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arXiv 2024年

作者： Wang, Yanhu Guo, Shuaishuai Dong, Anming Zhao, Hui School of Control Science and Engineering Shandong Key Laboratory of Wireless Communication Technologies Shandong University Jinan250061 China The School of Computer Science and Technology Qilu University of Technology Jinan250353 China The Communication Systems Department EURECOM Sophia Antipolis 06410 France

Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investigate the validity of this claim in interference scenarios compared to baseline approaches. Specifically, our focus is on general multiple-input multiple-output (MIMO) interference channels, where we propose an interference-robust semantic communication (IRSC) scheme. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Moreover, we establish a composite loss function for training IRSC transceivers, along with a dynamic mechanism for updating the weights of various components in the loss function to enhance system fairness among users. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches, particularly in low signal-to-noise (SNR) regimes. © 2024, CC BY.

关键词： Channel state information

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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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IEEE International Conference on Big Data

作者： Jia-Hao Syu Jerry Chun-Wei Lin Dariusz Mrozek Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland

ISBN: (纸本)9781665480468

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems.

关键词： Q-learning Remotely guided vehicles Big Data Benchmark testing Multitasking Energy management systems Anomaly detection

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Study on Degradation of Benzo[a]pyrene in Marine Environment by Chlorella vulgaris LH-1 with Heterotrophic Ability 44

Study on Degradation of Benzo[a]pyrene in Marine Environment...

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44th AMOP Technical Seminar on Environmental Contamination and Response 2022

作者： Chen, Qingguo Song, Zhao Chen, Yu Zhu, Baikang Liu, Mei Li, Yijing Chen, Zhi Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control Zhejiang Ocean University Zhoushan China School of Marine Science and Technology Zhejiang Ocean University Zhoushan China Department of Building Civil and Environmental Engineering Faculty of Engineering and Computer Sciences Concordia University MontrealQC Canada

After long-term weathering, the most toxic components of residual spilled oil in seawater are polycyclic aromatic hydrocarbons. Benzo[a]pyrene (BaP) is a typical polycyclic aromatic hydrocarbon with high molecular weight, wide distribution, stable properties, and strong carcinogenicity in marine environment. Marine microalgae play an important role in the removal of BaP. Previous studies have mainly focused on the photodegradation of BaP by microalga. However, there are few research on microalgae with heterotrophic ability to degrade BaP. In this study, a microalga, Chlorella vulgaris (C. vulgaris) LH-1, with heterotrophic ability to degrade BaP was studied, the effect of BaP concentration on microalgae growth was explored, and the possible degradation mechanism of BaP by this kind of microalgae was proposed. The results showed that low concentration BaP (-1) had less obvious influence on the growth of C. vulgaris under the mixotrophic condition (with illumination), but a higher concentration BaP (＞ 5 mg L-1) had a significant inhibitory effect on the growth of this microalgae. Under heterotrophic cultivation (without illumination), low concentration BaP (-1) promoted the growth of C. vulgaris LH-1, while high concentration BaP (＞20 mg L-1) led to inhibit the growth of this microalga significantly. The degradation rates of mixotrophic and heterotrophic C. vulgaris LH-1 were 62.56~74.13%, and 52.07~71.67%, respectively, when BaP concentration range from 0.5 to 2 mg/L. Microalgae RNA transcriptome sequencing analysis showed that the gene expression of functional enzyme of C. Vulgaris LH-1 such as phenol 2-monooxygenase activity, protocatechuate 3,4-dioxygenase activity, catechol 1,2-dioxygenase activity, styrene degradation, and benzoate degradation were up-regulated in the process of BaP degradation. Based on the results of metabolites detected by high performance liquid chromatography/ quadruple time-of-flight mass spectrometry (HPLC/ Q-TOF), C. Vulgaris LH-1 may de

关键词： Degradation

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Visual Tracking Nonlinear Model Predictive control Method for Autonomous Wind Turbine Inspection

Visual Tracking Nonlinear Model Predictive Control Method fo...

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International Conference on Advanced Robotics (ICAR)

作者： Abdelhakim Amer Mohit Mehndiratta Jonas le Fevre Sejersen Huy Xuan Pham Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark GIM Robotics Espoo Finland Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

Automated visual inspection of on- and off-shorewind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framework alongside a global wind turbine path planner is proposed to achieve distance-optimal coverage for wind turbine inspection. Unlike traditional MPC formulations, visual tracking NMPC (VT-NMPC) is designed to track an inspection surface, instead of a position and heading trajectory, thereby circumventing the need to provide an accurate predefined trajectory for the drone. An additional capability of the proposed VT-NMPC method is that by incorporating inspection requirements as visual tracking costs to minimize, it naturally achieves the inspection task successfully while respecting the physical constraints of the drone. Multiple simulation runs and real-world tests demonstrate the efficiency and efficacy of the proposed automated inspection framework, which outperforms the traditional MPC designs, by providing full coverage of the target wind turbine blades as well as its robustness to changing wind conditions. The implementation codes 1 1 https://***/open-airlab/VTNMPC-Autonomous-Wind-Turbine-Inspection are open-sourced.

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Continuous-Time LiDAR-Inertial-Vehicle Odometry Method with Lateral Acceleration Constraint

Continuous-Time LiDAR-Inertial-Vehicle Odometry Method with ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Bin He Weichen Dai Zeyu Wan Hong Zhang Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Computer Science Hangzhou Dianzi University Hangzhou China Key Laboratory of Collaborative sensing and autonomous unmanned systems of Zhejiang Province Hangzhou China

In this paper, we propose a continuous-time-based LiDAR-inertial-vehicle odometry method, which can tightly fuse the data from Light Detection And Ranging (LiDAR), inertial measurement units (IMU), and vehicle measurements. The lateral acceleration constraint is further added to trajectory estimation to make the estimated trajectory follow the motion characteristics of vehicles. In addition, since vehicle model parameters vary with different motion conditions and tyre pressure, we estimate vehicle correction factors that rectify changes in vehicle model parameters online, and also analyze the observability of these vehicle correction factors. In experiments, the proposed method is evaluated and compared with state-of-the-art methods in the public dataset. The experimental results show that the proposed method achieves more accurate results in all sequences since we add additional sensor measurements and utilize the characteristic of vehicle motion to restrict the trajectory estimation. The ablation study also proved the effectiveness of continuous-time representation, online correction factor estimation, and incorporation of lateral acceleration constraint.

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SEMI-SUPERVISED SEMANTIC SEGMENTATION VIA ENTROPY MINIMIZATION

SEMI-SUPERVISED SEMANTIC SEGMENTATION VIA ENTROPY MINIMIZATI...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wu, Jiawei Fan, Haoyi Zhang, Xiaoqing Lin, Shouying Li, Zuoyong College of Mecanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou350002 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9781665438643

In this paper, we propose a novel entropy minimization based semi-supervised method for semantic segmentation. Entropy minimization has proven to be an effective semi-supervised method for realizing the cluster assumption, where the decision boundary should lie in low-density regions. Inspired by the existing consistency training semi-supervised segmentation networks with encoder-decoder architecture, we found that there tend to be more large gradient values at the object edges than other positions in the feature map of the encoder, and therefore propose a feature gradient map regularization to enlarge inter-class distance in the feature space for low-entropy of segmentation prediction. Additionally, we introduce an adaptive sharpening scheme with aleatoric uncertainty, and a class consistency constraint regularization, to alleviate the interference of noise with pseudo labels. Extensive experiments on PASCAL VOC, PASCAL-Context, and Leukocyte datasets show that the proposed method achieves state-of-the-art semi-supervised semantic segmentation performance without almost additional calculations and network structures. © 2021 IEEE computer Society. All rights reserved.

关键词： Semantics

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Identify Influential Nodes in Complex Networks: A K-Orders Entropy-Based Method

SSRN

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SSRN 2023年

作者： Wu, Yali Dong, Ang Ren, Yuanguang Jiang, Qiaoyong Department of Information and Control Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China The School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China

Identifying influential nodes is a recognized challenge for the tremendous number of nodes in complex networks. Most of proposed methods detect the influential nodes based on their degree or topological location, which only consider the local or global information of the network causing inaccuracy. In this paper, we propose a k-orders entropy-based method to identify influential nodes. The influence of node is determined by its entropy with local and global information. The entropy reflecting local information is measured by the different order neighbors' information of nodes while the entropy reflecting global information by the betweenness centrality. The experiments conducted on real-world networks demonstrate the proposed method is more accurate than other methods. © 2023, The Authors. All rights reserved.

关键词： Complex networks

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PATH OPTIMIZATION FOR DYNAMIC OBSTACLE AVOIDANCE OF PIONEER ROBOT BASED ON DDPG SMART AGENT

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UPB Scientific Bulletin, Series D: Mechanical engineering 2023年第2期85卷 29-42页

作者： Zheng, Qingchun Peng, Zhi Zhu, Peihao Zhao, Yangyang Ma, Wenpeng Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin300384 China China School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

To improve the path efficiency for dynamic obstacle avoidance algorithms of Pioneer robots, this paper proposes an approach to create deep deterministic policy gradient (DDPG) smart agents with recurrent neural networks (RNN) to optimize the path strategy. This approach is realized by training RNN with the backpropagation (BP) algorithm to train the agent. First, the SolidWorks model and obstacle avoidance model of the Pioneer robot is established. The LIDAR sensor is used to obtain environmental information. Further, deep reinforcement learning obstacle avoidance algorithm based on RNN is presented. The dynamic obstacle avoidance path of the Pioneer robot is further simulated in two different virtual robot experiment platform (V-REP) scenarios. The simulation results show that the proposed algorithm shortens the training time by 26.3% compared with the DDPG algorithm. Our proposed scheme can improve the path efficiency for dynamic obstacle avoidance of the Pioneer robot. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Reinforcement learning

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A dual plasmonic core-shell Pt/[TiN@TiO_(2)]catalyst for enhanced photothermal synergistic catalytic activity of VOCs abatement

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Nano Research 2022年第8期15卷 7071-7080页

作者： Anqi Li Qianpeng Zhang Shuaiqi Zhao Yanan Chong Peng Wu Yifei Li Xiaojing Jin Guangxu Chen Yongcai Qiu Shihe Yang Daiqi Ye Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control School of Environment and EnergySouth China University of TechnologyGuangzhou 510006China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Guangzhou HKUST Fok Ying Tung Research Institute NanshaGuangzhou 511458China State Key Laboratory of Luminescent Materials and Devices South China University of TechnologyGuangzhou 510640China Guangdong Key Lab of Nano-Micro Material Research School of Chemical Biology and Biotechnology Shenzhen Graduate SchoolPeking UniversityShenzhen 518055China

Volatile organic compounds(VOCs)are ubiquitous organic pollutants affecting atmospheric environment and human *** development of new efficient and environmentally friendly materials utilizing photothermal synergistic catalysis for purification of VOCs is still ***,we design and prepare a core–shell TiN@TiO_(2)nanostructure integrating with nanoscaled Pt(Pt/[TiN@TiO_(2)])by an attractive quenching *** strong light-harvesting capability of Pt and TiN components improve light-to-heat utilization efficiency by their intrinsic surface plasmon resonance *** TiO_(2)component upon the surface and the coexisting coupling effect of Pt0 and Pt2+enhance the photocatalytic effect of the *** a result,the catalytic performance is significantly improved with toluene(120 ppm)conversion of 100%under the gas hourly space velocity of 72,000 mL·g^(−1)·h^(−1)and light illumination of 500 mW·cm^(−2).The desired catalyst thus achieves highly efficient coupling effect of photocatalysis and light-to-heat conversion for promoting VOCs abatement.

关键词： core-shell Pt/[TiN@TiO_(2)]catalyst volatile organic compounds(VOCs)abatement photothermal synergistic catalysis full spectrum sunlight utilization

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Distributed Prescribed-Time Observer for Nonlinear Systems

arXiv

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arXiv 2025年

作者： de Heij, Vincent Niazi, M.B. Umar Johansson, Karl H. Ahmed, Saeed Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper proposes a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite limited local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that asymptotically approach infinity as the prescribed time is approached. The theoretical convergence is rigorously proven and validated through numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear simulations

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