• Tunnel and Underground Space
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• v.28 no.6
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• pp.513-527
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• 2018

The Tunnel Boring Machine(TBM) is relatively insufficient to cope with unpredicted changes in ground conditions as compared with Conventional Tunnelling Methods. Therefore, it is very important to predict the TBM performance at the design stage and estimate the advance rate for the calculation of the construction period. In this study, we added data to 211 TBM databases constructed in the previous study and analyzed the correlation between TBM outer diameter, maximum thrust, maximum cutterhead torque, cutterhead driving power and RPM, which are the main design and manufacturing specifications of TBM. As a result of the analysis from results obtained in the previous studies, it was confirmed that TBM outer diameter is very effective and important in estimating maximum thrust, maximum cutterhead torque, and cutterhead driving power of the TBM. As a result of comparing the regression equations derived from other TBM databases outside the country and the regression equation obtained from the present study results, the maximum thrust showed a similar tendency to each other, but the maximum torque estimated from the regression equation of this study was higher than that of other countries in the case of the large scale TBM.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.2
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• pp.327-333
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• 2019

For sustainable lunar exploration, the most required resources should be procured on site because it takes tremendous cost to transfer the resources from the Earth to the Moon. The technologies required for use of lunar resources refers to In-Situ Resource Utilization (ISRU). As the ISRU technology cannot be verified in the Earth, a lunar surface environment simulator is necessary to be prepared in advance. The Moon has no atmosphere, and the average temperature of the lunar surface reaches to $107^{\circ}C$ during the daytime and $-153^{\circ}C$ at night. The lunar surface is also covered with very fine soils with sharp particles that are electrostatically charged by solar radiation and solar wind. In this research, generation of vacuum environment with lunar soil mass in a chamber and simulation of electrostatically charged soils are taken into consideration. It was successful to make a vacuum environment of a chamber including lunar soils without soil disturbance by controlling evacuation rate of a vacuum chamber. And an experiment procedure for simulating the charged lunar soil was suggested by theoretical consideration in charging phenomena on lunar dust.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.759-767
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• 2021

Most weapons systems that are Force Integration are expensive equipment that reflects the latest technology, and the operation and maintenance cost is increasing continuously. Factors that efficiently operate and maintain these weapon systems include maintenance plans, economic costs, and repair part requirements. Among them, predicting the repair parts requirements during the life cycle in advance is an important way to increase operation and maintenance cost efficiency and operating availability. The start of requirement analysis for repair parts is a calculation of the CSP (CSP: Concurrent Spare parts, CSP hereafter) that is distributed when the weapon system is deployed. The CSP is an essential component of achieving the operating availability during this period because the weapon system aims to successfully perform a given operation mission without resupply for an initial set period. In the present study, the CSP calculation method was analyzed, reflecting the failure rate and operating time of items, but the analyzed CSP was aimed at preparing for technical failure, but in the initial operating environment, it is limited in coping with unexpected failures caused by human error. The failure is not included in the scope of free maintenance and is a serious factor in making the weapon system inoperable during the initial operation period. To prevent the inoperable status of a weapon system, CSP that considers human error is required in the initial operating environment, and the calculation criteria and measures are proposed.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.583-589
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• 2021

This study is a study on the development of virtual reality to prepare for the increase in disasters of households living with companion animals due to the increase of companion animals. The increase in single-person households and DINKs(Double Income, No Kid) along with the low birth rate and aging population is raising the risk of disasters caused by companion animals in particular. Among these disasters, there is an increase in the occurrence of fires primarily due to the raising of companion animals. Electric stove fires caused by pets are the most common fires. In particular, the frequency of electric stove fires caused by cats is the highest. Careful precautions by the owner are necessary to reduce fires caused by pets. Parenting of companion animals causes pet loss syndrome due to emotional exchange. There are injuries to pets in escalators and injuries to owners in elevators due to disasters caused by the owner's negligence. In order to reduce injuries on escalators and elevators, basic etiquette for using escalators and elevators with pets is required as basic etiquette. It is necessary to utilize virtual reality to reduce disasters caused by such companion animals. Virtual reality can be experienced without a physical space in advance training to overcome disasters, so real disaster cases can be experienced immersively. Therefore, learning how to reduce fires caused by companion animals, disasters caused by owner's negligence, and petloss syndrome through virtual reality will greatly contribute to disaster prevention and reduction of social costs.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.391-404
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• 2021

This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.494-507
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• 2021

With the increasing use of TBM, research has recently been conducted in Korea to analyze TBM data with machine learning techniques to predict the ground in front of TBM, predict the exchange cycle of disk cutters, and predict the advance rate of TBM. In this study, classification prediction of rock characteristics of slurry shield TBM sites was made by combining traditional rock classification techniques and machine learning techniques widely used in various fields with machine data during TBM excavation. The items of rock characteristic classification criteria were set as RQD, uniaxial compression strength, and elastic wave speed, and the rock conditions for each item were classified into three classes: class 0 (good), 1 (normal), and 2 (poor), and machine learning was performed on six class algorithms. As a result, the ensemble model showed good performance, and the LigthtGBM model, which showed excellent results in learning speed as well as learning performance, was found to be optimal in the target site ground. Using the classification model for the three rock characteristics set in this study, it is believed that it will be possible to provide rock conditions for sections where ground information is not provided, which will help during excavation work.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.502-517
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• 2022

As the use of TBM increases, research has recently increased to to analyze TBM data with machine learning techniques to predict the exchange cycle of disc cutters, and predict the advance rate of TBM. In this study, a regression prediction of disc cutte wear of slurry shield TBM site was made by combining machine learning based on the machine data and the geotechnical data obtained during the excavation. The data were divided into 7:3 for training and testing the prediction of disc cutter wear, and the hyper-parameters are optimized by cross-validated grid-search over a parameter grid. As a result, gradient boosting based on the ensemble model showed good performance with a determination coefficient of 0.852 and a root-mean-square-error of 3.111 and especially excellent results in fit times along with learning performance. Based on the results, it is judged that the suitability of the prediction model using data including mechanical data and geotechnical information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of disc cutter data.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.41-49
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• 2022

The Internet Engineering Task Force (IETF) has standardized RPL (IPv6 Routing Protocol for Low-power Lossy Network) as a routing protocol for Low Power and Lossy Networks (LLNs), a low power loss network environment. RPL creates a route through an Objective Function (OF) suitable for the service required by LLNs and builds a Destination Oriented Directed Acyclic Graph (DODAG). Existing studies check the residual energy of each node and select a parent with the highest residual energy to build a DODAG, but the energy exhaustion of the parent can not avoid the network disconnection of the children nodes. Therefore, this paper proposes EC-RPL (Enhanced Connectivity-RPL), in which ta node leaves DODAG in advance when the remaining energy of the node falls below the specified energy threshold. The proposed protocol is implemented in Contiki, an open-source IoT operating system, and its performance is evaluated in Cooja simulator, and the number of control messages is compared using Foren6. Experimental results show that EC-RPL has 6.9% lower latency and 5.8% fewer control messages than the existing RPL, and the packet delivery rate is 1.7% higher.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.794-802
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• 2022

This retrospective study was to investigate the characteristics of unplanned readmission and factors affecting readmission within 30 days of discharge in patients who underwent heart valve surgery through electronic medical records. The participants were 423 unplanned re-hospitalization within 30 days after heart valve surgery at a tertiary hospital in Seoul from January 2018 to August 2019. A total of 48 patients (11.3%) were unplanned readmissions, and the most common causes were atrial fibrillation in 13 cases (27.1%) and pain at the surgical site in 13 cases (27.1%). Other causes were: 10 cases (20.8%) of warfarin inappropriate treatment concentration, 7 cases of general weakness (14.6%), 5 cases of hypotension (10.4%), 4 cases of pericardial effusion (8.3%), 3 cases of surgical wound infection (6.3%), 3 cases of hemorrhage (6.3%), 3 cases of high fever (6.3%), and 1 case of cerebral infarction (2.1%). Variables influencing readmission were history of cancer (OR = 2.60, 95% CI 1.13-6.03, p = .025) and the patients who went to a home rather than a hospital after discharge (OR = 2.91, 95% CI 1.33-6.36, p = .008), as a type of valve surgery, mitral valve valvuloplasty had a higher readmission rate than aortic valve replacement (OR = 1.21, 95% CI 1.21-4.98, p = .012). In order to reduce unplanned readmissions, an tailored education program is needed to enable patients and caregivers to manage their comorbid chronic diseases before discharge and assess risk factors for readmission in advance.

• Journal of the Korea Institute of Building Construction
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• v.22 no.4
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• pp.391-402
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• 2022

With the number of social overhead capital(SOC) projects that introduce private capital on the rise, overseas construction global companies today need to establish and advance their overseas order strategies. In this context, the purpose of this study is to develop the public private partnership(PPP) capacity evaluation model for developing countries and use it for domestic overseas construction companies to establish strategies for overseas expansion. The PPP competency evaluation model analyzes the importance of PPP competency evaluation items and infrastructure environment competency evaluation items through a review of previous studies and an interview survey with overseas construction experts. Through the above analysis results and expert surveys, problems that may occur when overseas construction companies enter the PPP market were derived, and improvement measures were proposed. Countries with a high probability of overseas construction companies entering the PPP market were determined to be those that have a mature PPP system, low risk in construction, and a good entry environment with a high infrastructure market size and growth rate. In addition, a lack of PPP investment experience, the absence of information on the infrastructure environment, and a shortage of PPP experts were identified as problems when entering the overseas construction PPP market. As an improvement measure, it was suggested to enter in cooperation with domestic and foreign companies. In addition, a plan was proposed to develop a curriculum to secure experts in areas such as PPP finance and contracts and to provide PPP information for each country. These findings are expected to contribute to overseas construction companies proposing strategies for entering the overseas construction market and using them for overseas expansion strategies and policy establishment.