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 September 2024
Urban Traffic Control Meets Decision Recommendation System: A Survey and Perspective
Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems. Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level, utilizing their knowledge and expertise. However, this process is cumbersome,...
04 September 2024
Evolution and Role of Optimizers in Training Deep Learning Models
To perform well, deep learning (DL) models have to be trained well. Which optimizer should be adopted? We answer this question by discussing how optimizers have evolved from traditional methods like gradient descent to more advanced techniques to address challenges posed by high-dimensional and non-convex problem space. Ongoing challenges include...
04 September 2024
Learning Sequential and Structural Dependencies Between Nucleotides for RNA N6-Methyladenosine Site Identification
N6-methyladenosine (m6A) is an important RNA methylation modification involved in regulating diverse biological processes across multiple species. Hence, the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level. Although a variety of identification algorithms have been proposed recently, most of...
04 September 2024
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04 September 2024
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15 August 2024
Data Driven Vibration Control: A Review
With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing, aviation, and healthcare, the data driven vibration control (DDVC) has attracted broad interests from both the industrial and academic communities. Input shaping (IS), as a simple and effective feedforward method, is greatly demanded in...
15 August 2024
Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems
This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time systems. The nonlinear system is modeled using linear parameter-varying (LPV) systems. A model-based probabilistic safe controller is first designed to guarantee probabilistic $lambda$-contractivity (i.e., stability and invariance) of the LPV system with...
19 July 2024
A Deep Residual PLS for Data-Driven Quality Prediction Modeling in Industrial Process
Partial least squares (PLS) model is the most typical data-driven method for quality-related industrial tasks like soft sensor. However, only linear relations are captured between the input and output data in the PLS. It is difficult to obtain the remaining nonlinear information in the residual subspaces, which may deteriorate the...
19 July 2024
Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems
Constrained multi-objective optimization problems (CMOPs) generally contain multiple constraints, which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions, thus they propose serious challenges for solvers. Among all constraints, some constraints are highly correlated with optimal feasible regions; thus they can provide effective...
12 June 2024
Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints
The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on...

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