• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1509-1521
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• 2020

Experiments for validation of surface reflectance produced by Korea Multi-Purpose Satellite (KOMPSAT-3A) were conducted using Chinese Baotou (BTCN) data among four sites of the Radical Calibration Network (RadCalNet), a portal that provides spectrophotometric reflectance measurements. The atmosphere reflectance and surface reflectance products were generated using an extension program of an open-source Orfeo ToolBox (OTB), which was redesigned and implemented to extract those reflectance products in batches. Three image data sets of 2016, 2017, and 2018 were taken into account of the two sensor model variability, ver. 1.4 released in 2017 and ver. 1.5 in 2019, such as gain and offset applied to the absolute atmospheric correction. The results of applying these sensor model variables showed that the reflectance products by ver. 1.4 were relatively well-matched with RadCalNet BTCN data, compared to ones by ver. 1.5. On the other hand, the reflectance products obtained from the Landsat-8 by the USGS LaSRC algorithm and Sentinel-2B images using the SNAP Sen2Cor program were used to quantitatively verify the differences in those of KOMPSAT-3A. Based on the RadCalNet BTCN data, the differences between the surface reflectance of KOMPSAT-3A image were shown to be highly consistent with B band as -0.031 to 0.034, G band as -0.001 to 0.055, R band as -0.072 to 0.037, and NIR band as -0.060 to 0.022. The surface reflectance of KOMPSAT-3A also indicated the accuracy level for further applications, compared to those of Landsat-8 and Sentinel-2B images. The results of this study are meaningful in confirming the applicability of Analysis Ready Data (ARD) to the surface reflectance on high-resolution satellites.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.193-215
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• 2022

Recently, ground structures have reached saturation, and underground construction using underground structures such as tunnels has been in the spotlight as a way to solve increasing traffic difficulties and environmental problems. However, due to the increasing number of underground structures, close construction is inevitable for continuous underground development. When a new underground structure is constructed closely, stability may become weak due to the influence on the existing tunnel, which may cause collapse. Therefore, analyzing the stability of existing tunnels due to new structures is an essential consideration. In this study, the effect of excavating new tunnels under parallel tunnels on existing parallel tunnels was analyzed using numerical analysis. Using the Displacement Control Model (DCM), the volume loss generated during construction was simulated into three case (0.5%, 1.0%, and 1.5%). Based on the center of the pillar, the distance where the new tunnel is located was set to 5 m, 6 m, 7 m, 8 m, 9 m, and the space for each distance were set to 5 (0D₁, 0.37D₁, 0.75D₁, 1.13D₁, 1.5D₁). In general, as the volume loss increased and the distance approached, the maximum displacement and angular displacement increased, and the strength/stress ratio to evaluate the stability of the pillar also decreased. As a result, when the distance between the new tunnel and the center of the pillar is 5 m, the space is 0D₁, and the volume loss is 1.5%, the stability of the existing parallel tunnel is the weakest.

• Journal of the Korea Organic Resources Recycling Association
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• v.30 no.3
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• pp.13-22
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• 2022

In order to improve water quality of the water system contaminated with dyes, biochars prepared using discarded waste resources were applied in this study. Biochars with a large specific surface area were manufactured using street tree pruning products or waste wood, and were applied to remove an organic dye in synthetic water. Biochars were made by pyrolysis of typical street tree porch products (Platanas, Ginkgo, Aak) and waste wood under air-controlled conditions. Methylene blue (MB), which is widely used in phosphofibers, paper, leather, and cotton media, was selected in this study. The adsorption capacity of Platanas for MB was the highest and the q_max value obtained using the Langmuir model equation was 78.47 mg/g. In addition, the adsorption energy (E) (kJ/mol) of MB using the Dubinin-Radushkevich (D-R) model equation was 4.891 kJ/mol which was less than 8 kJ/mol (a criteria distinguishing physical adsorption from chemical adsorption). This result suggests a physical adsorption with weak interactions such as van der Waals force between the biochar and MB. In addition, the physical adsorption may resulted from that Platanas-based biohar has the largest specific surface area and pore volume. The ∆G value obtained through the adsorption experiment according to temperature variation was -3.67 to -7.68, which also suggests a physical adsorption. Considering these adsorption results, the adsorption of MB onto Platanas-based biochar seems to occur through physical adsorption. Overall, it was possible to suggest that adsorption capacity of the biochr prepared from this study was equal to or greater than that of commercial activated carbon reported in other studies.

• Journal of the Korea Institute of Building Construction
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• v.22 no.5
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• pp.473-483
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• 2022

BIM(Building Information Modeling) is a technology that can manage information throughout the entire life cycle of the construction industry and serves as a platform for improving productivity and integrating the entire construction industry. Currently, BIM is actively applied in developed countries, and its use at various overseas construction sites is increasing This is unclear. due to air shortening and budget savings. However, there is still a lack of institutional basis and technical limitations in the domestic construction sector, which have led to the lack of utilization of BIM. Various activation measures and institutional frameworks will need to be established for the early establishment of these productive BIMs in Korea. Therefore, as part of the research for the domestic settlement and revitalization of BIM, this study derived a number of key factors necessary for the development of the construction industry through brainstorming and expert surveys using AHP techniques and analyzed the relative importance of each factor. In addition, prior surveys by a group of experts resulted in 1, 3 items in level, 2, 9 items in level, and 3, 27 items in level, and priorities analysis was performed through pairwise comparisons. As a result of the AHP analysis, it was found that the relative importance weight of policy aspects was highest in level 1, and the policy factors in level 2 and the cost-based and incentive system introduction factors were considered most important in level 3. These findings show that the importance of the policy guidance or institutions underlying the activation of BIM rather than research and development or corporate innovation is relatively high, and that the preparation of policy plans by public institutions should be the first priority. Therefore, it is considered that the development of a policy system or guideline must be prioritized before it can be advanced to the next activation stage. The use of BIM technologies will not only contribute to improving the productivity of the construction industry, but also to the overall development of the industry and the growth of the construction industry. It is expected that the results of this study can provide as useful information when establishing policies for activating BIM in central government, relevant local governments, and related public institutions.

• Korean Journal of Construction Engineering and Management
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• v.24 no.2
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• pp.59-69
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• 2023

FIDIC White Book is a Model Services Agreement between the Client and the Consultant. This study aimed to derive the Key Risk Sub-Clauses out of 63 Sub-Clauses of General Conditions of the FIDIC White Book by using the Delphi technique. A panel of 40 experts with more than 10 years of experience and expertise in overseas construction services agreements and FIDIC White Book was formed, and the reliability was improved in the direction of increasing the consensus of experts through a total of three Delphi survey processes. In the first Delphi survey, a closed-type survey was conducted on the impact of risk among 63 Sub-Clauses of General Conditions on a Likert 5-point scale, and 26 main risk Sub-Clauses were derived. The Content Validity of the results of the first Delphi survey was verified with the CVR value. In the 2nd and 3rd Delphi surveys, a closed-type survey was conducted on a Likert 10-point scale for 26 main risk Sub-Clauses and the risk possibility and impact of each main risk Sub-Clause were evaluated. The reliability of the 3rd Delphi survey result was verified with the COV value. Total 14 Key Risk Sub-Clauses were derived by applying the average risk possibility and impact of each of the 26 main risk Sub-Clauses to the PI Risk Matrix. The results of deriving Key Risk Sub-Clauses showed that agreement on specific scope of service, delay management, and change management were the most important. As a result of this study, from a practical point of view, consultants of consulting companies provide guidelines that should be reviewed to minimize contractual risks when signing service contracts with clients. From an academic point of view, the direction of research on deriving key risks related to service contracts for consultants participating in overseas construction is presented.

• Journal of the Korean Society of International Agriculture
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• v.31 no.1
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• pp.8-16
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• 2019

Alternatives for increasing the competitiveness of locally manufactured agricultural machinery in domestic and foreign markets has been proposed. This was done by analyzing the major agricultural machinery's price and market share as well as their performance and quality. In the Korean domestic market, the market share of Japanese agricultural machinery has been identified to be 14.5% for tractors, 31.1% for combine harvesters, and 35.8% for rice transplanters, and on track for further increase. Japanese manufacturers' domestic patent shares are 58.5% for tractors, 79.9% for combine harvesters, and 69.8% for rice transplanters, showing the dire need for Korean domestic firms to expand their technological rights. To strengthen the industrial competitiveness of agricultural machinery, therefore, researches that develop the fundamental and elemental technology to reduce the frequency of breakdown should be needed in the short term. To achieve this, it is imperative to establish technology roadmap, promote greater cooperation between academia and industry, and systematically increase research funding. In addition, as a long-term solution for enhancing the competitiveness, an establishment of Agricultural Equipment Technology Institute is strongly recommended to systematically support R&D for developing core technologies, particularly high-quality components that guarantee durability and quality.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Radiation Oncology Journal
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• v.21 no.1
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• pp.82-93
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• 2003

Purpose : To reduce the Irradiation dose to the lungs and heart in the case of chest wail irradiation using an oppositional electron beam, we used an Individualized custom bolus, which was precisely designed to compensate for the differences In chest wall thickness. The benefits were evaluated by comparing the normal tissue complication probablilties (NTCPS) and dose statistics both with and without boluses. Materials and Methods : Boluses were made, and their effects evaluated in ten patients treated using the reverse hockey-stick technique. The electron beam energy was determined so as to administer 80% of the irradiation prescription dose to the deepest lung-chest wall border, which was usually located at the internal mammary lymph node chain. An individualized custom bolus was prepared to compensate for a chest wall thinner than the prescription depth by meticulously measuring the chest wall thickness at 1 emf intervals on the planning CT Images. A second planning CT was obtained overlying the individuailzed custom bolus for each patient's chest wall. 3-D treatment planning was peformed using ADAC-Pinnacle$^{3}$ for all patients with and without bolus. NTCPS based on 'the Lyman-Kutcher' model were analyzed and the mean, maximum, minimum doses, V$_{50}$ and V$_{95}$ for 4he heari and lungs were computed. Results .The average NTCPS in the ipsliateral lung showed a statistically significant reduction (p<0.01), from 80.2${\pm}$3.43% to 47.7${\pm}$4.61%, with the use of the individualized custom boluses. The mean lung irradiation dose to the ipsilateral iung was also significantly reduced by about 430 cGy, Trom 2757 cGy to 2,327 cGy (p<0.01). The V$_{50}$ and V$_{95}$ in the ipsilateral lung markedly decreased from the averages of 54.5 and 17.4% to 45.3 and 11.0%, respectively. The V$_{50}$ and V$_{95}$ In the heart also decreased from the averages of 16.8 and 6.1% to 9.8% and 2.2%, respectively. The NTCP In the contralateral lung and the heart were 0%, even for the cases with no bolus because of the small effective mean radiation volume values of 4.4 and 7.1%, respectively Conclusion : The use of an Individualized custom bolus in the radiotherapy of postrnastectorny chest wall reduced the NTCP of the ipsilateral lung by about 24.5 to 40.5%, which can improve the complication free cure probability of breast cancer patients.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Progress in Medical Physics
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• v.26 no.3
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• pp.160-167
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• 2015

Radiation exposure from medical diagnostic imaging procedures to patients is one of the most significant interests in diagnostic x-ray system. A miniature x-ray intraoral tube was developed for the first time in the world which can be inserted into the mouth for imaging. Dose evaluation should be carried out in order to utilize such an imaging device for clinical use. In this study, dose evaluation of the new x-ray unit was performed by 1) using a custom made in vivo Pig phantom, 2) determining exposure condition for the clinical use, and 3) measuring patient dose of the new system. On the basis of DRLs (Diagnostic Reference Level) recommended by KDFA (Korea Food & Drug Administration), the ESD (Entrance Skin Dose) and DAP (Dose Area Product) measurements for the new x-ray imaging device were designed and measured. The maximum voltage and current of the x-ray tubes used in this study were 55 kVp, and 300 mA. The active area of the detector was $72{\times}72mm$ with pixel size of $48{\mu}m$. To obtain the operating condition of the new system, pig jaw phantom images showing major tooth-associated tissues, such as clown, pulp cavity were acquired at 1 frame/sec. Changing the beam currents 20 to $80{\mu}A$, x-ray images of 50 frames were obtained for one beam current with optimum x-ray exposure setting. Pig jaw phantom images were acquired from two commercial x-ray imaging units and compared to the new x-ray device: CS 2100, Carestream Dental LLC and EXARO, HIOSSEN, Inc. Their exposure conditions were 60 kV, 7 mA, and 60 kV, 2 mA, respectively. Comparing the new x-ray device and conventional x-ray imaging units, images of the new x-ray device around teeth and their neighboring tissues turn out to be better in spite of its small x-ray field size. ESD of the new x-ray device was measured 1.369 mGy on the beam condition for the best image quality, 0.051 mAs, which is much less than DRLs recommended by IAEA (International Atomic Energy Agency) and KDFA, both. Its dose distribution in the x-ray field size was observed to be uniform with standard deviation of 5~10 %. DAP of the new x-ray device was $82.4mGy*cm^2$ less than DRL established by KDFA even though its x-ray field size was small. This study shows that the new x-ray imaging device offers better in image quality and lower radiation dose compared to the conventional intraoral units. In additions, methods and know-how for studies in x-ray features could be accumulated from this work.