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RISS 인기검색어
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- 저자 : ( Jiangping He ) (검색결과 2 건)
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PERFORMANCE OF R-1234YF IN MOBILE AIR CONDITIONING SYSTEM UNDER DIFFERENT HEAT LOAD CONDITIONS
YU ZHAO,JIANGPING CHEN,BAIXING XU,BIN HE 대한설비공학회 2012 International Journal Of Air-Conditioning and Refr Vol.20 No.3
"Drop-in" tests of R-1234yf in a mobile air conditioning (MAC) system were conducted, and the system performance results were compared with those of the same system using R-134a. The performance tests were performed in a psychrometric calorimeter test facility based on enthalpy-difference method. The system performance was investigated under low-, middle- and high-load conditions. The results showed that the optimum refrigerant charge of R-1234yf was approximately 90% compared with that of R-134a under the same MAC system. When operated under optimum refrigerant charge, the system cooling capacity and COP of R-1234yf was 11% and 8.3% lower than that of the R-134a system, respectively. When wind tunnel test was conducted on a practical vehicle, the result showed that the carriage temperature in the vehicle with the R-1234yf system dropped slower. The average carriage temperature and blower outlet temperature were $2^{\circ}C$ higher than those of the R-134a system during the test.
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Scale Invariant Auto-context for Object Segmentation and Labeling
( Hongwei Ji ),( Jiangping He ),( Xin Yang ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.8
In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.
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