• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.176-183
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• 2009

Distributed generation(DG) of combined cooling, heat, and power(CCHP)has been gaining momentum in recent year as efficient, secure alternative for meeting increasing energy demands. This paper presents the energy performance of microturbine CCHP system equipped with an absorption chiller by modelling it in hospital building. The orders of study were as following. 1)The list and schedule of energy consumption equipment in hospital were examined such as heating and cooling machine, light etc. 2) Annual report of energy usage and monitoring data were examined as heating, cooling, DHW, lighting, etc. 3) The weather data in 2007 was used for simulation and was arranged by meteorological office data in Daejeon. 4) Reference simulation model was built by comparison of real energy consumption and simulation result by TRNSYS and ESP-r. The energy consumption pattern of building were analyzed by simulation model and energy reduction rate were calculated over the cogeneration. As a result of this study, power generation efficiency of turbine was about 30[%] after installing micro gas turbine and lighting energy as well as total electricity consumption can be reduced by 40[%]. If electricity energy and waste heat in turbine are used, 56[%] of heating energy and 67[%] of cooling energy can be reduced respectively, and total system efficiency can be increased up to 70[%].

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.481-486
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• 2019

Smart Factory requires real-time monitoring and analysis of all process processes for optimal production environment. Monitoring system for data collection from various sensors is necessary to make all production processes automatic. By storing and analyzing the collected data, we can check whether there are any signs of abnormalities in any machine or equipment. Thus, in this paper, an integrated monitoring system for smart factory incorporating a working environment monitoring system and an automatic storage system of measurement values was implemented. By using the automatic storage system of measurement values, it is possible to carry out reliable inspection in any place without misentry. Also, through working environment monitoring system using LoRa, production environments such as temperature, humidity and atmospheric pressure can be monitored in real time.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of Korean Library and Information Science Society
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• v.50 no.3
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• pp.191-216
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• 2019

This study is to suggest the revitalization plan of the medical school libraries in the future, on the basis of analysis for the overall operation situation of the medical school libraries among the medical libraries. So based on the website, it is divided into 1) whether independent homepage exists, 2) service target, 3) books, 4) classification system, 5) manpower, 6) facilities (area, number of seats available), 7) equipment (pc, printer, copy machine, etc.), 8) services, and then analyzed. Consequently, as the ways to revitalize the medical libraries, firstly, it is necessary to establish legal standards and develop guidelines for the medical school library's books, sizes, librarians, etc. Secondly, establishing a cooperative community network between medical school libraries is necessary. Thirdly, policies such as support at the national level, specialization education of librarians, development of operational guidelines, and activation of inter-library networks are needed to revitalize the medical school libraries. It is also expected that research on the actual situation of the medical libraries should be conducted at the national level or at the level of the association of medical libraries.

• Journal of the Korean Society of Safety
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• v.34 no.3
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• pp.83-88
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• 2019

The 7,993 cases of Survey Report of Fatal Industrial Accidents conducted jointly by the MEOL and the KOSHA for the recent seven years(2007-2013) were categorized according to personal and occupational characteristics, industry types, business sizes, job types, activities at the time accident, types of accidents, material agents(assailing materials), unsafe conditions, and unsafe acts. And it is found that among the 72.2 percent of fatal occupational accidents in the construction and manufacturing industries are caused by falling, sticking, bumping and being caught under objects & overturning. For this study, through the empirical analysis on causes of fatal industrial accidents, was used to identity high risk groups based on total data of 7,993 victims of occupational accidents. An annual fatal occupational injury (FOI) rate per 10,000 workers was about 0.47‱. The middle-aged group and the elderly group showed the highest FOI rates per 10,000 workers (0.73‱, 0.80‱), and the daily workers showed the highest FOI rate (1.46‱), and the craft and related trades workers showed the highest FOI rate (2.17‱). In case of industry type the mining industry (7.26‱) showed the highest FOI rate, followed by the sewerage, waste management, materials recovery and remediation activity industry (3.91‱) and the construction industry (2.71‱). The primary high risk target group that requires a strategy designed to reduce fatal occupation injuries caused by falling and bumping & contact(collision) is the construction industry, and the secondary high risk target group in the construction industry is classified as the equipment, machine operating and assembling workers in the construction industry, those aged 50 years old and above need the prevention measures against bumping & contact(collision) and being caught under an object & falling(objects), while those aged less than 50 years old need prevention measures against falling(persons).

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.1
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• pp.111-116
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• 2019

The fire detectors used in the engine rooms of ships offer only a slow response to emergencies because smoke or heat must reach detectors installed on ceilings, but the air flow in engine rooms can be very fluid depending on the use of equipment. In order to overcome these disadvantages, much research on video-based fire detection has been conducted in recent years. Video-based fire detection is effective for initial detection of fire because it is not affected by air flow and transmission speed is fast. In this paper, experiments were performed using images of smoke from a smoke generator in an engine room. Data generated using LBP and GLCM operators that extract the textural features of smoke was classified using SVM, which is a machine learning classifier. Even if smoke did not rise to the ceiling, where detectors were installed, smoke detection was confirmed using the image-based technique.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.7
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• pp.83-89
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• 2019

In the conventional casting and forging method, there is a disadvantage that a mold is an essential addition, and a production cost is increased when a small quantity is produced. In order to overcome this disadvantage, a metal 3D printing production method capable of directly forming a shape without a mold frame is mainly used. In particular, overseas research has been conducted on various materials, one of which is a metal printer. Similarly, domestic companies are also concentrating on the metal printer market. However, In this case of the conventional metal 3D printing method, it is difficult to meet the needs of the industry because of the high cost of materials, equipment and maintenance for product strength and production. To compensate for these weaknesses, printers have been developed that can be manufactured using sand mold, but they are not accessible to the printer company and are expensive to machine. Therefore, it is necessary to supply three-dimensional casting printers capable of metal molding by producing molds instead of conventional metal 3D printing methods. In this study, we intend to reduce the unit price by replacing the printing method used in the sand casting printer with the FDM method. In addition, Ag paste is used to design the output conditions and enable ceramic printing.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.49-58
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• 2019

In this paper, we propose an edge cloud platform architecture for implementing smart factory. The edge cloud platform is one of edge computing architecture which is mainly focusing on the efficient computing between IoT devices and central cloud. So far, edge computing has put emphasis on reducing latency, bandwidth and computing cost in areas like smart homes and self-driving cars. On the other hand, in this paper, we suggest not only common functional architecture of edge system but also light weight cloud based architecture to apply to the specialized requirements of smart factory. Cloud based edge architecture has many advantages in terms of scalability and reliability of resources and operation of various independent edge functions compare to typical edge system architecture. To make sure the availability of edge cloud platform in smart factory, we also analyze requirements of smart factory edge. We redefine requirements from a 4M1E(man, machine, material, method, element) perspective which are essentially needed to be digitalized and intelligent for physical operation of smart factory. Based on these requirements, we suggest layered(IoT Gateway, Edge Cloud, Central Cloud) application and data architecture. we also propose edge cloud platform architecture using lightweight container virtualization technology. Finally, we validate its implementation effects with case study. we apply proposed edge cloud architecture to the real manufacturing process and compare to existing equipment engineering system. As a result, we prove that the response performance of the proposed approach was improved by 84 to 92% better than existing method.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.717-726
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• 2020

The physical characteristics of the major varieties of perilla were analyzed to use as basic data for the design of a high-quality, high-efficiency perilla cleaner and stone separator. Because the size, thousand-grain weight, angle of repose, angle of friction, bulk density and terminal velocity of perilla have significant differences according to the perilla variety, the different of characteristics by variety should be considered for performance improvement of a perilla cleaner and stone separator. Therefore the cleaner and stone separator using a sieve could be improved by the application of a detachable sieve or by using equipment such as a 2 - 3 stage sieve and regulating the slope. Moreover, because differences in the terminal velocity occur due to the differences in the size and thousand-grain weight according to the perilla variety, a blower with an adjustable fan speed was considered for the design of the improved cleaner. Additionally, it was shown that the length of perilla has the greatest correlation based on a comparison of the coefficients of the other characteristics. Accordingly, the length of perilla could be used as a major factor for the fine adjustment and parts replacement of the device. These results can be used as basic data for a high-quality, high-efficiency perilla cleaner and stone separator. In the future, the development of the machine and follow-up studies based on the basic data are needed to determine the optimized operating conditions and mechanism of action.

• Journal of Biomedical Engineering Research
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• v.42 no.3
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• pp.100-106
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• 2021

Measuring Intima-media thickness (IMT) with ultrasound images can help early detection of coronary artery disease. As a result, numerous machine learning studies have been conducted to measure IMT. However, most of these studies require several steps of pre-treatment to extract the boundary, and some require manual intervention, so they are not suitable for on-site treatment in urgent situations. in this paper, we propose to use deep learning networks U-Net, Attention U-Net, and Pretrained U-Net to automatically segment the intima-media complex. This study also applied the HE, HS, and CLAHE preprocessing technique to wireless portable ultrasound diagnostic device images. As a result, The average dice coefficient of HE applied Models is 71% and CLAHE applied Models is 70%, while the HS applied Models have improved as 72% dice coefficient. Among them, Pretrained U-Net showed the highest performance with an average of 74%. When comparing this with the mean value of IMT measured by Conventional wired ultrasound equipment, the highest correlation coefficient value was shown in the HS applied pretrained U-Net.