• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.297-308
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• 2023

Displacement of structures is the most important parameter for safety and performance assessment and is measured to use for diagnosis and maintenance of bridges. Usually LVDT, Laser and GNSS are used for displacement measurement but these measurement instruments have problems in terms of field condition and cost. Therefore, in this study, displacements were evaluated using rotational angle measured by inclinometers and the proposed algorithm was experimentally verified. As the result, vertical displacements of cable supported bridges with traffic and temperature load were properly evaluated through the proposed algorithm. Therefore it is considered that the proposed algorithm can be used for displacement measurement by vehicle load test and long term displacement monitoring.

• Smart Structures and Systems
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• v.29 no.5
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• pp.653-660
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• 2022

Gravel scattering that is generated during operation of high-speed railway vehicle is cause to damage of vehicle such as windows, axle protector and so on. Especially, those are frequently occurred in winter season when snow ice is generated easily. Above all, damage of vehicle windows has not only caused maintenance cost but also increased psychological anxiety of passengers. Various methods such as heating system using copper wire, heating jacket and heating air are applied to remove snow ice generated on the under-body of vehicle. However, the methods require much run-time and man power which can be low effectiveness of work. Therefore, this paper shows that large-area heating system was developed based on heating coat in order to fundamentally prevent snow ice damage on high-speed railway vehicle in the winter season. This system gives users high convenience because that can remotely control the heating system using IoT-based wireless communication. For evaluating the applicability to railroad sites, a field test on an actual high-speed railroad operation was conducted by applying these techniques to the brake cylinder of a high-speed railroad vehicle. From the results, it evaluated how input voltage and electric power per unit area of the heating specimen influences exothermic performance to draw the permit power condition for icing. In the future, if the system developed in the study is applied at the railroad site, it may be used as a technique for preventing all types of damages occurring due to snow ice in winter.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.129-129
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• 2021

Constructed wetlands (CW) are artificially developed wetlands that are used to control water pollution. In central India, the field application of CW started on the late 1990s but are mostly focused on wastewater treatment. In this paper, different existing and experimental studies on constructed wetlands were reviewed to be able to determine the current status of wetlands in India to identify the type of CW that is more suitable in managing a specific target pollutant and type of wastewater. Wetlands were categorized into three types: vertical flow, horizontal flow, and hybrid while the wastewater were classified as domestic and industrial. Based on the review, 80% of constructed wetlands are used for treating domestic wastewater while 20% are treating industrial wastewater. Inflow analysis showed that industrial wastewater in hybrid constructed wetland produced the highest average concentration for parameters like COD (2851 mg/L) and BOD (5715 mg/L) while the lowest concentration was TN (13.97 mg/L) found in municipal wastewater. In terms of removing nonpoint source pollutants, it was revealed that vertical flow constructed wetlands (VFCW) are more effective at removing TSS and BOD as compared to horizontal flow constructed wetlands (HFCW) and hybrid constructed wetlands (HCW). HCW were found to be capable of efficiently removing COD and TN. Meanwhile, HFCW showed the highest TP removal among all the types of wetlands. In addition, VFCW were more effective for domestic wastewater while HCW are more effective for treating industrial wastewater. Lastly, there is currently a need to conduct further research on constructed wetlands for industrial wastewater as well as stormwater treatment to be able to gather more data and improve wetland design, performance, and maintenance.

• Journal of the Korean Society of Food Culture
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• v.25 no.2
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• pp.117-125
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• 2010

We surveyed 821 children (ages 12-15 y) in Daejeon city and Cunngcheong province, Korea for their use of vitamin mineral supplements (VMS), either over-the-counter drugs and/or health functional foods (HFF). We also considered the significant variables of VMS use, including demographic characteristics, health-related variables, eating habits and nutritional beliefs of VMS by subjects. The frequency of VMS use was 24.8% and, in comparison to counterparts, it was higher in subjects attending middle school (p<0.001), in those living in a large city (p<0.001), in those with a high monthly family income (p<0.05), and in those with high concerns about school performance by parents (p<0.01). Selfhealth concerns by subjects (p<0.05) and concerns about health of offspring by parents (p<0.01) were higher in users than in nonusers. Eating habits of subjects did not affect the use of VMS. Users had a more positive view concerning the potential health benefits of VMS than did nonusers (p<0.05). Most users took VMS when they were healthy (78.9%) and expected 'disease prevention and health maintenance' (44.6%). However, most users did not feel any special effects from VMS use (58.8%), and they got their information on VMS through family and relatives (53.9%). Users of VMS preferred vitamin C supplements (44.2%) and Ca-supplements (56.9%). Therefore, the results show that VMS use is widely spread among the subjects, and that use of VMS is influenced by multiple variables and subjective criteria. Therefore, practical guidelines on VMS use for children's health and growth should be given through nutritional education.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.549-555
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• 2023

Jacking force loss management inside the PSC Box girder of a common high-speed railway is a very important feature in girder performance, and requires detailed management during the maintenance of the girder. This study aimed to analyze the timing of re-tension prediction of PSC Box girder based on the reduction level of the packing force inside the girder and the results of the tension loss measured without the train load test. As a result of predicting the timing of re-tension according to the level of tension reduction of the PSC Box Girder, the Jacking Force Loss curve was gently analyzed before the structure reached 17 years after confirmed completion, and 17 years later, it was found that the jacking force loss curve progressed rapidly. The results confirmed that the tension of the structure decreases with the service life increase, but considerably decreases as the structure ages. Therefore, more data and research on tension loss of facilities over 20 years are much required.

• The Journal of Bigdata
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• v.7 no.2
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• pp.139-152
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• 2022

Engineering's work area for plants is a technical area that directly affects productivity, performance, and quality throughout the lifecycle from planning, design, construction, operation and disposal. Using the different types of data that occur to make decisions is important not only in the subsequent process but also in terms of cyclical cost reduction. However, there is a lack of systems to manage and analyze these integrated data. In this paper, we developed a knowledge base-based plant engineering analysis platform that can manage and utilize data. The platform provides a knowledge base that preprocesses previously collected engineering data, and provides analysis and visualization to use it as reference data in AI models. Users can perform data analysis through the use of prior technology and accumulated knowledge through the platform and use visualization in decision-support and systematically manage construction that relied only on experience.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.334-334
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• 2022

Improvement of old-fashioned rain gauge systems for automatic, timely, continuous, and accurate precipitation observation is highly essential for weather/climate prediction and natural hazards early warning, since the occurrence frequency and intensity of heavy and extreme precipitation events (especially floods) are recently getting more increase and severe worldwide due to climate change. Although rain gauge accuracy of 0.1 mm is recommended by the World Meteorological Organization (WMO), the traditional rain gauges in both weighting and tipping bucket types are often unable to meet that demand due to several existing technical limitations together with higher production and maintenance costs. Therefore, we aim to introduce a newly developed and cost-effective hybrid rain gauge system at 0.1 mm accuracy that combines advantages of weighting and tipping bucket types for continuous, automatic, and accurate precipitation observation, where the errors from long-term load cells and external environmental sources (e.g., winds) can be removed via an automatic drainage system and artificial intelligence-based data quality control procedure. Our rain gauge system consists of an instrument unit for measuring precipitation, a communication unit for transmitting and receiving measured precipitation signals, and a database unit for storing, processing, and analyzing precipitation data. This newly developed rain gauge was designed according to the weather instrument criteria, where precipitation amounts filled into the tipping bucket are measured considering the receiver's diameter, the maximum measurement of precipitation, drainage time, and the conductivity marking. Moreover, it is also designed to transmit the measured precipitation data stored in the PCB through RS232, RS485, and TCP/IP, together with connecting to the data logger to enable data collection and analysis based on user needs. Preliminary results from a comparison with an existing 1.0-mm tipping bucket rain gauge indicated that our developed rain gauge has an excellent performance in continuous precipitation observation with higher measurement accuracy, more correct precipitation days observed (120 days), and a lower error of roughly 27 mm occurred during the measurement period.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2021.11a
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• pp.132-133
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• 2021

In recent years, energy harvesting from natural sources and waste heat has been attracting more attention from researchers in response to ever-growing energy demands, high energy prices, and climate-change-mitigation purposes. It is also an important step towards future sustainable energy usages. In thermal dynamic cycles, expanders are playing as the most important equipment for waste heat recovery and energy harvesting as well. As a kind of expander, the bladeless turbine has a promising future and more widely using owning its advantages on relatively long life, good off-design performance, easy operation cleaning and maintenance, a simple structure, no blade corrosion, and low manufacturing costs. There are numerous studies about using the Tesla Turbine as a key technology for energy harvesting in a wide range of applications and conditions. They are presented to help identify technologies that have sufficient potential for applicating to our life and marine industrial engineering. This review paper, initially, presents an overview of current studies both theoretical and experimental of Tesla Turbine usage for waste heat recovery alongside its challenges and investigation on the effect of its configuration, working fluid selection as well. To conclude, future perspectives besides possible ways of transforming waste heat energy to electricity or work, which leads to circular energy, are discussed. The ambition of this paper is to act as a first-hand reference, through the well-defined possible directions, to the young researchers and senior scientists.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.155-161
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• 2023

In this paper, we present a WebRTC-based emergency video call system structure that builds a service system in a constant monitoring environment to increase the usability and stability of elevator emergency call devices. The proposed system provides a smooth call environment between the emergency call system in the elevator and maintenance managers in case of an emergency, performs rapid response processing to elevator emergency calls through monitoring of the target elevator, and handles any emergency calls that may occur in the physical space of the elevator. The purpose is to build an environment that can implement low-latency, real-time video call services of voice and video by overcoming the physical constraints required for video calls. To this end, we have established a service environment based on OpenAPI, which is currently used in various fields and its performance has been proven, and provides video calls and emergency situation dissemination through rapid messaging by providing low-latency call quality. The presented system structure will be able to provide a basis for expanding various functions and constructing a reliable service environment and intelligent model for the elevator system through combination with the elevator control panel and various devices.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.376-385
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• 2023

Fishing boat capsizing accidents account for more than half of all capsize accidents. These can occur for a variety of reasons, including inexperienced operation, bad weather, and poor maintenance. Due to the size and influence of the industry, technological complexity, and regional diversity, fishing ships are relatively under-researched compared to commercial ships. This study aimed to predict the rolling motion time series of fishing boats using an image-based deep learning model. Image-based deep learning can achieve high performance by learning various patterns in a time series. Three image-based deep learning models were used for this purpose: Xception, ResNet50, and CRNN. Xception and ResNet50 are composed of 177 and 184 layers, respectively, while CRNN is composed of 22 relatively thin layers. The experimental results showed that the Xception deep learning model recorded the lowest Symmetric mean absolute percentage error(sMAPE) of 0.04291 and Root Mean Squared Error(RMSE) of 0.0198. ResNet50 and CRNN recorded an RMSE of 0.0217 and 0.022, respectively. This confirms that the models with relatively deeper layers had higher accuracy.