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RISS 인기검색어
- 검색키워드
- 저자 : Youngjung Kim (검색결과 57 건)
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Kim, Youngjung,Park, Seokho,Kim, Youngjin,Kim, Chungkil Korean Society for Agricultural Machinery 2012 바이오시스템공학 Vol.37 No.5
Purpose: Combustion and fuel qualities of the animal-fats biodiesel as a heating fuel for agricultural hot air heater were studied. Methods: Biodiesel (BD) was made from animal-fats by reacting with methanol and potassium hydroxide in the laboratory. The biodiesel made in the laboratory was tested for fuel and combustion qualities. Results: The kinematic viscosity and the calorific values of the biodiesels were measured. Kerosene based biodiesel, BD20 (K) showed 18 cSt at $-20^{\circ}C$. It seemed that BD100 was not suitable for a heating fuel under some temperature. As BD content increased, the calorific value decreased up to 40,000 J/g for BD100, while the calorific value of light oil was 45,567 J/g showing difference of 5,567 J/g, about 12% difference. Several different fuels including BD20 (biodiesel 20% + light oil 80%), BD50 (biodiesel 50% + light oil 50%), BD100 (biodiesel 100%), and light oil were tested for fuel combustion qualities for agricultural hot air heater, and their combustion performances were compared and analyzed. Flame dimensions of biodiesels and light oils were almost the same shape at the same combustion condition. Generally, the $CO_2$ amounts of BDs were greater than light oil. However, in this study the differences were minor, so there was no significant difference existed between the BDs combustion and light oil. Conclusions: It seemed that quality was good for heating oil for agricultural hot air heater because of showing no barriers for continuous combustion and proper exhaust gas temperature and $CO_2$ amount discharged. But, for fuel fluidity for higher BD content fuel could be a detrimental problem in situations where the outdoor temperature is lowered. As BD content increased, calorific value decreased up to 40,000 J/g for BD100. Calorific value difference between BD20 and light oil was about 1,360 J/g.
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Characterizing Animal-fats Biodiesel as Heating Fuel for Agricultural Hot Air Heater
김영중(Kim, Youngjung),박석호(Park, Seokho),김충길(Kim, Chungkil),김영진(Kim, Yeoungjin) 한국신재생에너지학회 2011 한국신재생에너지학회 학술대회논문집 Vol.2011 No.11
Biodiesel (BD) was made from animal-fats reacting with methanol and potassium hydroxide in the laboratory. The biodiesel made in the laboratory was sent to K-petro, the government agency, to inspect the quality of animal-fats biodiesel, of which generally the quality was acceptable for heating oil for agricultural hot air heater. Kinematic viscosity and calorific values of the biodiesels were measured. BD20(K), kerosene based biodiesel, showed 18cSt at -20?C. It seems that BD100 can not be suitable for heating fuel under some temperature. As BD content increased calorific value decreased, up to 40,000J/g for 100% BD, while light oil calorific value was 45,567J/g, showing difference of 5,567J/g, about 12% difference. Several different fuels, BD20, BD50, BD100 and light oil, were prepared and tested for fuel combustion qualities for agricultural hot air heater and their combustion performances were compared and analyzed. Flame dimensions of biodiesels and light oil were almost same shape at the same combustion condition in the burner of the hot air heater. Generally CO₂ amounts of BDs are greater than light oil. But,the differences are so small that it is hard to tell there was significant difference existed between the BDs combustion and light oil.
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Fast Domain Decomposition for Global Image Smoothing
Youngjung Kim,Dongbo Min,Bumsub Ham,Kwanghoon Sohn IEEE 2017 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.26 No.8
<P>Edge-preserving smoothing (EPS) can be formulated as minimizing an objective function that consists of data and regularization terms. At the price of high-computational cost, this global EPS approach is more robust and versatile than a local one that typically has a form of weighted averaging. In this paper, we introduce an efficient decomposition-based method for global EPS that minimizes the objective function of L-2 data and (possibly non-smooth and non-convex) regularization terms in linear time. Different from previous decomposition-based methods, which require solving a large linear system, our approach solves an equivalent constrained optimization problem, resulting in a sequence of 1-D sub-problems. This enables applying fast linear time solver for weighted-least squares and -L-1 smoothing problems. An alternating direction method of multipliers algorithm is adopted to guarantee fast convergence. Our method is fully parallelizable, and its runtime is even comparable to the state-of-the-art local EPS approaches. We also propose a family of fast majorization-minimization algorithms that minimize an objective with non-convex regularization terms. Experimental results demonstrate the effectiveness and flexibility of our approach in a range of image processing and computational photography applications.</P>
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Structure Selective Depth Superresolution for RGB-D Cameras
Kim, Youngjung,Ham, Bumsub,Oh, Changjae,Sohn, Kwanghoon IEEE 2016 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.25 No.11
<P>This paper describes a method for high-quality depth superresolution. The standard formulations of image-guided depth upsampling, using simple joint filtering or quadratic optimization, lead to texture copying and depth bleeding artifacts. These artifacts are caused by inherent discrepancy of structures in data from different sensors. Although there exists some correlation between depth and intensity discontinuities, they are different in distribution and formation. To tackle this problem, we formulate an optimization model using a nonconvex regularizer. A nonlocal affinity established in a high-dimensional feature space is used to offer precisely localized depth boundaries. We show that the proposed method iteratively handles differences in structure between depth and intensity images. This property enables reducing texture copying and depth bleeding artifacts significantly on a variety of range data sets. We also propose a fast alternating direction method of multipliers algorithm to solve our optimization problem. Our solver shows a noticeable speed up compared with the conventional majorize-minimize algorithm. Extensive experiments with synthetic and real-world data sets demonstrate that the proposed method is superior to the existing methods.</P>
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Testing of Agricultural Tractor Engine using Animal-fats Biodiesel as Fuel
Kim, Youngjung,Lee, Siyoung,Kim, Jonggoo,Kang, Donghyeon,Choi, Honggi Korean Society for Agricultural Machinery 2013 바이오시스템공학 Vol.38 No.3
Purpose: Performances of a tractor diesel engine fueled by three different animal fats biodiesels were evaluated comparing with light oil tractor in terms of power, fuel consumption rate, exhaust gases, particulate matter amount and field work capacity. Methods: Animal fats based on pig biodiesel were manufactured manually and tested for its engine performance in the tractor diesel engine and fuel adoptability in the field works. Four different fuels, three different content of biodiesel (BD20, BD50, BD100) and light oil, were prepared and tested in the four strokes diesel engine. Power output, fuel consumption rate and exhaust gases of the four fuels in the diesel engine were compared and discussed. Results: Power output of light oil engine was the greatest showing 5.3% difference between light oil and BD100, but 0.37% better power than BD20 engine power. Less exhaust gases of $CO_2$, CO, $NO_X$ and THC were produced from animal fats biodiesel than light oil, which confirmed that biodiesel is environmental friendly fuel. For fuel adoptability in the tractor, biodiesel engine tractor showed its fuel competitiveness comparing with light oil for tractor works in the faddy field. Conclusions: With four different fuel types of animal-fats biodiesel, performances of a four cylinder diesel engine for tractor were evaluated in terms of power, exhaust gases, particulate matters (PM) and field work capacity. No significant differences observed in the engine performances including power output and exhaust gases emission rate. No significant power difference observed between the various fuels including light oil on the engine running, however, amounts of noxious exhaust gases including $CO_2$ and $NO_X$ decreased as biodiesel content increased in the fuels. Field performances of animal-fats biodiesel tractor were investigated by conducting plowing and rotary operation in the field. Tilling and rotary performance of light oil tractor and BD20 tractor in the field were compared, in which about 10% travelling speed difference on both operations were monitored that showed light oil tractor was superior to BD20 tractor by 10%. Animal-fats can be an alternative fuel source replacing light oil for agricultural machinery and an environmental friendly fuel to nature.
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Deep Monocular Depth Estimation via Integration of Global and Local Predictions
Kim, Youngjung,Jung, Hyungjoo,Min, Dongbo,Sohn, Kwanghoon IEEE 2018 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.27 No.8
<P>Recent works on machine learning have greatly advanced the accuracy of single image depth estimation. However, the resulting depth images are still over-smoothed and perceptually unsatisfying. This paper casts depth prediction from single image as a parametric learning problem. Specifically, we propose a deep variational model that effectively integrates heterogeneous predictions from two convolutional neural networks (CNNs), named global and local networks. They have contrasting network architecture and are designed to capture the depth information with complementary attributes. These intermediate outputs are then combined in the integration network based on the variational framework. By unrolling the optimization steps of Split Bregman iterations in the integration network, our model can be trained in an end-to-end manner. This enables one to simultaneously learn an efficient parameterization of the CNNs and hyper-parameter in the variational method. Finally, we offer a new data set of 0.22 million RGB-D images captured by Microsoft Kinect v2. Our model generates realistic and discontinuity-preserving depth prediction without involving any low-level segmentation or superpixels. Intensive experiments demonstrate the superiority of the proposed method in a range of RGB-D benchmarks, including both indoor and outdoor scenarios.</P>
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