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  • Open Access


    Coordinated Voltage Control of Distribution Network Considering Multiple Types of Electric Vehicles

    Liang Liu, Guangda Xu*, Yuan Zhao, Yi Lu, Yu Li, Jing Gao

    Energy Engineering, Vol.121, No.2, pp. 377-404, 2024, DOI:10.32604/ee.2023.041311

    Abstract The couple between the power network and the transportation network (TN) is deepening gradually with the increasing penetration rate of electric vehicles (EV), which also poses a great challenge to the traditional voltage control scheme. In this paper, we propose a coordinated voltage control strategy for the active distribution networks considering multiple types of EV. In the first stage, the action of on-load tap changer and capacitor banks, etc., are determined by optimal power flow calculation, and the node electricity price is also determined based on dynamic time-of-use tariff mechanism. In the second stage, multiple operating scenarios of multiple types… More >

  • Open Access


    AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

    Zain Anwar Ali1,2,*, Dingnan Deng1, Muhammad Kashif Shaikh3, Raza Hasan4, Muhammad Aamir Khan2

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 1-34, 2024, DOI:10.32604/csse.2023.041866

    Abstract Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in the agricultural field. In this… More >

  • Open Access


    Deep Learning Based Vehicle Detection and Counting System for Intelligent Transportation

    A. Vikram1, J. Akshya2, Sultan Ahmad3,4, L. Jerlin Rubini5, Seifedine Kadry6,7,8, Jungeun Kim9,*

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 115-130, 2024, DOI:10.32604/csse.2023.037928

    Abstract Traffic monitoring through remote sensing images (RSI) is considered an important research area in Intelligent Transportation Systems (ITSs). Vehicle counting systems must be simple enough to be implemented in real-time. With the fast expansion of road traffic, real-time vehicle counting becomes essential in constructing ITS. Compared with conventional technologies, the remote sensing-related technique for vehicle counting exhibits greater significance and considerable advantages in its flexibility, low cost, and high efficiency. But several techniques need help in balancing complexity and accuracy technique. Therefore, this article presents a deep learning-based vehicle detection and counting system for ITS (DLVDCS-ITS) in remote sensing images.… More >

  • Open Access


    Assessment of the Influence of Tunnel Settlement on Operational Performance of Subway Vehicles

    Gang Niu1,2, Guangwei Zhang1, Zhaoyang Jin1, Wei Zhang3,*, Xiang Liu3

    Structural Durability & Health Monitoring, Vol.18, No.1, pp. 55-71, 2024, DOI:10.32604/sdhm.2023.044832

    Abstract In the realm of subway shield tunnel operations, the impact of tunnel settlement on the operational performance of subway vehicles is a crucial concern. This study introduces an advanced analytical model to investigate rail geometric deformations caused by settlement within a vehicle-track-tunnel coupled system. The model integrates the geometric deformations of the track, attributed to settlement, as track irregularities. A novel “cyclic model” algorithm was employed to enhance computational efficiency without compromising on precision, a claim that was rigorously validated. The model’s capability extends to analyzing the time-history responses of vehicles traversing settlement-affected areas. The research primarily focuses on how… More >

  • Open Access


    Secure and Reliable Routing in the Internet of Vehicles Network: AODV-RL with BHA Attack Defense

    Nadeem Ahmed1,*, Khalid Mohammadani2, Ali Kashif Bashir3,4,5, Marwan Omar6, Angel Jones7, Fayaz Hassan1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 633-659, 2024, DOI:10.32604/cmes.2023.031342

    Abstract Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles (IoV). However, intricate security challenges are intertwined with technological progress: Vehicular ad hoc Networks (VANETs), a core component of IoV, face security issues, particularly the Black Hole Attack (BHA). This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability; also, BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether. Recognizing the importance of this challenge, we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor (AODV-RL). The significance of… More >

  • Open Access


    Introduction to the Special Issue on Passive and Active Noise Control for Vehicle

    Hui Guo*, Chao Yang

    Sound & Vibration, Vol.57, pp. 155-156, 2023, DOI:10.32604/sv.2023.043954

    Abstract This article has no abstract. More >

  • Open Access


    Study on Two-Tier EV Charging Station Recommendation Strategy under Multi-Factor Influence

    Miao Liu, Lei Feng, Yexun Yuan, Ye Liu, Peng Geng*

    Journal on Artificial Intelligence, Vol.5, pp. 181-193, 2023, DOI:10.32604/jai.2023.046066

    Abstract This article aims to address the clustering effect caused by unorganized charging of electric vehicles by adopting a two-tier recommendation method. The electric vehicles (EVs) are classified into high-level alerts and general alerts based on their state of charge (SOC). EVs with high-level alerts have the most urgent charging needs, so the distance to charging stations is set as the highest priority for recommendations. For users with general alerts, a comprehensive EV charging station recommendation model is proposed, taking into account factors such as charging price, charging time, charging station preference, and distance to the charging station. Using real data… More >

  • Open Access


    FIR-YOLACT: Fusion of ICIoU and Res2Net for YOLACT on Real-Time Vehicle Instance Segmentation

    Wen Dong1, Ziyan Liu1,2,*, Mo Yang1, Ying Wu1

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3551-3572, 2023, DOI:10.32604/cmc.2023.044967

    Abstract Autonomous driving technology has made a lot of outstanding achievements with deep learning, and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems. The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information, which is more accurate and reliable than object detection. However, the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed. Therefore, this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT, which fuses the ICIoU (Improved Complete Intersection over Union) and Res2Net for the YOLACT algorithm. Specifically, the… More >

  • Open Access


    Research on ECMS Based on Segmented Path Braking Energy Recovery in a Fuel Cell Vehicle

    Wen Sun1, Meijing Li2, Guoxiang Li1, Ke Sun1,*, Shuzhan Bai1,*

    Energy Engineering, Vol.121, No.1, pp. 95-110, 2024, DOI:10.32604/ee.2023.042096

    Abstract Proton exchange membrane fuel cells are widely regarded as having the potential to replace internal combustion engines in vehicles. Since fuel cells cannot recover energy and have a slow dynamic response, they need to be used with different power sources. Developing efficient energy management strategies to achieve excellent fuel economy is the goal of research. This paper proposes an adaptive equivalent fuel minimum consumption strategy (AECMS) to solve the problem of the poor economy of the whole vehicle caused by the wrong selection of equivalent factors (EF) in traditional ECMS. In this method, the kinematics interval is used to update… More >

  • Open Access


    Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

    Ting Zhao1, Weiping Ding1, Haibo Huang1, Yudong Wu1,2,*

    Sound & Vibration, Vol.57, pp. 133-153, 2023, DOI:10.32604/sv.2023.044203

    Abstract The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies. However, some micro-motors may exhibit design deficiencies, component wear, assembly errors, and other imperfections that may arise during the design or manufacturing phases. Consequently, these micro-motors might generate anomalous noises during their operation, consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers. Automobile micro-motors exhibit a diverse array of structural variations, consequently leading to the manifestation of a multitude of distinctive auditory irregularities. To address the identification of diverse forms of abnormal noise, this research presents a… More > Graphic Abstract

    Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

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