This article presents a novel method for controlling a doubly fed induction generator (DFIG) wind energy system in the presence of variable wind speeds. In order to achieve decoupling control of the active and reactiv...
This article presents a novel method for controlling a doubly fed induction generator (DFIG) wind energy system in the presence of variable wind speeds. In order to achieve decoupling control of the active and reactive power, the rotor-side converter (RSC) of the DFIG is controlled using field-oriented control (FOC). The system is very complicated and nonlinear, making it challenging to adjust the PI gains appropriately. The present research proposes the utilization of the Grey Wolf Optimization (GWO) algorithm as an approach to optimize the gains of a Proportional-Integral (PI) regulator. The simulation results demonstrate that the GWO-PI provides a lower criterion value and an improved response than the conventional PI.
The Internet of Vehicles (IoVs) is widely used to obtain information about vehicles and road conditions, which is transmitted in public channels. Hence, the most important requirement is the data security, which needs...
The Internet of Vehicles (IoVs) is widely used to obtain information about vehicles and road conditions, which is transmitted in public channels. Hence, the most important requirement is the data security, which needs to keep in a strict delay. Authentication is a common method to solve it. Due to limited resources and delay sensitivity of IoVs, vehicles must complete authentication within appropriate resources cost and delay. However, existing schemes are prone to physical, forgery and collusion attacks, and moreover, they are computationally heavy. Therefore, we propose a lightweight security identity authentication scheme for vehicle-road collaboration, which utilizes lightweight Physical Unclonable Function (PUF) as the trust root of entities to resist physical and collusion attacks; Besides, most of computation is offloaded to Road Side Units (RSUs) certified by Trusted Authority (TA) through the vehicle-road collaboration architecture. In addition, we utilize challenge-response pairs (CRPs) to protect privacy and expose malicious vehicular identities in identity tracking phase. Furthermore, there are formal and informal security analyses to prove our scheme is secure. Finally, the simulation experiment shows our scheme is more secure and efficient than other schemes in real scenarios.
The rapid expansion of data from diverse sources has made anomaly detection (AD) increasingly essential for identifying unexpected observations that may signal system failures, security breaches, or fraud. As datasets...
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This paper develops an iterative learning control law for a class of nonlinear systems. The approach used to represent the nonlinear system dynamics is a Takagi-Sugeno fuzzy repetitive process that considers the two d...
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Dual three-phase permanent magnet synchronous machine (DTP-PMSM) has attracted great attention due to its high reliability and high-power output capacities. However, the conventional single-voltage-vector-based predic...
Dual three-phase permanent magnet synchronous machine (DTP-PMSM) has attracted great attention due to its high reliability and high-power output capacities. However, the conventional single-voltage-vector-based predictive current control (SV-PCC) for DTP-PMSM presents high torque ripple and current harmonics, and high computational burden. To solve those issues, a modulated-virtual-vector-based PCC (MVV-PCC) for DTP-PMSM is proposed in this paper. Wherein, twenty-four VVs are synthesized by the inherent voltage vectors, and two VVs and one zero voltage vector with optimal duty cycles are determined and applied in each sampling period to improve the steady-state performance. The selection of optimal VVs and the calculation of the optimal duty cycles are simplified by integrating the deadbeat control and modulation scheme. Various comparisons are carried out to validate the effectiveness and superiority of the proposed MVV-PCC strategy.
Remote sensing technology is becoming more sophisticated and is extensively used for object tracking, urban planning, military reconnaissance and other fields. Complex backgrounds and diverse object scales are two imp...
Remote sensing technology is becoming more sophisticated and is extensively used for object tracking, urban planning, military reconnaissance and other fields. Complex backgrounds and diverse object scales are two important factors that affect the object detection effect of remote sensing images. To address this problem, this paper proposes a remote sensing object detection model that incorporates channel enhancement and multi-scale contextual features. Firstly, the multi-scale contextual feature enhancement module is constructed, which performs multi-order spatial interaction by cascading recursive convolution to obtain contextual information of feature maps at different scales, and introduces attention to reinforce unique features of objects and suppress background interference. Then, the spatial pyramid channel enhancement module combining sub-pixel convolution and adaptive sampling factor is designed to mitigate the semantic weakening of the depth feature maps caused by channel downscaling, thus enhancing the sampling effect between feature maps of different scales and reducing information loss. Finally, the effectiveness of the model is verified on the large-scale remote sensing image object detection dataset DIOR.
Unmanned Aerial Vehicles (UAVs) are widely deployed in transportation. Detection of traffic objects like vehicles in UAV images is helpful for traffic monitoring, urban transport planning, and estimation of traffic fl...
Unmanned Aerial Vehicles (UAVs) are widely deployed in transportation. Detection of traffic objects like vehicles in UAV images is helpful for traffic monitoring, urban transport planning, and estimation of traffic flow. However, detecting objects in UAV images has some inevitable problems, such as large changes in object scale, occlusion between objects in dense detection scenes, and interference caused by complex backgrounds. To reduce the influence of the above factors and achieve real-time detection, we propose RTOD-YOLO based on YOLOv5s in this study. Firstly, a small object detection layer is added to improve the model's performance to detect small objects. Secondly, the Global Context Attention Module (GCAM) is introduced to enable RTOD-YOLO to concentrate more on the target itself and reduce the interference caused by complex backgrounds. Thirdly, the YOLO detection head is replaced by the Shift Double Prediction Head (SDPH) to improve the bounding box regression and classification ability of the proposed model. Finally, RepBlock and CSPRepBottleneck are introduced to re-parameterize the model structure, for improving feature extraction capabilities and adequately mining semantic information in images. The experimental result shows that the mAP@0.5 of RTOD-YOLO reaches 44.6% on the VisDrone dataset, which is 10.7% higher than that of the baseline YOLOv5s, while still accommodating real-time detection.
Virtual reality (VR) has been adopted in various fields such as entertainments, education, healthcare, and military, due to its ability to provide immersive experience to users. However, 360° images, one of the m...
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Integrated sensing and communication (ISAC) technology is vital for vehicular networks, yet the time-varying communication channels and rapid movement of targets present significant challenges for real-time precoding ...
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In this paper, we present the fabrication of a 64-element patch antenna array optimized for 5G applications. The initial step involved constructing an individual antenna element, using a 0.035 mm-thick perfect conduct...
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