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Hybrid Water Quality Prediction with Multimodal Low-rank Fusion and Localized Attention

IEEE Internet of Things Journal 2025年第12期12卷 21158-21169页

作者： Bi, Jing Li, Yibo Yuan, Haitao Wang, Mengyuan Wang, Ziqi Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University School of Energy and Power Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

Water quality prediction methods forecast the short-or long-term trends of its changes, providing proactive advice for preventing and controlling water pollution. Existing water quality prediction methods typically fail to capture water quality's nonlinear characteristics accurately and only consider historical time series data. However, meteorology and other factors also significantly impact water quality indicators. Therefore, considering only historical data of water quality time series is not feasible. To solve this problem, this work proposes a hybrid water quality prediction model called CMLIP, which integrates ConvNeXt V2, Multimodal bottleneck transformer, Low-rank multimodal fusion, ITransformer, and PatchTST. CMLIP inputs water quality time series and meteorological remotely sensed rainfall images into a multimodal fusion module before prediction. Specifically, CMLIP integrates the model of ConvNeXt V2 to extract image features. Its multimodal fusion module combines a multimodal bottleneck transformer and the low-rank multimodal fusion to fuse the time series and images. Furthermore, CMLIP combines iTransformer and PatchTST to form an improved prediction module that realizes the prediction of fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMLIP when fusing meteorological data, achieves an average improvement of 17% in water quality forecasting accuracy compared to forecasts using only water quality time series. Moreover, CMLIP outperforms other state-of-the-art algorithms in both data fusion and prediction, with an average enhancement of 6% in fusion effectiveness and an average improvement of 22% in prediction accuracy. © 2025 IEEE.

关键词： Rain

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Position Measurement Based Slave Torque Feedback Control for Teleoperation Systems With Time-Varying Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 388-402页

作者： Xian Yang Jing Yan Changchun Hua Xinping Guan the Institute of Information Science and Engineering Yanshan University the Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China IEEE the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects *** the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback ***,due to hardware technology and cost constraints,the velocity measurements are not always *** addition,the time-varying communication delay makes it challenging to achieve tracking *** paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication *** order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity *** the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be ***,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically *** the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking *** particular,the relationship between the controller design parameters and the allowable maximum delay values is ***,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.

关键词： Asymptotic stability bilateral teleoperation time-varying delay velocity observer

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An Efficient Privacy-Preserving Asynchronous Federated Approach for Intelligent Decision Making in Equipment Maintenance 7

An Efficient Privacy-Preserving Asynchronous Federated Appro...

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Dou, Kangkang He, Fan Shang, Fanshu Li, Xingyi Dong, Yuhao Liu, Jiayu Jiangsu Automation Research Institute Nanjing University of Aeronautics and Astronautics Department of Information Technology Research Lianyungang China Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350352009

Federated learning enables collaborative training of a high-quality global model across multiple participants without sharing raw local data. Its advantages, such as decentralization, data isolation, and high computational performance, have made it a popular research direction in various fields. However, federated learning faces privacy risks during model parameter transmission. Additionally, reducing communication overhead and computational resource consumption while ensuring data privacy protection remains a critical challenge. To address these issues, this paper proposes an efficient privacy-preserving asynchronous federated learning method tailored for equipment maintenance scenarios. Leveraging asynchronous federated learning techniques, this method enables efficient collaborative training among multiple participants. It also employs homomorphic encryption to protect local data information. By utilizing time-series models, we construct a predictive model for equipment spare parts consumption, providing valuable insights for intelligent maintenance decision-making. Experimental results demonstrate that our proposed method has faster convergence. More importantly, the root mean square error decreases by at least 0.004, the mean square error decreases by at least 0.024, and the relative error decreases by at least 0.008 compared to the baseline. © 2024 IEEE.

关键词： Federated learning

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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Multi-layered access control based auto tuning relational key implications in enterprise-level multi-tenancy

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Multimedia Tools and Applications 2025年第17期84卷 17805-17836页

作者： Henge, Santosh Kumar Rajakumar, R. Prasanna, P. Parivazhagan, A. Hu, Yu-Chen Chen, Wu-Lin Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation KL University AP Vaddeswaram India Centre for Automation School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Veltech Rangarajan Dr Sagunthala R &D Institute of Science and Technology Chennai India Department of CSE Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Computer Science Xitun District Tunghai University No. 1727 Sec. 4 Boulevard Taichung City407224 Taiwan Department of Computer Science and Information Management Providence University Taichung City Taiwan

This article discusses the importance of cloud-based multi-tenancy in private–public-private secure cloud environments, which is achieved through the isolation of end-user data and resources into tenants to ensure data confidentiality and integrity. Maintaining secure access control is a complex and critical task in multi-tenant cloud environments, and researchers have proposed innovative methods for implementing security, privacy, and authenticity. However, several challenges remain. The proposed research addresses these challenges by presenting a layered approach to multi-tenancy in an enterprise-level cloud environment. The study has three main objectives: (1) to describe and analyze the complexities of enterprise-based multi-tenancy and proposed technical solutions in a tabular format;(2) to propose multi-tenant test cases across application, software, infrastructure, and network as a service environment;and (3) to present a collaborative, relational optimal key-based multi-tenancy access control model. The proposed access control model involves integrating various secure layers, such as intranet, extranet, intra-extranet, and using feasible relational optimal keys to provide secure multi-tenant access control among various tenants of single and multiple enterprises. This model provides secure data transmission by integrating various multi-layer data filters and secure blades. The layered multi-tenancy approach offers many benefits and technical feasibilities to avoid security and privacy issues in enterprise-based multi-tenant environments. The process achieved a 93% secure mapping rate to authenticate all IN and OUT data access transactions among the various multi-tenancy of similar and distinct enterprises. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Virtualization

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Event-Triggered Sampled-Data Iterative Learning Control for Linear Time-Varying Switched Systems

Event-Triggered Sampled-Data Iterative Learning Control for ...

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2023 China automation Congress, CAC 2023

作者： Ma, Xin Dai, Xisheng Zhou, Rusheng School of Automation Guangxi University of Science and Technology Liuzhou China School of Automation Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou China School of Computer Science and Technology Guangxi University of Science and Technology Liuzhou China

ISBN: (纸本)9798350303759

Consider a class of continues linear time-varying switched systems and study its sampled-data iterative learning control output tracking problem under event triggering. Firstly, in order to reduce the update batch of the control input in the direction of the iteration axis, an event-triggered sampled-data iterative learning control law is designed and the event-triggered conditions are derived by the Lyapunov function method. Secondly, a fixed threshold method is proposed to accelerate the convergence of the system. Then, according to the activation intervals of the switched subsystem,triggered and untriggered are analyzed for convergence, and the sufficient conditions for consistent convergence of the system are derived by the compression mapping method. Finally, the effectiveness of the algorithm is verified by the numerical simulation. © 2023 IEEE.

关键词： Learning algorithms

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Comparison of multiple reinforcement learning and deep reinforcement learning methods for the task aimed at achieving the goal

Mendel

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Mendel 2021年第1期27卷 1-8页

作者： Parak, Roman Matousek, Radomil Institute of Automation and Computer Science Brno University of Technology Czech Republic

Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physi-cal robots. In this paper, we compare several reinforcement learning (Q-Learning, SARSA) and deep reinforcement learning (Deep Q-Network, Deep Sarsa) methods for a task aimed at achieving a goal using robotics arm UR3. The main optimization problem of this experiment is to find the best solution for each RL/DRL scenario, respectively, minimize the Euclidean distance accuracy error and smooth the resulting path by the Bézier spline method. The simulation and real word application are controlled by the Robot Operating System (ROS). The learning environment is implemented using the OpenAI Gym library, which uses the RVIZ simulation tool and the Gazebo 3D modeling tool for dynamics and kinematics. © 2021, Brno University of technology. All rights reserved.

关键词： Reinforcement learning

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A Fully Sparse Detector with Cascade Heads for LiDAR-based 3D Object Detection 13

A Fully Sparse Detector with Cascade Heads for LiDAR-based 3...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Chen, Junnan Zhang, Gang Hu, Xiaolin Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science and Technology Institute for AI BNRist THU-Bosch JCML Center Tsinghua Universigy Beijing China

ISBN: (纸本)9798331516147

LiDAR-based 3D object detection is a critical component of perception systems in autonomous driving. Existing detectors typically rely on dense feature maps for 3D object prediction. However, as the perception range expands, the computational overhead associated with dense feature maps increases quadratically, creating significant challenges for scaling to long-range detection scenarios. To address this issue, fully sparse detectors have recently been proposed, but they often suffer from suboptimal performance or excessively complex architectures. In this paper, we present CHNet, a fully sparse detector designed to overcome these limitations by introducing a cascade head module, which consists of two sequential heads. By generating coarse object predictions, creating new feature points based on centers of the predicted objects, and aggregating features of the entire object to these new center points, CHNet enhances the qualities of object feature representations, achieving higher detection performance. Notably, CHNet effectively addresses the center feature missing problem in LiDAR-based 3D object detection by adding at most a hundred new feature points, offering a potential avenue for future research. On the widely used Waymo Open and Argoverse2 datasets, CHNet achieves impressive results without any additional embellishments. © 2025 IEEE.

关键词： Object recognition

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Mixed-integer programming model for ranking universities: Letting universities choose the weights

Mendel

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Mendel 2021年第1期27卷 41-48页

作者： Kudela, Jakub Institute of Automation and Computer Science Brno University of Technology Czech Republic

Regardless of the shortcomings and criticisms of world university rankings, these metrics are still widely used by students and parents to select universities and by universities to attract talented students and researchers, as well as funding. This paper proposes a new mixed-integer programming model for ranking universities. The new approach alleviates one of the criticisms – the issue of the "arbitrari-ness" of the weights used for aggregation of the individual criteria (or indicators) utilized in the contemporary rankings. Instead, the proposed model uses intervals of different sizes for the weights and lets the universities themselves "choose" the weights to optimize their position in the rankings. A numerical evaluation of the proposed ranking, based on the indicator values and weights from the Times Higher Education World University Ranking, is presented. © 2021, Brno University of technology. All rights reserved.

关键词： Students

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Design of large-scale hybrid, hydrogen and battery, energy storage systems for grid applications

IEEE Open Access Journal of Power and Energy

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IEEE Open Access Journal of Power and Energy 2025年 12卷 341-352页

作者： Dorn, Marvin Lotze, Jonas Kühnapfel, Uwe Weber, André Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Institute for Automation and Applied Informatics TransnetBW GmbH Stuttgart Germany Institute for Applied Computer Science Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

Due to the energy transition, which involves phasing out base load power plants such as coal, there is a need to establish storage systems within the energy system to compensate for fluctuations of renewable energies. Batteries are suitable for day-night cycles and particularly for short-cycle applications. To address the problem of dark-doldrums, when neither wind nor solar energy is available, gas and, in the more distant future, hydrogen power plants are to be used. By combining batteries and hydrogen power plants in a hybrid energy storage system, further advantages and application possibilities arise regarding grid stability and system design. This work illustrates interrelationships between the subsystems, optimizes proportions, and demonstrates logical system sizes, technologies, and their costs. A central part of the work are the self-derived methods for system design and the justification of these. Storage pressure, running times, availability time, annual cycles and design of the subsystems are described. Systems of this scale are difficult to imagine. A program developed as part of this work to implement the methods, visualizes the system, displays the system parameters, and shows the best-case and worst-case capital expenditures. An optimized system design is presented. Different combinations in the system design show the effects on capital expenditures. Starting from 2 to 4 hours of availability time, the hybrid system becomes cheaper than a pure battery system in terms of capital expenditures. © 2020 IEEE.

关键词： Electric power transmission networks

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