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Prediction of transmission spectrum from photonic energy band via autoencoder network

IEEE Photonics Technology Letters 2025年第13期37卷 737-740页

作者： Mo, Wenqin Zhu, Yaling Yin, Yuanxing Dong, Kaifeng Jin, Fang Song, Junlei China University of Geosciences School of Automation Wuhan430074 China China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Optical topological insulators, as an emerging type of photonic material, present substantial benefits for optical communication. The advanced pattern recognition capabilities of deep learning have propelled the inverse design of topological photonic structures into a hot area of research. However, existing methods encounter difficulties with the analysis of intricate band structures. Therefore, we construct a deep learning model capable of handling two-dimensional data of photonic energy band. Utilizing an autoencoder network, our model indirectly trains the photonic energy band and transmission spectrum, by converting them into feature coding. It integrates a transmission spectrum autoencoder and a photonic energy band autoencoder, processing energy band data to predict the corresponding transmission spectrum. The results indicate that compared to convolutional neural network (CNN), our model can accurately predict transmission spectrum, and perform better on both evaluation metrics of RMSE and R2. This work enhances prediction accuracy by leveraging energy band diagrams to constrain optical properties. © 1989-2012 IEEE.

关键词： Topological insulators

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Time-Optimal and Smooth Trajectory Planning Based On Continuous Pseudo-Acceleration

Time-Optimal and Smooth Trajectory Planning Based On Continu...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Xin Wang Yahui Gan Xianzhong Dai Key Laboratory of Measurement and Control of complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

The current time-optimal trajectory planning based on dynamics does not take into account the continuity of joint torque. Although the time obtained by the solution of the original time-optimal path parameterization (TOPP) algorithm is optimal, the joint torque obtained by the solution is not continuous, and the discontinuous joint torque will cause the manipulator to resonate, which will reduce the accuracy of the trajectory, making the obtained trajectory in practical engineering applications cannot be effectively applied. Therefore, this paper improves the problem of discontinuous torque on the basis of the TOPP algorithm, and proposes to use a quadratic polynomial curve to deal with the discontinuous part of the pseudo-acceleration in the phase plane composed of position and pseudo-acceleration, which not only ensures the integrity of the path, but also makes the joint torque continuous and smoothes the joint torque trajectory to some extent. This method is verified by the simulation experiment of the two-link manipulator.

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PWM Wave-Based Cooperative Vector control of Multiple Stepper Motors for Unmanned Surface Vehicles

PWM Wave-Based Cooperative Vector Control of Multiple Steppe...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Ruizhe Xu Yihang Xu Yongbao Wu Jian Liu Lei Xue Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

This paper studies the control system of multiple stepper motors. Four stepper motors are selected to design the vector control system for the actual application scenario of the unmanned surface vehicles. This paper designs a PWM wave-based motor control algorithm taking into account the characteristics of stepper motors. We proposes a closed-loop control algorithm incorporating magnetic encoders. The system applies CAN bus to realize the communication among stepper motors, enabling distributed cooperative control four stepper motors of the unmanned surface vehicles. In this paper, the multiple stepper motor system is modeled and finally simulated and experimentally verified to achieve cooperative vector control of multiple stepper motors for surface vehicles.

关键词： Protocols Surface waves Process control Pulse width modulation Motors control systems Vectors

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A Lightweight Object Detection Network for Industrial Robot Based YOLOv5

A Lightweight Object Detection Network for Industrial Robot ...

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2023 China Automation Congress, CAC 2023

作者： Zhong, Tao Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

ISBN: (纸本)9798350303759

With the rapid development of deep learning, it has been widely applied in fields such as computer vision, natural language processing, and robotics. Despite the superior performance of deep learning in object detection, most industrial vision robots still rely on traditional object detection methods due to computational constraints of robot controllers. In order to improve the performance of object detection for vision robots, a lightweight 3D object detection network based on You Only Look Once version 5 (YOLOv5) is proposed for satisfying industrial production. YOLOv5 work to efficiently object detection using deep convolutional networks. In terms of model deployment, we adopt a novel OpenVINO-based model deployment approach. The OpenVINO framework significantly enhances the inference speed of models by model optimization and compression. Our model achieves a 70% reduction in inference time compared to the baseline model on CPU. The effectiveness of the proposed method is demonstrated through experiments. © 2023 IEEE.

关键词： Object detection

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End-point Steady control for Whole-Body control Mobile Manipulators by Null Space Tunation

End-point Steady Control for Whole-Body Control Mobile Manip...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Enjin Zhang Yahui Gan Xianzhong Dai Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University NanJing China

This paper deal with the end-point steady control problem of a mobile manipulators(MM) at the velocity level. Mobile manipulators are usually kinematically redundant when performing tasks, so multiple subtasks can be performed simultaneously, such as tracking the trajectory of the end effector(EE), optimizing manipulability, etc. First, the mobile manipulators system is modeled as an ordinary jointed manipulator. The velocity of the EE has a higher priority, scheduling low-priority tasks in the null space of high-priority tasks leaves high-priority tasks unaffected. The damped least square method is used to generate a kinematic inverse solution with singular robustness, and the gradient projection method is used to optimize the manipulability measure. By analyzing the structure of the Jacobian matrix, the complexity of gradient calculation is reduced. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.

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Ensuring Port Safety: Improved YOLOX and DeepSORT for Accurate Detection and Tracking of Trucks and Truck Drivers

Ensuring Port Safety: Improved YOLOX and DeepSORT for Accura...

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第42届中国控制会议

作者： Xueqin Zheng Jinya Su Jun Yang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

An optimized YOLOX+DeepSORT method is proposed to accurately detect and track container trucks and truck drivers at the working position of automated rubber tire gantries in ports, while ensuring their safety during the whole working *** the proposed method, the improved YOLOX performs object detection and its output is used as the input for multi-object tracking using DeepSORT. The improved YOLOX model is developed through replacing standard convolution with depthwise separable convolution, adding the convolutional block attention module to enhance feature extraction, and using Focal Loss in the loss function to address sample imbalances. Comparative experiments were carried out on a self-built dataset, showing a4.32% increase in mAP and improved reasoning speed for improved YOLOX compared to the original YOLOX. Furthermore,the optimized method shows a 3.57% increase in Multi-Object Tracking Accuracy and a 1.73% increase in Multi-Object Tracking Precision compared to the benchmark YOLOX+DeepSORT.

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Autonomous Path Planning and Realization for Dual Robot Cooperation Based on ROS Framework

Autonomous Path Planning and Realization for Dual Robot Coop...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Jiawei Shao Yahui Gan Xianzhong Dai Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University NanJing China

To realize dual-robot autonomous path planning and realization, the trajectory planning research of dual-robot is carried out using the open-source robot operating system ROS as the simulation platform. The motion planning-related file configuration is completed by using MoveIt! In trajectory planning, cubic spline interpolation is performed on the trajectory points generated by the RRT-Connect path planning algorithm to complete the planning of the dual-arm assembly task. The communication between ROS nodes and ROS is established in the controller, and the processed trajectory points are communicated with the controller through ROS-Industrial. The experimental results show that the trajectory of the dual robot movement process is smooth and continuous, the stability is strong, and the error is small, which can ensure the completion of basic assembly tasks.

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Adaptive Augmented Kalman Filter-Based control for Electronic Throttle systems with Disturbances and Noises

Adaptive Augmented Kalman Filter-Based Control for Electroni...

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第42届中国控制会议

作者： Wen Li Jinya Su Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

In this study, we investigate the control problem of electronic throttle systems in the presence of practical challenges such as disturbances and measurement noises. To address these challenges, we propose an adaptive augmented Kalman filter(AAKF)based control approach that combines the strengths of extended state observer in disturbance estimation and adaptive Kalman filter in adaptive noise filtering. The outputs of AAKF are integrated into the Backstepping control design, resulting in a composite control that concurrently achieves fast disturbance rejection and noise suppression. We conduct a comparative simulation study against conventional methods without adaptive filtering to validate the effectiveness of the proposed AAKF-based control strategy, which exhibits superior position control accuracy and disturbance attenuation performance. We envision that our proposed control strategy will contribute to improving vehicle power, fuel economy, and emission performance.

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An Improved YOLOv5 Detection Algorithm with Pruning and OpenVINO Quantization

An Improved YOLOv5 Detection Algorithm with Pruning and Open...

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2023 China Automation Congress, CAC 2023

作者： Chen, Cheng Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

ISBN: (纸本)9798350303759

In order to address the problem of current object detection models being too large to be deployed on robot controllers, this paper proposes improvements to YOLOv5 for real-time detection. The YOLOv5s model is pruned at the Batch Normalization (BN) layer, and further optimized using the OpenVINO tool for quantization. The results demonstrate that our improvements effectively improve the model's inference speed while maintaining its accuracy. Compared to the original YOLOv5 model, the pruned model achieves the same accuracy without degradation, with a 36.6% reduction in parameter count and a 35% reduction in weight storage file size. Furthermore, after optimization with OpenVINO, the final model achieves a FPS of 56. © 2023 IEEE.

关键词： Object detection

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Research on Evolutionary Guiding Strategy for Self-organized Aggregates of Massive Flexible Resources 4

Research on Evolutionary Guiding Strategy for Self-organized...

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4th International Conference on Advanced Electrical and Energy systems, AEES 2023

作者： Mao, Tingfeng Zhang, Kaifeng Yin, Changwei Pan, Xiaohui Ma, Nan Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co. Ltd. Nanjing210019 China

ISBN: (纸本)9798350342659

With the improvement of communication technology and the management of demand-side, more and more researches focus on the aggregation technology of flexible resources on the demand side. This paper proposes to build a self-organizing response framework in the scenario of massive resources, and resources can independently choose to join or exit the aggregates to participate in the regulation of grid active power balance. However, self-organizing aggregates cannot generate aggregation scenarios that accurately match the dynamic needs of the power grid during autonomous operation, so the grid needs to further guide the evolution of the aggregates. Firstly, the process and principle of realizing self-organization and evolution of resources in cyberspace are outlined. Then, the evolutionary driving sources of the self-organizing aggregates are analyzed to construct the evolutionary dynamics model framework from both the steady and the dynamic dimensions, and the data-driven modeling is carried out through the back propagation neural network to obtain the evolution law of resource self-organization. Finally, an iterative evolutionary guiding strategy based on price feedback is designed. The optimal guiding value of the market price cap is obtained through iteration under the expected aggregation scale target of the power grid, and also the dynamic evolution time of the aggregates is predicted to determine the timing of the release of the guiding strategy. The proposed strategy is constructed and tested on the NETLOGO resource self-organization simulation platform. The results show that the self-organizing aggregates can evolve to the expected state of power grid demand with high accuracy. © 2023 IEEE.

关键词： Aggregates

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