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RISS 인기검색어
- 검색키워드
- 저자 : Xiaoqi Hu (검색결과 4 건)
- 무료
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- 유료
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Analysis and study of compact inductive power transfer systems for EV charging
Ai, Yongle,Hu, Xiaoqi,Li, Xing,Zhang, Xin The Korean Institute of Power Electronics 2021 JOURNAL OF POWER ELECTRONICS Vol.21 No.5
The double-sided LCC topology provides an efficient compensation method for electric vehicle (EV) wireless charging systems. However, the existence of two compensation coils results in an electric vehicle wireless charging device with a large volume, high power consumption, and low efficiency. To solve these problems, this paper proposes a wireless charging structure in which the compensation coils are separately integrated into the transmitting and receiving coils. First, the number of turns of the transmitting coil is optimized to maximize the coupling coefficient of the transmitting coil. Secondly, to minimize the redundant coupling effect, the relative placement of the compensation coils is studied. Based on the proposed coil integration method, it is possible to ignore the redundant coupling between the compensation coils and the transmitting and receiving coils. Then, the Ansys Maxwell and Ansys Twin Builder are used to build a joint simulation circuit to construct the proposed wireless charging system. Simulation and experimental results show that the system output power is 3.09 kW with a gap of 150 mm, and that the transmission efficiency is 95.49%. In addition, the integrated solution has a high transmission efficiency in the presence of front-to-back misalignment and vertical misalignment of electric vehicles.
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Accelerating Distance Transform Image based Hand Detection using CPU-GPU Heterogeneous Computing
Zhaohua Yi,Xiaoqi Hu,Eung Kyeu Kim,Kyung Ki Kim,Byunghyun Jang 대한전자공학회 2016 Journal of semiconductor technology and science Vol.16 No.5
Most of the existing hand detection methods rely on the contour shape of hand after skin color segmentation. Such contour shape based computations, however, are not only susceptible to noise and other skin color segments but also inherently sequential and difficult to efficiently parallelize. In this paper, we implement and accelerate our in-house distance image based approach using CPU-GPU heterogeneous computing. Using emerging CPU-GPU heterogeneous computing technology, we achieved 5.0 times speed-up for 320x240 images, and 17.5 times for 640x480 images and our experiment demonstrates that our proposed distance image based hand detection is robust and fast, reaching up to 97.32% palm detection rate, 80.4% of which have more than 3 fingers detected on commodity processors.
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Accelerating Distance Transform Image based Hand Detection using CPU-GPU Heterogeneous Computing
Yi, Zhaohua,Hu, Xiaoqi,Kim, Eung Kyeu,Kim, Kyung Ki,Jang, Byunghyun The Institute of Electronics and Information Engin 2016 Journal of semiconductor technology and science Vol.16 No.5
Most of the existing hand detection methods rely on the contour shape of hand after skin color segmentation. Such contour shape based computations, however, are not only susceptible to noise and other skin color segments but also inherently sequential and difficult to efficiently parallelize. In this paper, we implement and accelerate our in-house distance image based approach using CPU-GPU heterogeneous computing. Using emerging CPU-GPU heterogeneous computing technology, we achieved 5.0 times speed-up for $320{\times}240$ images, and 17.5 times for $640{\times}480$ images and our experiment demonstrates that our proposed distance image based hand detection is robust and fast, reaching up to 97.32% palm detection rate, 80.4% of which have more than 3 fingers detected on commodity processors.
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The Relative Influence of Diet and Physical Activity on Obesity in China
Cui Zhao-Hui,Li Yan-Ping,Di Yu-Feng,Ba Lei,Hu Xiaoqi,Ma Guan-Sheng The Korean Society of Community Nutrition 2004 Journal of community nutrition Vol.6 No.3
The purpose of this study is to investigate the relative influence of diet and physical activity on obesity. The subjects were 155 adults aged 35-52 years from 24 neighborhood committees in 4 urban districts of Beijing (male : 78, female : 77). They were divided into normal weight, overweight and obese groups according to their BMI. The general information of the subjects was collected by interview-administered questionnaire. Dietary intake was obtained by three-day(two weekdays and one weekend day) food weighted method, physical activity was assessed by a validated combination of data obtained from activity monitors, bicycling information and activity records. There were no significant differences of age, gender, height, educational, family economic level, smoking and drinking between different groups. The proportion of flour intake was higher in obese group compared to normal weight and overweight groups, and that of vegetables is lower in obese group. The physical activity (PAL) was not significantly different between two groups of the normal, overweight and obese groups. After the adjustment for confounding factors using logistic regression model, we found that the proportion of flour intake was positively associated with obesity, while the proportion of vegetable intake was inversely associated with obesity. It is concluded that dietary patterns were associated with obesity and diets composed of more vegetables and less staple combined with physical activities could contribute to obesity prevention.
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