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Experimental research on GM-APD LIDAR point cloud classification algorithm based on deep learning

Experimental research on GM-APD LIDAR point cloud classifica...

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2022 Applied Optics and Photonics China: Advanced Laser Technology and Applications, AOPC 2022

作者： Jiang, Yanze Sun, Jianfeng Ding, Yuanxue Jiang, Peng Zhang, Hailong Li, Sining Zhou, Shuaijun National Key Laboratory of Science and Technology on Tunable Laser Institute of OptoElectronic Harbin Institute of Technology Heilongjiang Harbin150001 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (纸本)9781510662223

Geiger mode Avalanche Photo Diode (Gm-APD) array lidar is a lidar that can perform single-photon detection. It offers a wide range of applications due to its low power consumption, small size, and extended detecting distance. There haven't been many research on this detector's target classification because of its late development and small detector array. The classification technique based on the Gm-APD array lidar point cloud is the focus of this paper's research: Firstly, the Gm-APD array lidar is utilized to perform imaging tests on four targets from various angles in order to create a target classification *** that, several data preprocessing methods were chosen and implemented based on the characteristics of the obtained data, such as filling in missing values, performing range image and intensity image interpolation, using the principle of keyhole imaging to convert the range image to point cloud data, realizing the information fusion of distance image and intensity image, and using multiple point cloud data enhancement methods. Finally, the point cloud classification networks PointNet and PointNet++ are trained on point cloud data with varying levels of preprocessing, the results are compared and analyzed, and the impact of different preprocessing methods on the classification accuracy of the two networks is determined. Inferences were made and experiments were carried out to verify the inferences. The data set preprocessing method with the highest classification accuracy of the two networks is discovered, laying the groundwork for future Gm-APD lidar target classification and detection research. © 2023 SPIE.

关键词： Optical radar

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BPH-YOLOv5: Improved yolov5 based on biformer prediction head for small target cigatette detection

BPH-YOLOv5: Improved yolov5 based on biformer prediction hea...

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2023 Jiangsu Annual Conference on Automation, JACA 2023

作者： Zheng, Xinyi Lu, Xiaobo College of Software Engineering Southeast University Suzhou215123 China School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

ISBN: (纸本)9781839539848

Small object detection in the cigarette detection field is a recent popular task. As cigarette targets in surveillance are tiny, the object scale leads to a great challenge of object detection. To solve the issue mentioned above, we propose BPH-YOLOv5 based on YOLOv5. We add one more prediction head to detect different-scale objects, which is more sensitive to tiny objects using multi-scale features. Then we replace the original prediction heads with BiFormer Prediction Heads (BPH) to explore the prediction potential with an attention mechanism. Experiments showed that the mean average precision (mAP) of BPHYOLOv5 in the cigarette dataset reached 90.34%, which was 6.94% higher than that of YOLOv5-s prototype, 7.20% higher than that of YOLOv7, which are encouraging and competitive. Tests on the actual scenarios verified that the accuracy of smallscale target detection was significantly improved. © The Institution of Engineering & Technology 2023.

关键词： Forecasting

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A Nonsmooth Observer Based on Friction Model Compensation for Sensorless Permanent Magnet Synchronous Motor 42

A Nonsmooth Observer Based on Friction Model Compensation fo...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Zaishan Zhu, Wenwu Yu, Ke Wang, Xiangyu Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China School of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China

ISBN: (纸本)9789887581543

In this paper, a nonsmooth observer considering friction compensation is proposed for the sensorless permanent magnet synchronous motor (PMSM) to estimate the rotor position and speed. Friction is one of the main factors affecting the tracking accuracy of the motor at low speed, especially in the back and forth motion of the motor. Firstly, based on the Stribeck friction model, a nonlinear friction model of the motor is established. Secondly, a nonsmooth observer with friction compensation is designed to estimate the position and speed of the rotor, and the lumped disturbance of the system at the same time. Further, the stability analysis of this nonsmooth observer is given. Finally, the effectiveness of the proposed method is verified by the simulation results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Sensorless control

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Non-smooth Speed control of Switched Reluctance Motor Based on Generalized Proportional Integral Observer

Non-smooth Speed Control of Switched Reluctance Motor Based ...

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第42届中国控制会议

作者： Qingzhao Yin Zheng Tian Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper investigates the speed regulation control of switched reluctance motor(SRM) *** improve the antidisturbance performance of SRM,a composite non-smooth control strategy is ***,the structure of SRM is analyzed,and a simplified nonlinear model is obtained based on a segmented representation of the varying phase inductance.A virtual control function is introduced to represent the nonlinear part of the torque equation,whose inverse function is cascaded to linearized the nonlinear ***,a generalized proportional integral observer(GPIO) is constructed to estimate the lumped disturbance of the system,which is used for feedforward compensation ***,a composite speed controller is designed based on a combination of finite time proportional feedback and feed-forward compensation based on GPIO(FTP+GPIO).The speed error closed-loop system can be regarded as a first-order finite time control system with bounded *** analysis shows that the proposed scheme can improve the anti-disturbance performance of the closed-loop *** effectiveness of the proposed method is verified by simulation ***,it is compared with proportional feedback combining feed-forward compensation(P+GPIO) method and proportional integral(PI) control method.

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The Current Research Status of Unmanned Aerial Vehicle Maneuver Penetration Decision-Making

The Current Research Status of Unmanned Aerial Vehicle Maneu...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Yutao Zhuang National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing P. R. China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

In modern warfare, maneuver penetration is an essential aspect of unmanned aerial vehicle (UAV) combat operations. As the capabilities of both offensive and defensive sides continue to improve, the operational patterns of maneuver penetration scenarios are constantly evolving. This paper first introduces the maneuver capabilities and detection characteristics of both offensive and defensive systems in contemporary warfare, providing a macro definition of the maneuver penetration problem. Then, it conducts a survey and analysis of the applications of traditional and new artificial intelligence algorithms in maneuver penetration decision-making. Finally, it discusses the future research directions and challenges of artificial intelligence algorithms in this field and envisions the development of maneuver penetration decision-making.

关键词： Surveys Heuristic algorithms Decision making Line-of-sight propagation Autonomous aerial vehicles Deep reinforcement learning Safety Trajectory Artificial intelligence Vehicle dynamics

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Manual Alignment of Dual End-Effectors for Piezo-Driven Microhand

Manual Alignment of Dual End-Effectors for Piezo-Driven Micr...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Pang, Jichao Liu, Dan Tang, Xiaoqing Chen, Yan Huang, Qiang Arai, Tatsuo Liu, Xiaoming Key Laboratory of Biomimetc Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems School of Mechatronical Engineerin Beijing Institute of Technology Beijing100081 China The University of Electro-Communications Center for Neuroscience and Biomedical Engineering Tokyo182-8585 Japan

ISBN: (纸本)9798350372601

In recent years, micromanipulator with dual end-effectors called microhand has been widely used in industrial and biological fields. Whether the tip positions of the two end-effectors of the microhand can be accurately aligned seriously affects the precise grasping of the microhand. However, few researchers have presented practical ways for precisely aligning the tip positions of a 6-DOF microhand's two end- effectors. This paper proposes a novel, inexpensive, lightweight, robust, and easy-to-use microhand dual end-effectors clamping device that can precisely and quickly align the two end-effectors of a 6-DOF microhand. First, we describe the microhand dual end-effectors clamping device's lightweight structural design. After that, we simplify the microhand dual end-effectors clamping device model and create a mathematical model to determine the relationship between the end-effectors' tip position and the rotation angles of screw No.1, screw No.2, screw No.3 of the microhand dual end-effectors clamping device. Through experimentation, we are able to attain precise alignment of the two end-effectors tip positions of the 6-DOF microhand, so we confirm that our mathematical model is true. Finally, we use the impulse response of PZT in the microhand's lower module to confirm the stability of the microhand dual end- effectors clamping device. We believe that the proposed the microhand dual end-effectors clamping device has great potential in industrial and biological applications as a universal tool for precise alignment of the tip positions of the two end-effectors of a 6-DOF microhand. © 2024 IEEE.

关键词： Clamping devices

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A semantic segmentation method for UAV image based on improved BiseNetv2 5

A semantic segmentation method for UAV image based on improv...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Guo, Fengjuan Han, Sen Zhao, Jiahang Han, Jiale Xin, Jing Shaanxi Key Laboratory of Integrated and Intelligent Navigation The 20th Research Institute of China Electronics Technology Corporation Shaanxi Xi’an710068 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Shaanxi Xi’an710048 China

ISBN: (纸本)9781510668621

Aiming at the problem of low semantic segmentation accuracy of tiny targets in UAV aerial images, a bilateral semantic segmentation network T-BiseNetv2 is proposed. The proposed segmentation network is based on BiseNetv2, and the Transformer structure is introduced into semantic branch, and the detail branch and feature fusion module are reconstructed, which improves the network’s capacity to capture both global context information and local semantic information. Several semantic segmentation experiments are conducted on the standard semantic segmentation dataset UAVID, experimental results show that the proposed semantic segmentation network T-BiseNetv2 has a MIoU of 69.52%, which is about 2.2% higher than the original BiseNetv2 network. Especially, for "Human", the IoU of TBiseNetv2 is about 4.3% higher than that of the popular UNetFormer. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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Pylon Reconstruction Based on NeRF

Pylon Reconstruction Based on NeRF

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International Conference on Electronics Information and Emergency Communication

作者： Zijian Hu Chenxing Wang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

In this paper, we propose a pylon reconstruction method based on Neural Radiance Fields (NeRF) technology. The advantage of this method lies in its ability to reconstruct pylons from images, with a multi-view model maintaining good performance even under complex lighting and occlusion conditions. We demonstrate the process of using NeRF to reconstruct electric power towers through experiments. During the experimental process, we collect images of electric power towers from different environments and angles, then apply NeRF to process these images, using deep learning to learn and generate a network model capable of expressing the scene. The experimental results show that our method can complete the reconstruction of scenes with only image inputs, and the reconstructed models have a certain accuracy in terms of geometric details and textures.

关键词： Surface reconstruction Image segmentation Image resolution Shape Poles and towers Lighting Reconstruction algorithms

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MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT images via a Deep Learning Architecture

MoB-NetV2-RVFL: Diagnosis of Coronavirus (COVID-19) from CT ...

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2024 Jiangsu Annual Conference on Automation, JACA 2024

作者： Tang, Xiaoyu Chai, Lin Song, Chengze School of Automation Southeast University Jiangsu Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Jiangsu Nanjing210096 China School of Sino-french Engineering Institute Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9781837242689

Computer-aided diagnosis has emerged as a crucial trend in modern medicine. Recently, CT-based automatic detection algorithms for lung infectious diseases suffer from issues of low accuracy and complex architecture. In this paper, three MoB-NetV2 models (MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL) for the detection of COVID-19 in CTs are proposed. the MoB-NetV2 model is proposed by inserting batch normalization layers between bottlenecks in MobileNetV2 and substituting the fully connected layer with the Net-1 model (SNN, ELM, RVFL), i.e. MoB-NetV2-SNN, MoB-NetV2-ELM, and MoB-NetV2-RVFL. After comparison, it is found that the proposed MoB-NetV2-RVFL achieves an average accuracy of 93.42% on a private dataset comprising of 2392 images, while the precision is reached at 97.33%. Furthermore, it is demonstrated that our MoB-NetV2-RVFL outperforms other existing algorithms in the literature. High classification accuracy and precision and low computing amount are achieved, thereby enhancing the potential application of the model in the field of medical image-aided diagnosis of COVID-19 virus. © The Institution of Engineering & Technology 2024.

关键词： COVID-19

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Traffic Signal control for Large-Scale Road Networks Based on Deep Reinforcement with PSR

Traffic Signal Control for Large-Scale Road Networks Based o...

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Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Zhicheng Zhou Hui Zhang Ya Zhang School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

This paper investigates the problem of Traffic Signal control (TSC) in large-scale road networks. In extensive road networks, it is customary to define each intersection as an agent, however, the issue of partial observability is particularly prominent. In this paper, Predictive State Representation (PSR) is employed to address the challenge of partial observability in large-scale multi-agent systems. A Multi-agent Deep Reinforcement Learning (DRL) model based on PSR called PSR-XLight is proposed in Large-Scale TSC systems. Multi-agent PSR is conducted with centralized training and independent filtering which overcome the challenge of prohibitive computations when the number of agents is large. Parameters sharing is adopted between each agent’s PSR model to enhance learning efficiency and facilitate utilization in large-scale multi-agent environments. Each agent undergoes independent DRL training and execution while parameters sharing is adopted. Experiments are conducted on real-world road networks and a large-scale road network comprising 1000 intersections.

关键词： Training Filtering Roads Computational modeling Deep reinforcement learning control systems Observability

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