• 한국노년학
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• v.28 no.4
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• pp.953-968
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• 2008

This research was performed to recognize anxiety level of older people, to find out anxiety level after or before regular exercises, and to find out how much anxiety level could be lowered by the sports activities. This research selected a population among the students who enrolled at sport activity classes (e, g., physical exercises, gate ball, volume dance) at L, S, J, Y (a), Y (b), D older people college located in Seoul and Gyoenggi area. 200 of the population were chosen as candidates by convenience sampling, nonprobability sampling. Considering the age of the candidates, total 200 cases were collected by using two methods (survey and interview) simultaneously. The data assessment was made by SPSS 12.0 Version. Error tolerance in statistics is .05. The data was analyzed by using frequency analysis, paired t-test and independent t-test. After analyzing anxiety awareness level after and before regular exercises, anxiety of older people are classified by four factors. First, anxiety for loss is 3.756 (M=3.756) before exercises, while 1.942 (M=1.942) after exercises. Second, fear for aging is 3.443 before exercises and 2.243 after exercises. Third, anxiety for physical appearance is 3.253 before exercises and 2.310 after exercises. Finally, anxiety caused by psychological insecurity is 3.060 before exercises, while 1.666 after exercises. Error of tolerance for all factors falls within .001. Anxiety score after exercises is lower than that of before exercises for every factor as well. As a result, regular physical exercises appeared to reduce anxiety level of older people.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• Journal of Applied Biological Chemistry
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• v.61 no.4
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• pp.391-395
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• 2018

There is an increasing concern over arsenic (As) contamination of paddy soil and rice with regard to food safety. This study was conducted to investigate total and inorganic As concentration in one hundred husked and polished rice samples collected at the non-contaminated paddy soil in Korea. Arsenic species in rice samples were extracted using 1% nitric acid ($HNO_3$) with a microwave oven and were measured using high performance liquid chromatography coupled with inductively coupled plasma-mass spectrometry. Mean concentrations of total As in husked rice and polished rice were 0.18 and $0.11mg\;kg^{-1}$, respectively. Also, average inorganic As concentrations in husked rice and polished rice were 0.11 and $0.07mg\;kg^{-1}$, respectively. These levels are lower than the standard guideline value 0.35 and $0.2mg\;kg^{-1}$ for inorganic As in husked and polished rice recommended by Codex Committee on Contaminants in Foods and Ministry of Food and Drug Safety, respectively. The mean of the inorganic As ratio for the total amount of As was 0.65 and 0.67 for husked rice and polished rice, respectively, and the range was from 0.08 to 1.0. For health risk assessment, the average value of cancer risk probability was $9.24{\times}10^{-5}$ and ranged from $2.30{\times}10^{-5}$ to $1.90{\times}10^{-5}$. Therefore, human exposure to As through dietary intake of surveyed rice samples might considered to be a low health risk.

• Journal of IKEEE
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• v.23 no.1
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• pp.112-119
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• 2019

In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.595-606
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• 2020

To find out the a Study on experiential consumption and development of the customized cosmetics on female university students in their 20s, this study conducted sampling using probability sampling from cosmetics major students in S City from September 1 to October 30, 2020. In this study, a study model was designed for a total of 30 people and studied as an Experience-Consume Experimentation. First, the result of the pre-purchase survey revealed that skincare cosmetics had the highest percentage for being selected by 30 people for "the preferred cosmetic type per the perception regarding customized cosmetics." Second, the result of the pre-purchase survey revealed that 11 people answered skincare cosmetics, 1 person answered shade cosmetics, and 2 people answered fragrance products (perfume, diffusers, etc.) for "the experience type for customized cosmetics." Third, the result of the post-purchase survey revealed that 29 people are willing to recommend the products, while 1 person is not. For the appropriateness of the price, 23 people answered yes; 7 people answered no. for the characteristics of the experience, 24 people (80%) answered that they selected ingredients according to their skin type; 9 people answered that the price is cheap considering they received 1:1 consultation; 18 people answered that they made a choice per their preferences (skin type) rather than per brands; 3 people answered that their self-esteem is stronger as if they received personal care. Therefore, customized cosmetics are expected to increase the attractiveness and purchase rate of female students in their twenties given that 'Human Touch,' genetic analysis, and 'hyper-customization technology,' which requires new development of customized cosmetics experience consumption for female college students in their 20s.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.623-631
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• 2021

The increased transport of marine hazardous and noxious substances (HNS) has resulted in frequent HNS spill accidents domestically and internationally. There are about 6,000 species of HNS internationally, and most of them have toxic properties. When an accidental HNS spill occurs, it can destroys the marine ecosystem and can damage life and property due to explosion and fire. Constructing a spectral library of HNS according to wavelength and developing a detection algorithm would help prepare for accidents. In this study, a ground HNS spill experiment was conducted in France. The toluene spectrum was determined through hyperspectral sensor measurements. HNS present in the hyperspectral images were detected by applying the spectral mixture algorithm. Preprocessing principal component analysis (PCA) removed noise and performed dimensional compression. The endmember spectra of toluene and seawater were extracted through the N-FINDR technique. By calculating the abundance fraction of toluene and seawater based on the spectrum, the detection accuracy of HNS in all pixels was presented as a probability. The probability was compared with radiance images at a wavelength of 418.15 nm to select abundance fractions with maximum detection accuracy. The accuracy exceeded 99% at a ratio of approximately 42%. Response to marine spills of HNS are presently impeded by the restricted access to the site because of high risk of exposure to toxic compounds. The present experimental and detection results could help estimate the area of contamination with HNS based on hyperspectral remote sensing.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.27-35
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• 2022

The purpose of this study is to analyze the metaverse-related issues in the news big data from an educational perspective, explore their characteristics, and provide implications for the educational applicability of the metaverse and future education. To this end, 41,366 cases of metaverse-related data searched on portal sites were collected, and weight values of all extracted keywords were calculated and ranked using TF-IDF, a representative term weight model, and then word cloud visualization analysis was performed. In addition, major topics were analyzed using topic modeling(LDA), a sophisticated probability-based text mining technique. As a result of the study, topics such as platform industry, future talent, and extension in technology were derived as core issues of the metaverse from an educational perspective. In addition, as a result of performing secondary data analysis under three key themes of technology, job, and education, it was found that metaverse has issues related to education platform innovation, future job innovation, and future competency innovation in future education. This study is meaningful in that it analyzes a vast amount of news big data in stages to draw issues from an education perspective and provide implications for future education.

• Journal of the Korean Geotechnical Society
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• v.26 no.3
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• pp.59-68
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• 2010

Systematic risk management is necessary in grand scaled urban construction because of the existence of complicated and various risk factors. Problems of obstructions, adjacent structures, safety, environment, traffic and geotechnical properties need to be solved because urban construction is progressed in limited space not as general earthwork. Therefore the establishment of special risk management system is necessary to manage not only geotechnical properties but also social and cultural uncertainties. This research presents the technique analysis by the current state of risk management technique. Risk factors were noticed and the importance of each factor was estimated through survey. The systemically categorized database was established. Risk extraction module, matrix and score module were developed based on the database. Expected construction budget and time distribution can be computed by Monte Carlo analysis of probabilities and influences. Construction budgets and time distributions of before and after response can be compared and analyzed 80 the risks are manageable for entire whole construction time. This system will be the foundation of standardization and integration. Procurement, efficiency improvement, effective time and resource management are available through integrated management technique development and application. Conclusively decrease in cost and time is expected by systemization of project management.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.307-319
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• 2023

This study aims to explain the key concepts and principles of text-based generative artificial intelligence (AI) that has been receiving increasing interest and utilization, focusing on its application in science education. It also highlights the potential and limitations of utilizing generative AI in science education, providing insights for its implementation and research aspects. Recent advancements in generative AI, predominantly based on transformer models consisting of encoders and decoders, have shown remarkable progress through optimization of reinforcement learning and reward models using human feedback, as well as understanding context. Particularly, it can perform various functions such as writing, summarizing, keyword extraction, evaluation, and feedback based on the ability to understand various user questions and intents. It also offers practical utility in diagnosing learners and structuring educational content based on provided examples by educators. However, it is necessary to examine the concerns regarding the limitations of generative AI, including the potential for conveying inaccurate facts or knowledge, bias resulting from overconfidence, and uncertainties regarding its impact on user attitudes or emotions. Moreover, the responses provided by generative AI are probabilistic based on response data from many individuals, which raises concerns about limiting insightful and innovative thinking that may offer different perspectives or ideas. In light of these considerations, this study provides practical suggestions for the positive utilization of AI in science education.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.45-52
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• 2009

In conventional structural design, deterministic optimization which satisfies codified constraints is performed to ensure safety and maximize economical efficiency. However, uncertainties are inevitable due to the stochastic nature of structural materials and applied loads. Thus, deterministic optimization without considering these uncertainties could lead to unreliable design. Recently, there has been much research in reliability-based design optimization (RBDO) taking into consideration both the reliability and optimization. RBDO involves the evaluation of probabilistic constraint that can be estimated using the RIA (Reliability Index Approach) and the PMA(Performance Measure Approach). It is generally known that PMA is more stable and efficient than RIA. Despite the significant advancement in PMA, RBDO still requires large computation time for large-scale applications. In this paper, A new reliability-based design optimization (RBDO) method is presented to achieve the more stable and efficient algorithm. The idea of the new method is to integrate a response surface method (RSM) with PMA. For the approximation of a limit state equation, the moving least squares (MLS) method is used. Through a mathematical example and ten-bar truss problem, the proposed method shows better convergence and efficiency than other approaches.