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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yi Yu Feipeng Da 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

With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By mapping the rotational periodicity of different cycles into the phase of different frequencies, we provide a unified framework for various periodic fuzzy problems caused by rotational symmetry in oriented object detection. Upon such a framework, common problems in oriented object detection such as boundary discontinuity and square-like problems are elegantly solved in a unified form. Visual analysis and experiments on three datasets prove the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance. The codes are publicly available at https://***/open-mmlab/mmrotate.

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Intrinsic-feature-guided 3D Object Detection

arXiv

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arXiv 2025年

作者： Zhang, Wanjing Wang, Chenxing School of Automation Southeast University 2# Sipailou Xuanwu Nanjing210096 China Key laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

LiDAR-based 3D object detection is essential for autonomous driving systems. However, LiDAR point clouds may appear to have sparsity, uneven distribution, and incomplete structures, significantly limiting the detection performance. In road driving environments, target objects referring to vehicles, pedestrians and cyclists are well-suited for enhancing representation through the complete template guidance, considering their grid and topological structures. Therefore, this paper presents an intrinsic-feature-guided 3D object detection method based on a template-assisted feature enhancement module, which extracts intrinsic features from relatively generalized templates and provides rich structural information for foreground objects. Furthermore, a proposal-level contrastive learning mechanism is designed to enhance the feature differences between foreground and background objects. The proposed modules can act as plug-and-play components and improve the performance of multiple existing methods. Extensive experiments illustrate that the proposed method achieves the highly competitive detection results. Code will be available at https://***/zhangwanjingjj/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Object recognition

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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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Chinese control Conference (CCC)

作者： Xueqin Zheng Jinya Su Jun Yang 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

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 process. In 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 a 4.32% 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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Robust Speed Regulation of PMSM Under Disturbances and Noises Using Composite Model Predictive control

Robust Speed Regulation of PMSM Under Disturbances and Noise...

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Chinese control Conference (CCC)

作者： Linxuan Mao Jinya Su Shihua Li 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

This paper presents a single-loop Model Predictive control strategy that incorporates a reduced-order Generalized Proportional Integral Observe and a Kalman filter to enhance the speed regulation of Permanent Magnet Synchronous Motor systems in the presence of complex disturbances and measurement noises. The proposed controller design seamlessly integrates the predictive control, disturbance observer, and state filter components, and it was evaluated through simulation comparisons. The performance of the proposed method is evaluated using various metrics, including maximum velocity drop, recovery time, and variance of steady-state error, which demonstrate its superior response performance and anti-disturbance ability when compared to other existing methods without state filtering.

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An Ensemble Rule Extraction Algorithm Based on Interpretable Greedy Trees for Detecting Malicious Traffic*

An Ensemble Rule Extraction Algorithm Based on Interpretable...

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

作者： Qian Ku Wu Tiejun Zhang Ya School of Automation Southeast University Nanjing Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This paper studies the detection and identification problem of malicious network traffic and proposes a rule extraction algorithm based on interpretable models. The algorithm utilizes interpretable greedy trees as the foundational model and extends its applicability to handle multi-classification problems, thereby enhancing interpretability and detection accuracy. This methodology furnishes a more dependable and secure framework for discerning attack categories within the domain of network security. The experiment results show that the proposed algorithm achieves better balance between interpretability and identification precision.

关键词： Accuracy Automation Telecommunication traffic Artificial neural networks Network security Classification algorithms complexity theory Computer security Robots

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Event-triggered receding horizon control via actor-critic design

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Science China(Information Sciences) 2020年第5期63卷 131-145页

作者： Lu DONG Xin YUAN Changyin SUN School of Automation Southeast University College of Electronics and Information Engineering Tongji University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast University

In this paper, we propose a novel event-triggered near-optimal control for nonlinear continuoustime systems. The receding horizon principle is utilized to improve the system robustness and obtain better dynamic control performance. In the proposed structure, we first decompose the infinite horizon optimal control into a series of finite horizon optimal problems. Then a learning strategy is adopted, in which an actor network is employed to approximate the cost function and an critic network is used to learn the optimal control law in each finite horizon. Furthermore, in order to reduce the computational cost and transmission cost, an event-triggered strategy is applied. We design an adaptive trigger condition, so that the signal transmissions and controller updates are conducted in an aperiodic way. Detailed stability analysis shows that the nonlinear system with the developed event-triggered optimal control policy is asymptotically *** results on a single-link robot arm with different noise types have demonstrated the effectiveness of the proposed method.

关键词： event-triggered control receding horizon control actor-critic design optimal control nonlinear systems neural networks

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Multi-BS Fusion Scheme for PHD Simultaneous Localization and Mapping 23

Multi-BS Fusion Scheme for PHD Simultaneous Localization and...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Du, Tao Yang, Jie Xia, Shuqiang Jin, Shi Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing211189 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China Zte Corporation Shenzhen518057 China State Key Laboratory of Mobile Network and Mobile Multimedia Shenzhen518057 China

ISBN: (纸本)9798350325959

Integrated sensing and communication (ISAC) is expected to improve the energy and spectral efficiency of communication systems. Probability hypothesis density (PHD) simultaneous localization and mapping (SLAM) without explicit data association is one of the mainstream algorithms for implementing sensing function in ISAC. However, most existing research focused on multi-sensor and multi-user sensing fusion, while few studies involve multi base station (BS) fusion schemes. In this study, we introduce the virtual reference point rather than common used virtual anchor to model reflecting surface to form a consistent description of the surrounding environment among different BSs. Then, the estimation results of BSs are sequentially utilized as prior information, which is compared with arithmetic average (AA) and generalized covariance intersection (GCI) fusion algorithms. To improve the poor performance of existing schemes when filed of view (FoV) of different BSs is partial overlapped, we propose the concept of establishing global cloud map. The AA fusion algorithm is integrated into cloud fusion to improve the map estimation performance. Simulation results demonstrate that the performance of sequential fusion is superior to AA and GCI schemes when FoVs of different BSs are fully overlapped. Besides, the cloud map fusion scheme can obtain global maps even if some features are no longer in FoV, as well as improve the accuracy and stability of local maps estimation for each BS. © 2023 IEEE.

关键词： integrated sensing and communication mmWave multi-BS fusion probability hypothesis density

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Finite Time Disturbance Observer-Based Tube Model Predictive control for Vehicle Platoon systems with Guaranteed L∞ String Stability

Finite Time Disturbance Observer-Based Tube Model Predictive...

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第34届中国控制与决策会议

作者： Qian Chen Jinhao Liu Jun Yang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

This paper investigates optimal longitudinal control problems for a vehicle platoon in presence of parameter uncertainties and external ***,a multi-constraint multi-objective optimization model is developed,where physical limits,safety constraints,driving comfort,and fuel economy are taken into *** reduce communication burden and avoid network congestion,the preceding vehicle's acceleration is obtained by employing a finite time disturbance observer(FTDO).As for the parameter uncertainties as well as external disturbances,they are estimated as a lumped disturbance by exploiting a ***,under a predecessor following communication topology,a FTDO-based tube model predictive control method with explicit consideration of string stability is ***,numerical simulations illustrate the effectiveness and superiority of the proposed control approach.

关键词： Vehicle Platoon Longitudinal control Finite Time Disturbance Observer Model Predictive control Predecessor-follower L_∞ String Stability

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Maximum power point tracking of photovoltaic array in partial shading condition based on improved Gray Wolf optimization algorithm

Maximum power point tracking of photovoltaic array in partia...

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第34届中国控制与决策会议

作者： Chuang Kou Chenxi Li Kanjian Zhang Jinxia Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of AutomationSoutheast University

The partial shading condition(PSC) makes it challenging for the PV system to find the maximum power *** this paper,an improved gray wolf algorithm(GWO) is proposed by introducing elimination mechanism,greedy mechanism and adjusting the convergence factor which overcomes the contradiction between global exploration ability and convergence *** fast varying irradiance and PSC,IGWO has been compared with GWO,particle swarm optimization(PSO),and adaptive particle swarm optimization(APSO),the results shows the superiority of IGWO in the MPPTs of the PV system.

关键词： MPPT IGWO Population selection mechanism Greedy mechanism Convergence factor

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MALT: Multi-scale Action Learning Transformer for Online Action Detection

arXiv

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arXiv 2024年

作者： Yang, Zhipeng Wang, Ruoyu Tan, Yang Xie, Liping The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518063 China

Online action detection (OAD) aims to identify ongoing actions from streaming video in real-time, without access to future frames. Since these actions manifest at varying scales of granularity, ranging from coarse to fine, projecting an entire set of action frames to a single latent encoding may result in a lack of local information, necessitating the acquisition of action features across multiple scales. In this paper, we propose a multi-scale action learning transformer (MALT), which includes a novel recurrent decoder (used for feature fusion) that includes fewer parameters and can be trained more efficiently. A hierarchical encoder with multiple encoding branches is further proposed to capture multi-scale action features. The output from the preceding branch is then incrementally input to the subsequent branch as part of a cross-attention calculation. In this way, output features transition from coarse to fine as the branches deepen. We also introduce an explicit frame scoring mechanism employing sparse attention, which filters irrelevant frames more efficiently, without requiring an additional network. The proposed method achieved state-of-the-art performance on two benchmark datasets (THUMOS’14 and TVSeries), outperforming all existing models used for comparison, with an mAP of 0.2% for THUMOS’14 and an mcAP of 0.1% for TVseries. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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