A Multidirectional Pendulum Kinetic Energy Harvester Based on Homopolar Repulsion for Low-Power Sensors in New Energy Driverless Buses

Zhang, Tingsheng; Tang, Minfeng; Li, Hai; Li, Jingbo; Zou, Yingquan; Pan, Yajia; Zhang, Zutao

doi:10.1007/s40684-021-00344-5

Cited by 16 publications

(5 citation statements)

References 39 publications

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“…The final output identifies the point cloud segmentation results for different regions. This point cloud segmentation method based on region growth can be applied to fields such as unmanned mining, providing accurate road information and environmental perception [17][18].…”

Section: Point Cloud Segmentationmentioning

confidence: 99%

Mining Autonomous Vehicle Driving Boundary Detection on Basis of 3D LiDAR

2024

jemm

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Mining area is very large, and the road conditions are also very complex. It is very difficult to familiarize oneself with the environment of the mine by driving. Based on 3D LiDAR technology, this research explores a driving boundary detection method for driverless vehicles in mines based on point cloud data. Through the use of 3D LiDAR sensors to obtain point cloud data of the environment, and the use of object shape recognition, high-precision ranging, multi-angle observation and multi-sensor fusion and other technologies, the accurate detection of mine environmental boundaries is realized. The experimental results show that the boundary detection method of mine driverless vehicles based on 3D LiDAR has high accuracy and real-time. Using the point cloud data obtained by the 3D LiDAR sensor, it can quickly capture and represent the shape and contour of objects in the mine environment, and realize accurate boundary detection.

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Section: Point Cloud Segmentationmentioning

confidence: 99%

Mining Autonomous Vehicle Driving Boundary Detection on Basis of 3D LiDAR

2024

jemm

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“…With the rapid development of science and technology, object detection technology has become a hot research problem [1]. Object detection has been useful in enhancing production and life efficiency in a variety of industries, including intelligent transportation, steel defect identification, face detection, and others.…”

Section: Introductionmentioning

confidence: 99%

An improved method MSS-YOLOv5 for object detection with balancing speed-accuracy

et al. 2023

Front. Phys.

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For deep learning-based object detection, we present a superior network named MSS-YOLOv5, which not only considers the reliability in complex scenes but also promotes its timeliness to better adapt to practical scenarios. First of all, multi-scale information is integrated into different feature dimensions to improve the distinction and robustness of features. The design of the detectors increases the variety of detection boxes to accommodate a wider range of detected objects. Secondly, the pooling method is upgraded to obtain more detailed information. At last, we add the Angle cost and assign new weights to different loss functions to accelerate the convergence and improve the accuracy of network detection. In our network, we explore four variants MSS-YOLOv5s, MSS-YOLOv5m, MSS-YOLOv5x, and MSS-YOLOv5l. Experimental results of MSS-Yolov5s show that our technique improves mAP on the PASCAL VOC2007 and PASCAL 2012 datasets by 2.4% and 2.9%, respectively. Meanwhile, it maintains a fast inference speed. At the same time, the other three models have different degrees of performance improvement in terms of balancing speed and precision in challenging detection regions.

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“…Zhang Zutao's team conducted extensive research on vehicle inertial energy collection in recent years. For instance, Zhang et al [ 33 ] developed a multidirectional pendulum kinetic energy harvester installed inside the vehicle. When the vehicle accelerates or decelerates, the pendulum swings forth or back, causing the PZT beam to generate bending deformation, capture the wasted inertial kinetic energy, and transform it into electrical power.…”

Section: Introductionmentioning

confidence: 99%

An Inertial Energy Harvester Based on Noncontact Magnetic Force for Self‐Powered Applications in New Energy Buses

Li,

Zhao,

Fan

et al. 2023

Energy Tech

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The advent of energy‐efficient, next‐generation driverless buses places substantial reliance on an array of wireless sensors. In response to this challenge, converting and utilizing these buses' local inertial kinetic energy emerges as a practical strategy for achieving self‐powered capabilities for low‐power appliances. Herein, an inertial energy harvester based on noncontact magnetic force for self‐powered applications in new energy buses is introduced. The harvester system comprises energy capture, mechanical amplification, conversion, and storage modules. The energy capture module proficiently captures and converts the otherwise wasted inertial kinetic energy into magnetic force. The magnified magnetic force produced by the mechanical amplification module efficiently transforms into electrical power within the energy conversion module. Afterward, the alternating current is regulated and stored within the supercapacitor by the energy storage module, achieving self‐powered capabilities for low‐power vehicle appliances. At the same time, combined with the bench test, the maximum output power of the harvester under different vehicle acceleration scenarios is obtained. Taking the campus bus of Liaocheng University as the application object, the average output power of the harvester in the actual operation of the campus is 23.71 mW, which can effectively promote the realization of wireless sensor self‐powered technology in new energy vehicles.

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A Multidirectional Pendulum Kinetic Energy Harvester Based on Homopolar Repulsion for Low-Power Sensors in New Energy Driverless Buses

Cited by 16 publications

References 39 publications

Mining Autonomous Vehicle Driving Boundary Detection on Basis of 3D LiDAR

Mining Autonomous Vehicle Driving Boundary Detection on Basis of 3D LiDAR

An improved method MSS-YOLOv5 for object detection with balancing speed-accuracy

An Inertial Energy Harvester Based on Noncontact Magnetic Force for Self‐Powered Applications in New Energy Buses

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