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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04 May 2026
Robust Brain Tumor Segmentation with Incomplete MRI Modalities Using Hölder Divergence and Mutual Information-Enhanced Knowledge Transfer
Multimodal MRI (magnetic resonance imaging) provides critical complementary information for accurate brain tumor segmentation. However, conventional methods struggle when certain modalities are missing due to issues like image quality, protocol inconsistencies, patient allergies, or financial constraints. To address this, we propose a robust single-modality parallel processing framework that achieves high...
04 May 2026
Vision-Guided Robotic System for Automatic Fish Quality Grading and Packaging
Dear Editor, Automated fish grading and packaging, critical in the seafood industry, have not been simultaneously addressed by prior works using machine vision and robotics. This letter presents a novel proof-of-concept robotic vision system for automatic, size-based fish grading and packaging. Our system classifies frozen fish steaks into two size...
04 May 2026
A Distributed Braking Scheme for Heavy-Haul Trains Using Coupler Force Compensation
In this paper, a novel distributed braking scheme is proposed for automatic heavy-haul trains equipped with an electronically controlled pneumatic (ECP) braking system. The scheme consists of a coupler force compensator and a cooperative controller. The compensator is designed to counteract the coupler force acting on each car, i.e., the...
04 May 2026
Model-Free Variable Impedance Control of Redundant Manipulators for Soft Tissue Puncture
Robotic-assisted medical technology has long been a key area of research in modern surgical medicine. Robotic-assisted puncture techniques, both theoretically and practically, hold significant potential to improve puncture precision and overall surgical outcomes in clinical practice. This paper presents a model-free variable impedance control (MFVIC) method for robotic soft tissue...
30 April 2026
Understanding Agentic AI: Algorithms and Infrastructure
The rapid evolution of large language models (LLMs) towards autonomous Agentic artificial intelligence (AI) necessitates a systemic overhaul across algorithms, infrastructure, and architectures. This paper presents a unified view of the “Agentic AI Infrastructure,” connecting research threads often studied in isolation. First, post-training algorithms are reviewed, contrasting traditional reinforcement learning...
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01 April 2026
Flexible Federated Learning in Machinery Fault Diagnostics with Light Communication
While data-driven fault diagnosis methods have been successfully developed in the past years, large amounts of high-quality condition monitoring data are generally required to ensure model performance. Due to the high economic and labor costs in data collection, it is difficult for a single user to build an effective database,...
01 April 2026
Prescribed-Time Formation Control for Multi-Agent Systems with Uncertain Nonlinear Dynamics and Non-Vanishing Random Disturbances
This paper investigates the problem of prescribed-time formation control for multi-agent systems with directed communication topology, uncertain nonlinear dynamics, and non-vanishing random disturbances. To drive the formation error to zero within a prescribed time, a novel prescribed-time control lemma is developed. A distributed observer is designed to allow each follower...
01 April 2026
Datasets, Metrics, Benchmarks and Future Research in Autonomous Driving: A Review
Data-driven autonomous driving is a hot topic in academic and industry research due to its impressive performance, flexible mobility, and reduced human intervention. However, the development of this technology relies heavily on large datasets that contain accurately annotated data, obtained through artificial or semi-automated strategies. Consequently, datasets play a crucial...
01 April 2026
M3Net: Meta-Reinforcement Learning-Based Open-Set Domain Generalization of Hyperspectral Image Classification Model
Hyperspectral image (HSI) classification models face dual challenges in open-set domain generalization: limited generalization ability due to unseen-domain shifts, and the need for unknown class recognition that breaks the closed-set assumption of traditional models. To address these challenges, we propose the Markov meta-Mamba network (M3Net), which provides a meta-reinforcement learning-based...
01 April 2026
CCDNN: A Novel Deep Learning Architecture for Multi-Source Data Fusion
With the rapid development of Industrial 4.0 and Industrial Internet of Things, the data collection with multi-source has significantly improved. How to effectively fuse these data for various engineering applications is still an open and challenge issue. To this end, we propose the canonical correlation guided deep neural network (CCDNN),...

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