Traditional PID control algorithms are limited in dynamic response and disturbance rejection, making them inadequate for handling the complex variations across different operating conditions in fuel cells. To tackle this challenge, this paper introduces a particle swarm optimization (PSO)-tuned active disturbance rejection control (ADRC) strategy aimed at improving efficiency and robustness in PEMFC thermal management. By integrating PSO with ADRC, the controller parameters are adaptively adjusted to adapt to system dynamics and external disturbances. Simulation results show that the PSO-optimized ADRC scheme achieves superior temperature stability, faster dynamic response and stronger disturbance rejection compared to a traditional ADRC. This improvement contributes to the long-term reliability and efficiency of PEMFC operation.

ABSTRACT The distributed electric drive vehicle is a highly nonlinear and time‐varying system. To address the issue of drive slip control under varying driving forces and road surface coefficients, a novel drive slip control strategy is proposed, which considers axle load transfer during vehicle acceleration. The strategy employs an improved PSO algorithm to obtain optimal parameters for the BP neural network, uses the BP neural network for forward propagation to calculate PID parameters in real‐time, and adjusts the weight matrix through backward propagation to achieve real‐time adaptive PID control for vehicle slip. Experimental results indicate that this strategy improves the ITAE index by 13.6% and response time by 74.8% compared to the anti‐saturation PID.

De-authentication attack is one of the major threats to Unmanned Aerial Vehicle (UAV) communication, in which the attacker continuously sends de-authentication frames to disconnect the UAV communication link. Existing defense methods are based on authentication by digital passwords or physical channel features. But they suffer from replay attacks or cannot adapt to the UAV mobility. In this paper, instead of enhancing the in-channel authentication, we leverage the ambient broadcasting signal to establish a low-cost additional channel for authentication. Different from methods using another dedicated secure communication channel to perform an independent authentication, we use the ambient FM radio broadcasting channel and couple the two channels by encoding parasitic bits on the host signals of the broadcasting channel, which is called parasitic coding. To further enhance the security, we propose the FM-based Parasitic Coding Authentication (FPCA) that leverages elaborate host signal processing and vector coding to ensure that the attacker cannot decode our authentication even knowing the FM receiving frequency. We implement FPCA on the embedded UAV platform. The extensive experiments show that FPCA can resist replay attacks and brute force searching, achieving reliable continuous authentication for UAVs.

Summary Based on the distributed drive intelligent electric vehicle, considering the collision avoidance effect of the planned path, the path tracking accuracy, and the body stability requirements in the process of steering collision avoidance, this article proposes a control strategy for emergency steering collision avoidance. First, the fifth‐order polynomial method is used to generate collision avoidance paths, and then a designed cost function is used to evaluate and select the optimal collision avoidance path. Second, a decision‐making strategy that integrates multiple risk judgment models is built. Based on the model prediction method, a path tracking controller is built, and an optimization objective function is established to improve the tracking accuracy. In order to ensure the stability of the vehicle body, a vehicle stability controller is designed based on fuzzy theory. Finally, a vehicle‐in‐the‐loop intelligent driving algorithm test platform is built, and the control algorithm is tested through this platform. The test results show that compared with the pure path tracking control method, the control strategy is reasonable and effective and can better reduce the path tracking error and improve the vehicle's driving stability in the collision avoidance process.

The prosperity of Internet of Things (IoT) brings forth the deployment of large-scale sensing systems such as smart cities. To enable the collaboration tasks among distributed devices, time synchronization is crucial. However, due to the long-range and device heterogeneity, accurate time synchronization for a large-scale IoT network is challenging. Existing GPS or NTP solutions either require an outdoor environment or only have low and unstable accuracy. In this paper, we propose AirSync, a novel synchronization method that leverages the widely existed aircraft signals, ADS-B, to synchronize large-scale IoT networks with nodes even in indoor environments. But ADS-B messages have no time stamp and cannot provide a reference time. We leverage the continuity of aircraft movements to estimate the aircraft traveling time. Then devices that observe common aircraft moving segments can calculate their time offset. To obtain the time skew, we propose a combined aircraft linear regression method. We also design a transitive synchronization for devices that cannot observe common aircraft. Besides, we also design a duty-cycled ADS-B message collection method for resource-limited IoT devices. We implement a prototype of AirSync and evaluate its performance in various real-world environments. The results show that AirSync can obtain the sub-ms accuracy.

WiFi-based Long Distance (WiLD) networks are promising to cover the rural and remote regions. But the explosive short-range WiFi deployments result in the long-short coexistence. Due to CSMA is ignorance of propagation delay, its carrier sensing is too short to detect long links, leading to the temporal hidden terminal problem that causes serious performance degradation and even starvation of long links. Existing methods for traditional hidden terminal problem are inefficient to cope with this problem because of the different causes. In this paper, we propose CSMA with Protective Jamming (CSMA/PJ), a new WiLD MAC protocol that solves the temporal hidden terminal problem with the minimized influence on uncontrollable short links. The key is generating protective jamming at the WiLD receiver that is sensible to the short links. By leveraging the asymmetric propagation delay of the WiLD transmitter and receiver, we make the jamming protective rather than destructive. We precisely control the jamming right before the arrivals of WiLD packets to set aside channel time for short links. We implement and evaluate CSMA/PJ on commercial devices. The experimental results show that CSMA/PJ can improve the throughput of the WiLD link by$6\times $and$5 \times $compared with the CSMA/CA and RTS/CTS methods.

Abstract Around the new four‐wheel independent steering system, the adjustment principle of Akman angle is studied in this article. In order to improve steering stability and reduce tire wear, a new algorithm for optimal Ackerman angle allocation at full speed is proposed. Furthermore, combined with the minimum load rule, the four‐wheel rotation angle and driving force are optimized. In this process, Carsim vehicle model and MATLAB/Simulink force and angle allocation algorithm model are established. The final system simulation and performance evaluation demonstrate the effectiveness of the proposed force and angle distribution algorithm for vehicle stability and tire wear optimization.

Variational Quantum Circuits (VQCs), or the so-called quantum neural-networks, are predicted to be one of the most important near-term quantum applications, not only because of their similar promises as classical neural-networks, but also because of their feasibility on near-term noisy intermediate-size quantum (NISQ) machines. The need for gradient information in the training procedure of VQC applications has stimulated the development of auto-differentiation techniques for quantum circuits. We propose the first formalization of this technique, not only in the context of quantum circuits but also for imperative quantum programs (e.g., with controls), inspired by the success of differentiable programming languages in classical machine learning. In particular, we overcome a few unique difficulties caused by exotic quantum features (such as quantum no-cloning) and provide a rigorous formulation of differentiation applied to bounded-loop imperative quantum programs, its code-transformation rules, as well as a sound logic to reason about their correctness. Moreover, we have implemented our code transformation in OCaml and demonstrated the resource-efficiency of our scheme both analytically and empirically. We also conduct a case study of training a VQC instance with controls, which shows the advantage of our scheme over existing auto-differentiation for quantum circuits without controls.

The prosperity of Internet of Things (IoT) brings forth the deployment of large-scale sensing systems such as smart cities. The distributed devices upload their local sensing data to the cloud and collaborate to fulfill the large-area tasks such as pollutant diffusion analysis and target tracking. To accomplish the collaboration, time synchronization is crucial. However, due to the long range and device heterogeneity, accurate time synchronization for a large-scale IoT network is challenging. Existing GPS or NTP solutions either require an outdoor environment or only have low and unstable accuracy. In this paper, we propose AirSync, a novel synchronization method that leverages the widely existed aircraft signals, ADS-B, to synchronize large-scale IoT networks with nodes even in indoor environments. But ADS-B messages have no time stamp and cannot provide a reference time. We leverage the continuity of aircraft movements to estimate the aircraft traveling time. Then devices that observe common aircraft moving segments can calculate their time offset. To obtain the time skew, we propose a combined aircraft linear regression method. We also design a transitive synchronization for devices that cannot observe common aircraft. We implement a prototype of AirSync and evaluate its performance in various real-world environments. The results show that AirSync can obtain the sub-ms accuracy.

Quantum computation is a topic of significant recent interest, with practical advances coming from both research and industry. A major challenge in quantum programming is dealing with errors (quantum noise) during execution. Because quantum resources (e.g., qubits) are scarce, classical error correction techniques applied at the level of the architecture are currently cost-prohibitive. But while this reality means that quantum programs are almost certain to have errors, there as yet exists no principled means to reason about erroneous behavior. This paper attempts to fill this gap by developing a semantics for erroneous quantum while-programs, as well as a logic for reasoning about them. This logic permits proving a property we have identified, called є-robustness, which characterizes possible “distance” between an ideal program and an erroneous one. We have proved the logic sound, and showed its utility on several case studies, notably: (1) analyzing the robustness of noisy versions of the quantum Bernoulli factory (QBF) and quantum walk (QW); (2) demonstrating the (in)effectiveness of different error correction schemes on single-qubit errors; and (3) analyzing the robustness of a fault-tolerant version of QBF.

Cross-view gait recognition is a challenging problem when view-interval and pose variation are relatively large. In this paper, we propose Cycle-consistent Attentive Generative Adversarial Networks (CA-GAN) to map different views' gait images to view-consistent and photorealistic gait images for cross-view gait recognition. In CA-GAN, the generative network is composed of two branches, which simultaneously perceives human's global contexts and local body parts information respectively. Moreover, we design a novel Attentive Adversarial Network (AAN) to adaptively learn different weights for the discriminator's receptive fields with attention mechanism. Furthermore, as it is hard to collect the pose-aligned gait image pairs from different views for training CA-GAN’ we combine forward cycle-consistency loss and adver-sarial loss to learn the transformation relationship from source views to target view. The combined loss function can also preserve the discriminative gait structures of different identities at the training stage. Finally, we directly exploit the synthesized view-consistent gait images for cross-view gait recognition task. Experimental results on CASIA-B demonstrate that our method not only outperforms the state-of-the-art methods in cross-view gait recognition, but also presents compelling perceptual results even across the large view-interval.