• 한국분말재료학회지
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• 제26권2호
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• pp.112-118
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• 2019

Infiltration is a popular technique used to produce valve seat rings and guides to create dense parts. In order to develop valve seat material with a good thermal conductivity and thermal expansion coefficient, Cu-infiltrated properties of sintered Fe-Co-M(M=Mo,Cr) alloy systems are studied. It is shown that the copper network that forms inside the steel alloy skeleton during infiltration enhances the thermal conductivity and thermal expansion coefficient of the steel alloy composite. The hard phase of the CoMoCr and the network precipitated FeCrC phase are distributed homogeneously as the infiltrated Cu phase increases. The increase in hardness of the alloy composite due to the increase of the Co, Ni, Cr, and Cu contents in Fe matrix by the infiltrated Cu amount increases. Using infiltration, the thermal conductivity and thermal expansion coefficient were increased to 29.5 W/mK and $15.9um/m^{\circ}C$, respectively, for tempered alloy composite.

• 한국인공지능학회지
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• 제11권3호
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• pp.17-22
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• 2023

In this paper, we propose a black ice detection system using Convolutional Neural Networks (CNNs). Black ice poses a serious threat to road safety, particularly during winter conditions. To overcome this problem, we introduce a CNN-based architecture for real-time black ice detection with an encoder-decoder network, specifically designed for real-time black ice detection using thermal images. To train the network, we establish a specialized experimental platform to capture thermal images of various black ice formations on diverse road surfaces, including cement and asphalt. This enables us to curate a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Additionally, in order to enhance the accuracy of black ice detection, we propose a multi-scale dilation convolution feature fusion (MsDC-FF) technique. This proposed technique dynamically adjusts the dilation ratios based on the input image's resolution, improving the network's ability to capture fine-grained details. Experimental results demonstrate the superior performance of our proposed network model compared to conventional image segmentation models. Our model achieved an mIoU of 95.93%, while LinkNet achieved an mIoU of 95.39%. Therefore, it is concluded that the proposed model in this paper could offer a promising solution for real-time black ice detection, thereby enhancing road safety during winter conditions.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2010년도 춘계학술대회 논문집
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• pp.42-42
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• 2010

We have characterized the previously undescribed parameters for engineering the electrical properties of single-walled carbon nanotube (SWCNT) films for technological applications. The surface tension of the top coating passivation material and matching coefficients of thermal expansion for the substrate and carbon nanotube network are two crucial parameters for the fabrication of reliable and highly conductive single-walled carbon nanotube network thin films.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2010년도 하계학술대회 논문집
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• pp.278-278
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• 2010

We have characterized the previously undescribed parameters for engineering the electrical properties of single-walled carbon nanotube (SWCNT) films for technological applications. The surface tension of the top coating passivation material and matching coefficients of thermal expansion for the substrate and carbon nanotube network are two crucial parameters for the fabrication of reliable and highly conductive single-walled carbon nanotube network thin films.

• Nuclear Engineering and Technology
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• 제53권6호
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• pp.2066-2073
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• 2021

The use of nuclear reactors is a large studied possible solution for thermochemical water splitting cycles. Nevertheless, there are several problems that have to be solved. One of them is to increase the efficiency of the cycles. Hence, in this paper, a thermal energy optimization of a Sulfur-Iodine nuclear hydrogen production cycle was performed by means a heuristic method with the aim of minimizing the energy targets of the heat exchanger network at different minimum temperature differences. With this method, four different heat exchanger networks are proposed. A reduction of the energy requirements for cooling ranges between 58.9-59.8% and 52.6-53.3% heating, compared to the reference design with no heat exchanger network. With this reduction, the thermal efficiency of the cycle increased in about 10% in average compared to the reference efficiency. This improves the use of thermal energy of the cycle.

• Composites Research
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• 제15권5호
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• pp.27-34
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• 2002

본 연구의 목적은 (주)한국와이바의 CF3327 평직 복합재료의 열전도도를 실험적으로 계측하고 이를 이론적인 예측과 비교하는데 있다. 열전도도 계측을 위하여 비교계측법의 원리를 이용한 실험 장치를 제작하였으며 열전도도가 잘 알려진 그라파이트를 실험함으로써 장비의 정확성을 확인하였다. 미시역학적인 방법은 섬유 및 기지의 물성, 섬유체적비, 직조 형태 등의 변수들이 복합재료의 유효물성치에 미치는 영향을 평가하는데 유용하다. 본 연구에서는 3차원 직-병렬 열저항 개념을 주기적으로 반복되는 평직의 단위구조에 적용하여 열전도도를 예측하였다. 해석 결과를 실험 결과와 비교한 결과 잘 일치함을 확인하였고 섬유체적비가 에폭시 수지 복합재료의 열전도도에 미치는 영향을 고찰하였다.

• 한국지능시스템학회논문지
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• 제25권5호
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• pp.437-443
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• 2015

이 논문에서는 가시광선 얼굴영상과 그로부터 예측한 열 적외선 텍스처의 데이터 융합에 의한 얼굴인식 방법에 관하여 연구하였다. 제안하는 얼굴인식 기법은 가시광선 얼굴영상과 열 적외선 텍스처를 PCA에 의하여 낮은 차원의 특징공간에서 특징벡터로 변환한 다음, 다층 신경회로망을 사용하여 가시광선 영상 특징으로부터 얼굴의 열적외선 특징을 예측하여 열 적외선 텍스처를 생성하였다. 학습과정에서는 주어진 개체로부터 획득한 한 쌍의 가시광선 및 열 적외선 영상에 대해서 PCA를 이용하여 낮은 차원의 특징공간으로 변환한 다음, 가시광선 영상특징으로부터 열 분포 특징으로 매핑시키는 비선형 함수에 해당하는 신경회로망의 내부 파라미터를 결정한다. 학습된 신경회로망은 입력 가시광선 얼굴 특징으로부터 열 에너지 분포 특성의 PCA계수를 예측하고, 이로부터 열 적외선 텍스처를 생성한다. 대표적인 두 가지 얼굴인식 알고리즘 Eigenfaces와 Fisherfaces을 사용하여 NIST/Equinox 데이터베이스에 대하여 얼굴인식에 관한 실험을 수행하였다. 예측한 열 적외선 텍스처와 가시광선 얼굴영상의 데이터 융합결과는 가시광선 얼굴영상만을 사용한 경우에 비해서 얼굴인식의 성능이 개선되었음을 수신자 조작특성 (ROC) 및 첫 번째 매칭성능에 의하여 검증하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 추계학술대회 논문집
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• pp.589-590
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• 2009

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2329-2334
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• 2018

This paper discusses the thermal effect of the number of permanent-magnet (PM) layers in an outer rotor machine. Depending on the number of axial-layer of PM, the operating temperature is compared analytically and experimentally. The electromagnetic analysis is performed using 3-dimensional time varying finite element method to get the heat sources depending on axial-layered PM models. Then thermal analysis is conducted using the lumped-parameter-thermal-network method for each case. Two outer rotor machines, which have the different number of axial-layer of PM, are manufactured and tested to validate the analysis results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.776-778
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• 1999

In this paper, identifier for thermal storage system using multi-layer feedforward neural network (MFNN) is designed. It is very difficult to control thermal storage system, since thermal storage system is nonlinear and its time constant is very large. Thus, in the MFNN, delta-bar-delta algorithm for high running speed and 2-bit status input are used. Also hardware using microprocessor for identifier is developed. The experimental results indicate that the proposed method can predict temperature more accurately.