• Korean Journal of Heritage: History & Science
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• 제55권1호
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• pp.111-132
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• 2022

The comprehensive examination on tendering system has been introduced to the Cultural Heritage repair and restoration field since 2016 to remedy the repair issues of South Gate in 2014. The Cultural Heritage Administration tried to attain the high performance of the cultural heritage repair and restoration works securing the proper payment for the repair and restoration works. It is high time to review the operating performance of the comprehensive examination on tendering system (hereinafter referred to as the "CEOTS"), as the system has been run for over 5 years to correspond with its original goal, i.e., "The Proper Payment in return for the High Performance of Repair and Restoration works." This study intends to analyze 114 tenders of CEOTS from 2016 to 2020. As a result of the analysis of 114 tenders, firstly, more than half of bid winners were in the top 20% of repair & restoration capacity disclosure amount list, which mostly fulfilled the goal of 'attaining high performance.' Secondly, as the winning bid rate is decreasing from 86.847% in 2017 to 85.488% in 2020, the goal of 'guarantee of a proper payment' is not achieved yet. Thirdly, the influence of Economic Evaluation section in CEOTS has been grown since the change of scoring system in CEOTS in 2019. This study identifies two reasons why the winning bid rate of CEOTS has decreased. Firstly, it is caused by the fact that 'the group that got more than 1st place' and 'the first place group' that are more than half of the total bidders have the decreasing bidding rate trend as the years go by. Secondly, the exclusion rate of 'the group that got more than 1st place' is higher than the exclusion rate of 'the group that got less than 1st place', which means the expected winning rate would be lowered. It is proposed that the revision of CEOTS code is needed, i.e. easing the strict rule concerning the exclusion rate as well as setting up the lower bidding limit to prevent the excessive decreasing winning bid rate.

• The Journal of Sustainable Design and Educational Environment Research
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• 제21권3호
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• pp.1-13
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• 2022

This study is a case study to identify the spatial composition and structural problems of existing schools for spatial innovation as a future school that can operate a credit system for old high schools and establish a mid-to-long-term arrangement plan as a credit system operating school capable of various teaching and learning in the future. The study results are as follows: First, most of the problems of the old high schools entailed that there was very poor connectivity between buildings as most of them were arranged in a single, standard design-type unit building and distributed in multiple buildings. In addition, the floor plan of each building is suggested to be a structure in which student exchange and rest functions cannot be achieved during the break period due to the spatial composition of the classroom and hallway concepts. Second, in the direction of the high school space configuration for future school space innovation, the arrangement plan should be established by reflecting the collective arrangement in consideration of the shortening of the movement route and the expansion of subject areas due to the movement of students on the premise of the subject classroom system. Moreover, it is desirable to provide a square-type space for rest and exchange in the central area where communication and exchange are possible according to the moving class. Third, as the evaluation criteria for relocating old high schools, a space program is prepared based on the number of classes in the future, and legal analysis of school land use and land use efficiency analysis considering regional characteristics are conducted. Based on such analysis data, mid-to-long-term land use plans and space arrangement plans for the entire school space such as the school facility complex are established.

• Journal of Korea Water Resources Association
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• 제55권11호
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• pp.923-929
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• 2022

The Chuncheon Mullori area is an underprivileged area of water welfare where local water supply is not supplied, and it is supplying water to the villages with small water supply facilities using lateral flow and groundwater as water sources. This is an area with poor water supply conditions, such as relying on water trucks due to water shortages during the recent severe drought. Therefore, in order to solve the problem of water shortage during drought and to prepare for the increasing water demand, a sand dam was installed along the valley, and this facility has been operating since May 2022. In this study, repeated simulations were performed according to the hydraulic conductivity of the filler material and the storage coefficient value for the inflow condition for about two years from mid-March 2020 to mid-March 2022. For each case, the amount of discharge through the perforated drain pipe was calculated. Overall, as the hydraulic conductivity increased, the amount of discharge and its ratio increased. However, when the hydraulic conductivity of the second floor was relatively low, the amount of discharge increased and then decreased as the hydraulic conductivity of the third floor increased. This is considered to be due to the fact that the water level was kept low due to the rapid drainage compared to the net inflow into the third floor because the water permeability of the third floor and the drainage coefficient of the drain pipe were large. As a result of simulating the flow of the open channel in the upper part of the sand dam as a hypothetical groundwater layer with very high hydraulic conductivity, the decrease in discharge rate was slower than the increase in the hydraulic conductivity of the hypothetical layer, but it was clearly shown that the discharge volume decreased relatively as the hydraulic conductivity of the virtual layer increased.

• Journal of the Korean Society of Marine Environment & Safety
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• 제29권5호
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• pp.479-487
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• 2023

Recently, the destructive power of typhoons is continuously increasing owing to global warming. In a situation where the installation of floating wind turbines is increasing worldwide, concerns about the huge loss and collapse of floating offshore wind turbines owing to strong typhoons are deepening. A new type of disconnectable mooring system must be developed for the safe operation of floating offshore wind turbines. A new submersible mooring pulley considered in this study is devised to more easily attach or detach the floating of shore wind turbine with mooring lines compared with other disconnectable mooring apparatuses. To investigate the structural safety of the initial design of submersible mooring pulley that can be applied to an 8MW-class floating type offshore wind turbine, scale-down structural models were developed using a 3-D printer and structural tests were performed on the models. For the structural tests of the scale-down models, tensile specimens of acrylonitrile butadiene styrene material that was used in the 3-D printing were prepared, and the material properties were evaluated by conducting the tensile tests. The finite element analysis (FEA) of submersible mooring pulley was performed by applying the material properties obtained from the tensile tests and the same load and boundary conditions as in the scale-down model structural tests. Through the FEA, the structural weak parts on the submersible mooring pulley were reviewed. The structural model tests were conducted considering the main load conditions of submersible mooring pulley, and the FEA and test results were compared for the locations that exceeded the maximum tensile stress of the material. The results of the FEA and structural model tests indicated that the connection structure of the body and the wheel was weak in operating conditions and that of the body and the chain stopper was weak in mooring conditions. The results of this study enabled to experimentally verify the structural safety of the initial design of submersible mooring pulley. The study results can be usefully used to improve the structural strength of submersible mooring pulley in a detailed design stage.

• Journal of Korea Water Resources Association
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• 제56권4호
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• pp.235-243
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• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.

• Journal of the Korean Society of Radiology
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• 제81권2호
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• pp.365-378
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• 2020

Purpose This study was performed to determine whether the T1 relaxation time of gadoxetic acid-enhanced liver MR imaging is useful for detecting and staging liver fibrosis in patients with chronic liver disease. Materials and Methods One hundred and three patients with suspected focal liver lesion underwent MR imaging and Fibroscan. Fibroscan was chosen as the reference standard for classifying liver fibrosis. T1 relaxation times were acquired before (preT1), 20 minutes after (postT1) contrast administration, and reduction rate of T1 relaxation time (rrT1) on transverse 3D VIBE (volumetric interpolated breath-hold examination) sequence using 3T MR imaging. The optimal cut-off values for the fibrosis staging were determined with ROC analysis. Results PreT1 and postT1 increased and rrT1 decreased constantly with increasing severity of liver fibrosis according to the METAVIR score (F0-F4). There were statistically significant differences between F2 and F3 in preT1 (F2, 836.0 ± 74.7 ms; F3, 888.6 ± 77.5 ms, p < 0.05) and between F3 and F4 in postT1 (F3, 309.0 ± 80.2 ms; F4, 406.6 ± 147.7 ms, p < 0.05) and rrT1 (F3, 65.4 ± 7.7%; F4, 57.3 ± 11.4%, p < 0.05). ROC analysis revealed that combination test (preT1 + postT1) was the best test for predicting liver fibrosis. Conclusion PreT1 and postT1 increased constantly with increasing severity of liver fibrosis. T1 mapping in gadoxetic acid-enhanced liver MR imaging could be a helpful complementary sequence to determine the liver fibrosis stage.

• Journal of the Korean Society of Marine Environment & Safety
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• 제30권1호
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• pp.127-134
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• 2024

With the growth of offshore wind power generation market, the corresponding installation vessel market is also growing. It is anticipated that approximately 100 installation vessels will be required in the of shore wind power generation market by 2030. With a price range of 300 to 400 billion Korean won per vessel, this represents a high-value market compared to merchant vessels. Particularly, the demand for large installation vessels with a capacity of 11 MW or more is increasing. The rapid growth of the offshore wind power generation market in the Asia-Pacific region, centered around China, has led to several discussions on orders for operational installation vessels in this region. The seabed geology in the Asia-Pacific region is dominated by clay layers with low bearing capacity. Owing to these characteristics, during vessel operations, significant spudcan and leg penetration depths occur as the installation vessel rises and descends above the water surface. In this study, using penetration variables ranging from 3 to 21 m, the unique vibration period, structural safety of the legs, and conductivity safety index were assessed based on penetration depths. As the penetration depth increases, the natural vibration period and the moment length of the leg become shorter, increasing the margin of structural strength. It is safe against overturning moment at all angles of incidence, and the maximum value occurs at 270 degrees. The conditions reviewed through this study can be used as crucial data to determine the operation of the legs according to the penetration depth when developing operating procedures for WTIV in soft soil. In conclusion, accurately determining the safety of the leg structure according to the penetration depth is directly related to the safety of the WTIV.

• Journal of the Korea Organic Resources Recycling Association
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• 제17권3호
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• pp.27-39
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• 2009

The main objective of this study is to investigate the characteristics of combustion by analyzing fuel gases from a combustion equipment with various combustion conditions for refuse-derived fuels (RDFs). CO gas is a parameter for indicating of incomplete combustion during a combustion process. The lowest CO gas was produced when the experiment conditions were m=2 under air-fuel condition and $800^{\circ}C$. $CO_2$ gas is a final product after complete combustions. The highest amount of $CO_2$ gas was produced when the experiment conditions were m=2 under air-fuel condition and $800^{\circ}C$. The highest level of $SO_2$ gas was produced in S.1 sample containing the highest sulfur. The highest level of NOx gas was produced in S.1 sample with the highest nitrogen content and air-fuel condition of m=2 under temperature of $800^{\circ}C$. HCl gas that is generated by reacting with metals catalyst through oxygen catalyst reaction during combustion process is a precursor of dioxin formation. The higher level of HCl gas was produced in the sample with higher chlorine content. The lowest level of HCl gas was produced when the experiment conditions were air-fuel condition of m=2 and $800^{\circ}C$. The lowest level of $NH_3$ gas was generated when the experiment condition was m=2 under air-fuel condition and after 3 minutes. Air-fuel condition is more important to create $NH_3$ gas than operating temperatures. Higher level of $H_2S$ gas was generated in S.1 sample with the higher sulfur content and was created in RDFs that contain higher mixture ratios of sewage sludge and food wastes. A result of combustion, gases and gases levels from the combustion of S.1 and S.2 were very similar to the combustion of a stone coal. As results of this research, when evaluating the feasibility of the RDFs, the RDFs could be used as auxiliary and main fuels.

• Journal of Intelligence and Information Systems
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• 제27권1호
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.