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  • Open Access

    ARTICLE

    IR-YOLO: Real-Time Infrared Vehicle and Pedestrian Detection

    Xiao Luo1,3, Hao Zhu1,2,*, Zhenli Zhang1,2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2667-2687, 2024, DOI:10.32604/cmc.2024.047988

    Abstract Road traffic safety can decrease when drivers drive in a low-visibility environment. The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents. To tackle the challenges posed by the low recognition accuracy and the substantial computational burden associated with current infrared pedestrian-vehicle detection methods, an infrared pedestrian-vehicle detection method A proposal is presented, based on an enhanced version of You Only Look Once version 5 (YOLOv5). First, A head specifically designed for detecting small targets has been integrated into the model to make full… More >

  • Open Access

    ARTICLE

    Improving the Accuracy of Vegetation Index Retrieval for Biomass by Combining Ground-UAV Hyperspectral Data–A New Method for Inner Mongolia Typical Grasslands

    Ruochen Wang1,#, Jianjun Dong2,#, Lishan Jin3, Yuyan Sun3, Taogetao Baoyin2, Xiumei Wang*

    Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 387-411, 2024, DOI:10.32604/phyton.2024.047573

    Abstract Grassland biomass is an important parameter of grassland ecosystems. The complexity of the grassland canopy vegetation spectrum makes the long-term assessment of grassland growth a challenge. Few studies have explored the original spectral information of typical grasslands in Inner Mongolia and examined the influence of spectral information on aboveground biomass (AGB) estimation. In order to improve the accuracy of vegetation index inversion of grassland AGB, this study combined ground and Unmanned Aerial Vehicle (UAV) remote sensing technology and screened sensitive bands through ground hyperspectral data transformation and correlation analysis. The narrow band vegetation indices were calculated, and ground and airborne… More >

  • Open Access

    ARTICLE

    Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

    Shuwei Zhong, Yanbo Che*, Shangyuan Zhang

    Energy Engineering, Vol.121, No.3, pp. 603-618, 2024, DOI:10.32604/ee.2023.044667

    Abstract Electric vehicle (EV) is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future. However, a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff. Therefore, this paper proposes a dynamic time-of-use tariff mechanism, which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean (FCM) clustering algorithm, and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period. Based on the proposed… More >

  • Open Access

    ARTICLE

    A Predictive Energy Management Strategies for Mining Dump Trucks

    Yixuan Yu, Yulin Wang*, Qingcheng Li, Bowen Jiao

    Energy Engineering, Vol.121, No.3, pp. 769-788, 2024, DOI:10.32604/ee.2023.044042

    Abstract The plug-in hybrid vehicles (PHEV) technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks. Meanwhile, plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies (EMS). Therefore, a series hybrid system is constructed based on a 100-ton mining dump truck in this paper. And inspired by the dynamic programming (DP) algorithm, a predictive equivalent consumption minimization strategy (P-ECMS) based on the DP optimization result is proposed. Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm, the P-ECMS strategy… More >

  • Open Access

    ARTICLE

    Prediction and Analysis of Vehicle Interior Road Noise Based on Mechanism and Data Series Modeling

    Jian Pang1,3, Tingting Mao2, Wenyu Jia3, Xiaoli Jia3,*, Peisong Dai2, Haibo Huang1,2,*

    Sound & Vibration, Vol.58, pp. 59-80, 2024, DOI:10.32604/sv.2024.046247

    Abstract Currently, the inexorable trend toward the electrification of automobiles has heightened the prominence of road noise within overall vehicle noise. Consequently, an in-depth investigation into automobile road noise holds substantial practical importance. Previous research endeavors have predominantly centered on the formulation of mechanism models and data-driven models. While mechanism models offer robust controllability, their application encounters challenges in intricate analyses of vehicle body acoustic-vibration coupling, and the effective utilization of accumulated data remains elusive. In contrast, data-driven models exhibit efficient modeling capabilities and can assimilate conceptual vehicle knowledge, but they impose stringent requirements on both data quality and quantity. In… More >

  • Open Access

    ARTICLE

    A Sound Quality Evaluation Method for Vehicle Interior Noise Based on Auditory Loudness Model

    Zhiheng He1, Hui Guo2, Houguang Liu1,*, Yu Zhao1,3, Zipeng Zhang1, Shanguo Yang1

    Sound & Vibration, Vol.58, pp. 47-58, 2024, DOI:10.32604/sv.2024.045470

    Abstract When designing and optimizing the hull of vehicles, their sound quality needs to be considered, which greatly depends on the psychoacoustic parameters. However, the traditional psychoacoustic calculation method does not consider the influence of the real human ear anatomic structure, even the loudness which is most related to the auditory periphery. In order to introduce the real physiological structure of the human ear into the evaluation of vehicle sound quality, this paper first carried out the vehicle internal noise test to obtain the experimental samples. Then, the physiological loudness was predicted based on an established human ear physiological model, and… More >

  • Open Access

    ARTICLE

    Prediction of Sound Transmission Loss of Vehicle Floor System Based on 1D-Convolutional Neural Networks

    Cheng Peng1, Siwei Cheng2, Min Sun1, Chao Ren1, Jun Song1, Haibo Huang2,*

    Sound & Vibration, Vol.58, pp. 25-46, 2024, DOI:10.32604/sv.2024.046940

    Abstract The Noise, Vibration, and Harshness (NVH) experience during driving is significantly influenced by the sound insulation performance of the car floor acoustic package. As such, accurate and efficient predictions of its sound insulation performance are crucial for optimizing related noise reduction designs. However, the complex acoustic transmission mechanisms and difficulties in characterizing the sound absorption and insulation properties of the floor acoustic package pose significant challenges to traditional Computer-Aided Engineering (CAE) methods, leading to low modeling efficiency and prediction accuracy. To address these limitations, a hierarchical multi-objective decomposition system for predicting the sound insulation performance of the floor acoustic package… More >

  • Open Access

    ARTICLE

    Lightweight Intrusion Detection Using Reservoir Computing

    Jiarui Deng1,2, Wuqiang Shen1,3, Yihua Feng4, Guosheng Lu5, Guiquan Shen1,3, Lei Cui1,3, Shanxiang Lyu1,2,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1345-1361, 2024, DOI:10.32604/cmc.2023.047079

    Abstract The blockchain-empowered Internet of Vehicles (IoV) enables various services and achieves data security and privacy, significantly advancing modern vehicle systems. However, the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks. As a result, an efficient intrusion detection system (IDS) becomes crucial for securing the IoV environment. Existing IDSs based on convolutional neural networks (CNN) often suffer from high training time and storage requirements. In this paper, we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats. Our approach achieves superior performance, as demonstrated by… More >

  • Open Access

    ARTICLE

    A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

    Yaoyao Du, Xiangkui Jiang*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 303-327, 2024, DOI:10.32604/cmc.2023.046068

    Abstract To address the challenges of high complexity, poor real-time performance, and low detection rates for small target vehicles in existing vehicle object detection algorithms, this paper proposes a real-time lightweight architecture based on You Only Look Once (YOLO) v5m. Firstly, a lightweight upsampling operator called Content-Aware Reassembly of Features (CARAFE) is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles, reducing the missed detection rate and false detection rate. Secondly, a new prediction layer for tiny targets is added, and the feature fusion network is redesigned to enhance the… More >

  • Open Access

    ARTICLE

    A New Vehicle Detection Framework Based on Feature-Guided in the Road Scene

    Tianmin Deng*, Xiyue Zhang, Xinxin Cheng

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 533-549, 2024, DOI:10.32604/cmc.2023.044639

    Abstract Vehicle detection plays a crucial role in the field of autonomous driving technology. However, directly applying deep learning-based object detection algorithms to complex road scene images often leads to subpar performance and slow inference speeds in vehicle detection. Achieving a balance between accuracy and detection speed is crucial for real-time object detection in real-world road scenes. This paper proposes a high-precision and fast vehicle detector called the feature-guided bidirectional pyramid network (FBPN). Firstly, to tackle challenges like vehicle occlusion and significant background interference, the efficient feature filtering module (EFFM) is introduced into the deep network, which amplifies the disparities between… More >

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