This article investigates the data-driven control problem of linear time-invariant (LTI) multi-input systems under aperiodic sampling, where the input of the system is determined by the corresponding agent. Focusing on partially unknown system models, we provide a methodology that integrates Integral Quadratic Constraints (IQC) with dissipation inequalities to derive the asymptotic stability criterion and controller design method for such systems. Firstly, addressing the challenge of incomplete system model knowledge, this article develops adaptive system identification algorithm for the above two scenarios, respectively. Each algorithm facilitates accurate model reconstruction directly from the available data. Then, the feedback interconnection between the LTI multi-input system and time-delay operator is established, and the asymptotic stability criterion for the system is demonstrated. In addition, by means of matrix decoupling techniques, a restricted frequency-domain data-driven framework which can effectively address complex coupling dynamics and overcome structural perturbation constraints is proposed, along with two corresponding controller algorithms. Finally, through the simulation analysis and result verification of numerical examples, the effectiveness of the proposed methods are demonstrated.

This study addresses the output feedback control synthesis problem for a quarter-vehicle active suspension system subject to input delay, using dynamic integral quadratic constraint. To account for the uncertain relationship between the real-time output of the controller and the delayed signal arriving at the actuator, a memory-driven control strategy is proposed. Furthermore, dynamic integral quadratic constraint multipliers are employed to reformulate the output feedback control synthesis into a set of linear matrix inequalities. A simplified memory-free dynamic output feedback controller is also introduced. Corresponding controller design algorithms are developed for both the memory-driven and memory-free schemes. Simulation results demonstrate the effectiveness of the proposed controllers in handling input delays, and comparative studies verify the superiority and feasibility of the proposed control approach.

This article investigates the problem of privacy-preserving average consensus for continuous-time heterogeneous multiagent systems with intermittent information transfer under asynchronous sampled-data interactions. To address the challenges posed by agent-specific asynchronous sampled-data and time-varying communication delays, a time-translation approach incorporating a shared sampling period strategy is introduced, effectively transforming the asynchronous problem into a synchronous framework. Next, integrated distributed hybrid controller with time-varying noise injection is designed, enabling agents to interact with sensitive information only at sampling instants, thereby preserving privacy while maintaining trajectory availability. Then, the time-varying step-size and noise parameters, which are tunable parameters of the dual control mechanism corresponding to the desired ε-differential privacy budget and system convergence accuracy are proposed, and the trade-off between control performance and privacy preservation is thoroughly analyzed. It is shown that the proposed protocol achieves asymptotically unbiased mean-square output consensus with predefined accuracy and privacy budget. Numerical examples validate the theoretical results.

In the mobile internet era, short videos are inundating people's lives. However, research on visual language models specifically designed for short videos has not yet received sufficient attention. Short videos are not just videos of limited duration. The prominent visual details and high information density of short videos differentiate them to long videos. In this paper, we propose the SpatioTemporal Fine-grained Description (STFVD) emphasizing on the uniqueness of short videos, which entails capturing the intricate details of the main subject and fine-grained movements. To this end, we create a comprehensive Short Video Advertisements Description (SVAD) dataset, comprising 34,930 clips from 5,046 videos. The dataset covers a range of topics, including 191 sub-industries, 649 popular products, and 470 trending games. Various efforts have been made in the data annotation process to ensure the inclusion of fine-grained spatiotemporal information, resulting in 34,930 high-quality annotations. Compared to existing datasets, samples in SVAD exhibit a superior text information density, suggesting that SVAD is more appropriate for the analysis of short videos. Based on the SVAD dataset, we develop a visual language model (SVAD-VLM) to generate spatiotemporal fine-grained description for short videos. We use a prompt-guided keyword generation task to efficiently learn key visual information. Moreover, we also utilize dual visual alignment to exploit the advantage of mixed-datasets training. Experiments on SVAD dataset demonstrate the challenge of STFVD and the competitive performance of proposed method compared to previous ones.

The concept of reachable set and state space ellipsoid is used in this article to determine the optimal safe range of the truck-trailer driving by allowing the constrained variables to move freely within a given range. A method of returning the truck to the desired position is proposed by analysing the movement trajectory of the truck. Two results are certified. The first result considers the issue of reachable set estimation (RSE) for the multirate sampled-data (MRSD) system in the aperiodic sampled-data framework based on the model knowledge. By constructing loop-based Lyapunov functional (LBLF), we obtain the sufficient condition that all the state trajectories are confined to target ellipsoid. This article also provides a computational method for an MRSD controller considering RSE. The second result provides the data-driven control tactics for the unknown sampled-data system to consider the RSE problem for the aperiodic sampled-data system, using only the noisy data. In addition, this article extends the data-driven control scheme to the design of MRSD controllers and ensures the stability of the system in agreement with the measured data. Simulation results show that the MRSD controller under both the model-driven method and the data-driven method is valid and achieves better control effect compared to the single-rate sampled-data (SRSD).

Energy harvesting (EH) endows device-to-device (D2D) communication and cellular equipment with the ability of continuous communication to provide internet-of-things (IoT) services in natural areas. While the available energy, which relies on EH, becomes an extra nonnegligible factor in resource allocation. Besides, we integrate uplink non-orthogonal multiple access (NOMA) with D2D communication to provide multiple access for D2D transmitters for more efficient IoT service and more efficient utilization of limited spectrum. In this scenario, ingenious resource allocation approach is a key focus for utilizing the advantages in energy and spectral efficiency. Aiming to investigate the inherent resource allocation issue, we set our goal as maximizing the energy efficiency for both NOMA-based D2D groups and cellular users (CUs), where the power and spectrum allocation are both considered. Then we propose a two-stage game approach, which is theoretically proved to be capable of obtaining the equilibrium and a stable result, to solve the unilateral energy efficiency maximization problems. Besides, an energy-aware screening method is proposed to reduce the computations based on the available energy of user equipment. Finally, the effectiveness of our proposed method is verified through elaborated simulation results.