• Structural Engineering and Mechanics
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• 제81권2호
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• pp.205-217
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• 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.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1310-1319
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• 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.

• 한국빅데이터학회지
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• 제3권2호
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• pp.59-70
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• 2018

특정 지역의 고객을 대상으로 열을 공급하는 지역난방 서비스의 안정적인 운영을 위해서는 단기간의 미래 수요를 보다 정확하게 예측하고, 효율적인 방법으로 생산 및 공급하는 것이 무엇보다 중요하다. 그러나 열 소비에 영향을 미치는 요소가 매우 다양할 뿐만 아니라 개별 소비자 및 지역적 특성에 따라 소비 형태가 달라지기 때문에 일반적인 상황에도 적용될 수 있는 범용적 열 수요 예측 모형을 개발하는 것은 매우 어렵다. 따라서 본 연구에서는 실시간으로 확보할 수 있는 제한적인 정보만을 바탕으로 딥러닝 기법을 활용한 수요예측 모형을 개발하고자 한다. 해당 지역의 외기온도와 날짜로만 구성된 과거 데이터를 입력 변수로 하여 텐서플로의 인공신경망을 학습시키는 방법으로 수요 예측 모형을 개발하였다. 기존의 회귀분석 기법을 통해 예측된 수요의 정확도와의 비교를 통해 제안된 모델의 성능을 평가하였다. 본 연구의 열 수요 예측 모델은 단기적 수요 예측을 위해 실시간으로 확보할 수 있는 제한적인 변수만으로도 수요 예측의 정확도를 높일 수 있음을 보였다. 나아가 개별 지역에서는 지역적 특수성을 추가하여 수요 예측 정확도를 높이는 데 활용할 수 있을 것이다.

• 자원환경지질
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• 제39권4호
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• pp.485-494
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• 2006

1920년대의 온천조사에서부터 현재에 이르기까지 우리나라 지열연구의 역사를 간략히 요약하고, 우리나라의 지열류량 연구 결과 및 추세, 지열의 근원 연구, 그리고 지열에너지 개발 및 활용분야에 대한 연구활동을 정리하였다. 우리나라에서의 지열연구는 1970년대까지 주로 온천조사와 관련되어 있다. 1980년대에 들어서 연구소와 학계에서 온천조사 뿐만 아니라, 지열류량에 대한 연구도 많이 수행하게 되었으며 1996년도에는 우리나라 전국적인 지온경사 분포도와 지열류량 분포도를 발간하게 되었다. 또한 우리나라 온천수에 대한 지화학적 동위원소 분석과 화강암 지대의 열생산율 측정도 1990년대에 주로 이루어졌다. 지열개발과 활용에 대한 시도는 1990년대 초반부터 시도되었으나 실제 개발을 위한 시추로 이어지게 된 것은 2000년대에 들어와서 가능해졌다. 최근의 활발한 심부 지열수 자원 개발이나 천부 지중열을 활용한 냉난방 수요의 증가 등 주변여건이 호전됨에 따라 우리나라 지열연구개발의 전망은 밝다고 판단된다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.957-966
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• 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.

• 열처리공학회지
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• 제35권6호
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• pp.327-336
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• 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
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• 제11권4호
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• pp.493-501
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• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.617-620
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• 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.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2005년도 춘계학술대회 논문집
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• pp.215-218
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• 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.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 하계학술대회
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• pp.717-720
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• 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.