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International Conference on computer Supported Cooperative Work in Design

作者： Tiantian Wang Lei Liu Hongbo Sun Jia Hu School of Software Shandong University Jinan China School of Computer and Control Engineering YanTai University YanTai China Computer Science Exeter University United Kingdom

Integrating large-scale sensors into the network has become a research hotspot for its promising flexibility in monitoring vitally critical wild areas. However, the existing Internet of Things (IoT) systems are limited due to the lack of a stable power supply, which seriously affects the system’s sustainability. The combination of sensors equipped with cordless power batteries and long-distance power transmission has ushered in a new era. Using the unmanned aerial vehicles (UAVs) to charge the battery ensures the flexibility and sustainability of the sensor in environmental detection. In this work, we aim to provide a solution for maintaining the sustainability of the sensors while optimizing UAV trajectory to minimize the overall energy consumption of UAV. Since deep reinforcement learning successfully solves the NP-hard combinatorial optimization problem, deep reinforcement learning is introduced in this work to obtain a feasible solution. We formulate the trajectory planning of UAV as a Markov decision problem and employ a deep reinforcement learning (DRL) model based on an attention mechanism to find the optimal policy efficiently, named the optimal trajectory planning algorithm based on DRL (OTPDRL). The experimental results suggest the OTPDRL obtains a good trade-off between performance gain and computational time.

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Trajectory control and Fair Communications for Multi-UAV Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

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IEEE Transactions on Wireless Communications 2025年

作者： Tarekegn, Getaneh Berie Tesfaw, Belayneh Abebe Juang, Rong-Terng Saha, Dola Tarekegn, Robel Berie Lin, Hsin-Piao Tai, Li-Chia Nycu Department of Electrical and Computer Engineering 1001 University Road Hsinchu300 Taiwan Nycu Institute of Electrical and Control Engineering 1001 University Road Hsinchu300 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Institute of Aerospace and System Engineering Taipei10608 Taiwan University at Albany Suny Department of Electrical and Computer Engineering AlbanyNY12222 United States

Due to their highly flexible deployment and agility features, unmanned aerial vehicles (UAVs) serving as aerial base stations are increasingly being used in challenging environments, including emergency communication, traffic offloading, and failures of existing communications infrastructure. A reliable and effective communication service requires 3D deployment and autonomous UAV trajectory optimization in each time slot. While most prior approaches focus on trajectory design that maximizes communication coverage or network throughput without considering fairness as well as computation time. This paper presents a multi-UAV trajectory control and fair communication (TCFC) scheme that maximizes ground user data rate and communication coverage while ensuring service fairness in a UAV-aided wireless communications system. The proposed TCFC scheme employs a two-stage learning approach. Firstly, it develops a gated recurrent unit-based link quality estimation model to assess each user's link quality over time. Then, a federated multi-agent deep reinforcement learning (FedMADRL) algorithm is utilized to continuously adjust the trajectory of the UAVs, optimizing communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. The results show that the proposed TCFC scheme reduces computation time by 26.91% and provides comparable network performance with the baseline methods while improving DRL agents' privacy. © 2025 IEEE.

关键词： Deep reinforcement learning

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Contracting Skeletal Kinematic Embeddings for Anomaly Detection

SSRN

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SSRN 2023年

作者： Flaborea, Alessandro D’Amely di Melendugno, Guido Maria D’Arrigo, Stefano Sterpa, Marco Aurelio Sampieri, Alessio Galasso, Fabio Department of Computer Science Sapienza University of Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Earth Sciences and Forecasting Research Center Prevention and Control of Geological Risks

Detecting the anomaly of human behavior is paramount to timely recognizing endangering situations, such as street fights or elderly falls. However, anomaly detection is complex, since anomalous events are rare and because it is an open set recognition task, i.e., what is anomalous at inference has not been observed at training. We propose COSKAD, a novel model which encodes skeletal human motion by an efficient graph convolutional network and learns to COntract SKeletal kinematic embeddings onto a latent hypersphere of minimum volume for Anomaly Detection. We propose and analyze three latent space designs for COSKAD: the commonly-adopted Euclidean, and the new spherical-radial and hyperbolic volumes. All three variants outperform the state-of-the-art, including video-based techniques, on the ShangaiTechCampus, the Avenue, and on the most recent UBnormal dataset, for which we contribute novel skeleton annotations and the selection of human-related *** source code and dataset will be released upon acceptance. © 2023, The Authors. All rights reserved.

关键词： Kinematics

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Ante-Hoc Generation of Task-Agnostic Interpretation Maps

Ante-Hoc Generation of Task-Agnostic Interpretation Maps

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Akash Guna R T Raul Benitez Sikha O K Department of Computer Science and Engineering Amrita School of Engineering Coimbatore India Departament of Automatic Control Universitat Politècnica de Catalunya-BarcelonaTech Spain

Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more reliable. However, current ante-hoc explanation methods mainly generate inexplicit concept-based explanations tailored to specific tasks. To address these limitations, we propose a task-agnostic ante-hoc framework that can generate interpretation maps to visually explain any CNN. Our framework simultaneously trains the CNN and a weighting network - an explanation generation module. The generated maps are self-explanatory, eliminating the need for manual identification of concepts. We demonstrate that our method can interpret classification, facial landmark detection, and image captioning tasks. We show that our framework is explicit, faithful, and stable through experiments. To the best of our knowledge, this is the first ante-hoc CNN explanation strategy that produces visual explanations generic to CNNs for different tasks.

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Highly Efficient 3D Human Pose Tracking from Events with Spiking Spatiotemporal Transformer

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zou, Shihao Mu, Yuxuan Ji, Wei Wang, Zi-An Zuo, Xinxin Wang, Sen Si, Weixin Cheng, Li Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Guangdong Shenzhen China University of Alberta Department of Electrical and Computer Engineering ABT6G 1H9 Canada Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Guangdong Shenzhen China

Event camera, as an asynchronous vision sensor capturing scene dynamics, presents new opportunities for highly efficient 3D human pose tracking. Existing approaches typically adopt modern-day Artificial Neural Networks (ANNs), such as CNNs or Transformer, where sparse events are converted into dense images or paired with additional gray-scale images as input. Such practices, however, ignore the inherent sparsity of events, resulting in redundant computations, increased energy consumption, and potentially degraded performance. Motivated by these observations, we introduce the first sparse Spiking Neural Networks (SNNs) framework for 3D human pose tracking based solely on events. Our approach eliminates the need to convert sparse data to dense formats or incorporate additional images, thereby fully exploiting the innate sparsity of input events. Central to our framework is a novel Spiking Spatio-temporal Transformer, which enables bi-directional spatio-temporal fusion of spike pose features and provides a guaranteed similarity measurement between binary spike features in spiking attention. Moreover, we have constructed a largescale synthetic dataset, SynEventHPD, that features a broad and diverse set of 3D human motions, as well as much longer hours of event streams. Empirical experiments demonstrate the superiority of our approach over existing state-of-the-art (SOTA) ANN-based methods, requiring only 19.1% FLOPs and 3.6% energy cost. Furthermore, our approach outperforms existing SNN-based benchmarks in this task, highlighting the effectiveness of our proposed SNN framework. © 1991-2012 IEEE.

关键词： Motion picture cameras

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MUFFIN-HGCN: Multi-feature Fusion Hierarchical-GCN for 3D Object Detection

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chang Jiang, Aimin Zhou, Jia Li, Min Zhu, Yanping Kwan, Hon Keung Hohai University College of Information Science and Engineering Changzhou213200 China Changzhou University School of Microelectronics and Control Engineering Changzhou213164 China University of Windsor Department of Electrical and Computer Engineering WindsorONN9B 3P4 Canada

3D object detection play a pivotal role in various applications, such as autonomous driving and environmental perception. However, the challenging task of detecting targets (e.g., vehicles and pedestrians) in 3D point clouds obtained from LiDAR sensors is hindered by two primary factors. Firstly, point clouds are non-Euclidean data and lack rigid data properties. Secondly, these point clouds are sparse and deficient in semantic information. In this paper, we propose a novel deep learning framework for 3D object detection. The proposed approach involves two stages. Specifically, we first present an innovative fusion technique that combines images and point clouds using transformers. This technique enhances the integration of semantic and geometric features while accurately mapping points to pixels without relying on camera extrinsic parameters. Additionally, a multi-level graph convolutional network (GCN) architecture is further introduced for object detection. This architecture effectively handles the non-Euclidean characteristics of point clouds while achieving precise object identification within them. A comprehensive series of experiments on the KITTl and nuScenes datasets validate the effectiveness of the proposed framework. © 1967-2012 IEEE.

关键词： Object detection

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Efficient Photothermal Catalytic Oxidation Enabled by Three-Dimensional Nanochannel Substrates

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Environmental science and Technology 2024年第11期58卷 5153-5161页

作者： Li, Yifei Zhang, Qianpeng Chong, Yanan Huang, Wei-Hsiang Chen, Chi-Liang Jin, Xiaojing Chen, Guangxu Fan, Zhiyong Qiu, Yongcai Ye, Daiqi School of Environment and Energy State Key Laboratory of Luminescent Materials and Devices Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control South China University of Technology Guangdong Guangzhou510000 China State Key Laboratory of Photovoltaic Science and Technology Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai200433 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong National Synchrotron Radiation Research Center 101 Hsin-Ann Road Hsinchu Science Park Hsinchu30076 Taiwan Taipei10607 Taiwan College of Light Chemical Industry and Materials Engineering Shunde Polytechnic Foshan528333 China

Photothermal catalysis exhibits promising prospects to overcome the shortcomings of high-energy consumption of traditional thermal catalysis and the low efficiency of photocatalysis. However, there is still a challenge to develop catalysts with outstanding light absorption capability and photothermal conversion efficiency for the degradation of atmospheric pollutants. Herein, we introduced the Co3O4 layer and Pt nanoclusters into the three-dimensional (3D) porous membrane through the atomic layer deposition (ALD) technique, leading to a Pt/Co3O4/AAO monolithic catalyst. The 3D ordered nanochannel structure can significantly enhance the solar absorption capacity through the light-trapping effect. Therefore, the embedded Pt/Co3O4 catalyst can be rapidly heated and the O2 adsorbed on the Pt clusters can be activated to generate sufficient O2- species, exhibiting outstanding activity for the diverse VOCs (toluene, acetone, and formaldehyde) degradation. Optical characterization and simulation calculation confirmed that Pt/Co3O4/AAO exhibited state-of-the-art light absorption and a notable localized surface plasmon resonance (LSPR) effect. In situ diffuse reflectance infrared Fourier transform spectrometry (in situ DRIFTS) studies demonstrated that light irradiation can accelerate the conversion of intermediates during toluene and acetone oxidation, thereby inhibiting byproduct accumulation. Our finding extends the application of AAO’s optical properties in photothermal catalytic degradation of air pollutants. © 2024 American Chemical Society.

关键词： Catalysis

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Three matrix conditions for the reduction of finite automata based on the theory of semi-tensor product of matrices

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science China(Information sciences) 2020年第2期63卷 227-229页

作者： Jumei YUE Yongyi YAN Zengqiang CHEN College of Agricultural Engineering Henan University of Science and Technology College of Information Engineering Henan University of Science and Technology College of Computer and Control Engineering Nankai University

Dear editor,Reduction of finite automata (FA) is of great importance because of its practical applications in engineering; for example the memory space of hardware realization grows exponentially with the number of states of FSMs. Existing results for reducing FA can roughly be classified into four categories:merging of states [1], refining of the state

关键词： STP Sr Three matrix conditions for the reduction of finite automata based on the theory of semi-tensor product of matrices

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Modelling, Analysis and control of OmniMorph: an Omnidirectional Morphing Multi-rotor UAV

arXiv

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arXiv 2023年

作者： Aboudorra, Youssef Gabellieri, Chiara Brantjes, Ralph Sablé, Quentin Franchi, Antonio Robotics and Mechatronics group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy

This paper introduces for the first time the design, modelling, and control of a novel morphing multirotor Unmanned Aerial Vehicle (UAV) that we call the OmniMorph. The morphing ability allows the selection of the configuration that optimizes energy consumption while ensuring the needed maneuverability for the required task. The most energy-efficient uni-directional thrust (UDT) configuration can be used, e.g., during standard point-to-point displacements. Fully-actuated (FA) and omnidirectional (OD) configurations can be instead used for full pose tracking, such as, e.g., constant attitude horizontal motions and full rotations on the spot, and for full wrench 6D interaction control and 6D disturbance rejection. Morphing is obtained using a single servomotor, allowing possible minimization of weight, costs, and maintenance complexity. The actuation properties are studied, and an optimal controller that compromises between performance and control effort is proposed and validated in realistic simulations. Preliminary tests on the prototype are presented to assess the propellers' mutual aerodynamic interference. © 2023, CC BY.

关键词： Maneuverability

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Industrial Teleassistance with the Help of Augmented Reality

Industrial Teleassistance with the Help of Augmented Reality

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Roedunet International Conference (RoEduNet)

作者： Valentin Bobeica Dan Popescu Nicoleta Angelescu Loretta Ichim Faculty of Automatic Control and Computer Science National University of Science and Technology Politehnica Bucharest Bucharest Romania Faculty of Electrical Engineering Electronics and Information Technology Valahia University of Targoviste Targoviste Romania

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

This paper details the integration of augmented reality (AR) in a new factory, focusing on enhancing maintenance efficiency and optimization. The innovative approach involves combining AR technology, an Android application, and Siemens PLCs (Programmable Logic controller), for efficient automation. The robustness of the PLC enables seamless integration within manufacturing environments, accommodating diverse programming paradigms. The software aspect features Human Machine Interface (HMI) configuration using Siemens’ WinCC software, offering dynamic visualizations, alarm handling, and data logging. WinCC’s scalability suits applications of varying sizes. The HMI architecture is designed for easy parameter display. The Android application, developed with Java and Python, leverages their respective strengths in Android app development and image processing. Data exchange is facilitated by OPC-UA, supporting multiple programming languages for seamless communication. The AR Teleassistance is highlighted as a revolutionary solution, fostering real-time communication among global experts, technicians, and operators. Beyond gaming origins, AR teleassistance enhances efficiency in various sectors, minimizing downtime and facilitating global knowledge transfer. The integration of Teleassistance promotes effective collaboration between human expertise and automated systems, crucial for optimal performance in modern industrial settings. Continuous development is suggested through the addition of more 3D mechanical drawings and real-time stock information for parts in the application, aiming for future optimization and performance enhancement.

关键词： Technological innovation Three-dimensional displays Scalability Real-time systems Software Robustness Optimization Augmented reality Videos Python

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