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Secure Cross-Domain Authentication and Data Sharing Scheme for IIoT in Cloud-Fog automation Architecture

ieee Journal on Selected Areas in Communications 2025年

作者： Chen, Xi Hu, Chunqiang Cai, Bin Hu, Pengfei Yu, Jiguo Ministry of Education of China Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing China Chongqing University School of Big Data and Software Engineering Chongqing China Shandong University School of Computer Science and Technology Shandong China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Qilu University of Technology Big Data Institute Jinan China

Cloud-fog automation architecture has propelled the advancement of the Industrial Internet of Things (IIoT), significantly enhancing production efficiency and intelligence through extensive data collection and connectivity. Simultaneously, industrial cyber-physical system leverages this data to achieve intelligent control and optimization of production processes. As industrial production becomes increasingly specialized and complex, independent operations within a single domain are no longer sufficient to meet demands, making cross-domain collaborative production inevitable. Consequently, ensuring the security of cross-domain communication and data sharing has become a critical issue for IIoT under the cloud-fog automation architecture. Existing solutions encounter substantial management and computational burdens in cross-domain communication and data sharing, and they are vulnerable to privacy leakage risks. To address these challenges and enhance industrial production efficiency, this paper uses consortium blockchain to co-design a cross-domain authentication and data sharing scheme. The scheme ensures secure and private cross-domain communications with minimal computational, communication, and storage overhead. And, the proposed time-specific plaintext checkable encryption protocol can secure data during cross-domain sharing. Security and performance analyses show that the proposed scheme effectively reduces computational and communication resource demands while maintaining communication and data security. © 1983-2012 ieee.

关键词： Sensitive data

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Continuous Shortest Path Vector Field Navigation on 3D Triangular Meshes for Mobile Robots

Continuous Shortest Path Vector Field Navigation on 3D Trian...

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ieee International Conference on Robotics and automation (ICRA)

作者： Sebastian Pütz Thomas Wiemann Malte Kleine Piening Joachim Hertzberg Knowledge Based Systems Institute of Computer Science Osnabrück University Plan-Based Robot Control German Research Center for Artificial Intelligence DFKI

We present a highly efficient approach to compute continuous shortest path vector fields on arbitrarily shaped 3D triangular meshes for robot navigation in complex real-world outdoor environments. The continuity of the vector field allows to query the shortest distance, direction and geodesic path to the goal at any point within the mesh triangles, resulting in accurate paths. In order to avoid impassable areas, our wavefront propagation method runs on a modular extendable multilayer map architecture taking different geometric cost layers into account. We describe the mathematical foundation of the geodesic distances and continuous vector field computation and demonstrate the performance in real-world and multilevel environments on our campus with a tunnel, ramps and stair- cases, and in a difficult, steep forest area with a stone quarry. For reproducibility, we provide a ready-to-use ROS software stack as well as Gazebo simulations.

关键词： Three-dimensional displays Runtime Costs Navigation Robot kinematics computational modeling Forestry

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Resilient Optimal Virtual Synchronous Generator control Under DoS Attacks

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ieee Transactions on Circuits and Systems II: Express Briefs 2025年

作者： Zhang, Bowei Deng, Chao Wang, Bohui Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence Nanjing210003 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology for Carbon Neutrality Nanjing210023 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China

This brief leverages a novel resilient reinforcement learning (RL) technique to tackle the model-free optimal transient problem for virtual synchronous generators (VSGs) under denial-of-service (DoS) attacks. An optimal control strategy is established from real-time data of the VSGs under DoS attacks using the iterative algebraic Riccati equation (ARE). Furthermore, with a theoretical upper limit for DoS attack durations, system resilience is analyzed using the Lyapunov method. The resilient hybrid iteration (RHI) algorithm introduced in this brief eliminates the assumption of initial stable control in previous approaches, removes the need for precise system dynamics, and reduces computational requirements. Finally, the effectiveness of this approach is demonstrated through simulation experiments. © 2004-2012 ieee.

关键词： Reinforcement learning

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Load Prediction Model of Gas Boiler Generator Set Based on CNN-LSTM 11th

Load Prediction Model of Gas Boiler Generator Set Based on...

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11th International symposium on computational intelligence and Industrial Applications, ISCIIA 2024

作者： Xu, Yan Wu, Min Hu, Jie Peng, Fu-Sheng Zhang, Wen Song, Wen-Shuo Li, Hui-Hang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789819647521

With the proposal of carbon peak and carbon neutrality targets and the construction of a new power system, improving energy efficiency and reducing carbon emissions have become the key to the sustainable development of industrial boiler generator sets. However, the current load scheduling of gas boiler power plants is mainly based on manual experience, which limits achieving more efficient and stable operation. Accurate short-term load forecasting can help dispatchers to make reasonable production schedules. Therefore, this paper proposes a hybrid load forecasting model for generator sets based on convolutional neural networks combined with long short-term memory networks. First, the Isolation Forest algorithm eliminates anomalies in the historical data set. Then, variables related to unit load are selected by combining mechanistic analysis with the Spearman algorithm. Considering the multiple operating conditions and high complexity of unit loads, the CNN-LSTM network is used to fully extract the spatial and temporal characteristics to build the load prediction model. Finally, experiments are conducted using actual production data, showing that the proposed method is effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric load dispatching

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Analysis Platform of Student Learning Data Combined with Computer Vision

Analysis Platform of Student Learning Data Combined with Com...

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ieee International symposium on Information (IT) in Medicine and Education, ITME

作者： Xinghui Ding Zhijun Zhang Zhaohui Yang Zhiyun Yu College of Cyberspace Security Zhengzhou University Zhengzhou China College of Computer Science and Technology Shandong Jianzhu University Jinan China

At present, most teaching platforms based on artificial intelligence are designed to launch learning tasks for students. However, those teaching platforms are less to analyze the knowledge mastery of students and less to carry out personalized and accurate teaching according to the learning situation. Teachers using those platforms generally check the learning situation of the whole student in a coarse granularity, which will lead to the lack of data analysis and learning situation of a single student, and therefore there is no feedback to adjust the teaching strategy. Those factors contribute to the insufficiency of communication and interaction between teachers and students. The effect of online teaching is inferior to offline teaching. In this paper, we proposed a learning platform that combines artificial intelligence. This platform aims to strengthen teachers' control over students' learning process and help teachers improve their teaching strategies. This paper combines deep learning technology with computer vision to collect data and analyze students' learning status, using machine algorithms to achieve identity binding and data feedback. Finally, we also established the environment for big data for the future expansion of data analysis functions.

关键词： Deep learning Computer vision Analytical models Data analysis Face recognition computational modeling Education

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Adaptation of a wheel loader automatic bucket filling neural network using reinforcement learning

Adaptation of a wheel loader automatic bucket filling neural...

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International Joint Conference on Neural Networks (IJCNN) held as part of the ieee World Congress on computational intelligence (ieee WCCI)

作者： Dadhich, Siddharth Sandin, Fredrik Bodin, Ulf Andersson, Ulf Martinsson, Torkjorn Lulea Univ Technol S-97187 Lulea Sweden Volvo CE Bolindervagen 5 S-63185 Eskilstuna Sweden

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169262

Bucket-filling is a repetitive task in earth-moving operations with wheel-loaders, which needs to be automated to enable efficient remote control and autonomous operation. Ideally, an automated bucket-filling solution should work for different machine-pile environments, with a minimum of manual retraining. It has been shown that for a given machine-pile environment, a time-delay neural network can efficiently fill the bucket after imitation-based learning from 100 examples by one expert operator. Can such a bucket-filling network be automatically adapted to different machine-pile environments without further imitation learning by optimization of a utility or reward function? This paper investigates the use of a deterministic actor-critic reinforcement learning algorithm for automatic adaptation of a neural network in a new pile environment. The algorithm is used to automatically adapt a bucket-filling network for medium coarse gravel to a cobble-gravel pile environment. The experiments presented are performed with a Volvo L180H wheel-loader in a real-world setting. We found that the bucket-weights in the novel pile environment can improve by five to ten percent within one hour of reinforcement learning with less than 40 bucket-filling trials. This result was obtained after investigating two different reward functions motivated by domain knowledge.

关键词： reinforcement learning imitation learning bucket filling wheel loader automation construction

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Desired Footprint by Technology Mapping Modification using a Genetic Algorithm in Odin II 31

Desired Footprint by Technology Mapping Modification using a...

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31st International Workshop on Rapid System Prototyping (RSP) - Shortening the Path from Specification to Prototype

作者： Damghani, Seyed Alireza Legault, Jean-Philippe Kent, Kenneth B. Univ New Brunswick Fredericton NB Canada

ISBN: (纸本)9781728184661

Technology mapping is the transformation of a general Boolean logic network into a functional equivalent K-LUT network that can be implemented by the target FPGA device. Because an FPGA architecture is pre-determined, technology mapping is limited to the available resources. However, circuits can be optimized before the low-level synthesis phase. Odin II, part of the Verilog-to-routing project, is responsible for synthesis and elaboration. In the partial mapping phase of Odin II, some modifications are still possible for high-level modules-adder, multiplier-when there is no hard block available. When Odin II performs partial mapping to create soft logic, we can choose which implementation of a high-level module works best with respect to the desired goals: area versus speed. In this paper, we describe a method to modify circuit characteristics based on placement criteria. More specifically, after partial mapping circuit components during Verilog HDL code synthesis, there are still potential modifications in soft-logic circuit generation. We propose using a genetic algorithm during synthesis to adjust soft-logic circuit implementation in order to achieve the desired synthesis goal. We show that the approach provides promising results for a marginal cost in runtime.

关键词： Design automation Logic design Reconfigurable logic computational intelligence Genetic algorithms CAD flow Odin II

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Saturated Adaptive Output-Constrained control of Cooperative Spacecraft Rendezvous and Docking

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ieee/CAA Journal of Automatica Sinica 2019年第6期6卷 1462-1470页

作者： Liang Sun IEEE the Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation and Electrical Engineering and also with the Institute of Artificial Intelligence University of Science and Technology Beijing

This paper investigates the robust relative pose control for spacecraft rendezvous and docking with constrained relative pose and saturated control inputs.A barrier Lyapunov function is used to ensure the constraints of states,so that the computational singularity of the inverse matrix in control command can be avoided,while a linear auxiliary system is introduced to handle with the adverse effect of actuator *** tuning rules for designing parameters in control command and auxiliary system are derived based on the stability analysis of the closed-loop *** is proved that all closed-loop signals always keep bounded,the prescribed constraints of relative pose tracking errors are never violated,and the pose tracking errors ultimately converge to small neighborhoods of *** experiments validate the performance of the proposed robust saturated control strategy.

关键词： Output-constrained control pose tracking control rendezvous and docking saturated control singularity-free control spacecraft control

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Smooth Reference Line Generation for a Race Track with Gates based on Defined Borders 31

Smooth Reference Line Generation for a Race Track with Gates...

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31st ieee Intelligent Vehicles symposium, IV 2020

作者： Zubaca, Jasmina Stolz, Michael Watzenig, Daniel Institute of Automation and Control Graz University of Technology Graz8010 Austria Institute of Automation and Control Graz University of Technology Virtual Vehicle Research Center Graz8010 Austria

As racing sports pushed the technological limits of vehicles in the past, automated racing has the potential to directly evaluate new technologies from research in a competitive environment. The required speed of implementation and the fair evaluation criteria enable very fast progress in selecting potential strategies, architectures and algorithms. By this, automated racing strongly contributes to science as well as to future every day implementations of driving automation. In this publication, the approaches used by the team Autonomous-Racing-Graz at the ROBORACE final races in season alpha are revealed. The contribution describes how to generate a smooth reference line in case of non-smooth borders and additional precision gates on a race track. The generation of an initial smooth reference line is key to assure convergence in later optimization. The described data processing is based on track geometry defined by borders only. The specific challenge addressed in the approach was the incorporation of tight, short gates and the low computational effort of the algorithms, enabling fast tuning and adaptation during racing events onsite. © 2020 ieee.

关键词： Autonomous vehicles

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CPSoSaware: Cross-layer Cognitive Optimization Tools & Methods for the Lifecycle Support of Dependable CPSoS 19

CPSoSaware: Cross-layer Cognitive Optimization Tools & Metho...

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19th ieee-Computer-Society Annual symposium on VLSI (ISVLSI)

作者： Keramidas, Georgios Antonopoulos, Christos P. Voros, Nikolaos Jaaskelainen, Pekka Cid, Marisa Catalan Zacharaki, Evangelia, I Fournaris, Apostolos P. Lalos, Aris Aristotle Univ Thessaloniki Sch Informat Thessaloniki Greece Univ Peloponnese Elect & Comp Engn Dept Tripoli Greece Tampere Univ Dept Pervas Comp Tampere Finland I2Cat Fdn Mobile & Wireless Internet Unit MWI Barcelona Spain Univ Patras Elect & Comp Engn Dept Patras Greece Athena Res & Innovat Ctr Informat Commun & Knowle Ind Syst Inst Maroussi Greece

ISBN: (纸本)9781728157757

Cyber-physical Systems of Systems (CPSoS) are large complex systems where physical elements interact with and are controlled by a large number of distributed and networked computing elements as well as human users. Their increasingly stringent demands on efficient use of resources, high service and product quality levels and, of course low cost and competitiveness on the world market introduce big challenges related to the design operation continuum of dependable connected CPSs. The CPSoSaware project aims at developing the models and software tools to allocate computational power/resources to the CPS end devices and autonomously determining what cyber-physical processes will be handled by the devices' heterogeneous components (CPUs, GPUs, FPGA fabric, software stacks). The project relies on Artificial intelligence (AI) support to strengthen reliability, fault tolerance and security at system level and also to lead to CPS designs that work in a decentralized way, collaboratively, in an equilibrium, by sharing tasks and data with minimal central intervention. The CPSoSaware system will interact with the human users/operators through extended reality visual and touchable interfaces increasing situational awareness. The CPSoSaware system will be evaluated: i) in the automotive sector, in mixed traffic environments with semi-autonomous connected vehicles and ii) in the manufacturing industry where inspection and repair scenarios are employed using collaborative robots.

关键词： CPS Embedded Systems Monitoring and control Systems Computer Hardware and Architecture System of Systems Automotive Manufacturing

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