• Journal of Powder Materials
• /
• v.26 no.2
• /
• pp.112-118
• /
• 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.

• Korean Journal of Artificial Intelligence
• /
• v.11 no.3
• /
• pp.17-22
• /
• 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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2010.03b
• /
• pp.42-42
• /
• 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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2010.06a
• /
• pp.278-278
• /
• 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
• /
• v.53 no.6
• /
• pp.2066-2073
• /
• 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
• /
• v.15 no.5
• /
• pp.27-34
• /
• 2002

The purpose of this study is to measure and predict the thermal conductivity of CF3327 plain-weave fabric composite made by Hankuk Fiber, Co. An experiment apparatus based on the comparative method has been made to measure the thermal conductivities of the composite material. Its accuracy was proved by measuring the thermal conductivity of graphite which is well-known. Micro-mechanical approaches are useful to assess the effect of parameters such as fiber and matrix material properties, fiber volume fraction and fabric geometric parameters on the effective material properties of composites. In this study, prediction was based on the concept of three dimensional series-parallel thermal resistance network. Thermal resistance network was applied to unit ceil model that characterized the periodically repeated pattern of a plain weave. The numerical results were compared with experimental one and good agreement was observed. Also, the effects of fiber volume fraction on the thermal conductivity of several composites has been investigated.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.5
• /
• pp.437-443
• /
• 2015

This paper presents face recognition based on the fusion of visible image and thermal infrared (IR) texture estimated from the face image in the visible spectrum. The proposed face recognition scheme uses a multi- layer neural network to estimate thermal texture from visible imagery. In the training process, a set of visible and thermal IR image pairs are used to determine the parameters of the neural network to learn a complex mapping from a visible image to its thermal texture in the low-dimensional feature space. The trained neural network estimates the principal components of the thermal texture corresponding to the input visible image. Extensive experiments on face recognition were performed using two popular face recognition algorithms, Eigenfaces and Fisherfaces for NIST/Equinox database for benchmarking. The fusion of visible image and thermal IR texture demonstrated improved face recognition accuracies over conventional face recognition in terms of receiver operating characteristics (ROC) as well as first matching performances.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2009.10a
• /
• pp.589-590
• /
• 2009

• Journal of Electrical Engineering and Technology
• /
• v.13 no.6
• /
• pp.2329-2334
• /
• 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.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.776-778
• /
• 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.