• Journal of The Korea Institute of Healthcare Architecture
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• v.24 no.3
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• pp.49-57
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• 2018

Purpose: The increase in patients requiring hemodialysis has resulted in an increase dialysis-associated infections risk. but there are no Renal Dialysis unit design standard meet specified safety and quality standards. Therefore, appropriate Establish standards and legal regulation is important for the provision of initial certification and maintenance of facility, equipment, and human resource quality. Methods: Literature survey on the design guideline and standards of Renal Dialysis unit design in Korea, U.S, Germany, Singapore, Hongkong, Dubai. Results: There are no established standards for facilities in dialysis units in Korea. To prevent infections in dialysis patients, necessary establish standards. Considering the domestic and overseas Health-care facilities standards, the major factors to be considered in the medical environment for Renal Dialysis Unit are as follows. First, planning to separate Clean areas(treatment area) from contaminated areas(medical waste storage area). Second, ensure sufficient space and minimum separation distance. Although there may be differences depending on the circumstances of individual institutions, renal dialysis unit consider the space to prevent droplet transmission. Third, secure infrastructure of infection prevention such as sufficient amount of hand hygiene sinks. Hand washing facilities for staff within the Unit should be readily available. Hand hygiene sinks should be located to prevent water from splashing into the treatment area. Fourth, Heating, ventilation and air conditioning (HVAC) system for Renal Dialysis Unit is all about providing a safer environment for patients and staff. Implications: The results of this paper can be the basic data for the design of the Renal Dialysis Units and relevant regulations.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.5
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• pp.115-123
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• 2010

Cool-down performance after soaking is important because it affects passenger's thermal comfort. The cooling capacity of HVAC system determines initial cool down performance in most cases, the performance is also affected by location, and shape of panel vent, indoor seat arrangement. Therefore, optimal indoor designs are required in developing a new car. In this paper, initial cool down performance is predicted by CFD(computational fluid dynamics) analysis. Experimental time-averaging temperature data are used as inlet boundary condition. For more reliable analysis, real vehicle model and human FE model are used in grid generation procedure. Thermal and aerodynamic characteristics on re-circulation cool vent mode are investigated using CFX 12.0. Thermal comfort represented by PMV(predicted mean vote) is evaluated using acquired numerical data. Temperature and velocity fields show that flow in passenger's compartment after soaking is considerably unstable at the view point of thermodynamics. Volume-averaged temperature is decreased exponentially during overall cool down process. However, temperature monitored at different 16 spots in CFX-Solver shows local variation in head, chest, knee, foot. The cooling speed at the head and chest nearby panel vent are relatively faster than at the knee and foot. Horizontal temperature contour shows asymmetric distribution because of the location of exhaust vent. By evaluating the passenger's thermal comfort, slowest cooling region is found at the driver's seat.

• Architectural research
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• v.17 no.3
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• pp.109-115
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• 2015

The dynamic pricing of electricity, where the electricity rate increases in a time zone with a high demand for electricity is typically applied to a building whose power reception capacity is greater than a certain size. This includes the time of use(TOU) electricity pricing in Korea which can induce the effect of reducing the power demand of a building. Meanwhile, a VRF (Variable Refrigerant Flow) system that uses electricity is regarded as one of the typical heating and cooling systems along with central air conditioning (central HVAC) for its easy operation and application to the building. Thus, to reduce power energy and operating costs of a building in which the TOU and VRF systems are applied simultaneously, we suggested a control for changing the indoor temperature setting within the thermal comfort range or limiting the rotational speed of an inverter compressor. In this study, to describe the features of the above-mentioned control and verify its effects, we evaluated the results obtained from the analysis of its operation data. Through the actual measurements in winter operations for 73 days since mid- December 2014, we confirmed a reduction of 10.9% in power energy consumption and 12.2% in operating costs by the new control. Also, a reduction of 13.3% in power energy consumption was identified through a regression analysis.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).