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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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Specificity-preserving RGB-D saliency detection

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Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou Deng-Ping Fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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Distributionally Robust control Synthesis for Stochastic systems with Safety and Reach-Avoid Specifications

arXiv

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arXiv 2025年

作者： Chen, Yu Li, Yuda Li, Shaoyuan Yin, Xiang Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

We investigate the problem of synthesizing distributionally robust control policies for stochastic systems under safety and reach-avoid specifications. Using a game-theoretical framework, we consider the setting where the probability distribution of the disturbance at each time step is selected from an ambiguity set defined by the Wasserstein distance. The goal is to synthesize a distributionally robust control policy that ensures the satisfaction probability exceeds a specified threshold under any distribution within the ambiguity set. First, for both safety and reach-avoid specifications, we establish the existence of optimal policies by leveraging the dynamic programming principles. Then we demonstrate how the associated optimization problem can be efficiently solved using the dual representation of Wasserstein distributionally robust optimization. Furthermore, for safety specifications in particular, we introduce a novel concept of distributionally robust control barrier certificates and show how these enable the efficient synthesis of controllers through sum-of-squares programming techniques. Finally, our experimental results reveal that incorporating distributional robustness during the synthesis phase significantly improves the satisfaction probability during online execution, even with limited statistical knowledge of the disturbance distribution. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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The CEEMDAN-ps-gcGRU Model On Water Pressure Prediction With Strong Irregularity

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IFAC-PapersOnLine 2023年第2期56卷 7108-7113页

作者： Tzuyi Kan Jingcheng Wang Shunyu Wu Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

As for urban water dispatch, multiple steps of water pressure prediction are necessary. Due to the strong irregularity of water pressure, both machine learning and original deep learning methods can't accurately predict water pressure. In order to predict the water pressure with significant irregularity, this paper has developed a prediction model. The CEEMDAN approach seeks to reduce the noise of the raw water pressure data, which is directly recorded by sensors. The extraction of pump state signals has the ability to introduce extra useful information to the training model. Gradient Correction Methodology is a new strategy for increasing the precision of water pressure prediction in multi-step models. It could alleviate the gradient disappearing issue for long horizon prediction. In general, the designed prediction model on water pressure performs better than other models with long step sizes.

关键词： CEEMDAN states of pump signals Gradient Correction GRU

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Vision-based Autonomous Landing of UAV on Dynamic Apron

Vision-based Autonomous Landing of UAV on Dynamic Apron

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2023 IEEE International Conference on Unmanned systems, ICUS 2023

作者： Yue, Pengyu Xin, Jing Mao, Zhijie Lu, Yongchang Zhang, Youmin Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China Industrial & Aerospace Engineering Concordia University Department of Mechanical MontrealQCH3G 1M8 Canada

ISBN: (纸本)9798350316308

Aiming at the landing problem of Unmanned Aerial Vehicle (UAV) without landmarks and global coordinates, this paper proposes an efficient vision-based UAV autonomous landing solution. Firstly, image information is obtained from the bottom visual sensor of the UAV, and the YOLOV5 object detection method is employed to determine the initial position of the dynamic apron. Subsequently, an improved dynamic apron tracking method based on Spatio-Temporal Transformer is designed. This method updates an initial template through target detection algorithm and utilizes multi-source perceptual data for confidence evaluation and dynamic template updates. It outputs the distribution estimation of the predicted bounding box corner coordinates, and achieves the high reliability continuous positional smoothing estimation and dynamic apron loss re-detection based on UAV vision. Finally, the UAV calculates the horizontal control quantity based on the horizontal relative position between the UAV and the apron obtained. The vertical control quantity is calculated based on the vertical relative position between the UAV and the apron. The vertical relative position is estimated using laser ranging and visual odometry techniques. Based on the results of horizontal tracking control and vertical regulation, velocity commands for the UAV in all three directions are generated, enabling the UAV to achieve autonomous landing on the dynamic apron. This paper achieves precise landing for dynamic apron in real-world environments. The experimental results demonstrate that this method avoids the limitations of labeled data and global information on autonomous landing of UAV. Even under GPS-denied conditions, the UAV can generate control commands based on visual data and achieve autonomous landing for randomly moving apron. © 2023 IEEE.

关键词： Antennas

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signal Fusion-based Distributed Detection in Heterogeneous Radar Scenarios

Signal Fusion-based Distributed Detection in Heterogeneous R...

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IEEE International Conference on Radar

作者： Aoya Wang Shenghua Zhou Man Zhang Ziwei Wang National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Beijing institute of Control and Electronics Technology Beijing China

In distributed radar system, local radar may form heterogeneous statistics which probability density functions (PDFs) are different, but it is typically difficult to combine such observations for better detection performance. In this paper, taking fusion of the cell-average constant false alarm rate detector and the square-law detector for instance, we propose a signal-fusion based distributed detection algorithm and its global test statistic is a sum of linearly weighted local statistics, which the local weights depend on the channel SNRs. According to the Neyman-Pearson criterion and under the assumption of independence of local observations, the optimal test statistic is a sum of complicated functions of local test statistics and thus its PDFs are difficult to obtain. We first find the equivalent local test statistics to ensure that the PDF of a sum of the equivalent local test statistics under the null hypothesis has a closed-form expression. Then we approximate the global test statistic by the first-order Taylor series of the local likelihood ratio functions and local test statistics are properly weighted. Numerical results show that the detection performance of the proposed method outperforms the decision fusion algorithm. It is also found that the proposed algorithm is robust to SNR estimation errors.

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FoldExplorer: Fast and Accurate Protein Structure Search with Sequence-Enhanced Graph Embedding

arXiv

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arXiv 2023年

作者： Liu, Yuan Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

The advent of highly accurate protein structure prediction methods has fueled an exponential expansion of the protein structure database. Consequently, there is a rising demand for rapid and precise structural homolog search. Traditional alignment-based methods are dedicated to precise comparisons between pairs, exhibiting high accuracy. However, their sluggish processing speed is no longer adequate for managing the current massive volume of data. In response to this challenge, we propose a novel deep-learning approach FoldExplorer. It harnesses the powerful capabilities of graph attention neural networks and protein large language models for protein structures and sequences data processing to generate embeddings for protein structures. The structural embeddings can be used for fast and accurate protein search. And the embeddings also provide insights into the protein space. FoldExplorer demonstrates a substantial performance improvement of 5% to 8% over the current state-of-the-art algorithm on the benchmark datasets. Meanwhile, FoldExplorer does not compromise on search speed and excels particularly in searching on a large-scale dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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ACBL:Attentive CNN-BiLSTM Model For Trajectory Prediction

ACBL:Attentive CNN-BiLSTM Model For Trajectory Prediction

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第43届中国控制会议

作者： Gan Yang Guangwei Wang Yichen Li Wenbin Yu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Tianjin Navigation Instruments Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Predicting the future trajectory of a target becomes particularly challenging while dealing with occlusion conditions,where current information is unavailable,and historical data becomes the only basis for *** this paper,we propose a trajectory prediction model that utilizes scaled dot product attention mechanism,a convolution neural network,and a bidirectional long short term memory *** scaled dot product attention mechanism gauges the impact of historical sequences,while the convolution neural network extracts intricate feature information from these ***,these data is fed into the bidirectional long short term memory network to forecast the target's navigation *** results highlight that the Attentive CNN-Bi LSTM(ACBL) network which we proposed has lower root mean square error(RMSE) by 23% on average than current methods.

关键词： Occlusion conditions Scaled dot product attention mechanism Convolution neural networks Bidirectional long short term memory networks Trajectory prediction

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Research on Risk Management of Petrochemical Supply Chain Based on Network Dynamic Evolution Model

Research on Risk Management of Petrochemical Supply Chain Ba...

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Chinese Intelligent Automation Conference, CIAC 2021

作者： Pan, Chang-chun Huang, Xiaohao Sun, Weiqi Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9789811663710

This paper studies the quantitative measurement method of risk occurrence and possible spread among petrochemical supply chain enterprise nodes. Combined with the characteristics of petrochemical supply chain network, a risk propagation model of the supply chain based on cascading failure process is proposed, and a simulation experiment of risk propagation is carried out to investigate the different model parameters and different risk source enterprises on the communication process. According to experimental results, this paper puts forward targeted preventive measures to achieve the prevention and control of supply chain risk, which has important guiding significance for the regular operation of the petrochemical supply chain. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk management

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Distributed Model Predictive control for Asynchronous Multi-agent systems with Self-Triggered Coordinator

arXiv

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arXiv 2024年

作者： Chen, Qianqian Li, Shaoyuan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

This paper investigates the distributed model predictive control for an asynchronous nonlinear multi-agent system with external interference via a self-triggered generator and a prediction horizon regulator. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enable the robustness of the system. Then, the trigger interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade-off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the multi-agent system is achieved under asynchronous communication. Finally, the recursive feasibility and stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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