• International Journal of Fluid Machinery and Systems
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• 제10권3호
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• pp.254-263
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• 2017

The cavitation erosion remains an industrial issue for many applications. This paper deals with the cavitation intensity, which can be described as the fluid mechanical loading leading to cavitation damage. The estimation of this quantity is a challenging problem both in terms of modeling the cavitating flow and predicting the erosion due to cavitation. For this purpose, a numerical methodology was proposed to estimate cavitation intensity from 3D unsteady cavitating flow simulations. CFD calculations were carried out using Code_Saturne, which enables U-RANS equations resolution for a homogeneous fluid mixture using the Merkle's model, coupled to a $k-{\varepsilon}$ turbulence model with the Reboud's correction. A post-process cavitation intensity prediction model was developed based on pressure and void fraction derivatives. This model is applied on a flow around a hydrofoil using different physical (inlet velocities) and numerical (meshes and time steps) parameters. The article presents the cavitation intensity model as well as the comparison of this model with experimental results. The numerical predictions of cavitation damage are in good agreement with experimental results obtained by pitting test.

• 벤처창업연구
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• 제2권3호
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• pp.103-122
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• 2007

본 연구는 지역발전을 위한 다양한 이론모형들을 살펴보았고, 경쟁적으로 추진되고 있는 각국이 혁신클러스터 육성정책과 성공요인, 대전지역을 중심으로 혁신클러스터 활성화를 위한 정책과제 등을 살펴보았다. 혁클러스터의 활성화를 통해 지역을 발전시키기 위해서는 산업혁신클러스터가 발전할 수 있는 여건의 조성과 체계적이고 지속적인 정부의 지원정책이 중요하다. 세계적으로 성공적이라고 평가받고 있는 미국의 실리콘밸리, 샌디에고의 UCSD, 중국의 중관촌(中關村), 스웨덴의 시스타(Kista), 핀란드의 울루(Oulu) 등은 연구개발, 생산, 지원시스템 외에 이들 요소들을 유기적으로 연결시켜주는 역동성과 해외 클러스터 및 시장과의 긴밀한 네트워크가 발달되어 있다. 그러나 유리한 여건에도 불구하고 대전지역의 벤처산업은 수도권 등에 비하여 활성화되지 못하고 있다. 산업혁신 클러스터가 성공적으로 작동하고 발전하기 위해서는 혁신클러스터와 산업(생산)클러스터, 지원시스템과 인프라 혹은 선도적 대기업 등이 유기적인 네트워크를 구축하여 역동성을 발휘해야 한다. 구체적인 활성화 전력으로는 (1)기업환경 개선과 연구 성과의 사업화, (2)벤처금융지원 강화, (3)첨단기업의 유치와 네트워크 강화, (4)정부의 체계적인 지원과 혁신클러스터 구축 등이 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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• pp.2862-2878
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• 2020

Data Security is the most challenging area in Internet communication, where most of the secret sharing schemes are proposed for binary images. But still it lacks in providing security for data communication, especially in image transmission. Traditional visual cryptography scheme generate meaningless diwies and the reconstruction phase leads to quality degradation over the secret image. In this work, an intelligent two dimensional secret share construction scheme is proposed. A secret image is expanded into n diwies with the choice of scheme selection. By Stacking all the qualified diwies to revert the secret image without content loss and less than s^* - 1 shares could not reveal any information about the secret image. The experimental results emphasize that the proposed secret share scheme is highly secured for image transmission.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.855-870
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• 2020

Windows are key components of building facades. Detecting windows, crucial to 3D semantic reconstruction and scene parsing, is a challenging task in computer vision. Early methods try to solve window detection by using hand-crafted features and traditional classifiers. However, these methods are unable to handle the diversity of window instances in real scenes and suffer from heavy computational costs. Recently, convolutional neural networks based object detection algorithms attract much attention due to their good performances. Unfortunately, directly training them for challenging window detection cannot achieve satisfying results. In this paper, we propose an approach for window detection. It involves an improved Faster R-CNN architecture for window detection, featuring in a window region proposal network, an RoI feature fusion and a context enhancement module. Besides, a post optimization process is designed by the regular distribution of windows to refine detection results obtained by the improved deep architecture. Furthermore, we present a newly collected dataset which is the largest one for window detection in real street scenes to date. Experimental results on both existing datasets and the new dataset show that the proposed method has outstanding performance.

• 한국연소학회지
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• 제20권2호
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• pp.40-45
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• 2015

This study is aimed to study combustion characteristics of low $NO_X$ burner using petroleum cokes as fuel. The petroleum coke, which is produced through the oil refining process, is an attractive fuel in terms of its high heating value and relatively low price. But petroleum coke is a challenging fuel because of its low volatile content, high sulfur and nitrogen content, which give rise to undesirable emission characteristics and low ignitability. The petroleum cokes burner is operated at fuel rich condition, and overfire air are supplied to achieve fuel lean condition. The low $NO_X$ burner is designed to control fuel and air mixing to achieve air staged combustion, in addition secondary and tertiary air are supplied through swirler. Air distribution ratio of triple staged air are optimized experimentally. The result showed that $NO_X$ concentration is lowest when overfire air is used, and the burner function at a fuel rich condition.

• 제어로봇시스템학회논문지
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• 제22권7호
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• pp.536-542
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• 2016

To enhance tracking efficiency, a target-tracking filter with a resource management algorithm is required. One of the resource management algorithms chooses or evaluates the proper sampling time using cost functions which are related to the target tracking filter. We propose a resource management algorithm for bearing only tracking environments. Since the tracking performance depends on the system observability, the bearing-only tracking is one of challenging target-tracking fields. The proposed algorithm provides the adaptive sampling time using the variation rate of the error covariance matrix from the target-tracking filter. The simulation verifies the efficiency performance of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• 제9권3호
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• pp.893-898
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• 2014

Recently, the interior permanent synchronous motor (IPMSM) has been applied to an integrated starter and generator (ISG) for hybrid electric vehicles. In the design of such a motor, thermal analysis is necessary to maximize the power density because the loss is proportional to the power of a motor. Therefore, a cooling device as a heat sink is required internally. Generally, a cooling system designed with a water jacket structure is widely used for electric motors because it has advantages of simple structure and cooling effectiveness. An effective approach to analyze an electric machine with a water jacket is a thermal equivalent network. This network is composed of thermal resistance, a heat source, and thermal capacitance that consider the conduction, convection, and radiation. In particular, modeling of the cooling channel in a network is challenging owing to the flow of the coolant. In this paper, temperature prediction using a thermal equivalent network is performed in an ISG that has a water cooled system. Then, an experiment is conducted to verify the thermal equivalent network.

• 전자통신동향분석
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• 제36권3호
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• pp.133-144
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• 2021

High-performance computing (HPC) is the underpinning for many of today's most exciting new research areas, to name a few, from big science to new ways of fighting the disease, to artificial intelligence (AI), to big data analytics, to quantum computing. This report captures the summary of a 9-day program of presentations, keynotes, and workshops at the SC20 conference, one of the most prominent events on sharing ideas and results in HPC technology R&D. Because of the exceptional situation caused by COVID-19, the conference was held entirely online from 11/9 to 11/19 2020, and interestingly caught more attention on using HPC to make a breakthrough in the area of vaccine and cure for COVID-19. The program brought together 103 papers from 21 countries, along with 163 presentations in 24 workshop sessions. The event has covered several key areas in HPC technology, including new memory hierarchy and interconnects for different accelerators, evaluation of parallel programming models, as well as simulation and modeling in traditional science applications. Notably, there was increasing interest in AI and Big Data analytics as well. With this summary of the recent HPC trend readers may find useful information to guide the R&D directions for challenging new technologies and applications in the area of HPC.

• 센서학회지
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• 제30권6호
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• pp.376-380
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• 2021

The accurate detection of hydrogen gas molecules is considered to be important for industrial safety. However, the selective detection of the gas using semiconductive metal oxides (SMOs)-based sensors is challenging. Here, we describe the fabrication of H₂ sensors in which a nanocellulose/graphene oxide (GO) hybrid membrane is attached to SnO₂ nanosheets (NSs). One-dimensional (1D) nanocellulose fibrils are attached to the surface of GO NSs (GONC membrane) by mixing GO and nanocellulose in a solution. The as-prepared GONC membrane is employed as a sacrificial template for SnO₂ NSs as well as a molecular sieving membrane for selective H₂ filtration. The combination of GONC membrane and SnO₂ NSs showed substantial selectivity to hydrogen gas (R_air / R_gas > 10 @ 0.8 % H₂, 100 ℃) with noise level responses to interfering gases (H₂S, CO, CH₃COCH₃, C₂H₅OH, and NO₂). These remarkable sensing results are attributed mainly to the molecular sieving effect of the GONC membrane. These results can facilitate the development of a highly selective H₂ detector using SMO sensors.