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6th International Conference on Signal and Image Processing, ICSIP 2021

作者： Hu, Haiyang Chen, Yuhao Li, Shihua Yang, Jun Sun, Zhenxing Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing China

ISBN: (纸本)9780738133737

Aiming at the problem that Seismocardiography (SCG) signal detection cannot be promoted due to the bulkiness of the equipment and the SCG containing 7 fiducial points is difficult to extract, this paper proposes a non-contact SCG signal detection algorithm based on the circulant singular spectrum analysis (CiSSA) that runs on a millimeter-wave radar platform. In order to make the detection equipment portable and achieve non-contact detection, this paper uses a millimeter-wave radar working in the 60-64GHz frequency band to collect the signal returned from the surface of the human chest cavity. Then the signal is processed by static clutter elimination and maximum energy distance gate positioning algorithm to improve the signal-to-noise ratio, especially to reduce the interference problem caused by people's tiny movement. For the key problem that the SCG curve extracted by traditional filtering methods are fuzzy and the fiducial points are difficult to identify, the CiSSA method is used to improve the accuracy and completeness of SCG curve extraction. By CiSSA, the signal is embedded into trajectory matrix, and then decomposed into component matrices containing various frequency characteristics. Next, the component matrices are grouped according to the frequency distribution characteristics of the SCG signal, and finally the result of the grouping is reconstructed into a high-precision SCG signal with clear and distinguishable fiducial points. Experiments show that the proposed SCG detection method can accurately extract the SCG curve, clearly distinguish 7 fiducial points of the SCG, and the success rate of detection can reach 97%. This provides the possibility for the popularization of SCG signal detection. © 2021 IEEE.

关键词： Signal detection

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A Two-step Model for Multi-object Tracking 21

A Two-step Model for Multi-object Tracking

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5th International Conference on Computer Science and Application Engineering, CSAE 2021

作者： Zhang, Shuaishuai Lu, Xiaobo Du, Songlin Southeast University Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education School of Automation Nanjing China

ISBN: (纸本)9781450389853

Multi-object tracking is widely used in video analysis. However, due to the limitation of detector performance, many multi-object tracking models have the problem of detecting two objects into one object in some occlusion scenes. In this paper, we propose a twostep model for handling this problem. In the first step model, the non-occlusion targets are detected and embeddings are extracted, while the occlusion areas are identified. The second step model processes the occlusion areas to obtain occlusion targets' accurate positions and embeddings. Finally, we integrate and optimize the output results of the two steps models. Experiments show that the number of false positives and missed positives in our model's object detection is significantly reduced. The multi-object tracking performance (MOTA metric) is improved by nearly 3% compared with other models.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Embeddings

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Multi Scale Attention Network for Crowd Counting 21

Multi Scale Attention Network for Crowd Counting

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5th International Conference on Computer Science and Application Engineering, CSAE 2021

作者： Yang, Xiangpeng Lu, Xiaobo Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9781450389853

Reasonable management and control of extra crowded scenes have become a hot topic in recent years. Counting people from density map generated from the object location annotations is an effective way to analyze crowd information and control crowds in severely congested scenes. In this paper, we propose a novel endto- end crowd counting method called MSANet for crowd counting. MSANet consists of the VGG16 backbone as the fronted part, two branches as the back-end part, including the attention map extractor to predict crowd states (means with people or not), and density map branch to regress the density map. What is more, to obtain high-resolution density map, we combine different scale maps from the front part to the back-end part. On the design of the loss function, to enhance the resolution of the predicted map and its structural similarity to ground truth, we proposed a new loss function for crowd counting. The test result based on the public dataset ShanghaiTech and Subway Crowd Counting Dataset supported by the Nanjing Metro demonstrates the effectiveness of our method.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Statistical tests

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A camera calibration method based on scene and human semantics 37

A camera calibration method based on scene and human semanti...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Shu, Yuan Wang, Yangang Southeast University Chien-Shiung Wu College Nanjing China Southeast University School of Automation Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781665465366

Camera calibration is an important preliminary for many applications such as 3D reconstruction, motion capture and human-computer interactions. Although great achievements have been made in the past several years, it still remains two main problems. The one is that conventional calibration methods always depend on specific calibration tools (e.g., checkerboard). The other is that the calibration procedure is complex when these methods are used in multi-view large scenarios. In this paper, we propose a camera calibration method which can be applied to large-scale sports scenarios without a special calibration object. To tackle these challenges, our key-idea is to use scene and human semantics detected from RGB images to calibrate the intrinsic and extrinsic parameters of multi-view cameras. We first detect the scene and human keypoints, then the initial camera parameters are calculated with the static scene keypoints and a predefined sports field model. Finally, we combine the scene and human semantics to refine the camera intrinsic and extrinsic parameters. Experimental results demonstrate that our method successfully obtains accurate camera intrinsic and extrinsic parameters for large-scale sports scenarios. © 2022 IEEE.

关键词： Semantics

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Stabilization of Port-controlled Hamiltonian systems subject to exogenous disturbances via compensation control approach

Stabilization of Port-Controlled Hamiltonian systems subject...

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Data Driven control and Learning Systems (DDCLS)

作者： Baozeng Fu Qingzhi Wang Yongchao Liu Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Qingdao China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

The stabilization of Port-controlled Hamiltonian (PCH) systems under exogenous disturbances by designing a compensation control approach is studied in the paper. Firstly, a baseline feedback control is proposed utilizing the damping injection method. To estimate disturbances, then a novel exogenous disturbance observer is constructed, based on which a feedforward compensation control is developed. Next, a composite control law is proposed based on the feedforward compensation and the feedback control. For the Hamiltonian system, an asymptotic stability analysis is followed finally. The feasibility of the compensation control strategy is revealed by a numerical simulation example.

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DeepTQ: Predictive TSN Switch Queue Length Based on Deep Learning 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Jia, Yanxin Xu, Long Xiong, Wei Wang, Xin Shang, Zhijun Yuan, Zijun 3onedata Co. Ltd. Shenzhen China Ministry of Education School of Automation and the Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing China 3onedata Qitong Co. Ltd. Shanghai China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9789819733361

ISBN: (纸本)9789819733354

With the rapid development of network technology, TSN (Time-Sensitive Networking) is in the stage of rapid development. It needs to ensure the deterministic transmission of time-sensitive flow, further improve the network throughput, and meet the increasing traffic demand. SDN (Software-Defined Networking) can realize the automatic management of network resources by decoupling the control plane and the data plane. Through global network information, it uses efficient route scheduling to achieve load balancing and improve network throughput. However, the prerequisite for route scheduling is that accurately predict the queue length of TSN switch, so that route scheduling can be carried out according to the predicted results. To address the queue length prediction problem of TSN switch, DeepTQ (Predictive TSN Switch Queue Length Based on Deep Learning) is proposed, which predicts the queue length of TSN switches in the next time slot using deep learning methods. It consists of the feature selection phase and the model prediction phase. In DeepTQ, the feature selection phase uses the Spearman rank correlation coefficient to remove redundant features in the datasets. The model prediction phase uses an improved Transformer Encoder and GRU (Gate Recurrent Unit) to construct the prediction model. Simulation results demonstrate that DeepTQ achieves superior performance compared to existing methods and individual components contribute to its overall effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software defined networking

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Research on Drag control Algorithm of Cooperative Robot Based on Torque Compensation

Research on Drag Control Algorithm of Cooperative Robot Base...

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2021 China Automation Congress, CAC 2021

作者： Zhu, Dezhi Gan, Yahui Dai, Xianzhong School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781665426473

At present, the teaching methods of collaborative robot drag based on joint current feedback mainly include integral and partial dynamics model, both of which can make robots dragged. However, there are few researches aimed at the advantages and disadvantages of the drag effect and the specific application requirements of the systematic comparative study. Based on the above problems, two zero-force control methods are used on the same 6 degree-of-freedom(DOF) cooperative robot in this paper. And method of torque compensation is improved by parameter identification. Firstly, both of the two methods need to model and analyse the robotic arm. However, processes of the method based on integral dynamics model are modeling, excitation trajectory, filtering, identification and vertification. The zero-force control based on partial dynamics model mainly focuses on the compensation of gravity torque and static friction torque. The gravity term in the dynamic equation is adopted by modeling. The difference in this part is that the parameters of gravity moment function are identified, that is, the weight moment is calculated under the condition of the mass and center of mass deviation are unknown. Finally, NaiWei robot which is a six-joints cooperative robot in China is taken as the experimental object to test and compare. The experimental results show that method of partial dynamics model needs smaller force than integral dynamics model. And the comparison of drag power can be displayed by sensor. © 2021 IEEE

关键词： Force control

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A High-Quality View Synthesis System for Sparse Rgb-D Input

SSRN

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SSRN 2024年

作者： Zhao, Ziyu Lu, Xiaobo Zhou, Sifan Chiang, Patrick Y.I.N. School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Key Laboratory of ASIC and System Fudan University Shanghai China

Recent research in neural rendering has achieved impressive results in the field of novel view synthesis. However, high-quality view synthesis with sparse source views remains a challenging task, as the reduction in the number of source views significantly degrades the synthesis quality. In this paper, we present a novel view synthesis system that exploits RGB-D pairs from sparse source views. First, we introduce a two-branch feature extraction network to embed the geometric clues from depth maps into the texture information of color images and fuse it with regularized Fourier mapping features. Second, a distance-aware feature query method, which constructs multi-level feature maps, is proposed to ensure high-fidelity details across different distance views. Then, we apply an occupancy network to perform occupancy prediction on raw point cloud. By integrating occupancy probabilities and extracted features, we use a prediction network with geometric information as an additional input to obtain the color and density of sampled points, and synthesize the target view using neural volume rendering. To prevent model degradation and enhance efficiency, we design an efficient sampling strategy based on Gaussian Mixture Model. Moreover, we introduce pixel-wise local regularization and full-image global geometric regularization to optimize the model. Extensive experiments show that the proposed method can achieve the state-of-the-art results for novel view synthesis with sparse inputs, and the model performance has been significantly improved in different scenarios. © 2024, The Authors. All rights reserved.

关键词： Geometry

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Information Entropy Guided Height-aware Histogram for Quantization-friendly Pillar Feature Encoder

arXiv

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arXiv 2024年

作者： Zhou, Sifan Yuan, Zhihang Yang, Dawei Zhao, Ziyu Hu, Xing Shi, Yuguang Lu, Xiaobo Wu, Qiang School of Automation Southeast University the Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Houmo AI Nanjing210046 China

Real-time and high-performance 3D object detection plays a critical role in autonomous driving and robotics. Recent pillar-based 3D object detectors have gained significant attention due to their compact representation and low computational overhead, making them suitable for onboard deployment and quantization. However, existing pillar-based detectors still suffer from information loss along height dimension and large numerical distribution difference during pillar feature encoding (PFE), which severely limits their performance and quantization potential. To address above issue, we first unveil the importance of different input information during PFE and identify the height dimension as a key factor in enhancing 3D detection performance. Motivated by this observation, we propose a height-aware pillar feature encoder, called PillarHist. Specifically, PillarHist statistics the discrete distribution of points at different heights within one pillar with the information entropy guidance. This simple yet effective design greatly preserves the information along the height dimension while significantly reducing the computation overhead of the PFE. Meanwhile, PillarHist also constrains the arithmetic distribution of PFE input to a stable range, making it quantization-friendly. Notably, PillarHist operates exclusively within the PFE stage to enhance performance, enabling seamless integration into existing pillar-based methods without introducing complex operations. Extensive experiments show the effectiveness of PillarHist in terms of both efficiency and performance. © 2024, CC BY-NC-ND.

关键词： Object detection

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Constraint-Following Based Adaptive Robust control for Underactuated Mechanical Systems

Constraint-Following Based Adaptive Robust Control for Under...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Cui Wei Ye-Hwa Chen Yanling Wei Institute of Intelligent Manufacturing Nanjing Tech University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA USA Key Laboratory of Measurement and Control of Complex System of Engineering School of Automation Southeast University Nanjing China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This paper introduces an adaptive robust control approach tailored for underactuated mechanical systems encountering matched and mismatched uncertainty, employing a constraint-following methodology. The control strategy unfolds in two phases: initially, a nominal control scheme is devised neglecting uncertainty and deviations in initial conditions from constraints. Subsequently, uncertainty is categorized into matched and mismatched components, ensuring that mismatched uncertainties remain unobservable. Leveraging the structural characteristics of the uncertainty bound, a novel segmented adaptive law is proposed and seamlessly integrated into the adaptive robust control framework. By employing the Lyapunov minimax approach, the method ensures uniform boundedness and uniform ultimate boundedness simultaneously, thereby ensuring approximate adherence to constraints for underactuated mechanical systems facing both matched and mismatched uncertainties alongside initial condition deviations.

关键词： Robust control Uncertainty Adaptive systems Costs Automation Nonlinear dynamical systems Mechanical systems Closed loop systems Robots

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