Research on Vehicle Automatic Driving Target Perception Technology Based on Improved MSRPN Algorithm

Yang, Min

doi:10.47852/bonviewjcce20514

Cited by 24 publications

(7 citation statements)

References 14 publications

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“…[13] addressed the issue of poor stability of intelligent systems using the radial basis function neural network model, improving the anti-jamming ability of vehicles and protecting critical vehicle data. Similarly, [14] found that autonomous driving technology can effectively improve the safety performance of vehicles. They proposed a better target recognition algorithm by combining it with vehicle perception technology.…”

Section: Related Workmentioning

confidence: 96%

“…Data security [12] Machine Learning Machine Learning Platform [13] RBF neural network Radial Basis Function neural network sliding mode controller [14] Fast R-CNN Multi-strategy region proposal network algorithm [15] Data fusion approaches Data collection framework Asymmetric network [16] Blockchain-as-a-Service multi-preference matching [17] Deep reinforcement learning Cloud-edge cooperative content-delivery strategy [18] Mobile edge computing Task offloading and fault tolerance algorithm traditional consensus algorithms [19] PoW [20] PoS [21] DPoS consensus algorithms in IoV [23] PoW,PoS Decentralized trust management system [24] PoW,PBFT Blockchain-based TM with conditional privacy-preserving scheme [25] DPoS Soft security solution [26] PBFT,EigenTrust T-PBFT…”

Section: Literature Basic Algorithm Innovationmentioning

confidence: 99%

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AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

Sun

Wang

et al. 2023

Symmetry

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With the enhanced interoperability of information among vehicles, the demand for collaborative sharing among vehicles increases. Based on blockchain, the classical consensus algorithms in collaborative IoV (Internet of Vehicle), such as PoW (Proof of Work), PoS (Proof of Stake), and DPoS (Delegated Proof of Stake), only consider the node features, which is hard to adapt to the immediacy and flexibility of vehicles. On the other hand, classical consensus algorithms often require mass computing, which undoubtedly increases the communication overhead, resulting in the inability to achieve collaborative IoV under asymmetric networks. Therefore, proposing a low failure rate consensus algorithm that takes into account running time and energy consumption becomes a major challenge in IoV applications. This paper proposes an AI-enabled consensus algorithm with vehicle features, combining vehicle-based metrics and neural networks. First, we introduce vehicle-based metrics such as vehicle online time, performance, and behavior. Then, we propose an integral model and a hierarchical classification method, which combine with a BP neural network to obtain the optimal solution for interconnection. Among them, we also use Informer to predict the future online duration of vehicles, which effectively solves the situation that the primary node vehicle drops off in collaborative IoV. Finally, the experimentations show that the vehicle-based metrics eliminate the problem of the primary node vehicle being offline, which realizes the collaborative IoV considering vehicle features. Meanwhile, it reduces the vehicle network system delay and energy consumption.

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Section: Related Workmentioning

confidence: 96%

Section: Literature Basic Algorithm Innovationmentioning

confidence: 99%

AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

Sun

Wang

et al. 2023

Symmetry

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show abstract

“…The mechanical control of the portable sorting device uses STM32 as the development board, and wireless communication will be between the lower computer and the control board to adjust the operation of the camera acquisition module. The motion unit of the device adopts a brushless DC motor, and the driving control signal is a wide pulse [23][24]. The device is also equipped with an encoder to record movement and an obstacle avoidance device for obstacle detection.…”

Section: Design Of Hplc/dmpsd Based On DL and Ipmentioning

confidence: 99%

Detection of Apparent Defects in HPLC/Dual Mode Portable Sorting Device Based on Deep Learning and Image Processing

Wang,

Li,

Jiang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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As the growth of automated detection technology, traditional manual detection has gradually been replaced. To improve the effectiveness of defect detection, a HPLC/dual mode portable sorting device with deep learning and image processing is raised for apparent defect detection. The product image is segmented using flood filled mean shift method, and defect detection is achieved using Yolo v3 algorithm. An LME2918 chip is the main component of the communication module in the portable device. Based on experimental data, image segmentation accuracy can range from 80% to 100%, and image matching accuracy can range from 85% to 95%. Under the power line carrier mode, the average success rate of apparent defect detection in portable sorting devices can reach 85%, and the change in success rate is not significant, indicating that the detection is relatively stable; Under wireless communication mode, the average success rate can reach 83%, and the change in success rate is relatively obvious, because wireless communication is easily affected by the external environment. The experimental data shows that the defect detection effect of the HPLC/dual mode portable sorting device based on deep learning and image processing meets the design requirements.

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“…The number of resonant stages and harmonic frequency will increase the impact. This makes it difficult to design the system, while increasing the consumption of the system (Yang, 2022;Zhang X. et al, 2023a). In the wireless charging system for electric vehicles, the parking of electric vehicles is affected by the coupling structure, resulting in changes in the parking position.…”

Section: Optimal Efficiency Point Membership and Electric Vehicle Cha...mentioning

confidence: 99%

Optimal efficiency point membership incorporating fuzzy logic and automatic control of various charging stages for electric vehicles

Wang,

2024

Front. Mech. Eng.

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Introduction: The rapid development of electric vehicle technology in the field of renewable energy has brought significant challenges to wireless charging systems. The efficiency of these systems is crucial for improving availability and sustainability. The main focus of the research is to develop an intelligent charging strategy that utilizes fuzzy logic to optimize the efficiency of wireless charging systems for electric vehicles.Method: Introduce a model that combines fuzzy logic algorithm with automatic control system to improve the wireless charging process of electric vehicles. The model adopts dynamic tracking and adaptive control methods by analyzing the characteristics of static wireless charging systems. Utilizing primary phase shift control and secondary controllable rectifier regulation, combined with optimized fuzzy control algorithm.Result and discussion: The experimental results show that when the secondary coil is stable, the model maintains a stable duty cycle of about 75.6% and a stable current of 5A. It was observed that when the mutual inductance values were set to 10, 15, and 20 uH, the efficiency of the primary coil before applying control decreased with increasing resistance.Conclusion: The proposed system has shown great potential for application in real-world electric vehicle charging systems, demonstrating good applicability and feasibility in controlling the charging process and tracking the optimal efficiency point. The integration of fuzzy logic enhances the system’s ability to adapt to different operating conditions, which may lead to wider implementation and improved operational efficiency.

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Research on Vehicle Automatic Driving Target Perception Technology Based on Improved MSRPN Algorithm

Cited by 24 publications

References 14 publications

AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

Detection of Apparent Defects in HPLC/Dual Mode Portable Sorting Device Based on Deep Learning and Image Processing

Optimal efficiency point membership incorporating fuzzy logic and automatic control of various charging stages for electric vehicles

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