With the rapid development of information technology, artificial intelligence, and robotics, unmanned autonomous systems have demonstrated tremendous application potential in fields such as military, aerospace, marine exploration, disaster rescue, and intelligent transportation. Distributed cooperative control, as a key technology for achieving efficient and flexible collaboration among multiple unmanned autonomous systems, has become a research focus. A review is conducted on the research progress in distributed cooperative control of unmanned autonomous systems. Firstly, it discusses the core theories in the aspects of consensus problems, formation control, and distributed optimization. Then, combining the practical applications of current multiple unmanned autonomous systems, it presents the latest research achievements in unmanned aerial vehicles, unmanned ground vehicles, unmanned surface vessels, unmanned underwater vehicles, and multi-modal cooperative control. Finally, it explores the future challenges and development in this field.

Piezoelectric actuators have the advantages of high displacement resolution, compact size, rapid response, strong load-driving capability, the ability to provide multi-degree of freedom outputs, and immunity to electromagnetic interference etc, which are widely used in micro-nano processing, micro-electromechanical system packaging, bio-medicine, aerospace engineering and other fields. However, the nonlinear characteristics of piezoelectric actuators, such as hysteresis and creep, make affection for the stability and can cause unstable and inaccurate outputs, which requires certain appropriate control strategies to overcome the above problems. In this research, the nonlinear characteristics of piezoelectric actuators are firstly summarized.Secondly, research progress of hysteresis model and creep model is reviewed. Then, the compensation of control strategy and research progress of piezoelectric actuators are introduced. Finally, the trend of piezoelectric actuator future development in control technology is discussed and prospected.

The inclusion of connected and automated vehicles as leading vehicles in mixed platoons has the potential to achieve smoother and safer road traffic, but current research seldom focuses on the formation of mixed platoons with multi-lane distribution characteristics. To address this problem, a game-based lane-changing decision model is proposed for leading CAVs in mixed platoons. The model establishes a lane-changing decision mechanism that combines optimized and game-based approaches. It initiates a leading CAVs' target lane initialization process with the objective of minimizing the number of CAVs lane changes. Based on the payoff from CAVs' game-based lane-changing, it updates the target lanes of leading CAVs to achieve multi-lane distribution of mixed platoons. Furthermore, a non-cooperative game matrix between CAVs and HDVs is established based on game theory, considering both lane-changing efficiency and safety. Time and safety payoff functions are designed to quantify the lane-changing risk of CAVs. Microscopic simulations are conducted using the traffic software SUMO. Experimental results demonstrate that compared to the baseline model, the proposed game-based lane-changing strategy maintains a mixed formation completion rate of over 97% under different mixed traffic volumes, with an average reduction of about 40% in the lane-changing time for each group of leading CAVs, while keeping the lane-changing frequency of each group of leading CAVs at a low level.

The ever-increasing functionalities and escalating complexity of existing Advanced Driver Assistance Systems inevitably cause the problem of Safety of The Intended Functionality. The identification and generation of trigger conditions play a critical role in SOTIF activities. Most existing trigger condition identification approachesare mainly based on the System-Theoretic Process Analysis method, which however neglect the issues within the system's functional state transitions. This paper adopts a knowledge-driven approach to construct a trigger condition identification mechanism by integrating STPA and Finite State Machine theories to establish an expanded system control structure. Safety analysis is conducted concerning the expanded control architecture and functional state transitions. By considering system limitations and human misuse, trigger conditions are identified, generated, described, classified, and labeled. Finally, the proposed trigger condition generation mechanism is applied to an Integrated Cruise Assistance system, obtaining trigger conditions and their classifications. The generated mechanism is compared with existing trigger condition generation methods, demonstrating its practicality, feasibility, and effectiveness.

In the multi-agent systems with switching topology, to address the consistency problem weaken by the high and low density information, a Layered Neighbor Selection(LNS) algorithm is proposed. First, this algorithm divides the neighborhood of the agent into layers, and selects the representative neighbor agents from each layer for communication, state update and state evolution. Then, it designs a layer adjustment strategy and a layer fusion strategy to accelerate the convergence speed, Finally, it designs a layer neighborhood selection consistency protocol, and provides the influence of the number of layers on the convergence. The convergence effect of the traditional consistency protocol is limited to a specific density range and is greatly affected by different densities. Differently, the proposed protocol of this paper can adapt to different density ranges and improve the convergence speed under the condition of system stability. The stability of the consistency protocol is proved by the Lyapunov function method. The effectiveness of the proposed consistency protocol is verified by simulation in comparison with several consistency protocols.

Considering the situation that multi-agent systems subject to an external disturbance in the complex environment and malicious attacks in the controller, the leader-following quasi-consensus of a class of disturbed nonlinear multi-agent system under the impulsive control and an event-triggered mechanism is studied. Unlike existing literature that requires a minimum triggering interval to be specified in advance, Zeno behavior is avoided by setting appropriate parameters in the designed dynamic event-triggered mechanism. Based on this mechanism, some sufficient conditions to achieve quasi-consensus are further proposed, and the upper bound of error states is also estimated. Finally, a numerical simulation example verifies the feasibility of the results.

To address the performance optimization issues in federated learning for unmanned aerial vehicle (UAV) digital twin edge networks, a resource scheduling strategy is proposed based on deep reinforcement learning for UAV digital twin edge networks. Considering dynamic and time varying features of UAV digital twin edge networks environment, a twin network model is built consisting of base station (BS) , intelligent terminals, UAV and wireless transmission channel. Then an adaptive resource optimization model is formulated which jointly optimized UAV flying distance, flying angle and spectrum resource allocation, in order to minimize time delay of federated learning. Moreover, an UAV digital twin edge networks empowered multi-agent deep deterministic policy gradient (MA-DDPG) algorithm is designed to solve the adaptive resource optimization model. The algorithm training process adopts centralized training and decentralized execution. Each UAV agent considers the states and actions of other agents when evaluating the value of actions, but decides actions based only on its own local observations during execution. The above training process is conducted in digital twin environment, and after the algorithm converges, and it is applied to the real world, minimizing the resource overhead of physical entities to the greatest extent. Numerical results illustrate that the proposed algorithm can significantly decrease the service latency of federated learning, while guaranteeing the superiority of training loss and accuracy of federated learning.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems can improve their edge computing performance by taking the advantage of flexible deployment of UAVs and the ability to establish high-quality communication links between UAVs and ground users. The existing works on UAV-enabled MEC systems usually assume that the blocklength of the offloading transmission is long, and their results cannot be directly applied to MEC scenarios with strict requirements on computing delay. A UAV-enabled MEC system with ultra-reliable and low-latency communication (URLLC)-based task offloading is considered, in which a UAV carrying a computing server provides MEC service for multiple ground users and the users offload parts of their computing tasks to the UAV through URLLC. The UAV's deployment location, the offloading bandwidths of the users, and the computing central processing unit (CPU) frequencies of the UAV and users are jointly optimized to minimize the computation delay of the system. To solve the resulting non-convex optimization problem, the block coordinate descent method is used to decompose the problem into two subproblems that optimize the UAV's deployment location, and the offloading bandwidths and CPU frequencies, respectively, and the two subproblems are solved alternately. In solving the two subproblems, logarithmic functions are used to make nonlinear approximation to the expression of URLLC offloading rate to simplify the subproblems, and the two subproblems are both transformed into convex optimization problems by applying the successive convex approximation method. Simulation results show that the proposed algorithm can effectively balance the communication capability and computing capability of the system and reduce the system’s computing delay compared to other benchmark schemes.

Parked vehicle assisted edge computing (PVEC) is effective to alleviate the imbalance between supply and demand of resources in vehicular edge computing, by utilizing the idle resources in parked vehicles (PVs). However, the computing services provided by the PVs can be abruptly aborted due to uncertain parking behaviours. This makes it hard to meet the requirements of users on service reliability. To address this issue, this paper formulates an optimization problem for service reliability guarantee. Then, a task replication technique is introduced to transform the formulated problem into a replication offloading problem, with the goal of minimizing the average completion time of task replications. The NP-hardness of the formulated problem is proved. A greedy algorithm (GA) is proposed to solve the formulated problem, by carefully offloading the replicas of the tasks with large data sizes to the PVs, which can provide the computing services with service guarantee and short completion time. Meanwhile, an enhanced genetic algorithm (EGA) is proposed to refine the solution generated by the proposed algorithm GA. Experimental results show that the proposed GA and EGA algorithms outperform the baseline algorithms in terms of the average completion time of task replications for different requirements of users on service reliability.

Aiming at the shortcomings that the existing image-text matching algorithms based on common-sense learning cannot effectively match the intractable negative samples in image-text sample pairs, and the generalization ability of the models is weak and ineffective on large-scale datasets, a novel image-text matching model called Active Mining Sample Pair Semantics image-text matching model is proposed. Firstly, the proposed Adaptive Hierarchical Reinforcement Loss has diversified learning modes, and on top of the traditional triple loss, predictive candidate instances (pairs of intractable sample pairs) are added to aid in training. Its active learning mode enables model to more focus on the intractable negative samples through a penalizing mechanism to enhance the discriminative ability. In addition, the proposed model can also adaptively mine more hidden relevant semantic representations from uncommented items, which greatly improves the performance and generalization ability of model. Finally, experimental results on Flickr30K and MSCOCO datasets show that this proposed method is superior to the existing advanced comparison methods.

Multi-view learning has attracted wide attention because of the ability to integrate information from different views. For the issue of multi-view data fusion, a co-consensus multi-view spectral clustering method is proposed. The method adds two consensus constraints in the model of spectral clustering to utilize the feature relationship and the similarity relationship of different views’ spectral embedding matrices which enhances the consistency of multi views. Simultaneously, this method obtains closed solution of the consensus variables in the optimization process, which further improves the clustering performance. The experiment tests the convergence, parameter sensitivity and clustering performance of the proposed method in three real-world datasets. The experiment results show that this method has the best performance in multiple performance metrics compared with the existing methods, and the maximum improvement in clustering accuracy is more than 10%. The experiment proves the co-consensus method effectively improves the performance of multi-view spectral clustering algorithm.

A single decryption private key is subordinate to multiple users in traditional ciphertext attribute-based encryption schemes, which makes it possible for malicious users or semi-trusted attribute authorities to reveal the decryption private key to third unauthorized parties in order to gain benefits. Moreover, the decryption stage requires numerous pairing calculations, resulting to a huge burden to the end users with limited computing power. To address these issues, this paper proposes an accountable and verifiable outsourced decryption for ciphertext-policy attribute-based encryption. By deploying verifiable outsourcing decryption technology, the majority of the encryption overhead is shifted to the decryption agents , such that the computational load on end users can be reduced. By embedding user identity information and secret information that remains invisible to attribute authority into the user's private key, public accountability of both users and attribute authority is achieved. Security analysis demonstrates that our proposed scheme provides selective security, accountability, and verifiability of outsourcing decryption under the standard model. Performance analysis also indicates that the decryption cost of this scheme mainly lies on the decryption agent side, making it applicable for mobile device users with limited resources.

The deep learning technology has significantly improved the speed and accuracy of compressed sensing reconstruction. However, the existing deep compressive sensing algorithms usually use the same sampling rate to process different blocks of an image, ignoring the fact that different image blocks have different reconstruction difficulties. In this paper, a compressive sensing algorithm with adaptive sampling and memory enhancement is proposed. Firstly, the reconstruction difficulty of different blocks is estimated based on the reconstruction error of the measurement domain. Then, the rules are designed to adaptively assign the sampling rates, and the sampling matrix is used to sample each image block at a specific sampling rate with the help of a sampling rate mask. Furthermore, the two-branch aggregation module is added to the reconstruction network to enhance the interaction of context memory, and the reconstruction ability of the network is improved by adjusting the channel weight of different memory branches. The experimental results show that the proposed algorithm increases the average SSIM by approximately 0.0269 and the average PSNR by approximately 1.66 dB over other methods on several common datasets.

In the research of extension innovation design, many innovative design elements can be formalized by multi-dimensional complex-elements, and many of them belong to heterogeneous multi-dimensional compound-elements (HMdCes) , and there are various complex relationships among them. At present, there is a lack of research on the conductive rules among these HMdCes. Based on the conductive rules of basic-element transformations, the conductive rules among HMdCes without basic-elements’ operations internally and HMdCes with multiple basic-elements or basic-element’ operations internally are established, which can provide more reasoning basis for the ideas generation of extension innovative design. Taking the design for the function and structure of ZDY series reducer as an example, the conductive rules among HMdCes established in this research are used to verify the universality and effectiveness of the rules.

In the context of promoting cultural self-confidence and self-improvement in the new era, the extension innovative method is applied to the redesign of national patterns in order to preserve the national patterns, make the traditional national patterns revitalized in the new era, and improve the efficiency of the national pattern redesign. An extension design method for the redesign of national patterns is proposed, which characterizes the explicit information such as morphological features and the implicit information such as semantic description of pattern primitives through primitive theory and extension analysis method, and establishes a pattern primitive library for pattern information storage, and generates a sufficient amount of creative design solutions at one time in the design practice stage by using the extension transformation method. Finally, the Zhuang brocade patterns are taken as examples to realize the application of creative patterns with national characteristics and good meanings on modern clothing, verifying that this method can provide an idea for the protection and inheritance of national patterns and help to promote the development of modern creative design practice for traditional culture.