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1,975 results on '"Wang, Wensheng"'

3. UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detection

Author

Wang, Yuchao, Cheng, Peirui, Tian, Pengju, Yuan, Ziyang, Zhao, Liangjin, Tian, Jing, Wang, Wensheng, Wang, Zhirui, and Sun, Xian

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

With the advancement of collaborative perception, the role of aerial-ground collaborative perception, a crucial component, is becoming increasingly important. The demand for collaborative perception across different perspectives to construct more comprehensive perceptual information is growing. However, challenges arise due to the disparities in the field of view between cross-domain agents and their varying sensitivity to information in images. Additionally, when we transform image features into Bird's Eye View (BEV) features for collaboration, we need accurate depth information. To address these issues, we propose a framework specifically designed for aerial-ground collaboration. First, to mitigate the lack of datasets for aerial-ground collaboration, we develop a virtual dataset named V2U-COO for our research. Second, we design a Cross-Domain Cross-Adaptation (CDCA) module to align the target information obtained from different domains, thereby achieving more accurate perception results. Finally, we introduce a Collaborative Depth Optimization (CDO) module to obtain more precise depth estimation results, leading to more accurate perception outcomes. We conduct extensive experiments on both our virtual dataset and a public dataset to validate the effectiveness of our framework. Our experiments on the V2U-COO dataset and the DAIR-V2X dataset demonstrate that our method improves detection accuracy by 6.1% and 2.7%, respectively.

Published
2024

7. Small values and functional laws of the iterated logarithm for operator fractional Brownian motion

Author

Wang Wensheng and Dong Jingshuang

Subjects
operator self-similarity, operator fractional brownian motion, small ball probability, chung type laws of the iterated logarithm, strong local nondeterminism, 60f15, 60g17, 60g22, 60g60, Mathematics, QA1-939
Abstract

The multivariate Gaussian random fields with matrix-based scaling laws are widely used for inference in statistics and many applied areas. In such contexts, interests are often Hölder regularities of spatial surfaces in any given direction. This article analyzes the almost sure sample function behavior for operator fractional Brownian motion, including multivariate fractional Brownian motion. We obtain the estimations of small ball probability and the strongly locally nondeterministic for operator fractional Brownian motion in any given direction. By applying these estimates, we obtain Chung type laws of the iterated logarithm for operator fractional Brownian motion. Our results show that the precise Hölder regularities of these spatial surfaces are completely determined by the real parts of the eigenvalues of self-similarity exponent and the covariance matrix at time point 1.

Published
2024
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13. The impact of carbon trading mechanism and technology application on carbon emission reduction in China’s coal-fired power industry:Multi-agent simulation based on reinforcement learning

Author

LIAN Xu, PEI Tongxin, WANG Wensheng, TANG Yansheng

Subjects
carbon emission reduction, carbon trading mechanism, energy saving technology, multi-intelligence modelling, low carbon policy instruments, coal-fired power industry, china, Environmental sciences, GE1-350, Biology (General), QH301-705.5
Abstract

[Objective] The “3060” dual carbon target introduces new requirements for the low-carbon development of China’s coal-fired power industry. With decreasing carbon quota limits, the focus on low-carbon transformation in this industry has increased among industry and academia. The carbon trading mechanism directs coal-fired power enterprises to reduce emissions and innovate through policy constraints. By enhancing energy efficiency and reducing emissions, it is vital in driving coal-fired power enterprises toward a low-carbon model. However, the micro-level driving mechanism of this transformation process remains underexplored [Methods] This study uses a sample of 2628 coal-fired power enterprises in China from 2022, employing reinforcement learning theory and multi-agent models. Through the Netlogo platform, it simulates and analyzes the interactive and collaborative effects between the carbon market, government, and coal-fired power enterprises with varying carbon reduction tendencies under the carbon trading mechanism. [Results] The research reveals that: (1) The carbon trading mechanism effectively promotes technological carbon reduction in coal-fired power enterprises, achieving both environmental and economic benefits, with significant impacts expected from 2032 onwards. (2) The carbon reduction tendency of these enterprises is influenced by the degree of carbon trading implementation. Before reaching the carbon peak target by 2030, these enterprises did not actively employ carbon reduction technologies as anticipated. (3) A reasonable carbon trading price effectively encourages investment in technology application. Higher carbon prices stimulate greater carbon reduction efforts and increase market activity. The carbon price growth is most significant from 2022 to 2030 and continues to rise steadily from 2032 to 2060. [Conclusion] China’s coal-fired power industry faces challenges in achieving low-carbon transformation. Accelerating the construction of carbon trading market mechanisms, strengthening carbon asset management, improving carbon reduction incentives, and optimizing carbon quota pricing mechanisms are essential for promoting technological emission reduction and the high-quality development of China’s coal-fired power industry.

Published
2024
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20. Evaluation of the analgesic and anesthetic properties of silver nanoparticles supported over biodegradable acacia gum-modified magnetic nanoparticles

Author

Tan Binbin, Chen Jun, and Wang Wensheng

Subjects
gum acacia, magnetic, silver nanoparticles, anti-inflammatory, anesthesia, Chemistry, QD1-999
Abstract

An ecofriendly and green procedure was adopted to immobilize Ag NPs over acacia gum (GA)-modified magnetic nanoparticles (Fe3O4-GA/Ag NPs) with analgesic and anesthetic properties. In the stepwise modification method, the pre-synthesized Fe3O4 NPs were coated by the gum acacia (GA) that has hydroxyl groups on its backbone and can be used for as a capping, reducing, and stabilizing agent for immobilized Ag nanoparticles. The capped Ag ions were reduced and immobilized over the acacia gum because of hydrogen bonding of the hydroxyl groups. The final bio-material was characterized by various analytical techniques such as X-ray diffraction, energy dispersive X-ray spectroscopy, inductively coupled plasma-optical emission microscope, transmission electron microscopy, field emission scanning electron microscopy, and fourier transformed infra red spectroscopy. In the in vivo design, the results of anesthetic activity indicated that the Fe3O4-GA/Ag NPs made local anesthetic effects in guinea pig and frog models. After oral administration of Fe3O4-GA/Ag NPs, paw edema was significantly decreased in mice. Acetic acid-induced writhing episodes were dose-dependently and significantly decreased in all groups treated with Fe3O4-GA/Ag NPs. In addition, the analgesic and anti-inflammatory properties were determined in several doses of Fe3O4-GA/Ag NPs. The above results protect the analgesic and anesthetic properties of silver nanoparticles supported over biodegradable acacia gum-modified magnetic nanoparticles for the next research or clinical trial studies in human.

Published
2023
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38. A Real-time Low-cost Artificial Intelligence System for Autonomous Spraying in Palm Plantations

Author

Qin, Zhenwang, Wang, Wensheng, Dammer, Karl-Heinz, Guo, Leifeng, and Cao, Zhen

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

In precision crop protection, (target-orientated) object detection in image processing can help navigate Unmanned Aerial Vehicles (UAV, crop protection drones) to the right place to apply the pesticide. Unnecessary application of non-target areas could be avoided. Deep learning algorithms dominantly use in modern computer vision tasks which require high computing time, memory footprint, and power consumption. Based on the Edge Artificial Intelligence, we investigate the main three paths that lead to dealing with this problem, including hardware accelerators, efficient algorithms, and model compression. Finally, we integrate them and propose a solution based on a light deep neural network (DNN), called Ag-YOLO, which can make the crop protection UAV have the ability to target detection and autonomous operation. This solution is restricted in size, cost, flexible, fast, and energy-effective. The hardware is only 18 grams in weight and 1.5 watts in energy consumption, and the developed DNN model needs only 838 kilobytes of disc space. We tested the developed hardware and software in comparison to the tiny version of the state-of-art YOLOv3 framework, known as YOLOv3-Tiny to detect individual palm in a plantation. An average F1 score of 0.9205 at the speed of 36.5 frames per second (in comparison to similar accuracy at 18 frames per second and 8.66 megabytes of the YOLOv3-Tiny algorithm) was reached. This developed detection system is easily plugged into any machines already purchased as long as the machines have USB ports and run Linux Operating System., Comment: 19 pages,18 figures

Published
2021

40. Adaptive Control for Compact Vector-Propelled ROVs in Underwater Detection: Enhancing Stability and Maneuverability

Author

Lan, Qi, Chen, Bonan, Wang, Xu, Xu, Tian, Wang, Wensheng, Lei, Yong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Huayong, editor, Liu, Honghai, editor, Zou, Jun, editor, Yin, Zhouping, editor, Liu, Lianqing, editor, Yang, Geng, editor, Ouyang, Xiaoping, editor, and Wang, Zhiyong, editor

Published
2023
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41. Research on High-Speed Permanent Magnet Brushless DC Motor with High Power Density

Author

Liu, Chao, Wang, Dongwen, Wang, Wensheng, Chen, Qiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, Dong, Xuzhu, editor, and Ma, Weiming, editor

Published
2023
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43. Extending Visibly Pushdown Automata over Multi-matching Nested Relations

Author

Liu, Jin, Xiao, Yeqiu, Wang, Haiyang, Wang, Wensheng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Shaoying, editor, Duan, Zhenhua, editor, and Liu, Ai, editor

Published
2023
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50. Making Streett Determinization Tight

Author

Tian, Cong, Wang, Wensheng, and Duan, Zhenhua

Subjects
Computer Science - Formal Languages and Automata Theory
Abstract

Optimal determinization construction of Streett automata is an important research problem because it is indispensable in numerous applications such as decision problems for tree temporal logics, logic games and system synthesis. This paper presents a transformation from nondeterministic Streett automata (NSA) with $n$ states and $k$ Streett pairs to equivalent deterministic Rabin transition automata (DRTA) with $n^{5n}(n!)^{n}$ states, $O(n^{n^2})$ Rabin pairs for $k=\omega(n)$ and $n^{5n}k^{nk}$ states, $O(k^{nk})$ Rabin pairs for $k=O(n)$. This improves the state of the art Streett determinization construction with $n^{5n}(n!)^{n+1}$ states, $O(n^2)$ Rabin pairs and $n^{5n}k^{nk}n!$ states, $O(nk)$ Rabin pairs, respectively. Moreover, deterministic parity transition automata (DPTA) are obtained with $3(n(n+1)-1)!(n!)^{n+1}$ states, $2n(n+1)$ priorities for $k=\omega(n)$ and $3(n(k+1)-1)!n!k^{nk}$ states, $2n(k+1)$ priorities for $k=O(n)$, which improves the best construction with $n^{n}(k+1)^{n(k+1)}(n(k+1)-1)!$ states, $2n(k+1)$ priorities. Further, we prove a lower bound state complexity for determinization construction from NSA to deterministic Rabin (transition) automata i.e. $n^{5n}(n!)^{n}$ for $k=\omega(n)$ and $n^{5n}k^{nk}$ for $k=O(n)$, which matches the state complexity of the proposed determinization construction. Besides, we put forward a lower bound state complexity for determinization construction from NSA to deterministic parity (transition) automata i.e. $2^{\Omega(n^2 \log n)}$ for $k=\omega(n)$ and $2^{\Omega(nk \log nk)}$ for $k=O(n)$, which is the same as the state complexity of the proposed determinization construction in the exponent.

Published
2020

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