作者：
Wang, YufeiXiao, BaihuaChinese Academy of Sciences
University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China Chinese Academy of Sciences
The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China
Deep convection can cause a variety of severe weather conditions such as thunderstorms, strong winds, and heavy rainfall. Satellite observations provide all-weather and multi-directional observations, facilitating the...
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This issue comprises 2 Perspectives, 2 Letters, and 82 Regular Papers. After Scanning the Issue, I would like to initiate a broad discussion about Intelligent Vehicles (IVs), with an emphasis on smart logistics and au...
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This issue comprises 2 Perspectives, 2 Letters, and 82 Regular Papers. After Scanning the Issue, I would like to initiate a broad discussion about Intelligent Vehicles (IVs), with an emphasis on smart logistics and autonomous mobility (SLAM) for smart societies.
Minimally invasive surgery, which relies on surgical robots and microscopes, demands precise image segmentation to ensure safe and efficient procedures. Nevertheless, achieving accurate segmentation of surgical instru...
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Minimally invasive surgery, which relies on surgical robots and microscopes, demands precise image segmentation to ensure safe and efficient procedures. Nevertheless, achieving accurate segmentation of surgical instruments remains challenging due to the complexity of the surgical environment. To tackle this issue, this paper introduces a novel multiscale dual-encoding segmentation network, termed MSDE-Net, designed to automatically and precisely segment surgical instruments. The proposed MSDE-Net leverages a dual-branch encoder comprising a convolutional neural network (CNN) branch and a transformer branch to effectively extract both local and global features. Moreover, an attention fusion block (AFB) is introduced to ensure effective information complementarity between the dual-branch encoding paths. Additionally, a multilayer context fusion block (MCF) is proposed to enhance the network's capacity to simultaneously extract global and local features. Finally, to extend the scope of global feature information under larger receptive fields, a multi-receptive field fusion (MRF) block is incorporated. Through comprehensive experimental evaluations on two publicly available datasets for surgical instrument segmentation, the proposed MSDE-Net demonstrates superior performance compared to existing methods.
This paper presents the first survey of vehicle dynamics modeling methods for autonomous racing. Previous surveys have covered dynamics models for standard autonomous vehicles or, alternatively, concentrated on planni...
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This paper presents the first survey of vehicle dynamics modeling methods for autonomous racing. Previous surveys have covered dynamics models for standard autonomous vehicles or, alternatively, concentrated on planning and control methods in autonomous racing with vehicle dynamics models briefly mentioned. However, previous surveys overlook the importance of vehicle dynamics under challenging conditions of top speeds and non-steady state driving, which are unique characteristics in autonomous racing. Recognizing the vital role of vehicle dynamics modeling in an autonomous racecar's prediction, planning, and control modules, this survey seeks to ascertain to what degree the nominal full-scale racecar dynamics can be streamlined without sacrificing accuracy for simplicity. Furthermore, this survey provides essential guidance for organizers of virtual autonomous races, helping them choose vehicle dynamics models that meet the required level of precision. This paper begins with a review of previous surveys on vehicle dynamics modeling, highlighting their limitations in the context of autonomous racing. Following this, it investigates the existing dynamics models for autonomous racing vehicles, along with a comprehensive examination of the existing physical/virtual testing platforms. The paper concludes by discussing emerging trends and offering perspectives in the field of vehicle dynamics modeling for autonomous racing, paving the way for groundbreaking research and innovations in autonomous racing.
WE are in an exciting new intelligent era where various Web 3.0 systems emerge and flourish.[1]–[3].In this new epoch,the collaboration of data and knowledge,humans and machines,actual and virtual worlds is undergoin...
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WE are in an exciting new intelligent era where various Web 3.0 systems emerge and flourish.[1]–[3].In this new epoch,the collaboration of data and knowledge,humans and machines,actual and virtual worlds is undergoing an unprecedented diversification and community-driven transformation,unveiling an open future full of boundless ***,the value of dispersed data extends far beyond passive storage and application.
An investigation and outline of Metacontrol and Decontrol in Metaverses for control intelligence and knowledge automation are *** control with prescriptive knowledge and parallel philosophy is proposed as the starting...
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An investigation and outline of Metacontrol and Decontrol in Metaverses for control intelligence and knowledge automation are *** control with prescriptive knowledge and parallel philosophy is proposed as the starting point for the new control philosophy and technology,especially for computational control of metasystems in cyberphysical-social *** argue that circular causality,the generalized feedback mechanism for complex and purposive systems,should be adapted as the fundamental principle for control and management of metasystems with metacomplexity in ***,an interdisciplinary approach is suggested for Metacontrol and Decontrol as a new form of intelligent control based on five control metaverses:MetaVerses,MultiVerses,InterVerses,TransVerse,and DeepVerses.
In addressing the complex challenge of Traffic Signal control (TSC), Deep Reinforcement Learning (DRL) has emerged as a popular solution. In traditional DRL methods applied to TSC problems, deep neural networks are se...
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In addressing the complex challenge of Traffic Signal control (TSC), Deep Reinforcement Learning (DRL) has emerged as a popular solution. In traditional DRL methods applied to TSC problems, deep neural networks are sensitive to minor input changes, which complicates accurate predictions. This ambiguity hampers algorithm convergence, speed, and overall performance. Additionally, existing DRL methods for TSC employ high-dimensional state spaces, escalating computational complexity. This study addresses these challenges by introducing an innovative approach, SLFMLight, that integrates a stochastic traffic flow model with DRL algorithm for TSC. Our method employs an innovative network update algorithm that integrates traffic flow prediction in Q-value learning process to enhance interpretability and accelerate algorithm convergence. Utilizing mode-based multi-actor networks to handle diverse traffic conditions, SLFMLight excels in decision-making towards complex traffic scenarios, especially in congested ones. Concise state definition improves computational efficiency. SLFMLight contributes to the advancement of intelligent traffic management by providing an effective DRL solution that improves interpretability, efficiency, and adaptability in TSC.
With the acceleration of economic globalization, a large amount of research studies have been conducted for the exploration of industrial economics. In the visualization community, common visualization tools present p...
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With the acceleration of economic globalization, a large amount of research studies have been conducted for the exploration of industrial economics. In the visualization community, common visualization tools present potential features of industrial economics. They hardly meet the various and complex user requirements for insightful analysis and decision-making. In this article, we design VIEA, a web-based visualization system that integrates a rich set of views and tailored interactions, enabling users to easily perceive economic features, such as geographical distributions, trade relationships, and pattern comparisons. Case studies and user studies based on real-world datasets have been conducted to demonstrate the effectiveness of our system in the exploration of industrial economics.
Real-time transportation surveillance is an essential part of the intelligent transportation system (ITS). However, images captured under low-light conditions often suffer poor visibility with types of degradation, su...
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Real-time transportation surveillance is an essential part of the intelligent transportation system (ITS). However, images captured under low-light conditions often suffer poor visibility with types of degradation, such as noise interference and vague edge features, etc. With the development of imaging devices, the quality of the visual surveillance data is continually increasing, like 2K and 4K, which have more strict requirements on the efficiency of image processing. To satisfy the requirements on both enhancement quality and computational speed, this paper proposes a double domain guided real-time low-light image enhancement network (DDNet) for ultra-high-definition (UHD) transportation surveillance. Specifically, we design an encoder-decoder structure as the main architecture of the learning network. In particular, the enhancement processing is divided into two subtasks (i.e., color enhancement and gradient enhancement) via the proposed coarse enhancement module (CEM) and LoG-based gradient enhancement module (GEM), which are embedded in the encoder-decoder structure. It enables the network to enhance the color and edge features simultaneously. Through the decomposition and reconstruction on both color and gradient domains, our DDNet can restore the detailed feature information concealed by the darkness with better visual quality and efficiency. The evaluation experiments on standard and transportation-related datasets demonstrate that our DDNet provides superior enhancement quality and efficiency compared with state-of-the-art methods. Besides, the object detection and scene segmentation experiments indicate the practical benefits for higher-level image analysis under low-light environments in ITS. The source code is available at https://***/QuJX/DDNet.
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