecosystem and urban ecosystem education to find out how to plan and create an effective urban ecosystem and how to utilize urban ecosystem education and educational media according to age groups. To this end, an online survey of 416 Suwon citizens was conducted, and based on the responses of the respondents, cross-analysis, multiple-response analysis, and correlation analysis were conducted using the IBM SPSS Statistics Statistical Program. The study found that fewer respondents showed a high understanding of urban ecosystem concepts compared to those who recognized the importance of environmental issues. Nevertheless, most of the respondents were aware of the importance of preserving and protecting the urban ecosystem and responded positively to the inconvenience. In addition, most of the respondents were aware of the need for urban ecosystem education and were found to have different preferred information media depending on age. It has been confirmed that the establishment of facilities such as ecological learning centers and seasonal environmental schools is the top priority among all age groups. Citizens are also aware of the importance of preserving and protecting the urban ecosystem and the need for education, but it is deemed necessary to supplement it because effective urban ecosystem conservation and protection plans and systematic education are not provided that citizens can sympathize with. In addition, it is deemed that various measures should be presented in selecting responsible organizations and educational media that host the education for effective education and promotion of urban ecosystem education according to conduct urban ecosystem education.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.75-85
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• 2021

In this paper, we present a deep neural network-based prediction model that processes and analyzes the corporate credit and personal credit information of individual business owners as a new method to predict the default rate of individual business more accurately. In modeling research in various fields, feature selection techniques have been actively studied as a method for improving performance, especially in predictive models including many features. In this paper, after statistical verification of macroeconomic indicators (macro variables) and credit information (micro variables), which are input variables used in the default rate prediction model, additionally, through the credit information feature selection method, the final feature set that improves prediction performance was identified. The proposed credit information feature selection method as an iterative & hybrid method that combines the filter-based and wrapper-based method builds submodels, constructs subsets by extracting important variables of the maximum performance submodels, and determines the final feature set through prediction performance analysis of the subset and the subset combined set.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.113-127
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• 2021

Topic modeling has been receiving much attention in academic disciplines in recent years. Topic modeling is one of the applications in machine learning and natural language processing. It is a statistical modeling procedure to discover topics in the collection of documents. Recently, there have been many attempts to find out topics in diverse fields of academic research. Although the first Department of Industrial Engineering (I.E.) was established in Hanyang university in 1958, Korean Institute of Industrial Engineers (KIIE) which is truly the most academic society was first founded to contribute to research for I.E. and promote industrial techniques in 1974. Korean Society of Industrial and Systems Engineering (KSIE) was established four years later. However, the research topics for KSIE journal have not been deeply examined up until now. Using topic modeling algorithms, we cautiously aim to detect the research topics of KSIE journal for the first half of the society history, from 1978 to 1999. We made use of titles and abstracts in research papers to find out topics in KSIE journal by conducting four algorithms, LSA, HDP, LDA, and LDA Mallet. Topic analysis results obtained by the algorithms were compared. We tried to show the whole procedure of topic analysis in detail for further practical use in future. We employed visualization techniques by using analysis result obtained from LDA. As a result of thorough analysis of topic modeling, eight major research topics were discovered including Production/Logistics/Inventory, Reliability, Quality, Probability/Statistics, Management Engineering/Industry, Engineering Economy, Human Factor/Safety/Computer/Information Technology, and Heuristics/Optimization.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.75-82
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• 2022

There are various exhibition halls, shopping malls, theme parks around us and analysis of interest in exhibits or contents is mainly done through questionnaires. These questionnaires are mainly depend on the subjective memory of the person being investigated, resulting in incorrect statistical results. Therefore, it is possible to identify an exhibition space with low interest by tracking the movement and counting the number of visitors. Based on this, it can be used as quantitative data for exhibits that need replacement. In this paper, we use deep learning-based artificial intelligence algorithms to recognize visitors, assign IDs to the recognized visitors, and continuously track them to identify the movement path. When visitors pass the counting line, the system is designed to count the number and transmit data to the server for integrated management.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.548-562
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• 2022

This study was conducted to diagnose the issues of AI ethics in global content and to discuss what kind of discourse is needed to strengthen human identity. To this end, the study selected Netflix original content "The Social Dilemma" for analysis and adopted narrative analysis as the research method. The analysis results confirmed that "Social Dilemma" showed the structure of a traditional current affairs documentary and mainly used experts and statistical data to develop the story. It also reinforced core content claims by enumerating domestic and foreign cases such as the 2021 Myanmar massacre and the spread of fake news. In addition, the relationship between the characters clearly revealed the binary opposition between developers and media companies as well as users and advertisers. For the solution to the problem, strong regulations on businesses and the suspension of social media use were reached. However, "The Social Dilemma" merely pointed out the misuse of AI technology and had a narrative that ignored human identity and social relationships. Such results raise the need for creating contents that emphasize the importance of human sociality, relationships, and learning ability in the age of AI.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• Journal of Korean society of Dental Hygiene
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• v.21 no.6
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• pp.813-820
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• 2021

Objectives: This study was conducted to verify the effectiveness of the VR-applied head and neck anatomy class and to be used as basic data for the development of a teaching method using VR in the basic dental hygiene field. Methods: A convenience sample was extracted from 128 students and graduates who completed the head and neck anatomy class at the department of dental hygiene at a university in Gwangju. From June 1 to July 31, 2021, an online survey was conducted on class attitude and satisfaction, and statistical analysis was performed using frequency analysis and independent sample t-test. Results: Class attitude (3.79), interest (3.64), attention (3.88), learning motivation (3.80), and achievement motivation (3.84) were all high in the head and neck anatomy VR application group (p<0.01). Satisfaction (3.99), relevance of class content (4.06) and class achievement (3.96) were high in the head and neck anatomy VR application group (p<0.01). The suitability of class attitude (3.65) and class content (sub-item of class satisfaction) (4.13) was high in the group with the 4th industrial revolution education experience (p<0.05). Conclusions: The effect of VR application in the head and neck anatomy class was confirmed to increase students' class attitude and satisfaction. In the basic dental hygiene curriculum, it will be necessary to develop and utilize teaching methods related to the 4th industrial revolution and VR application classes.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.545-557
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• 2022

This study used high-resolution satellite images and supervised classification technique based on machine learning method in order to detect the areas affected by wildfires in the demilitarized zone (DMZ) where direct access is difficult. Sentinel-2 A/B was used for high-resolution satellite images. Land cover map was calculated based on the SVM supervised classification technique. In order to find the optimal combination to classify the DMZ wildfire damage area, supervised classification according to various kernel and band combinations in the SVM was performed and the accuracy was evaluated through the error matrix. Verification was performed by comparing the results of the wildfire detection based on satellite image and data by the wildfire statistical annual report in 2020 and 2021. Also, wildfire damage areas was detected for which there is no current data in 2022. This is to quickly determine reliable results.

• Journal of People, Plants, and Environment
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• v.23 no.3
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• pp.321-332
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• 2020

Background and objective: The concept of 'money' in the numbers and operations domain is a fundamentally necessary domain of economic life. This study was conducted to examine the effects of a horticulture-mathematics integration program on mathematical attitude and money calculating ability of high school students with intellectual disabilities. Methods: We analyzed the changes in the mathematical attitude and money calculating ability of students with mild intellectual disabilities in S special school in the city of D, Republic of Korea, with 12 students in the control group and 12 students in the experimental group, from August 27 to October 29, 2019. Results: The results of the comparison showed no statistically significant changes in the three items of mathematical attitude for the control group, while the experimental group, which took part in the horticulture-mathematics integration program, showed statistically significant differences across all three items, such as self-concept about the subject (p = .003), attitude toward the subject (p = .004), and study habit related to the subject (p = .012). The horticulture-mathematics integration program, which was developed by integrating horticultural activities and the mathematics curriculum, used plants and horticultural activities to provide students with positive experiences in mathematics. These included the sense of closeness, curiosity, interest, attention, and enjoyment, leading to positive changes in mathematical attitude. In terms of money calculating ability, both the control group and experimental group showed statistical differences across the three items, but the experimental group showed greater degrees of increase, 15.0 or more, in the scores compared to the control group. Conclusion: These results suggest that utilizing horticultural materials as a part of purchase learning programs with elements of money calculation chapters in the mathematics curriculum could lead to the improvement of students' ability in money calculation. These positive changes are thought to be related to the high degrees of interest in horticulture among students, which led to active participation in the program and enabled the simple and repeated purchase activities in the program to generate positive changes in the money calculation ability of the students.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.