• Proceedings of the KOSOMBE Conference
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• v.1985 no.06
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• pp.9-12
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• 1985

This paper covers the design and implementation process of RF hypertermia system for cancer therapy. Among many hyperthermic methods, RF capacitive heating method is discussed because it can heat the deep-seated tumors selectively. The RF power oscillator and its applicators were designed and implemented. And the experiments were performed with agar phantom and dog to prove that the system can heat any depth selectively. And the electrical safety and appropriateness of clinical application was proved through the human living-body test.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.303-308
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• 2022

Simulating the heat transfer in a composite material is an important topic in material science. Difficulties arise from the fact that adjacent materials cannot match perfectly, resulting in discontinuity in the temperature variables. Although there have been several numerical methods for solving the heat-transfer problem in imperfect contact conditions, the methods known so far are complicated to implement, and the computational times are non-negligible. In this study, we developed a ResNet-type deep neural network for simulating a heat transfer model in a composite material. To train the neural network, we generated datasets by numerically solving the heat-transfer equations with Kapitza thermal resistance conditions. Because datasets involve various configurations of composite materials, our neural networks are robust to the shapes of material-material interfaces. Our algorithm can predict the thermal behavior in real time once the networks are trained. The performance of the proposed neural networks is documented, where the root mean square error (RMSE) and mean absolute error (MAE) are below 2.47E-6, and 7.00E-4, respectively.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.19-28
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• 2020

The Urban Heat Island (UHI) Effect has intensified due to urbanization and heat management at the urban level is treated as an important issue. Green space improvement projects and environmental policies are being implemented as a way to alleviate Urban Heat Islands. Several studies have been conducted to analyze the correlation between urban green areas and heat with linear regression models. However, linear regression models have limitations explaining the correlation between heat and the multitude of variables as heat is a result of a combination of non-linear factors. This study evaluated the Heat Island alleviating effects in Seoul during the summer by using a deep neural network model methodology, which has strengths in areas where it is difficult to analyze data with existing statistical analysis methods due to variable factors and a large amount of data. Wide-area data was acquired using Landsat 8. Seoul was divided into a grid (30m × 30m) and the heat island reduction variables were enter in each grid space to create a data structure that is needed for the construction of a deep neural network using ArcGIS 10.7 and Python3.7 with Keras. This deep neural network was used to analyze the correlation between land surface temperature and the variables. We confirmed that the deep neural network model has high explanatory accuracy. It was found that the cooling effect by NDVI was the greatest, and cooling effects due to the park size and green space proximity were also shown. Previous studies showed that the cooling effects related to park size was 2℃-3℃, and the proximity effect was found to lower the temperature 0.3℃-2.3℃. There is a possibility of overestimation of the results of previous studies. The results of this study can provide objective information for the justification and more effective formation of new urban green areas to alleviate the Urban Heat Island phenomenon in the future.

• Journal of Power System Engineering
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• v.20 no.2
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• pp.79-84
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• 2016

The thermodynamic performance of ocean thermal energy conversion with 1 kg/s geothermal water flow rate as a heat source was evaluated to obtain the basic data for the optimal design of cycle with respect to the classification of the working fluid. The basic thermodynamic model for cycle is rankine cycle and the geothermal water and deep seawater were adapted for the heat source of evaporator and condenser, respectively. R245fa, R134a are better to use as a working fluid than others in view of the use of geothermal water. It is important to select the proper working fluid to operate the ocean thermal energy conversion. So, this paper can be used as the basic data for the design of ocean thermal energy conversion with geothermal water and deep seawater.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.05a
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• pp.73-77
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• 2005

In this work, the influences of tool temperature on the formability of AZ31 sheet material in warm deep drawing processes of square cup were investigated. Deep drawing tests under different tool temperatures for magnesium alloy sheet at elevated temperature $250^{\circ}C$, where AZ31 sheet shows a good formability, and FE analyses were carried out. The successfully formed part without any defects was obtained when temperature of tool was over $100^{\circ}C$ while the fracture was occurred at the corner of the square cup below $100^{\circ}C$. It is shown that lower temperature of tool than that of magnesium sheet causes the temperature drop of the material by heat transfer and thus Interrupts the dynamic recrystallization of it. Therefore, in order to obtain successful part of magnesium alloys, it is necessary that the tool temperature is limited to the same or slightly lower temperature than sheet material.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.424-431
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• 2019

The aim of the present study was to develop model for detonation cell sizes prediction based on a deep artificial neural network of hydrogen, methane and propane mixtures with air and oxygen. The discussion about the currently available algorithms compared existing solutions and resulted in a conclusion that there is a need for a new model, free from uncertainty of the effective activation energy and the reaction length definitions. The model offers a better and more feasible alternative to the existing ones. Resulting predictions were validated against experimental data obtained during the investigation of detonation parameters, as well as with data collected from the literature. Additionally, separate models for individual mixtures were created and compared with the main model. The comparison showed no drawbacks caused by fitting one model to many mixtures. Moreover, it was demonstrated that the model may be easily extended by including more independent variables. As an example, dependency on pressure was examined. The preparation of experimental data for deep neural network training was described in detail to allow reproducing the results obtained and extending the model to different mixtures and initial conditions. The source code of ready to use models is also provided.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.434-440
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• 2007

The Effluent ground water overflows in deep and broad ground space building. Temperature of effluent ground water is in 12$\sim$18$^{\circ}C$ annually and the quality of that water is as good as living water. Therefore if the flow rate of effluent ground water is sufficient as source of heat pump, that is good heat source and heat sink of heat pump. Effuent ground water contain the thermal energy of surrounding ground. So this is a new application of ground source heat pump. In this study open type and close type heat pump system using effluent ground water was installed and tested for a church building with large and deep ground space. The effluent flow rate of this building is 800$\sim$1000 ton/day. The heat pump capacity is 5RT each. The heat pump system heating COP was 3.0$\sim$3.3 for the open type and 3.3$\sim$3.8 for the close type system. The heat pump system cooling COP is 3.2$\sim$4.5 for the open type and 3.8$\sim$4.2 for close type system. This performance is up to that of BHE type ground source heat pump.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.1
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• pp.45-49
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• 2013

High frequency system has been used for the purpose of skin care and obesity treatment, by high-frequency energy is applied to the human body generates deep heat. Conventional high frequency system could not selection control by depending on the body load fluctuations. Such as burns and side effects have been reported by system instability and then therapeutic effect is insufficient. During treatment, objective information about the status of the patient was no. Because of treatment methods are subjective, and so tailored treatments were impossible. In this paper, high frequency medical system with sinusoidal frequency characteristics without distortion of the Push pull switching scheme for pain relief therapy was designed. And control circuit that was designed by feedback using the output changes according to the body-load fluctuation. Last, power circuit for efficient control the heat generated from the hardware was proposed.

• Journal of the Korean Society for Precision Engineering
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• v.5 no.4
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• pp.59-65
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• 1988

This paper describes the transmutation layer of CNC Wire electrical discharte machined surface. In order to analayze and invesigate transmutation layer of the carbon tool steel, workpieces was heat-treated by quenching, tempering, normaling. The obtained results are summarized as follows. 1. The result showed that wire electrical discharge machined surface region was transmuted into the recdast layer in the range of about 10${\mu}$m deep by resolidification and next zone was transmuted into the heat affected zone in the range of about 15${\mu}$m deep by high temperature. 2. The hardness of the recast layer and heat affected zone was decreased on its machined surface. 3. The more wire feedrate was increased, the more electrical discharge machine gap was decreased.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.77-83
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• 2020

The heat treatment facility is in a situation where the scope of application of the remote IOT system is expanding due to the harsh environment caused by high heat and long working hours among the root industries. In this heat treatment process environment, the IOT middleware is required to play a pivotal role in interpreting, managing and controlling data information of IoT devices (sensors, etc.). Until now, the system controlled by the heat treatment remotely was operated with the command of the operator's batch system without overall monitoring of the site situation. However, for the safety and precise control of the heat treatment facility, it is necessary to control various sensors and recognize the surrounding work environment. As a solution to this, the heat treatment safety support system presented in this paper proposes a support system that can detect the access of the work manpower to the heat treatment furnace through thermal image detection and operate safely when ordering work from a remote location. In addition, an OPEN CV-based deterioration analysis system using DNN deep learning network was constructed for faster and more accurate recognition than general fixed hot spot monitoring-based thermal image analysis. Through this, we would like to propose a system that can be used universally in the heat treatment environment and support the safety management specialized in the heat treatment industry.