• Structural Engineering and Mechanics
• /
• v.81 no.2
• /
• pp.205-217
• /
• 2022

The thermodynamic properties of shaft lining concrete (SLC) are important evidence for the design and construction, and the spatial variability of concrete materials can directly affect the stochastic thermal analysis of the concrete structures. In this work, an array of field experiments of the concrete materials are carried out, and the statistical characteristics of thermophysical parameters of SLC are obtained. The coefficient of variation (COV) and scale of fluctuation (SOF) of uncertain thermophysical parameters are estimated. A three-dimensional (3-D) stochastic thermal model of concrete materials with heat conduction and hydration heat is proposed, and the uncertain thermodynamic properties of SLC are computed by the self-compiled program. Model validation with the experimental and numerical temperatures is also presented. According to the relationship between autocorrelation functions distance (ACD) and SOF for the five theoretical autocorrelation functions (ACFs), the effects of the ACF, COV and ACD of concrete materials on the uncertain thermodynamic properties of SLC are analyzed. The results show that the spatial variability of concrete materials is subsistent. The average temperatures and standard deviation (SD) of inner SLC are the lowest while the outer SLC is the highest. The effects of five 3-D ACFs of concrete materials on uncertain thermodynamic properties of SLC are insignificant. The larger the COV of concrete materials is, the larger the SD of SLC will be. On the contrary, the longer the ACD of concrete materials is, the smaller the SD of SLC will be. The SD of temperature of SLC increases first and then decreases. This study can provide a reliable reference for the thermodynamic properties of SLC considering spatial variability of concrete materials.

• Journal of Korea Multimedia Society
• /
• v.19 no.8
• /
• pp.1310-1319
• /
• 2016

Face detection is the first step in a wide range of face applications. However, detecting faces in the wild is still a challenging task due to the wide range of variations in pose, scale, and occlusions. Recently, many deep learning methods have been proposed for face detection. However, further improvements are required in the wild. Another important issue to be considered in the face detection is the computational complexity. Current state-of-the-art deep learning methods require a large number of patches to deal with varying scales and the arbitrary image sizes, which result in an increased computational complexity. To reduce the complexity while achieving better detection accuracy, we propose a fully convolutional network-based face detection that can take arbitrarily-sized input and produce feature maps (heat maps) corresponding to the input image size. To deal with the various face scales, a multi-scale network architecture that utilizes the facial components when learning the feature maps is proposed. On top of it, we design multi-task learning technique to improve detection performance. Extensive experiments have been conducted on the FDDB dataset. The experimental results show that the proposed method outperforms state-of-the-art methods with the accuracy of 82.33% at 517 false alarms, while improving computational efficiency significantly.

• The Journal of Bigdata
• /
• v.3 no.2
• /
• pp.59-70
• /
• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• Economic and Environmental Geology
• /
• v.39 no.4 s.179
• /
• pp.485-494
• /
• 2006

This paper summarizes the history of geothermal research in Korea since 1920s and also describes the present status of research on heat flow, origin of thermal waters and geothermal exploitation and utilization. Geothermal research in Korea has been mainly related with hot spring investigation until 1970s. 1t was not until 1980s before heat flow study became continuous by research institute and academia and first nation-scale geothermal gradient map and heat flow map were published in 1996. Also in 1990s, geochemical isotope analysis of Korean hot spring waters and measurements of heat production rate of some granite bodies were made. Attempts to develop and utilize the deep geothermal water has been tried from early 1990s but field scale exploitations for geothermal water was activated in 2000s. Considering recent increase of demands on both deep and shallow geothermal energy utilization, outlook on future goethermal research and development is encouraging.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 1996.06c
• /
• pp.957-966
• /
• 1996

An attempt was made to measure the availability of waste heat, released from the cooling system of a small engine, which can be utilized for grain drying. An engine powered flat-bed rough rice dryer was constructed and the performance of the dryer with available engine-waste heat was analyzed for 10 , 20, 30 and 40 cm rough rice bulk depths with a constant dryer base area of 0.81$m^2$/min. The waste heat was sufficient to increase the drying air temperature 7 to 12$^{\circ}C$ at an air flow rate of 8.8 to 5.7㎥/min, while the average ambient temperature and relative humidity were 24$^{\circ}C$ and 70%. The minimum energy requirement was 3.26 MJ/kg of water removed in drying a 40 cm deep grain bed in 14h. A forty to fifty centimeter deep grained seems to be optimum in order to avoid over-drying in the top layers. On the basis of minimum energy requirement (3.26 MJ/kg ) , an estimation was made that the waste heat harvest from an engine of a power range of 1 to 10.5PS can dry about 0.1 to 1 metric on of rough rice from 23% to 15% m.c. (w.b) in 12 h at an average ambient temperature and relative humidity of $25^{\circ}C$ and 80%, respectively. The engine-waste heated grain dryer can be used in the rural areas of non industrialized countries where electricity is not available.

• Journal of the Korean Society for Heat Treatment
• /
• v.35 no.6
• /
• pp.327-336
• /
• 2022

In this study, a model for automatically recognizing several steel parts through a camera before charging materials was developed under the assumption that the temperature distribution in the pre-air atmosphere was known. For model development, datasets were collected in random environments and factories. In this study, the YOLO-v5 model, which is a YOLO model with strengths in real-time detection in the field of object detection, was used, and the disadvantages of taking a lot of time to collect images and learning models was solved through the transfer learning methods. The performance evaluation results of the derived model showed excellent performance of 0.927 based on mAP 0.5. The derived model will be applied to the model development study, which uses the model to accurately recognize the material and then match it with the temperature distribution in the atmosphere to determine whether the material layout is suitable before charging materials.

• Physical Therapy Rehabilitation Science
• /
• v.11 no.4
• /
• pp.493-501
• /
• 2022

Objective: The purpose of this study is to present basic data for appropriate therapeutic intervention by confirming changes in the autonomic nervous system and pain by applying high-frequency deep diathermy to the lower abdomen in patients with primary dysmenorrhea. Design: A randomized controlled clinical trial. Methods: Thirty-eight women aged 18-50 years who complained of regular menstrual cycles (24-32 days) and primary dysmenorrhea symptoms were randomly assigned to a high-frequency therapy group (5, 7, or 9 mins) and a superficial heat therapy group (20 min). High frequency treatment group: The subject was in a supine position, and radio frequency was applied to the lower abdomen below the umbilicus. The radio frequency therapy device used in this study uses a 300 kHz capacitive electrode and a 500 kHz resistive electric transfer to deliver deep heat. Superficial heat treatment Group: Subjects applied a hot pack to the lower abdomen for 20 minutes while lying on their back. Evaluations were made of Heart rate variability and Visual Analogue Scale. Results: In subjects with menstrual pain, there was a significant difference in pain between the high-frequency therapy group and the superficial heat therapy group (p=0.026). However, there was no significant difference between the autonomic nervous system and the stress resistance (p>0.05). Conclusions: As a result of this study, high-frequencytreatment using radiofrequency was effective in relieving pain because it can penetrate deeper tissues than conventional hot packs using superficial heat. In particular, it was found that the optimum effect was obtained when high frequency was applied forfive-seven minutes.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.10a
• /
• pp.617-620
• /
• 1997

In this paper, the thermal stress analysis of spent nuclear fuel disposal canister in a deep repository at 500m underground is done for the underground pressure variation. Since the nuclear fuel disposal usually emits much heat and radiation, its careful treatment is required. And so a long term safe repository at a deep bedrock is used. Under this situation, the canister experiences some mechanical external loads such as hydrostatic pressure of underground water, swelling pressure of bentonite buffer, and the thermal load due to the heat generation of spent nuclear fuel in the basket etc.. Hence, the canister should be designed to designed to withstand these loads. In this paper, the thermal stress analysis is done using the finite element analysis code, NISA.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2005.05a
• /
• pp.215-218
• /
• 2005

For the production of cold forged parts with near-net-shape attributes, the quality of the tool system is responsible for an essential portion of costs fer the finished components. Therefore, a tool lift is one of the important issues on cold forging industry. There are many complicated variables related with tool life, such as material, heat-treatment, coating, lubricant, process design. In this study, heat-treatment of tool material and lubricant are investigated to improve the tool life. Deep cryogenic treatment of tool steel is very efficient to improve the wear resistance due to the fine carbide. And, friction factor of lubricants for cold forging are measured by the ring compression test. Zinc-Phosphate and $MoS_2$ lubricant is effective to sustain the friction factor under 0.1.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2020.07a
• /
• pp.717-720
• /
• 2020

An indoor localization system that uses Wi-Fi RSSI signals for localization gives accurate user position results. The conventional Wi-Fi RSSI signal based localization system uses raw RSSI signals from access points (APs) to estimate the user position. However, the RSSI values of a particular location are usually not stable due to the signal propagation in the indoor environments. To reduce the RSSI signal fluctuations, shadow fading, multipath effects and the blockage of Wi-Fi RSSI signals, we propose a Wi-Fi localization system that utilizes the advantages of Wi-Fi RSSI heat maps. The proposed localization system uses a regression model with deep convolutional neural networks (DCNNs) and gives accurate user position results for indoor localization. The experiment results demonstrate the superior performance of the proposed localization system for indoor localization.