• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.353-359
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• 2021

The Army is constructing a training system using Miles equipment that applies the latest science and technology to carry out military training. The Miles training system is a system that uses Miles equipment to simulate the damage situation of combat personnel and equipment in the same way as an actual battlefield by conducting practiced maneuvers in the field. Through this, the training force can experience conditions similar to an actual battle. In particular, the training effects of the warriors participating in the training can be maximized by establishing an integrated system that utilizes cutting-edge science technologies, such as information communication and computer simulation. This study analyzed the effects of Miles training in the army using scientific techniques targeted at the mid-range Miles. In particular, the effect index for analyzing the training effect was derived from a literature survey and expert opinions. The weight of each effect index was calculated by applying the Swing method. The final training effect was calculated by combining the results of the survey from train-experienced people. The Miles training effect was 2.6 times more effective than previous training without using Miles, and the satisfaction rate with Miles training according to status was high through variance analysis, and the difference was statistically significant.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.363-373
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• 2021

The purpose of this research is to analyze the Spill-over economic effect of the cultural and creative industries(CCI) in Henan Province, China. The research object is the CCI of Henan Province, which is mainly based on five sectors out of 42 industries in the industrial association table of the Statistical Bureau of Henan Province, China in 2017 (culture, sports; recreation and research sector; experimental development and integrated technical services sector; information transmission, computer services and software sector; education sector, etc), and is analyzed through secondary integration and redefinition of the CCI of Henan Province. Through the analysis of Henan Province Industry Association Table, this paper provides some enlightenment to the future direction of the cultural and creative industries. The main analysis results are as follows. The total production inducement of the CCI in Henan province is 48,848 billion yuan, and in particular, the production inducement coefficient of the industry in Henan province is 2.72809, 2.23909 (total of columns and rows), Index of the power of dispersion is 0.26325, and the index of the sensitivity of dispersion is 0.87535. Income induction coefficient is 0.55211, production tax induction coefficient is 0.09291. Because CCI of Henan Province has full development potential, the government needs to provide active support and policy support, in addition to the need for legal provisions and supervision of market management. In order to improve the innovative development of the CCI, it is necessary to develop a new model of "CCI+X".

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Food Engineering Progress
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• v.22 no.4
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• pp.344-352
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• 2018

The effects of chemical compositions (protein, lipid, and dietary fiber) on the physical properties of dried biji powders were investigated. The raw biji was freeze-dried (control) and hot-air dried (untreated). The untreated biji was further defatted and deproteinated. The prepared biji powders were analyzed for the proximate composition, total dietary fiber (TDF), water absorption index (WAI), water solubility index (WSI), swelling power, solubility (including the quantification of soluble carbohydrate and protein fractions), and final viscosity (using a rapid visco analyzer). Control and untreated biji powders exhibited the similar chemical compositions. The defatted biji possessed higher TDF, although its protein content did not significantly differ for control and untreated ones. The deproteinated biji consisted mainly of TDF. WAI and swelling power increased in the order: deproteinated > defatted > control > untreated biji powders. WSI and solubility increased in the order: control > untreated > defatted > deproteinated biji powders. The similar patterns were observed for soluble carbohydrate and protein fractions. The deproteinated biji revealed the highest viscosity over applied temperatures, while the untreated one was lowest. Overall results suggested that the physical properties of the dried biji powder were reduced by protein and fat, but enhanced by dietary fiber.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.2
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• pp.142-148
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• 2022

This survey was performed to monitor the spread of specific mosquito-borne pathogens at Jeonbuk. The frequency of occurrence of mosquito borne pathogens including Japanese encephalitis virus, West Nile virus, Zika virus, and yellow fever virus was assessed by collecting mosquitoes twice a month from March to December 2021 from various areas in Jeonbuk. A total of 15,975 mosquitoes from 15 species and 7 genera were collected. The highest number of 9,116 mosquitoes (trap index: TI, 506.4) were collected in the Wanju cattle pen, followed by the habitat for migratory birds and the downtown area in Jeonju. In the Gunsan habitat for migratory birds, 3,217 mosquitoes (TI, 178.7) were collected in the reed fields, 356 (TI, 19.7) in the men's toilets, and 1,948 (TI, 108.2) in the women's toilets. In Jeonju, 677 mosquitoes (TI, 37.6) were collected in the Deokjin park, 358 (TI, 19.8) in the Deokjin-gu office, and 303 (TI, 16.8) at the Jeonbuk National University. The largest population of mosquitoes was collected in the men's toilets in Gunsan and the Deokjin Park in downtown Jeonju. The results of the RT-PCR confirmation to determine the pathogen infection of the collected mosquitoes were all negative. These results provide a basis for tackling integrated mosquito-borne diseases in the Jeonbuk region.

• Journal of Industrial Convergence
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• v.20 no.3
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• pp.33-40
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• 2022

Recently, IoT-linked services have been used in various environments, and IoT and artificial intelligence technologies are being fused. However, since technologies that process IoT data stably are not fully supported, research is needed for this. In this paper, we propose a processing technique that can optimize IoT data after generating embedded vectors based on machine learning for IoT data. In the proposed technique, for processing efficiency, embedded vectorization is performed based on QR such as index of IoT data, collection location (binary values of X and Y axis coordinates), group index, type, and type. In addition, data generated by various IoT devices are integrated and managed so that load balancing can be performed in the IoT data collection process to asymmetrically link IoT data. The proposed technique processes IoT data to be orthogonalized based on hash so that IoT data can be asymmetrically grouped. In addition, interference between IoT data may be minimized because it is periodically generated and grouped according to IoT data types and characteristics. Future research plans to compare and evaluate proposed techniques in various environments that provide IoT services.

• Korea Trade Review
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• v.48 no.3
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• pp.1-22
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• 2023

Many major country have struggled to build a block of the secondary battery industry supply chain by considering their interests first. And their supply chain due diligence agreement mandates due diligence on human rights and environmental risks that may occur throughout the supply chain. So the integrated approach called supply chain ESG is needed. But there isn't to be a global standard for ESG yet. And the disclosure standards for each country are different, adding to companies' confusion. In this perspective, to present guidelines for establishing a supply chain ESG management strategy accompanied by Korean SMEs, this study presents environmental evaluation indicators of global secondary battery supply chain ESG customized for Korean SMEs and then performs weight analysis using AHP methodology. Through this, this study aims to suggest implications for accepting sustainability within the supply chain of Korean SMEs by presenting indicators to be considered first among environmental evaluation indicators in preparation for ESG due diligence of the global secondary battery supply chain.

• Journal of the Korean Society for Marine Environment & Energy
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• v.14 no.4
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• pp.213-223
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• 2011

The status and changes of water quality of national fishing harbors and designated ports in East Coast of Korea were analyzed to support establishment effective water environmental management. COD (Chemical Oxygen Demand) concentration was satisfied to designated water quality criteria in most areas, but TN (Total Nitrogen) and TP (Total Phosphorus) exceeded the criteria frequently. Also, peak concentration was summer in COD and SS (Suspended Solid), but winter in TP. Eutrophication index of Ganggu and Pohang (old) area were the highest. Pollution index by function of COD, TN, and TP of Ganggu, Pohang, Jumunjin, and Guryongpo was high with gradual increasing recently, on the contrary, that of Samcheok, Imwon, and Chuksan was decreased. Pollution index involving multi-indictors relation to organics and inorganics was necessary for water quality assessment. Designated water quality criteria needed to be improved because the criteria of Jukbyun and Chuksan was applied more strictly compared to the other regions although without difference of environmental characteristics. Furthermore, the criteria notified lately needed to be related to management pollutants from land-based sources. The continuous diagnosis and monitoring on sediment quality within the study area were necessary for prevention of water pollution and eco-friendly disposal of dredged sediment. Especially, monitoring of Designated Ports was implemented partially, however monitoring ratio of National Fishing Har-bors was 7% to whole part. Therefore, systematic and integrated environmental monitoring for ports and harbors with charge of national management was reestablished by strengthening and securing a legal basis.

• Korean Journal of Ecology and Environment
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• v.39 no.3 s.117
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• pp.320-330
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• 2006

An integrated health of a lotic ecosystem, Cho River, was evaluated by various approaches such as conventional water quality analysis, physical assessments of Qualitative Habitat Evaluation Index (QHEI), and the bioassay of Index of Biological Integrity (IBI) durin August${\sim}$September 2005. The IBI model used in the study was based on original multivariate metric model and then modified the metric attributes of the model for the regional application. Physical habitat health, based on the QHEI, was estimated using eleven metrics. During the study, values of IBI model averaged 36, which was judged as 'fair' to 'good' conditions. Spatial variations in the model values were evident: the headwater site (S1) was estimated as 48, indicating an 'excellent' condition, and the other sites were estimated 32${\sim}$38, 'good' condition. Values of the QHEI in the all sites averaged 148, which is judged as a good condition. The QHEI values varied from 120 (fair condition) to 199 (excellent condition) depending on the location of the stream. Site 5 (S5) was estimated as 'fair${\sim}$good' condition, while Site 7 (S7) was estimated as 'excellent' condition. The biological health, based on the IBI, reflected the habitat health. However, chemical conditions in terms of pH, turbidity, electric conductivity, dissolved oxygen (DO) did not make a difference in the biological health because of minor chemical differences among the locations.

• Journal of agricultural medicine and community health
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• v.38 no.4
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• pp.217-228
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• 2013

Objective: This study was performed to identify associations between the practice of health behaviors and awareness of metabolic syndrome (MetS) among adults aged 19 to 64 years in the Gwangju-Jeonnam area. Methods: This study utilized data from the 2010 Community Health Survey (CHS). Health behaviors considered in this study were smoking, alcohol drinking, physical activity, low-salt diet, and perception of stress. The index for the health behaviors was calculated as the sum of the practice of each health behavior (range: 0-5). The analysis was weighted with a complex sampling design, and the chi-square test and multiple logistic regression analysis were used to identify the association between the practice of health behaviors and awareness of MetS. Results: A total of 19.8% of the population were aware of MetS. The perception of MetS was statistically significantly associated with healthy behaviors, including nonsmoking (aOR = 1.33, 95% CI = 1.14-1.56), non-high-risk drinking (aOR = 1.54, 95% CI = 1.27-1.88), engagement in physical activity (aOR = 1.48, 95% CI = 1.28-1.72), and a low-salt diet (aOR = 1.30, 95% CI = 1.13-1.51). The ORs of the perception of MetS were significantly higher in patients with a health behavior index of 2 to 3 (aOR = 1.64, 95% CI = 1.01-2.66) and in those with an index of ${\geq}4$ (aOR = 2.47, 95% CI = 1.51-4.04) than in those with an index of 0. Among all health behaviors, physical activity had the highest OR for the perception of MetS (aOR = 1.50, 95% CI = 1.29-1.74). Conclusions: This study revealed associations between health behaviors, especially physical activity, and awareness of MetS. Therefore, integrated health promotion programs may be needed to enhance awareness of MetS and to effectively prevent MetS and non-communicable diseases.