• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.29-40
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• 2020

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.

• Journal of Korean Society of Forest Science
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• v.104 no.2
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• pp.206-212
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• 2015

This study was carried out to assess the characteristics of gabion on road cut-slopes, and analyze the scenic view preference for the gabion in Korea. 97 gabion sites from road cut-slopes were selected and were classified into 10 application types of gabion. The classification types of gabion were mostly related to the erosion and collapse prevention across road cut-slope. Gabion work sites in this study were classified into 30 sites (31%) for below 10% gradient, followed by 31 sites (32%) for 11~30% gradient, 20 sites (21%) for 31~50% gradient, and 16 sites (16%) for 51~80% gradient. Gabion works were constructed mostly in low gradient than in high gradient. 34 gabion sites (35%) among 97 sites were not covered by vegetation and 52 gabion sites (54%) showed vegetation cover rates of 1~30%. On the scenic preference analysis, public groups understood that the scenic view of gabion in cutting slope can be improved by vegetation cover, whereas expert groups prefer to scenic view of gabion only. However, expert groups encouraged subsequently vegetation covering to improve scenic view during gabion works in cutting slope.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.321-332
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• 2024

Understanding the status of surface cover in riparian zones is essential for river management and flood disaster prevention. Traditional survey methods rely on expert interpretation of vegetation through vegetation mapping or indices. However, these methods are limited by their ability to accurately reflect dynamically changing river environments. Against this backdrop, this study utilized satellite imagery to apply the Random Forest method to assess the distribution of vegetation in rivers over multiple years, focusing on the Naeseong Stream as a case study. Remote sensing data from Sentinel-2 imagery were combined with ground truth data from the Naeseong Stream surface cover in 2016. The Random Forest machine learning algorithm was used to extract and train 1,000 samples per surface cover from ten predetermined sampling areas, followed by validation. A sensitivity analysis, annual surface cover analysis, and accuracy assessment were conducted to evaluate their applicability. The results showed an accuracy of 85.1% based on the validation data. Sensitivity analysis indicated the highest efficiency in 30 trees, 800 samples, and the downstream river section. Surface cover analysis accurately reflects the actual river environment. The accuracy analysis identified 14.9% boundary and internal errors, with high accuracy observed in six categories, excluding scattered and herbaceous vegetation. Although this study focused on a single river, applying the surface cover classification method to multiple rivers is necessary to obtain more accurate and comprehensive data.

• Journal of Industrial Technology
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• v.36
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• pp.49-55
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• 2016

Different criteria for evaluating vulnerability of debris flow hazard proposed by various institutes such as Korea Forest Service(KFS), Korea Institute of Geoscience and Mineral Resources(KIGAM), Ministry of Public Safety and Security (MPSS) and Korea Expressway Corporation (KEC) were reviewed and discussed. Assessment of debris flow hazard for natural slopes around land for house development was carried out on the basis of the report about results of performing in-situ survey. Results of evaluating vulnerability of debris flow hazard by using these methods were compared to each other to discuss appropriateness of their evaluation and to recommend improvement.

• Korean Journal of Agricultural Science
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• v.21 no.2
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• pp.92-102
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• 1994

This study examined Public Evaluation for functions of urban open space environment using a marketing strategy called Importance-Performance analysis. 12 main function attributes for urban open space environment were selected 150 respondents were sampled in Taejon city by personal interview. Ratings used mean values from a seven-point scale. Results were then graphically displayed on an easily interpreted two-dimensional 'Action Grid'. Generally Importance Ratings were high, but Performance Ratings were low relatively for various open space environment functions. In air pollution control function the difference was the most large. The results of rating scale analysis indicated repondents' sexual and residence period difference.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.219-222
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• 2007

Today, use of high resolution satellite image with at least 1m resolution is expanding into many more areas including forest, river way, city, seashore and so forth for disaster prevention. Interest in this medium is increasing among the general public due to the roll-out to the private sector as Google earth, Virtual Earth and so forth. However, pre-processing process that revises the geometrical distortion that result at the time of photographing is required in order to use high resolution satellite image. The purpose of this research is to search the most accurate GCP(Ground Control Point) information acquisition method that is used for the revision of high resolution satellite image's geometrical distortion through automated processing. Through this, it is possible to contribute to increasing the level of accuracy at the time of high resolution satellite image revision and to secure promptness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1118-1133
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• 2017

Computer vision-based human activity recognition (HAR) has become very famous these days due to its applications in various fields such as smart home healthcare for elderly people. A video-based activity recognition system basically has many goals such as to react based on people's behavior that allows the systems to proactively assist them with their tasks. A novel approach is proposed in this work for depth video based human activity recognition using joint-based motion features of depth body shapes and Deep Belief Network (DBN). From depth video, different body parts of human activities are segmented first by means of a trained random forest. The motion features representing the magnitude and direction of each joint in next frame are extracted. Finally, the features are applied for training a DBN to be used for recognition later. The proposed HAR approach showed superior performance over conventional approaches on private and public datasets, indicating a prominent approach for practical applications in smartly controlled environments.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.142-145
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• 2001

Recently transmission and distribution power facilities have been often damaged severely by fire which broke out around the facilities in forest. It causes a power failure and thus gives an economic losses to both the public and the power utilities. Sometimes the fire can happen by an electrical accident such as the electrical short circuit or the ground short circuit. In this paper, trend of breaking out the fire has been investigated and an countermeasure against the economic losses due to the fire has been studied.

• Journal of the Korea Furniture Society
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• v.12 no.1
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• pp.113-124
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• 2001

This study explored the characteristics of contemporary furniture called New Design furniture design in terms of aesthetics of communication in the postmodern era. Qualitative conceptural analysis as the principal methodology was used to explore the characteristics of New Design furniture, which is accessible to the broadest possible public. Thereby, the communicative elements of symbol, metaphor, narrative, animation, imagination, humor, and/or wit expressed in New Design furniture were analyzed according to the designers'concept and work. As a result the postmodern aesthetics of communication made New Design furniture accessible to the largest number of People through cultural considerations in New Design furniture as it influences designers 'concept and work. However, it showed problems of New Design furniture in connection with postmodern aesthetics affecting mass production. Therefore, the designers have begun rethinking, redefining, and redesigning their furniture aesthetically, functionally, economically, and ecologically.

• Journal of information and communication convergence engineering
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• v.9 no.1
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• pp.69-73
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• 2011

Twitter is a microblogging service that allows its user to share short messages called tweets with each other. All the tweets are visible on a public timeline. These tweets have the valuable geospatial component and particularly time critical events. In this paper, our interest is in the rapid detection of disaster events such as tsunami, tornadoes, forest fires, and earthquakes. We describe the detection system of disaster events and show the way to detect a target event from Twitter data. This research examines the three disasters during the same time period and compares Twitter activity and Internet news on Google. A significant result from this research is that emergency detection could begin using microblogging service.