IEEE SMC: Systems, Man, & Cybernetics Society

IEEE/CAA Journal of Automatica Sinica

Scope

The scope of the IEEE/CAA Journal of Automatica Sinica includes the field of automation. The objective of this journal is high quality and rapid publication of articles, with a strong focus on new trends, original theoretical and experimental research and developments, emerging technologies, and industrial standards in automation. Specifically, the Journal focuses on such areas as automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network based automation, robotics, computer-aided technologies for automation systems, sensing and measurement, navigation, guidance, and control, smart city, smart grid, big data and data mining, internet of things, cyber-physical systems, blockchain, cloud computing for automation, mechatronics.

IEEE/CAA Journal of Automatica Sinica was lunched on January 1, 2014.

Editor-in-Chief

Qing-Long Han
Editor-In-Chief

Distinguished Professor, PhD, MAE, FIEEE, FIFAC, FIEAust
Member of the Academia Europaea (The Academy of Europe)

Pro Vice-Chancellor (Research Quality)

Swinburne University of Technology
EN Building, Level 6, Room 602c
John Street, Hawthorn, Melbourne, Victoria 3122, Australia

Tel.:  +61 3 9214 3808

Fax.:  +61 9214 8264

Email: [email protected]

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Articles

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31 March 2025
Parallel Medical Devices and Instruments: Integrating Edge and Cloud Intelligence for Smart Treatment and Health Systems
With the rapid development of technologies such as Artificial Intelligence (AI), edge computing, and cloud intelligence, the medical field is undergoing a fundamental transformation [1]. These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs. In this wave...
31 March 2025
$mathrm{E}^{2}text{AG}$: Entropy-Regularized Ensemble Adaptive Graph for Industrial Soft Sensor Modeling
Adaptive graph neural networks (AGNNs) have achieved remarkable success in industrial process soft sensing by incorporating explicit features that delineate the relationships between process variables. This article introduces a novel GNN framework, termed entropy-regularized ensemble adaptive graph $(mathbf{E}^{mathbf{2}}mathbf{AG})$, aimed at enhancing the predictive accuracy of AGNNs. Specifically, this work pioneers...
31 March 2025
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31 March 2025
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31 March 2025
Collision-Free Maneuvering for a UAV Swarm Based on Parallel Control
The maneuvering of a large-scale unmanned aerial vehicle (UAV) swarm, notable for flexible flight with collision-free, is still challenging due to the significant number of UAVs and the compact configuration of the swarm. In light of this problem, a novel parallel control method that utilizes space and time transformation is...
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21 January 2025
Chattering-Free Fault-Tolerant Cluster Control and Fault Direction Identification for HIL UAV Swarm with Pre-Specified Performance
In this paper, the problem of pre-specified performance fault-tolerant cluster consensus control and fault direction identification is solved for the human-in-the-loop (HIL) swarm unmanned aerial vehicles (UAVs) in the presence of possible non-identical and unknown direction faults (NUDFs) in the yaw chan-nel. The control strategy begins with the design of...
21 January 2025
Adaptive Control of a Flexible Manipulator with Unknown Hysteresis and Intermittent Actuator Faults
In this study, we consider a single-link flexible manipulator in the presence of an unknown Bouc-Wen type of hysteresis and intermittent actuator faults. First, an inverse hysteresis dynamics model is introduced, and then the control input is divided into an expected input and an error compensator. Second, a novel adaptive...
21 January 2025
Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy
In this paper, the attack detection problem is inves-tigated for a class of closed-loop systems subjected to unknown-but-bounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission. A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken...
20 January 2025
Decentralized Federated Learning Algorithm Under Adversary Eavesdropping
In this paper, we study the decentralized federated learning problem, which involves the collaborative training of a global model among multiple devices while ensuring data privacy. In classical federated learning, the communication channel between the devices poses a potential risk of compromising private information. To reduce the risk of adversary...
20 January 2025
When Software Security Meets Large Language Models: A Survey
Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in...

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