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Your search keyword '"Zhou, Dacheng"' showing total 733 results
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733 results on '"Zhou, Dacheng"'

1. A New World Framework: The Three Realms and Six Layers Model

Author

Zhou, Dacheng

Subjects
Physics - Physics and Society
Abstract

This paper introduces an innovative framework for understanding the world, termed the "Three Realms and Six Layers Model". Based on the concept of scale, the world is divided into three realms, each encompassing six layers, with a ten-thousand-fold difference in scale between adjacent layers. This unique division reveals the variations in laws at different levels and the fundamental changes in laws when transitioning between realms. The model offers a new perspective for addressing interdisciplinary issues, especially in understanding the behavior of large-scale systems and their connection to microscopic phenomena. In summary, the "Three Realms and Six Layers Model" provides a novel tool for comprehending the diversity and complexity of the universe., Comment: 3 pages, 0 figures

Published
2024

2. UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite

Author

Guo, Sicen, Li, Jiahang, Feng, Yi, Zhou, Dacheng, Zhang, Denghuang, Chen, Chen, Su, Shuai, Zhu, Xingyi, Chen, Qijun, and Fan, Rui

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

In the nascent domain of urban digital twins (UDT), the prospects for leveraging cutting-edge deep learning techniques are vast and compelling. Particularly within the specialized area of intelligent road inspection (IRI), a noticeable gap exists, underscored by the current dearth of dedicated research efforts and the lack of large-scale well-annotated datasets. To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI. Along with this article, we introduce the road pothole detection task, the first online competition published within this benchmark suite. This task provides a well-annotated dataset, comprising 1,000 RGB images and their pixel/instance-level ground-truth annotations, captured in diverse real-world scenarios under different illumination and weather conditions. Our benchmark provides a systematic and thorough evaluation of state-of-the-art object detection, semantic segmentation, and instance segmentation networks, developed based on either convolutional neural networks or Transformers. We anticipate that our benchmark will serve as a catalyst for the integration of advanced UDT techniques into IRI. By providing algorithms with a more comprehensive understanding of diverse road conditions, we seek to unlock their untapped potential and foster innovation in this critical domain., Comment: Database webpage: https://www.udtiri.com/, Kaggle webpage: https://www.kaggle.com/datasets/jiahangli617/udtiri

Published
2023

17. Asymptotic and Finite-time Cluster Synchronization of Neural Networks via Two Different Controllers

Author

Cao, Juan, Ren, Fengli, and Zhou, Dacheng

Subjects
Mathematics - Dynamical Systems
Abstract

In this paper, by using pinning impulse controller and hybrid controller respectively, the research difficulties of asymptotic synchronization and finite time cluster synchronization of time-varying delayed neural networks are studied. On the ground of Lyapunov stability theorem and Lyapunov-Razumikhin method, a novel sufficient criterion on asymptotic cluster synchronization of time-varying delayed neural networks is obtained. Utilizing Finite time stability theorem and hybrid control technology, a sufficient criterion on finite-time cluster synchronization is also obtained. In order to deal with time-varying delay and save control cost, pinning pulse control is introduced to promote the realization of asymptotic cluster synchronization. Following the idea of pinning control scheme, we design a progressive hybrid control to promote the realization of finite time cluster synchronization. Finally, an example is given to illustrate the theoretical results.

Published
2021

18. Road Environment Perception for Safe and Comfortable Driving

Author

Guo, Sicen, Jiang, Yu, Li, Jiahang, Zhou, Dacheng, Su, Shuai, Bocus, Mohammud Junaid, Zhu, Xingyi, Chen, Qijun, Fan, Rui, Singh, Sameer, Founding Editor, Kang, Sing Bing, Series Editor, Bischof, Horst, Advisory Editor, Bowden, Richard, Advisory Editor, Dickinson, Sven, Advisory Editor, Jia, Jiaya, Advisory Editor, Lee, Kyoung Mu, Advisory Editor, Lin, Zhouchen, Advisory Editor, Sato, Yoichi, Advisory Editor, Schiele, Bernt, Advisory Editor, Sclaroff, Stan, Advisory Editor, Fan, Rui, editor, Guo, Sicen, editor, and Bocus, Mohammud Junaid, editor

Published
2023
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40. Quantitative Assessment of Muscle Fatigue Based on Improved Gramian Angular Difference Field

Author

Liu, Yu, Zhou, Jian, Zhou, Dacheng, and Peng, Linna

Abstract

Accurately quantifying muscle fatigue and identifying relative risk are crucial for manual laborers and patients with muscle diseases. The existing studies have insufficiently quantified muscle fatigue and have not fully utilized subjective fatigue information. This study proposes a dynamic muscle fatigue recognition method based on the Gaussian normalized Gramian angular difference field (G-GADF) image encoding method, utilizing the multichannel surface electromyography (sEMG) signal fusion method. Thirty-one healthy participants performed a focused bicep curl movement, and the sEMG signals were categorized into ten fatigue states based on the rating of muscle fatigue (RMF) values. The multichannel sEMG signal data are preprocessed and fused into a single-channel signal using the local maxima of different channels at each time. Then, the signals are encoded into 2-D images using the G-GADF method, and these images are fed into the EfficientNet_B0 model. Finally, the EfficientNet_B0 model is compared with other models. The results indicate that the image dataset, generated through preprocessing, multichannel fusion, and G-GADF methods, achieves a test accuracy of 97.058% for ten categorized muscle fatigue classifications using the EfficientNet_B0 model. These methods improve the accuracy of identifying muscle fatigue for individuals engaged in high-intensity physical labor and muscle rehabilitation, aiding in exercise intensity management and muscle injury prevention due to overexertion.

Published
2024
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43. Three-Dimensional Coverage Path Planning for Cooperative Autonomous Underwater Vehicles: A Swarm Migration Genetic Algorithm Approach.

Author

Xie, Yangmin, Hui, Wenbo, Zhou, Dacheng, and Shi, Hang

Subjects
GENETIC algorithms, MULTIAGENT systems, ENERGY consumption, COOPERATION, SATISFACTION
Abstract

Cooperative marine exploration tasks involving multiple autonomous underwater vehicles (AUVs) present a complex 3D coverage path planning challenge that has not been fully addressed. To tackle this, we employ an auto-growth strategy to generate interconnected paths, ensuring simultaneous satisfaction of the obstacle avoidance and space coverage requirements. Our approach introduces a novel genetic algorithm designed to achieve equivalent and energy-efficient path allocation among AUVs. The core idea involves defining competing gene swarms to facilitate path migration, corresponding to path allocation actions among AUVs. The fitness function incorporates models for both energy consumption and optimal path connections, resulting in iterations that lead to optimal path assignment among AUVs. This framework for multi-AUV coverage path planning eliminates the need for pre-division of the working space and has proven effective in 3D underwater environments. Numerous experiments validate the proposed method, showcasing its comprehensive advantages in achieving equitable path allocation, minimizing overall energy consumption, and ensuring high computational efficiency. These benefits contribute to the success of multi-AUV cooperation in deep-sea information collection and environmental surveillance. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Er3+/Yb3+ ions doped chiral inorganic nanostructured BiOCl phosphor as a novel temperature sensing material.

Author

Zhang, Haoqiang, Chen, Hao, Xu, Liang, Li, Yongjin, Song, Zhiguo, Zhou, Dacheng, Wang, Qi, Long, Zhangwen, Yang, Yong, Wen, Yugeng, Han, Jin, Gao, Yuan, and Qiu, Jianbei

Subjects
RED light, LIGHT sources, PHOTOMETRY, SUGAR alcohols, OPTICAL sensors
Abstract

Inorganic chiral materials have attracted increasing attention due to their application prospects. In this work, we synthesized the chiral nanostructured bismuth oxychloride co‐doped with Er3+/Yb3+ phosphor (D‐BYEP) by hydrothermal growth with a subsequent air‐annealing strategy using chiral sugar alcohols as the chiral inducer. The D‐BYEP emitted bright red light under the excitation of 980 nm near‐infrared laser. The temperature sensing behavior was evaluated by employing the thermally coupled (TCLs) or non‐thermally coupled (NTCLs) levels of Er3+. The maximum relative sensitivities (SR) obtained are 0.84% K−1 and 1.30% K−1 (298 K), respectively. The results show that the D‐BYEP may be ideal candidates for optical temperature sensors. We anticipate that this new type of inorganic chiral phosphor may have a range of applications in temperature measurement of light sources. [ABSTRACT FROM AUTHOR]

Published
2024
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