This article proposes a high-order robust dynamic surface control method for high-order strict-feedback systems (SFSs) with asymmetric output constraints and external disturbances, based on the fully actuated system approach. By introducing a class of nonlinear transformation functions, the original system's output constraint problem is transformed into a bounded problem in a new system representation. The proposed method directly designs a controller for each higher order subsystem using the fully actuated system framework, avoiding transformation to a first-order system and thereby simplifying the control design process. Stability analysis demonstrates that all closed-loop signals are uniformly ultimately bounded, while the system output successfully tracks the reference signal without violating the prescribed constraints. Numerical simulations on a robotic manipulator and an electromechanical system validate the effectiveness of the proposed approach.

In modern urban areas, inefficiency traffic management is one of the main causes of road congestion, leading to reduced fuel efficiency and increased traffic safety hazards. Traditional researches typically focus only on enhancing the throughput of intersections by optimizing traffic signals or individual vehicle trajectories. However, these methods often overlook the dynamic nature of the traffic system and the potential benefits of vehicle platooning, limiting their effectiveness in complex traffic environments. Addressing this challenge, this article presents MARP, a Cooperative Multiagent deep reinforcement learning (DRL) System for connected autonomous vehicle (CAV) Platooning. Utilizing vehicle to vehicle (V2I) and vehicles to infrastructure (V2V) technologies, MARP integrates sensing, computing, and communication to collect and process real-time data on traffic conditions, thereby achieving dynamic synchronization between traffic signal controllers and CAV platoons. By constructing platoons that collaborates with the infrastructure through a multiagent DRL collaboration model, MARP adapts to real-time traffic flow changes, significantly optimizing the fluidity and efficiency of the entire traffic network. Detailed experiments show that MARP effectively reduces traffic congestion, shortens intersection travel times, and cuts fuel consumption and emissions, surpassing the state-of-the-art approach.

In smart distribution networks (SDNs), false data injection attacks (FDIAs) on the state of charge (SoC) estimation of battery energy storage systems (BESSs) can successfully escape bad data detection, making SDNs suffer serious security risks. Some valuable work has been done on FDIAs and the detection of FDIAs, but how to defend FDIAs is still tough. Considering the spatio-temporal characteristics of the measurement data in SDNs containing BESSs, a TSCW-GAN defense method is proposed in this article. The method consists of the following two parts: 1) a generator and 2) a discriminator. The generator combines the transformer with the extraction of valid path features to form path features, which are then used to represent the regularity of the data in terms of the expected values. In addition, the discriminator uses conditional signature Wasserstein-1 metrics, thus capturing the conditional joint law of the measurement data. In IEEE13 and IEEE33 bus systems, the experimental results show that the proposed method achieves good performance with 97.5% accuracy.

How to efficiently process concurrent data tasks such as online analytical queries in datacenter environments is still a big challenge for current computing techniques. One of the fundamental reasons is that their task execution normally involves large numbers of distributed data operators, which are always expensive in terms of communication time. To improve the general performance, various advanced approaches on the execution optimization of data operators have been proposed in the past years. However, most of them focus on application-level optimization, such as using data locality scheduling to reduce network traffic. Moreover, few of them has considered the optimization opportunities for concurrent execution of multiple data operators. In this paper, we propose a novel coflow-based scheduling system called CoFlop, which aims to improve network communication time for multiple distributed operators at a query level, and on that basis to lay a solid foundation for the development of a network-aware query execution system in datacenter networks. We introduce the detailed system design of CoFlop and conduct a simulation-based evaluation with large concurrent distributed join operations. Compared to existing methods, the experimental results show that CoFlop can perform better in the presence of different large workloads.

Recent advances in quantum computing systems attract tremendous attention. Commercial companies, such as IBM, Amazon, and IonQ, have started to provide access to noisy intermediate-scale quantum computers. Researchers and entrepreneurs attempt to deploy their applications that aim to achieve a quantum speedup. Grover’s algorithm and quantum phase estimation are the foundations of many applications with the potential for such a speedup. While these algorithms, in theory, obtain marvelous performance, deploying them on existing quantum devices is a challenging task. For example, quantum phase estimation requires extra qubits and a large number of controlled operations, which are impractical due to low-qubit and noisy hardware. To fully utilize the limited onboard qubits, we propose IQuCS, which aims at index searching and counting in a quantum-classical hybrid system. IQuCS is based on Grover’s algorithm. From the problem size perspective, it analyzes results and tries to filter out unlikely data points iteratively. A reduced data set is fed to the quantum computer in the next iteration. With a reduction in the problem size, IQuCS requires fewer qubits iteratively, which provides the potential for a shared computing environment. We implement IQuCS with Qiskit and conduct intensive experiments. The results demonstrate that it reduces qubits consumption by up to 66.2%.

We exploit a coding technique called position-based coding in the entanglement-assisted setting to establish lower bounds for error exponents, lower bounds on the second-order coding rate, and one-shot lower bounds, and we also demonstrate that position-based coding can be a powerful tool for analyzing other communication settings. In particular, we reduce the quantum simultaneous decoding conjecture for entanglement-assisted or unassisted communication over a quantum multiple access channel to open questions in multiple quantum hypothesis testing. We then determine an achievable rate region for entanglement-assisted or unassisted classical communication over a quantum multiple-access channel, when using a particular quantum simultaneous decoder. The achievable rate regions given in this latter case are generally suboptimal, involving differences of Rényi-two entropies and conditional quantum entropies.