• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.77-87
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• 2010

Currently, our society has been changed from the industrial society to the information society. As the war progresses to Information Warfare, Network-Centric Warfare, Long-Range Precision Engagement and Robot Warfare, the military should advance to High-tech Scientific force. For this creation of the war potential, it is regarded as the warfighting experiment is a critical method. Surely it is rational that LVC(Live Virtual Constructive simulation) is desirable to make the warfighting experiment. But because it is limited by the cost, the time, the place and the resource, the constructive simulation(M&S : Modeling&Simulation) is a good tool to solve those problems. There are some studies about the evaluation process for developing the model, but it is unsatisfying in the process of the constructive simulations' operation. This study focuses on the way of constructive simulation operation, which is supplied with the evaluation process(VV&A : Verification Validation & Accreditation). We introduce the example of the rear area operation simulation for "appropriateness evaluation to the organization of logistic corps" by the AWAM(Army Weapon Analysis Model). This study presents the effective methods of the constructive simulations, which is based on the reliable evaluation process, so it will contribute to the warfighting experiments.

• Journal of Life Science
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• v.33 no.8
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• pp.680-691
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• 2023

Peak oil, climate change, and microplastics caused by the production and usage of petroleum-based plastics have threatened the sustainability of our daily life, and this has emerged as a recent global issue. To solve this global issue, the production and usage of biodegradable eco-friendly bioplastics such as polyhydroxyalkanoates (PHAs) has been suggested as an alternative. Therefore, in this review, the present status of global PHA manufacturers, the advantages of the production of PHAs using marine-origin microorganisms (with their productivity potential) and further required research and development strategies for cost-competitive production of PHAs using marine-based microorganisms were investigated. In this review, PHAs produced from marine microorganisms were found to have similar physical properties to petroleum-based plastics but with several advantages that can reduce the costs of PHA production. Those advantages include, seawater used in the medium preparation step, and osmotic-based cell lysis technology used in the separation and purification steps. However, the PHA productivities from marine microorganisms showed somewhat lower efficiencies than those from the commercial strains isolated from terrestrial environments. In order to solve the problem, further research strategies using synthetic microbiology-based technology, the development of long-term continuous culture technology, and solutions to improve PHA efficiency are required to meet future market demands for alternative bioplastics.

• Journal of Korea Entertainment Industry Association
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• v.13 no.1
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• pp.111-119
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• 2019

The purpose of study was to evaluate the economic evaluation of Chuncheon curling stadium. In oder to estimate economic evaluation, benefit/cost ratio, net present value and internal rate of return were used. Additionally, in order to investigate the socio-economic spread effect, literature review and input-output analysis were used. The results of study were as follows. First, as a result of analyzing the demand for curling stadiums in Chuncheon, it was believed that the construction of the stadium will attract athletes from the Chuncheon region as well as athletes from Seoul, Incheon, Gyeonggi Province and North Chungcheong Province. Second, economic validity analysis showed that the initial investment did not make sense, but the players' training and competitions and the advantages of the potential experience of curling events for citizens in nearby areas, including Chuncheon, make the construction and operation reasonable. Third, as a result of the review of the social and policy validity of the curling stadium, the project to build a curling stadium in Chuncheon was secured with a policy validity as a public sports facility necessary for both professional and living athletes. Finally, the analysis of socio-economic spread effect of curling stadiums had shown that it would have a positive effect on the level of satisfaction of the general public as well as the discovery of elite athletes.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.367-373
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• 2023

Aircrews and passengers are exposed to radiation from cosmic rays and secondary scattered rays generated by reactions with air or aircraft. For aircrews, radiation safety management is based on the exposure dose calculated using a space-weather environment simulation. However, the exposure dose varies depending on solar activity, altitude, flight path, etc., so measuring by route is more suggestive than the calculation. In this study, we developed an instrument to measure the cosmic radiation dose using a general-purpose Si sensor and a multichannel analyzer. The dose calculation applied the algorithm of CRaTER (Cosmic Ray Telescope for the Effects of Radiation), a space radiation measuring device of NASA. Energy and dose calibration was performed with Cs-137 662 keV gamma rays at a standard calibration facility, and good dose rate dependence was confirmed in the experimental range. Using the instrument, the dose was directly measured on the international line between Dubai and Incheon in May 2023, and it was similar to the result calculated by KREAM (Korean Radiation Exposure Assessment Model for Aviation Route Dose) within 12%. It was confirmed that the dose increased as the altitude and latitude increased, consistent with the calculation results by KREAM. Some limitations require more verification experiments. However, we confirmed it has sufficient utilization potential as a cost-effective measuring instrument for monitoring exposure dose inside or on personal aircraft.

• The Journal of Bigdata
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• v.7 no.1
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• pp.125-137
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• 2022

The transportation industry is one of the important industries due to the geographical requirements surrounded by the sea on three sides of Korea and the problem of resource poverty, which relies on imports for most of its resource consumption. Among them, the proportion of the shipping industry is large enough to account for most of the transportation industry, and maintenance in the shipping industry is also important in improving the operational efficiency and reducing costs of ships. However, currently, inspections are conducted every certain period of time for maintenance of ships, resulting in time and cost, and the cause is not properly identified. Therefore, in this study, the proposed methodology, LSTM-AutoEncoder, is used to detect abnormalities that may cause ship failure by considering the time of actual ship operation data. In addition, clustering is performed through clustering, and the potential causes of ship main engine failure are identified by grouping outlier by factor. This enables faster monitoring of various information on the ship and identifies the degree of abnormality. In addition, the current ship's fault monitoring system will be equipped with a concrete alarm point setting and a fault diagnosis system, and it will be able to help find the maintenance time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.635-647
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• 2006

Korean construction companies first advanced to the international markets in 1960's and so far have brought more than 4,900 projects which account for 193 billion dollars approximately. With the large increase of national employment and income being followed by the achievement, Korea's construction industry has made an enormous contribution to the improvement of domestic economy for the last 40 years. However, recently the increased risk in international markets as well as the sharpening competition with foreign companies promising in terms of advanced technologies and low labor cost have been driving Korean construction away from the market shares. According to ENR (Engineering News Record, 1994~2003), it is revealed that 15.1% of top 225 global contractors are suffering from loss in international construction markets. This phenomenon is largely due to the highly uncertain characteristics of international projects, which are inherently exposed to various and complicated risky situations. Furthermore, especially for Korean construction companies, it is often the case that the failure in an international construction project cannot be offset by even a sufficient number of successful domestic achievements. Therefore, not only the selective screening among the nominated projects which have strong possibility of collapse but the systematic strategies for controlling potential risk factors are also considered indispensable in international construction portfolio management. The purpose of this study is to first analyze the causal relationships of the profit-influencing variables and the project success, and develop the profitability forecasting model in international construction projects.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.446-454
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• 2023

The impact of climate change on agriculture is substantial, especially as global warming is projected to lead to varying temperature and humidity patterns in the future. These changes pose a higher risk for both crops and livestock, exposing them to environmental stressors under altered climatic conditions. Specifically, as temperatures are expected to rise, the risk of heat stress is assessable through the Temperature-Humidity Index (THI), derived from temperature and relative humidity data. This study involved the comparison of THI collected from 10 Korea Meteorological Administration ASOS stations spanning a 60-year period from 1961 to 2020. Moreover, high-resolution temperature and humidity distribution data from 1981 to 2020 were employed to generate high-resolution TH I distributions, analyzing temporal changes. Additionally, the number of days characterized by heat stress, derived from TH I, was compared over different time periods. Generally, TH I showed an upward trend over the past, albeit with varying rates across different locations. As TH I increased, the frequency of heat stress days also rose, indicating potential future cost increases in the livestock industry due to heat-related challenges. The findings emphasize the feasibility of evaluating heat stress risk in livestock using THI and underscore the need for research analyzing THI under future climate change scenarios.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.