A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

Vol. 10,  No. 11, 2023

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The ChatGPT After: Building Knowledge Factories for Knowledge Workers with Knowledge Automation
Yutong Wang, Xiao Wang, Xingxia Wang, Jing Yang, Oliver Kwan, Lingxi Li, Fei-Yue Wang
2023, 10(11): 2041-2044. doi: 10.1109/JAS.2023.123966
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Containment-Based Multiple PCC Voltage Regulation Strategy for Communication Link and Sensor Faults
Meina Zhai, Qiuye Sun, Rui Wang, Huaguang Zhang
2023, 10(11): 2045-2055. doi: 10.1109/JAS.2023.123747
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The distributed AC microgrid (MG) voltage restoration problem has been extensively studied. Still, many existing secondary voltage control strategies neglect the co-regulation of the voltage at the point of common coupling (PCC) in the AC multi-MG system (MMS). When an MMS consists of sub-MGs connected in series, power flow between the sub-MGs is not possible if the PCC voltage regulation relies on traditional consensus control objectives. In addition, communication faults and sensor faults are inevitable in the MMS. Therefore, a resilient voltage regulation strategy based on containment control is proposed. First, the feedback linearization technique allows us to deal with the nonlinear distributed generation (DG) dynamics, where the PCC regulation problem of an AC MG is transformed into an output feedback tracking problem for a linear multi-agent system (MAS) containing nonlinear dynamics. This process is an indispensable pre-processing in control algorithm design. Moreover, considering the unavailability of full-state measurements and the potential faults present in the sensors, a novel follower observer is designed to handle communication faults. Based on this, a controller based on containment control is designed to achieve voltage regulation. In regulating multiple PCC voltages to a reasonable upper and lower limit, a voltage difference exists between sub-MGs to achieve power flow. In addition, the secondary control algorithm avoids using global information of directed communication network and fault boundaries for communication link and sensor faults. Finally, the simulation results verify the performance of the proposed strategy.

Disturbance Observer-Based Safe Tracking Control for Unmanned Helicopters With Partial State Constraints and Disturbances
Haoxiang Ma, Mou Chen, Qingxian Wu
2023, 10(11): 2056-2069. doi: 10.1109/JAS.2022.105938
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In this paper, a disturbance observer-based safe tracking control scheme is proposed for a medium-scale unmanned helicopter with rotor flapping dynamics in the presence of partial state constraints and unknown external disturbances. A safety protection algorithm is proposed to keep the constrained states within the given safe-set. A second-order disturbance observer technique is utilized to estimate the external disturbances. It is shown that the desired tracking performance of the controlled unmanned helicopter can be achieved with the application of the backstepping approach, dynamic surface control technique, and Lyapunov method. Finally, the availability of the proposed control scheme has been shown by simulation results.

Can Digital Intelligence and Cyber-Physical-Social Systems Achieve Global Food Security and Sustainability?
Yanfen Wang, Mengzhen Kang, Yali Liu, Juanjuan Li, Kai Xue, Xiujuan Wang, Jianqing Du, Yonglin Tian, Qinghua Ni, Fei-Yue Wang
2023, 10(11): 2070-2080. doi: 10.1109/JAS.2023.123951
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Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration behavior. From the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability.Digital intelligence (DI) and cyber-physical-social systems (CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence (AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semi-arid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the upstream, midstream and downstream areas. Furthermore, establishing mechanisms for water resource transfer and trade among different industries can be instrumental in maximizing the benefits derived from water resources. Finally, we envisage a future of agriculture characterized by the integration of digital, robotic and biological farming techniques. This vision aims to incorporate small tasks, big models, and deep intelligence into the regular ecological practices of intelligent agriculture.
An Optimal Control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network
Zhe Chen, Ning Li
2023, 10(11): 2081-2093. doi: 10.1109/JAS.2022.105992
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This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential equation. Hence, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning (RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.

Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation
Shaohua Teng, Zefeng Zheng, Naiqi Wu, Luyao Teng, Wei Zhang
2023, 10(11): 2094-2107. doi: 10.1109/JAS.2023.123318
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Domain adaptation (DA) aims to find a subspace, where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples well. Existing approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity (AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity (GECS), and adaptive graph embedding (AGE). GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS, the neighborhood samples with the same label are rewarded, while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.
GraphCA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing
Xinhua Wang, Shasha Zhao, Lei Guo, Lei Zhu, Chaoran Cui, Liancheng Xu
2023, 10(11): 2108-2123. doi: 10.1109/JAS.2023.123678
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With the popularity of online learning in educational settings, knowledge tracing (KT) plays an increasingly significant role. The task of KT is to help students learn more effectively by predicting their next mastery of knowledge based on their historical exercise sequences. Nowadays, many related works have emerged in this field, such as Bayesian knowledge tracing and deep knowledge tracing methods. Despite the progress that has been made in KT, existing techniques still have the following limitations: 1) Previous studies address KT by only exploring the observational sparsity data distribution, and the counterfactual data distribution has been largely ignored. 2) Current works designed for KT only consider either the entity relationships between questions and concepts, or the relations between two concepts, and none of them investigates the relations among students, questions, and concepts, simultaneously, leading to inaccurate student modeling. To address the above limitations, we propose a graph counterfactual augmentation method for knowledge tracing. Concretely, to consider the multiple relationships among different entities, we first uniform students, questions, and concepts in graphs, and then leverage a heterogeneous graph convolutional network to conduct representation learning. To model the counterfactual world, we conduct counterfactual transformations on students’ learning graphs by changing the corresponding treatments and then exploit the counterfactual outcomes in a contrastive learning framework. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed GraphCA method compared with several state-of-the-art baselines.

Multi-Objective Optimization for an Industrial Grinding and Classification Process Based on PBM and RSM
Xiaoli Wang, Luming Liu, Lian Duan, Qian Liao
2023, 10(11): 2124-2135. doi: 10.1109/JAS.2023.123333
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The grinding and classification process is one of the key sub-processes in mineral processing, which influences the final process indexes significantly and determines energy and ball consumption of the whole plant. Therefore, optimal control of the process has been very important in practice. In order to stabilize the grinding index and improve grinding capacity in the process, a process model based on population balance model (PBM) is calibrated in this study. The correlation between the mill power and the operating variables in the grinding process is modelled by using the response surface method (RSM), which solves the problem where the traditional power modeling method relies on some unobservable mechanism-related parameters. On this basis, a multi-objective optimization model is established to maximize the useful power of the grinding circuit to improve the throughput of the grinding operation and improve the fraction of –0.074 mm particles in the hydrocyclone overflow to smooth the subsequent flotation operation. The elite non-dominated sorting genetic algorithm-II (NSGA-II) is then employed to solve the multi-objective optimization problem. Finally, subjective and objective weighting methods and integrated multi-attribute decision-making methods are used to select the optimal solution on the Pareto optimal solution set. The results demonstrate that the throughput of the mill and the fraction of –0.074 mm particles in the overflow of the cyclone are increased by 3.83 t/h and 2.53%, respectively.

Regularization by Multiple Dual Frames for Compressed Sensing Magnetic Resonance Imaging With Convergence Analysis
Baoshun Shi, Kexun Liu
2023, 10(11): 2136-2153. doi: 10.1109/JAS.2023.123543
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Plug-and-play priors are popular for solving ill-posed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-and-play priors relies on the assumption of bounded denoisers. However, the bounded properties of existing plugged Gaussian denoisers have not been proven explicitly. To bridge this gap, we detail a novel provable bounded denoiser termed as BMDual, which combines a trainable denoiser using dual tight frames and the well-known block-matching and 3D filtering (BM3D) denoiser. We incorporate multiple dual frames utilized by BMDual into a novel regularization model induced by a solver. The proposed regularization model is utilized for compressed sensing magnetic resonance imaging (CSMRI). We theoretically show the bound of the BMDual denoiser, the bounded gradient of the CSMRI data-fidelity function, and further demonstrate that the proposed CSMRI algorithm converges. Experimental results also demonstrate that the proposed algorithm has a good convergence behavior, and show the effectiveness of the proposed algorithm.

Diverse Deep Matrix Factorization With Hypergraph Regularization for Multi-View Data Representation
Haonan Huang, Guoxu Zhou, Naiyao Liang, Qibin Zhao, Shengli Xie
2023, 10(11): 2154-2167. doi: 10.1109/JAS.2022.105980
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Deep matrix factorization (DMF) has been demonstrated to be a powerful tool to take in the complex hierarchical information of multi-view data (MDR). However, existing multi-view DMF methods mainly explore the consistency of multi-view data, while neglecting the diversity among different views as well as the high-order relationships of data, resulting in the loss of valuable complementary information. In this paper, we design a hypergraph regularized diverse deep matrix factorization (HDDMF) model for multi-view data representation, to jointly utilize multi-view diversity and a high-order manifold in a multi-layer factorization framework. A novel diversity enhancement term is designed to exploit the structural complementarity between different views of data. Hypergraph regularization is utilized to preserve the high-order geometry structure of data in each view. An efficient iterative optimization algorithm is developed to solve the proposed model with theoretical convergence analysis. Experimental results on five real-world data sets demonstrate that the proposed method significantly outperforms state-of-the-art multi-view learning approaches.
A Range-Based Node Localization Scheme for UWASNs Considering Noises and Aided With Neurodynamics Model
Lijuan Wang, Xiujuan Du, Chong Li
2023, 10(11): 2168-2170. doi: 10.1109/JAS.2023.123348
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Intelligent Electric Vehicle Charging Scheduling in Transportation-Energy Nexus With Distributional Reinforcement Learning
Tao Chen, Ciwei Gao
2023, 10(11): 2171-2173. doi: 10.1109/JAS.2023.123285
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A Model Predictive Control Algorithm Based on Biological Regulatory Mechanism and Operational Research
Jinying Yang, Yongjun Zhang, Tanju Yildirim, Jiawei Zhang
2023, 10(11): 2174-2176. doi: 10.1109/JAS.2023.123303
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