• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1799-1802
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• 2008

More than 7 million people use the Seoul metropolitan subway network daily. This number tends to increase due to the increase of oil price. Indoor air quality of electrical multiple unit (EMU) is strongly affected by outdoor air quality, however, indoor thermal comfort is subjected to heating, ventilating, and air conditioning (HVAC) system of EMU. In general, air temperature, humidity, air velocity, surface temperature, and illumination are key parameters affecting thermal comfort of passenger. It is known that the well-designed HVAC system should improve the thermal comfort of passengers and should increase the energy efficiency of HVAC system also. In this study, we analyzed the thermal comfort of advanced EMU developed by Korea Railroad Research Institute by using the computational fluid dynamics (CFD) in order to find the optimum HVAC system which can improve thermal comfort of passengers with a minimal energy use.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.5
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• pp.239-248
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• 2017

At the $21^{st}$ Conference of the Parties (COP) of the United Nations Climate change Conference, representatives of the 195 member countries reached an agreement requiring all participating countries, including Korea, to establish proactive measures to fight climate change. Under this vision, energy network technologies are deemed as a key site of research towards meeting this goal. Herein, the headquarters of the Korea Institute of Energy Research (KIER) is a worthy site for carrying out energy network technology research insofar as it contains various heat sources. To prepare for this research, a study was conducted analyzing the heat sources at KIER based on measured data. The study also consisted of developeding simulation models to predict the amount of energy savings that could be derived by replacing an absorption chiller/heater with an absorption heat pump during winter seasons. In our simulation results, we observed a primary energy saving ratio of 65~72% based on the water temperature from the heat source of a coal power plant.

• Journal of IKEEE
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• v.26 no.3
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• pp.349-354
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• 2022

In thermal power plants, boiler models have been used widely in evaluating logic configurations, performing system tuning and applying control theory, etc. Furthermore, proper plant models are needed to design the accurate controllers. Sometimes, mathematical models can not exactly describe a power plant due to time varying, nonlinearity, uncertainties and complexity of the thermal power plants. In this case, a neural network can be a useful method to estimate such systems. In this paper, the models of boiler steam system in a thermal power plant are developed by using a generalized regression neural network(GRNN). The models of the superheater, reheater, attemperator and drum are designed by using GRNN and the models are trained and validate with the real data obtained in 540[MW] power plant. The validation results showed that proposed models agree with actual outputs of the drum boiler well.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.10
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• pp.1445-1453
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• 2017

In this paper, we propose a fault detection model based on multi-layer neural network using data mining technique for faults due to boiler tube leakage in a thermal power plant. Major measurement data related to faults are analyzed using statistical methods. Based on the analysis results, the number of input data of the proposed fault detection model is simplified. Then, each input data is clustering with normal data and fault data by applying K-Means algorithm, which is one of the data mining techniques. fault data were trained by the neural network and tested fault detection for boiler tube leakage fault.

• Journal of Ocean Engineering and Technology
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• v.20 no.4 s.71
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• pp.24-30
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• 2006

Different methods exist for the estimation of thermaldeformation of plates in the line heating process. These are based on the assumption of residual strains in the heat-affected zone, known as the method of inherent strains, or simulated relations between heating conditions and residual deformations. The purpose of this paper is to develop a simulator of thermal deformation in the line heating, using the artificial neural network. Curvature deformations for the plate-forming are investigated, which can be used as a prime deformation parameter in the process. The curvature of plates are calculated using the approximation of plate surface by NURBS. Line heating experiments for 11 specimens of different thickness and heating conditions were performed. Two neural networks predicting the maximum temperature and curvature deformations at the heating line are studied. It was concluded that the thermal deformations predicted by the neural network can be used in a line heating simulator, which is considered an attractive and practical alternative to the existing methods.

• International Journal of Naval Architecture and Ocean Engineering
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• v.2 no.3
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• pp.132-138
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• 2010

In the present study, the real-time monitoring system is developed based on the wireless sensor network (WSN) and power line communication (PLC) employed in the 3,000-ton-class training ship. The WSN consists of sensor nodes, router, gateway and middleware. The PLC is composed of power lines, modems, Ethernet gateway and phase-coupler. The basic tests show that the ship has rather good environments for the wired and wireless communications. The developed real-time monitoring system is applied to recognize the thermal environments of main-engine room and one cabin in the ship. The main-engine room has lots of heat sources and needs careful monitoring to satisfy safe operation condition or detect any human errors beforehand. The monitoring is performed in two regions near the turbocharger and cascade tank, considered as heat sources. The cabin on the second deck is selected to monitor the thermal environments because it is close to the heat source of main engine. The monitoring results of the cabin show the thermal environment is varied by the human activity. The real-time monitoring for the thermal environment would be useful for the planning of the ventilation strategy based on the traces of the human activity against inconvenient thermal environments as well as the recognizing the temperature itself in each cabin.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1343-1351
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• 2000

Thermally induced errors have been significant factors affecting the machine tool accuracy. In this paper, the spindle thermal error has been focused, where the 5 degree of freedom thermal error components are considered. An effective measurement system has been devised for the 5 DOF thermal errors, consisting of gap sensors and thermocouples around the micro-computer interfaced environment. Several thermal error modeling techniques are also implemented for the thermal error prediction: multiple linear regression, neural network and system identification methods, etc. The performance of the thermal error modeling techniques is evaluated and compared, giving the system identification method as the optimum model having the least deviation. The developed system for the thermal error measurement and modeling was practically applied to a CNC machining center, and the spindle thermal errors were effectively compensated around the micro computer-machine tool interfaced networks. The machine tool accuracy was improved about 4-5 times typically.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.361-368
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• 2018

As autonomous driving research is rapidly developing, pedestrian detection study is also successfully investigated. However, most of the study utilizes color image datasets and those are relatively easy to detect the pedestrian. In case of color images, the scene should be exposed by enough light in order to capture the pedestrian and it is not easy for the conventional methods to detect the pedestrian if it is the other case. Therefore, in this paper, we propose deep neural network (DNN)-based multi-spectrum pedestrian detection method using color and thermal images. Based on single-shot multibox detector (SSD), we propose fusion network structures which simultaneously employ color and thermal images. In the experiment, we used KAIST dataset. We showed that proposed SSD-H (SSD-Halfway fusion) technique shows 18.18% lower miss rate compared to the KAIST pedestrian detection baseline. In addition, the proposed method shows at least 2.1% lower miss rate compared to the conventional halfway fusion method.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.16-23
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• 2004

Thermally induced errors of machine tools have been recognized as one of the most important issues in precision machining. This is probably the most formidable obstacle to obtain high level of machining accuracy. To this regard, the experimental compensation methodologies such as software-based method or origin shift of machine tool axes have been suggested. In this research, to cope with thermal deformation, a model based correction was carried out with the function of an external machine coordinate shift. Models with multi-linear regression or neural network were investigated to selected a good one for thermal compensation. Consequently, multi-linear regression model combined with origin shift was verified good enough form the machining of dot matrices of plate with ball end milling.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.600-603
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• 2009

We compared relative importance of thermal conductivity and initial ground temperature in designing borehole heat exchanger network and also we test accuracy of ground temperature estimation in thermal response test using a proven 3-D T-H modeler. The effect of error in estimating ground temperature on calculated total length of borehole heat exchanger was more than 3 times larger than the case of thermal conductivity in maximum 20% error range. Considering 10% of error in estimating thermal conductivity is generally acceptable, we have to define the initial ground temperature within 5% confidence level. Utilizing the mean annual ground surface temperature and the geothermal gradient map compiled so far can be a economic way of estimating ground temperature with some caution. When performing thermal response test for estimating ground temperature as well as measuring thermal conductivity, minimum 100 minutes of ambient circulation is required, which should be even more in case of very cold and hot seasons.