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T. Shen, J. Sun, S. Kong, Y. Wang, J. Li, X. Li, and F.-Y. Wang, "The journey/DAO/TAO of embodied intelligence: From large models to foundation intelligence and parallel intelligence," IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1313–1316, Jun. 2024. 

> A novel kernel framework of embodied intelligence.

> Correspondence between embodied intelligence and parallel intelligence.

> Journey/DAO/TAO and prospects of embodied intelligence.

J. Ren, J. Wen, Z. Zhao, R. Yan, X. Chen, and  A. Nandi,  “Uncertainty-aware deep learning: A promising tool for trustworthy fault diagnosis,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1317–1330, Jun. 2024. 

> Multivariate Gaussian distribution is employed into the deep architecture and some methods of representing diversity are combined to quantify both aleatoric and epistemic uncertainties simultaneously.

> On the basis of uncertainty decomposition graph given by our simplified algorithm, we proposed a unified trustworthy fault diagnosis framework, named as UU-DLF.

> Through the powerful performance and promising prospects shown by experiments, the uncertainty of deep learning proposed by our paper may provide a promising way for deep learning models to gain industrial users’ trust.

Z. Zhang, S. Gao, M. C. Zhou, M. Yan, and  S. Cao,  “Mapping network-coordinated stacked gated recurrent units for turbulence prediction,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1331–1341, Jun. 2024. 

> Proposes a novel deep learning method for solving turbulence prediction.

> Proposed method can extract the spatial-temporal feature in the turbulence well.

> Proposed method outperforms other state-of-the-art methods.

S. Qi, R. Wang, T. Zhang, X. Yang, R. Sun, and  L. Wang,  “A two-layer encoding learning swarm optimizer based on frequent itemsets for sparse large-scale multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1342–1357, Jun. 2024. 

> Discussed the limitations of the prevailing two-layer encoding SLMOEAs.

> Propose an innovative masking learning (ML) strategy based on the frequent itemsets.

> Propose a dynamic mutation strategy for binary variables suitable for ML.

Z. Wu, Y. Zhao, F. Li, T. Yang, Y. Shi, and  W. Gui,  “Asynchronous learning-based output feedback sliding mode control for semi-Markov jump systems: A descriptor approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1358–1369, Jun. 2024. 

> Arbitrary distribution of sojourn time and asynchronous strategy are applied to controller design.

> Chattering of traditional sliding mode control is alleviated by a novel iterative learning term.

> Sliding surface synthesis under incompletely available state information is presented.

J. Li, R. Qin, S. Guan, W. Ding, F. Lin, and  F.-Y. Wang,  “Attention markets of blockchain-based decentralized autonomous organizations,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1370–1380, Jun. 2024. 

> Design attention markets based on the Haberger Tax for DAOs.

> Formulate the Stackelberg game model in DAOs’ attention markets to capture the dynamics of attention trading.

> Discuss the equilibrium trading strategies to explore the attention pricing mechanisms among DAO members.

Z. Zuo, J. Tang, R. Ke, and  Q.-L. Han,  “Hyperbolic tangent function-based protocols for global/semi-global finite-time consensus of multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1381–1397, Jun. 2024. 

> New hyperbolic tangent function-based protocols for achieving finite-time consensus in multi-agent systems, offering a user-prescribed bounded control level with an explicit estimate of the settling time bound.

> Simplified design and stability analysis surpassing traditional saturation function and homogeneity theory approaches.

> Proven effectiveness and practicability via illustrative examples and applications in multi-BLDC motor systems.

L. Feng, B. Huang, J. Sun, Q. Sun, and  X.  Xie,  “Adaptive event-triggered time-varying output group formation containment control of heterogeneous multiagent systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1398–1409, Jun. 2024. 

> Studies a multilevel multigroup problem where agents are categorised into tracking leaders, formation leaders, and followers.

> Investigates the time-varying output group formation containment problem for general linear heterogeneous multi-agent systems under a directed topology, where the agents have different characteristics and dynamic models.

> Control protocol proposed is fully distributed and adopts new dynamic event-triggered mechanisms to efficiently save communication resources, and the Zeno behavior is ruled out by giving positive lower bounds of the inter-event intervals of two subsequences.

X. Tan, C. Hu, G. Cao, Q. Wei, W. Li, and  B. Han,  “Fixed-time antidisturbance consensus tracking for nonlinear multiagent systems with matching and mismatching disturbances,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1410–1423, Jun. 2024. 

> For MASs with dynamics in the form of strict feedback affine nonlinearity, a DFTO is developed for each follower to obtain an estimate of the leader's output by utilizing the topology of the communication network. 

> A FTDO is given to estimate the lumped disturbances for feedforward compensation.

> A nonsmooth antidisturbance backstepping tracking controller with feedforward compensation for lumped disturbances is designed base on the proposed DFTO and FTDO.

Q. Ge, Y. Cheng, H. Li, Z. Ye, Y. Zhu, and  G. Yao,  “A non-parametric scheme for identifying data characteristic based on curve similarity matching,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1424–1437, Jun. 2024. 

> On the condition of unknown data distribution, a Parzen window method on the sliding window technology is adopted for the data PDF rough estimation. 

> With the shape difference between a Gaussian-like PDF curve and a Gaussian PDF curve, the intelligent optimization algorithm adjusts mean and variance to analyse the difference between two curve shapes (slope, kullback-leibler divergence).

> Multi-index evaluation based on the method of weighted mean is proposed to improve the correct recognition rate between Gaussian-like PDF curve and standard Gaussian PDF curve.

Y. Li, X. Wang, Z. He, Z. Wang, K. Cheng, S.  Ding, Y. Fan, X. Li, Y. Niu, S. Xiao, Z. Hao, B. Gao, and  H. Wu,  “Industry-oriented detection method of PCBA defects using semantic segmentation models,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1438–1446, Jun. 2024. 

> Proposes a novel defect detection method for complex PCBAs which is geared towards actual production lines.

> A semantic segmentation model combined with computer vision methods is proposed to build up a defect detection framework. 

> Proposed method can significantly improve the throughput of the PCBA production line with a lower false-call rate and missing detection rate.

Q. Ma, P. Jin, and  F. L. Lewis,  “Guaranteed cost attitude tracking control for uncertain quadrotor unmanned aerial vehicle under safety constraints,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1447–1457, Jun. 2024. 

> Safety, robustness, and optimality are important, but sometimes they are conflicting criteria in controller design. 

> A simple control barrier function and a special disturbance attenuation function are designed to characterize the violations of safety constraints and tolerance of uncertain disturbances.

> A new weight update law for critic neural networks is designed by combining the concurrent learning technique with gradient descent method, which enhances the utilization rate of system data and relaxes the requirement for persistent excitation condition.

J. Liang, H. Lin, C. Yue, P. Suganthan, and  Y. Wang,  “Multiobjective differential evolution for higher-dimensional multimodal multiobjective optimization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1458–1475, Jun. 2024. 

> A new framework of MMO test functions is proposed, incorporating various components with different functions to control convergence difficulty, multimodal characteristics, and the extensibility of decision variables.

> Fifteen test instances are generated, allowing for a more comprehensive evaluation of the performance of MMOEAs.

> A new MMOEA is developed, called HDMMODE.

Z. Chen, J. Tang, and  Z. Zuo,  “A novel prescribed-performance path-following problem for non-holonomic vehicles,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1476–1484, Jun. 2024. 

> A novel paradigm termed the PPPF problem is introduced.

> A guiding vector field adhering to prescribed-performance criteria is advanced to facilitate adaptable path following tasks.

> Leveraging the prescribed-time sliding mode control approach, a novel path-following control framework is posited to propel a controlled vehicle towards specific segments of a designated trajectory.

Y. Zhao, X. He, M. Zhou, and  T. Huang,  “Accelerated primal-dual projection neurodynamic approach with time scaling for linear and set constrained convex optimization problems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1485–1498, Jun. 2024. 

> Proposed APDPNA-S with fast convergence rates.

> APDPNA-S can handle convex optimization problems with linear and set constraints, providing wider applicability.

> Extended APDPNA-S to differential inclusion dynamical approach (APDPNA-NS) with same results.

S. Shi and  J. Chen,  “Adaptive space expansion for fast motion planning,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1499–1514, Jun. 2024. 

> An ASE framework for motion planning which first focuses on exploring in a small promising region and then explores the outward expanded space.

> A particular hyper-ellipsoid ring expansion strategy such that the algorithm can also avoid repeatedly exploring the same space. 

> An adaptive division strategy divides a global hyper-ellipsoid into several small local hyper-ellipsoids.

C. Liu, W. Liu, T. Wei, and X. Li, “Input-to-state stability of impulsive switched systems involving uncertain impulse-switching moments,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1515–1517, Jun. 2024. 

L. Xue, J. Ye, Y. Wu, J. Liu, and D. C. Wunsch, “Prescribed-time Nash equilibrium seeking for pursuit-evasion game,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1518–1520, Jun. 2024. 

M. Yang, “New second-level-discrete zeroing neural network for solving dynamic linear system,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1521–1523, Jun. 2024. 

S. Wu, “Partially-observed maximum principle for backward stochastic differential delay equations,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1524–1526, Jun. 2024. 

J. Liu, S. Li, and R. Liu, “Recurrent neural network inspired finite-time control design,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1527–1529, Jun. 2024. 

M. Deng and C. Wang, “Gait recognition under different clothing conditions via deterministic learning,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1530–1532, Jun. 2024. 

F. Yan, X. Liu, and T. Feng, “Distributed minimum-energy containment control of continuous-time multi-agent systems by inverse optimal control,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1533–1535, Jun. 2024. 

P. Wu, H. Li, L. Hu, J. Ge, and N. Zeng, “A local-global attention fusion framework with tensor decomposition for medical diagnosis,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1536–1538, Jun. 2024.