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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zike Cheng Hengwang Zhao Qiyuan Shen Weihao Yan Chunxiang Wang Ming Yang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D moving object segmentation (MOS) is vital for autonomous systems, providing essential information for downstream tasks like mapping and localization. However, current MOS methods face challenges due to the limitation of existing datasets, which are sparse in moving objects and limited in scene diversity. Meanwhile, the prevalent methods are projection-based, struggling with the challenge of blurred boundaries. To tackle the dataset issue, we introduce a nuScenes-based MOS dataset, which provides richer scenes and more dynamic instances. To alleviate the boundary blur-ring issue and further improve accuracy and generalizability, we propose a dual-branch multimodal fusion MOS network, MOSFormer. The Transformer structure is incorporated to extract spatio-temporal information better, while image semantic information is utilized to refine the boundaries of moving objects. Finally, experiments on two datasets show that our method achieves state-of-the-art performance, and a mapping experiment with our method confirms its effectiveness in downstream tasks such as mapping and localization.

关键词： Location awareness Three-dimensional displays Autonomous systems Semantics Object segmentation Transformers Real-time systems Data mining Faces Intelligent robots

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A Depression Level Classification Model Based on EEG and GCN Network with Domain Generalization

A Depression Level Classification Model Based on EEG and GCN...

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第43届中国控制会议

作者： Zhongyi Zhang Chenyang Xu Licheng Jin Huirang Hou Qinghao Meng Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Electroencephalography(EEG) contains a wealth of information, including neuron activity, allowing for a partial understanding of the brain's state. As a reliable tool, EEG, combined with deep neural networks, has been increasingly utilized to assist in the detection of depression and has achieved satisfactory results. However, there are still some limitations in the current models. Firstly, it is worth noting that many existing models are binary classification models. These models can determine whether a subject is depressed or not, but they lack the capability to differentiate the severity of depression. Besides, the variations in EEG signals among various individuals have not been thoroughly taken into account, leading to subpar performance when applying the trained model to new subjects. To address the aforementioned issues, we suggest implementing a multi-classification model that utilizes a graph convolutional neural network(GCN) to identify levels of depression. Additionally,to mitigate the problem of imbalanced samples in multi-classification tasks, we introduce a penalty coefficient for the smaller categories. At the same time, the issue of signal discrepancies among subjects was taken into account, leading to the introduction of the domain generalization module. Finally, the depression level identification task on the MODMA and PRED+CT dataset achieved an accuracy of 75.47% and 77.97% respectively, surpassing the performance of the state-of-the-art(SOTA) model. In addition, numerous ablation experiments were conducted to validate the efficacy of each module.

关键词： Depression Level EEG Graph Convolution Network Domain Generalization

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Laser tracking leader-follower automatic cooperative navigation system for UAVs

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International Journal of Agricultural and Biological Engineering 2022年第2期15卷 165-176页

作者： Rui Ming Zhiyan Zhou Zichen Lyu Xiwen Luo Le Zi Cancan Song Yu Zang Wei Liu Rui Jiang College of Engineering South China Agricultural University/Guangdong Laboratory for Lingnan Modern AgricultureGuangzhou 510642China College of Computer and Control Engineering Minjiang University/Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlFuzhou 350108China Guangdong Provincial Key Laboratory for Agricultural Artificial Intelligence(GDKL-AAI) Guangzhou 510642China Guangdong Engineering Research Center for Agricultural Aviation Application(ERCAAA) Guangzhou 510642China Key Laboratory of Key Technology on Agricultural Machine and Equipment South China Agricultural UniversityMinistry of Education of P.R.ChinaGuangzhou 510642China

Currently,small payload and short endurance are the main problems of a single UAV in agricultural applications,especially in large-scale *** is one of the important methods to solve the above problems of UAVs by improving operation efficiency through multi-UAV cooperative *** study proposed a laser tracking leader-follower automatic cooperative navigation system for *** leader in the cluster fires a laser beam to irradiate the follower,and the follower performs a visual tracking flight according to the light spot at the relative position of the laser tracking *** on the existing kernel correlation filter(KCF)tracking algorithm,an improved KCF real-time spot tracking method was *** with the traditional KCF tracking algorithm,the recognition and tracking rate of the optimized algorithm was increased from 70%to 95%in indoor environment,and was increased from 20%to 90%in outdoor *** navigation control method was studied from two aspects:the distance coordinate transformation model based on micro-gyroscope and navigation control *** error of spot position was reduced from the maximum(3.12,−3.66)cm to(0.14,0.12)cm by correcting the deviation distance of the spot at different angles through a coordinate correction *** image coordinate conversion model was established for a complementary metal-oxide-semiconductor(CMOS)camera and laser receiving device at different mounting *** laser receiving device was divided into four regions,S0-S3,and the speed of the four regions is calculated using an uncontrollable discrete Kalman *** outdoor flight experiments of two UAVs were carried out outdoors using this *** experiment results show that the average flight error of the two UAVs on the X-axis is 5.2 cm,and the coefficient of variation is *** average flight error on the Z-axis is 7.3 cm,and the coefficient of variation is *** study demonstrated the possibility

关键词： two-UAVs cooperative visual navigation laser tracking

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Accelerated Simulation of Non-Markovian Quantum systems Using Quantum Trajectory

Accelerated Simulation of Non-Markovian Quantum Systems Usin...

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第42届中国控制会议

作者： Yanfang Wang Bo Yue Shibei Xue Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

Quantum trajectory can be viewed as a numerical simulation method for wavefunctions which consume smaller memories compared to density matrices in master equations. In this paper, we adopt quantum trajectory to simulate the evolution of a non-Markovian quantum system modeled by an augmented system and further use parallel computing to improve *** to the Lindblad master equation solver in QuTiP, it can run more efficiently using less memory. In the example of a8-spin-chain non-Markovian system, it only takes about 0.57% of the memory footprint and 4.12% of the runtime of the solver for the Lindblad master equation.

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Multi-Temporal Images Generation for Building Change Detection Performance Promotion

Multi-Temporal Images Generation for Building Change Detecti...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yute Li Wei Li Nan Wang Chenzhong Gao Yin Zhuang He Chen Beijing Key Laboratory of Fractional Signals and Systems Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The changes in building are important basis for urban monitoring. However, due to the rarity and sparsity of the occurrence of changes in buildings, collecting effective bitemporal image pairs is challenging, as it requires long-term observation over several months or even years. Additionally, annotating large-scale change detection datasets is time-consuming and labor-intensive. Consequently, data scarcity issues lead to insufficient training of building change detection models. To address this, we propose a data generation method Building Generation GAN (BG-GAN). Different from other GANs, the BG-GAN is trained based on adversarial consistency loss, enabling the model to generate new bi-temporal image pairs with various types of building changes. To verify the effectiveness of the proposed methods on change detection task, BG-GAN is utilized to perform building change samples generation on two building change detection datasets (LEVIR-CD and WHU-CD). The experimental results demonstrate that the proposed method can improve the robustness and generalization of change detection model to detect pseudo changes.

关键词： Training Accuracy Image synthesis Buildings Geoscience and remote sensing Generative adversarial networks Data models

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HTEC foot:A novel foot structure for humanoid robots combining static stability and dynamic adaptability

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Defence Technology(防务技术) 2025年第2期44卷 30-51页

作者： Jintao Zhang Xuechao Chen Zhangguo Yu Lianqiang Han Zhifa Gao Qingrui Zhao Gao Huang Ke Li Qiang Huang School of Mechatronical Engineering Beijing Institute of TechnologyNo.5South StreetZhongguancunHaidian DistrictBeijing100081China Key Laboratory of Biomimetic Robots and Systems Ministry of EducationNo.5South StreetZhongguancunHaidian DistrictBeijing100081China School of Information Science and Technology Beijing University of TechnologyNo.100PingleyuanChaoyang DistrictBeijing100124China Innovation Center for Control Actuators Beijing Institute of Precision Mechatronics and ControlsDonggaodiFengtai DistrictBeijing100076China

Passive bionic feet,known for their human-like compliance,have garnered attention for their potential to achieve notable environmental *** this paper,a method was proposed to unifying passive bionic feet static supporting stability and dynamic terrain adaptability through the utilization of the Rigid-Elastic Hybrid(REH)dynamics ***,a bionic foot model,named the Hinge Tension Elastic Complex(HTEC)model,was developed by extracting key features from human ***,the kinematics and REH dynamics of the HTEC model were *** on the foot dynamics,a nonlinear optimization method for stiffness matching(NOSM)was ***,the HTEC-based foot was constructed and applied onto BHR-B2 humanoid *** foot static stability is *** enhanced adaptability is observed as the robot traverses square steel,lawn,and cobblestone *** proposed design method and structure,the mobility of the humanoid robot is improved.

关键词： Humanoid robot Bionic structure Passive foot structure Rigid-elastic hybrid dynamics model Optimal stiffness matching

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Security analysis of simultaneous classical communication and CVQKD with a local local oscillator under the phase reference pulse intensity attack

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Optics Express 2025年第11期33卷 23893-23906页

作者： Weiqi Liu Minghui Zhang Jin Qi Tao Wang College of Information Science and Technology Northwest University Xi’an 710127 Shaanxi China State Key Laboratory of Advanced Optical Communication Systems and Networks Center of Quantum Information Sensing and Processing Shanghai Jiao Tong University Shanghai 200240 China tonystar@***

The simultaneous classical and continuous variable quantum communication can allow classical communication and continuous variable quantum key distribution with a local local oscillator (LLO CVQKD) to be implemented simultaneously using the same communication architecture. Such scheme enables LLO CVQKD to be integrated with classical communication at the minimum cost, but the existence of reference pulses will bring security loopholes to the simultaneous communication system, allowing eavesdroppers to launch attacks. In this paper, we analyze and simulate the performance of the system by introducing the trusted noise model under the phase reference pulse intensity attack, where Eve can hide her attack by manipulating the intensity of the reference pulse using a phase insensitive amplifier. A reconstructed noise model is proposed to solve this problem. Moreover, we show that the reconstructed noise model can strengthen the security of the practical simultaneous communication system.

关键词： Optical signals Phase contrast Phase noise Quantum key distribution Raman scattering Signal transmission

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AMG: A Depression Detection Model with Autoencoder and Multi-Head Graph Convolutional Network 42

AMG: A Depression Detection Model with Autoencoder and Multi...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Han-Guang Meng, Qing-Hao Jin, Li-Cheng Wang, Jian-Bang Hou, Hui-Rang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Process Measurement and Control Tianjin300072 China China Southern Power Grid Company Limited Guangzhou510623 China

ISBN: (纸本)9789887581543

Depression is a common chronic mental disorder characterized by high prevalence, recurrence, suicide rate, disability rate and heavy disease burden. Depression assessment based on questionnaires is subjective, therefore, a novel framework to is hereby designed to detect depression automatically and obj ectively. The framework first utilizes a two-way autoencoder to extract the hidden features from the original differential entropy features. The new features are used to generate the channel connectivity graph, using the multi-head attention mechanism. Next, the new features and graphs are input into the graph convolutional network (GCN) to aggregate spatial information. Finally, the output from GCN is passed to a fully connected layer and a soft layer to obtain the predicted label. Experiments are performed on Multimodal Open Dataset for Mental-disorder Analysis dataset. A final accuracy of 88.68% and a cross entropy loss of 2.4532 are obtained, when using ten-fold cross validation, outperforming other traditional machine learning methods including SVM, KNN, decision tree. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Decision trees

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Aggregating Correlation Filter with Multiple Kernels Learning for Robust Visual Object Tracking 22

Aggregating Correlation Filter with Multiple Kernels Learnin...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Wei, Yanxia Xiao, Hairong Feng, Deying Mu, Jian Yang, Jie School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Liaocheng University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781450396110

Recently, Discriminative Correlation Filter based trackers have increasingly become popular in the domain of visual object tracking, which is benefited by their effective and robustness in terms of tracking performance. However, the significant variations of target appearance, such as Occlusion or Deformation, always suppress the performance improvement. Although the multi-feature fusion strategy partly alleviates the above factors, the synthetic scheme also ambiguates the overall presentation ability of multiple features and results in degradation of discriminative power during classification filter learning. In this paper, we present a novel tracking framework based on the correlation filter with multiple kernel learning. Instead of directly integrating different features, we decompose a tracking process into several module, including low-dimension modules and high-dimension modules. The handcraft features extracted from low-dimension modules are robust to the small variation of target while the semantic convolutional features from the high-dimension modules can distinguish the target object from the relative complex tracking environment. In addition, we also design a reliability detection mechanism based on the confidence map to accurately measure the important degree of each kernel learning and precisely estimate the target state in the subsequent frames. For verifying our proposed tracker, we conduct extensive experiments on several standard sequence benchmark. Experiment results show that our proposed trackers perform favorably against other state-of-the-art trackers. © 2022 ACM.

关键词： Semantics

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Optimization of Tiered Pricing Strategy for Smart Grid Considering Clean Energy

Optimization of Tiered Pricing Strategy for Smart Grid Consi...

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Chinese Automation Congress (CAC)

作者： Xiaocheng Wang ZeLong Li Pengjiao Sun Qiaoni Han Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Ministry of Education College of Electronic and Communication Engineering Tianjin Normal University Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin Normal University Key Laboratory of System Control and Information Processing Ministry of Education School of Electrical and Information Engineering Tianjin University Shanghai Jiao Tong University

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

With the rapid development of new energy technology, it has become a trend to combine wind and solar power generation into the smart grid. In this paper, wind power generation and photovoltaic power generation are introduced into the smart grid, and taking into account the consumption capacity of consumers at different levels, a tiered pricing scheme considering the uncertainty of wind and solar energy is proposed. Firstly, we establish probability density functions of power generation for wind power generation and photovoltaic power generation respectively to express the uncertainty of wind energy and solar energy. To meet the energy demand of consumers at all levels, we present the energy demand model of consumers after considering their electricity demand and temperature influence. Then, we formulate the optimal pricing problem for the electricity market under the framework of tiered pricing, so as to make the energy market get satisfactory income while meeting the energy demand of consumers. Finally, we use the particle swarm optimization algorithm to determine the optimal pricing strategy of the energy market. The simulation results show that when meeting the energy demand of consumers in different temperature conditions, the optimal pricing can be found that meets the satisfactory income of the energy market.

关键词： Photovoltaic systems Wind energy generation Energy consumption Uncertainty Wind energy Electricity Pricing Solar energy Wind power generation Smart grids

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