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213 results on '"Gao, Weijie"'

8. Effective-medium-cladded dielectric waveguides for terahertz communications

Author

Gao, Weijie, Yu, Xiongbin, Fujita, Masayuki, Nagatsuma, Tadao, Fumeaux, Christophe, and Withayachumnankul, Withawat

Subjects
Physics - Optics
Abstract

Terahertz communications is a promising modality for future short-range point-to point wireless data transmission at rates up to terabit per second. A milestone towards this goal is the development of an integrated transmitter and receiver platforms with high efficiency. One key enabling component is a planar waveguiding structure with wide bandwidth and low dispersion. This work proposes substrate-less all-dielectric waveguides cladded by an effective medium for low-loss and low dispersion terahertz transmission in broadband. This self-supporting structure is built solely into a single silicon wafer with air perforations to mitigate significant absorptions in metals and dielectrics at terahertz frequencies. The realized waveguides can cover the entire 260 to 400 GHz with single dominant modes in both orthogonal polarizations. The simulation shows that for the E_11^x mode the attenuation ranges from 0.003 to 0.024 dB/cm over the entire band, while it varies from 0.008 to 0.023 dB/cm for the E_11^y mode. Limited by the measurement setup, the maximum error-free data rate of 28 Gbit/s is experimentally achieved at 335 GHz on a 3-cm waveguide. We further demonstrate the transmission of uncompressed 4K-resolution video across this waveguide. This waveguide platform promises integration of diverse active and passive components. Thus, we can foresee it as a potential candidate for the future terahertz integrated circuits, in analogy to photonic integrated circuits at optical frequencies.

Published
2019
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15. Survey of Physical Adversarial Attacks Against Object Detection Models.

Author

CAI Wei, DI Xingyu, JIANG Xinhao, WANG Xin, and GAO Weijie

Subjects
OBJECT recognition (Computer vision), ARTIFICIAL neural networks, DEEP learning
Abstract

Deep learning models are highly susceptible to adversarial samples, and even minuscule image perturbations that are not perceptible to the naked eye can disable well-trained deep learning models. Recent research indicates that these perturbations can exist in the physical world. This paper provides insight into physical adversarial attacks on deep learning object detection models, clarifying the concept of physical adversarial attack and outlining the general process of such attacks on object detection. According to the different attack tasks, a series of physical adversarial attack methods against object detection networks in recent years are reviewed from vehicle detection and pedestrian detection. Other attacks against target detection models, other attack tasks and other attack methods are briefly introduced. The current challenges of physical adversarial attack are discussed, the limitations of adversarial training are leaded out, and future development directions and application prospect are suggested. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Spatio-Temporal Evolution Characteristics and Driving Factors of Typical Karst Rocky Desertification Area in the Upper Yangtze River.

Author

Gao, Weijie, Zhou, Siyi, and Yin, Xiaojie

Abstract

Karst rocky desertification (KRD) has become the most serious ecological disaster in the southwest of China and is a major obstacle to the sustainable development of the karst region in the southwest. Remarkably, scientific understanding of the spatial-temporal evolution of rocky desertification and the corresponding driving mechanism is the primary prerequisite crucial to controlling rocky desertification. Hence, the typical rocky desertification area of Qujing City, located in the upper reaches of the Yangtze River, was selected as the research object. On the basis of the Google Earth Engine (GEE) cloud platform and decision tree classification, the spatial-temporal evolution process of rocky desertification in Qujing City from 1990 to 2020 was investigated, and the driving factors of rocky desertification were explored in terms of the natural environment and socio-economic aspects. Consequently, over this period, the area of rocky desertification had decreased by 1728.38 km2, while the no rocky desertification area had increased by 1936.61 km2. Notably, the major driving factors of rocky desertification were fractional vegetation cover (FVC) (q = 0.41), land use type (q = 0.26), slope (q = 0.21), and land reclamation rate (q = 0.21). Typically, rocky desertification is likely to occur in areas with moderate or low FVC (<0.7), a low slope (0–8°) or high slope (35°–80°), a land type of cultivated-land or grassland, and a land reclamation rate of 10–70%. In addition, all two-factor interactions acted as drivers that exacerbate rocky desertification. Furthermore, FVC ∩ slope (q = 0.79) and slope ∩ land use type (q = 0.56) were two interacting drivers that promote rocky desertification strongly. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Study on the design and cutting performance of stepped bi-directional milling cutters for hole making of CFRP

Author

Li Rui, Gao Weijie, Wang Yongsheng, Xiang Jiupeng, and Chen Tao

Subjects
0209 industrial biotechnology, Materials science, Mechanical Engineering, Mechanical engineering, 02 engineering and technology, Grinding wheel, Edge (geometry), Fibre-reinforced plastic, Industrial and Manufacturing Engineering, Computer Science Applications, Grinding, 020901 industrial engineering & automation, Machining, Control and Systems Engineering, Milling cutter, Tool wear, Software, Groove (music)
Abstract

During hole making of carbon fiber reinforced plastic (CFRP) by helical milling, the tool wear is a main factor in machined surface damage, being unable to guarantee machining quality and accuracy, and restricting the improvement of hole making efficiency. In order to prolong the service life of cutters, this paper has carried out the research into the design, manufacture, and cutting performance of stepped bi-directional milling cutters, combining with hole making of CFRP by bi-directional helical milling. By the method of differential geometry, the geometric model of the profile shape and the mathematical model of the helical edge are established of the stepped bi-directional milling cutter. Additionally, based on the transformation matrix between grinding wheel and workpiece coordinates of any point, the track equation of grinding wheel is derived from grinding the side edge helical groove of the stepped bi-directional milling cutter. What is more, the grinding process and accuracy are measured about the designed stepped bi-directional milling cutter. The experiment of CFRP holes made by bi-directional helical milling is designed, and the experimental results show that the axial cutting force of stepped bi-directional milling cutters is smaller and fluctuates more gently than that of symmetrical bi-directional milling cutters in the backward milling. Especially, the former has better distributed and slower flank wear on the backward cutting edge, and better machining quality than the latter.

Published
2020

50. Orphan nuclear receptor TLX promotes immunosuppression via its transcriptional activation of PD-L1 in glioma

Author

Zhou, Jiayi, primary, Pei, Xiaojuan, additional, Yang, Yingui, additional, Wang, Zhu, additional, Gao, Weijie, additional, Ye, Ran, additional, Zhang, Xiantong, additional, Liu, Jiangang, additional, Liu, Zhuohao, additional, Yang, Xinzhi, additional, Tao, Jingli, additional, Gu, Chunshan, additional, Hu, Wei, additional, Chan, Franky Lueng, additional, Li, Xin, additional, Mao, Jie, additional, and Wu, Dinglan, additional

Published
2021
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