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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Shen, Chengyang Chen, Weidong Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation

ISBN: (纸本)9781665436786

The detection of soft packaging bags is a key step in the process of soft packaging bags unpacking. Time-of-flight(ToF) camera is used as vision sensor and a multi-scale detection method based on deep learning is proposed to solve the deformation and shielding problems of soft packaging bags. This method is improved on the basis of YOLOv3. Aiming at the texture disorder and shape change caused by the deformation of the soft packaging bags, the deformation convolution is used to replace the standard convolution for feature extraction. In view of the slow detection speed of YOLOv3, the inverted residual module based on depth separable convolution is used to replace the residual module. Aiming at the problem of non-detection caused by the occlusion between soft packaging bags, the loss function in YOLOv3 is improved and the size of anchor box is adjusted. The test is carried out when there were different degrees of shielding between the soft packaging bags and different degrees of deformation of the soft packaging bags. The experimental results show that the detection speed of this method reaches 0.8s/frame, the recall rate reaches 99%, and the relative positioning accuracy reaches 2 cm. © 2021 IEEE.

关键词： Convolution

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Analysis of Belief Propagation Based Cooperative Localization and Target Tracking Schemes

Analysis of Belief Propagation Based Cooperative Localizatio...

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2021 China Automation Congress, CAC 2021

作者： Li, Yichen Yu, Wenbin Wang, Lin Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781665426473

The target tracking is an fundamental ability for many oceanic applications, such as ocean surveillance, underwater rescue and etc. Due to the good mobility of autonomous underwater vehicles (AUVs), they are the ideal devices to carry out the target tracking. However, their position accuracy severely affect the target tracking performance. Thus, in this paper, aiming at the problem of target tracking using multiple AUVs, we introduce two AUV cooperative localization and target tracking schemes. The two schemes are designed based on belief propagation and carry out the localization and tracking serially and in parallel, respectively. The features and differences of both schemes are compared and analyzed in detail. Moreover, their application prospects in underwater scenarios are discussed and further validated through simulations. As a result, two schemes have their own advantages in different situations and are suitable for multiple-AUV applications. © 2021 IEEE

关键词： Belief propagation

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A Modified RC and Diode Co-Triggered ESD Clamp Circuit 1

A Modified RC and Diode Co-Triggered ESD Clamp Circuit

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1st Annual International EOS/ESD Symposium on Design and system, IEDS 2020

作者： Yang, Zhaonian Zhang, Yibo Mao, Pan Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

ISBN: (纸本)9781585373284

This paper presents a new ESD clamp circuit which is co-triggered by RC and diode. It is immune to the false triggering caused by fast power-up events due to the diode detection mechanism. With the addition of a common source amplifier, the detection signal is enhanced, and the clamp device is turned on more quickly. The proposed circuit is designed in a 0.18 μm 1.8-V CMOS process and verified by numerical simulation. The characteristics and improvements of the new circuit are discussed. © 2021 EOS/ESD ASSOCIATION INC.

关键词： Diodes

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent Transportation systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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EMOST: A dual-branch hybrid network for medical image fusion via efficient model module and sparse transformer

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Computers in Biology and Medicine 2024年 179卷 108771-108771页

作者： Wang, Wenqing He, Ji Liu, Han School of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

Multimodal medical image fusion fuses images with different modalities and provides more comprehensive and integrated diagnostic information. However, current multimodal image fusion methods cannot effectively model non-local contextual feature relationships, and due to direct aggregation of the extracted features, they introduce unnecessary implicit noise into the fused images. To solve the above problems, this paper proposes a novel dual-branch hybrid fusion network called EMOST for medical image fusion that combines a convolutional neural network (CNN) and a transformer. First, to extract more comprehensive feature information, an effective feature extraction module is proposed, which consists of an efficient dense block (EDB), an attention module (AM), a multiscale convolution block (MCB), and three sparse transformer blocks (STB). Meanwhile, a lightweight efficient model (EMO) is used in the feature extraction module to exploit the efficiency of the CNN with the dynamic modeling capability of the transformer. Additionally, the STB is incorporated to adaptively maintain the most useful self-attention values and remove as much redundant noise as possible by developing the top-k selection operator. Moreover, a novel feature fusion rule is designed to efficiently integrate the features. Experiments are conducted on four types of multimodal medical images. The proposed method shows higher performance than the art-of-the-state methods in terms of quantitative and qualitative evaluations. The code of the proposed method is available at https://***/XUTauto/EMOST. © 2024 Elsevier Ltd

关键词： Feature extraction

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A Probabilistic Topology Inference Method for Networked Dynamical system via Single Excitation

A Probabilistic Topology Inference Method for Networked Dyna...

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IEEE Conference on Decision and control

作者： Qing Jiao Yushan Li Jianping He Dept. of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

A growing number of works have investigated inferring the topology of networked dynamical systems from observations, such as to better understand the system behaviour. Despite the tremendous advances, most of them require the observations to be abundant. This paper focuses on inferring the topology by injecting single excitation on a node and collecting several steps of noisy observations. The problem is challenging because the noises cannot be depressed in several observations and are mixed with the injected excitation, making it hard to directly reveal the topology. To practice, we develop a probabilistic method based on the hypothesis test framework. First, we infer the neighbors that are within h-hop of the excited node and derive the accuracy guarantees. Then, we extend the method to infer the exact h-hop neighbors. A computable lower bound for the accuracy probability is established to provide confidence support in the inference procedures. Furthermore, we give the conditions of excitation input to ensure a desired inference probability, which provides guidance for the input design. Numerical simulations are conducted to verify the effectiveness of the proposed method.

关键词： Lower bound Accuracy Network topology Noise Probabilistic logic Numerical simulation Topology Noise measurement Reliability Dynamical systems

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A Communication-Efficient Distributed Optimization Algorithm with Linear Convergence over Directed Graphs

A Communication-Efficient Distributed Optimization Algorithm...

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Asian control Conference

作者： Yuzhu Duan Ziwen Yang Shanying Zhu Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Ministry of Education of China Shanghai P. R. China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The Industrial Internet of Things (IIoT) is increasingly emerging as a novel paradigm for information exchange within industrial production environments. To facilitate the efficient operation of IIoT, there is an urgent requirement for communication-efficient methods to transmit extensive production data. This paper proposes a communication-efficient distributed optimization algorithm tailored for the strongly convex and smooth convex optimization problems over a directed graph. To encompass a broader class of compressors, the assumption of compressor is relaxed to contain both relative and absolute errors. To mitigate compression errors, we adopt differential compression technique and dynamic scaling factor in the algorithm. In particular, under this compression assumption, a non-uniform quantizer is introduced to enable finite-bit transmission over communication channels and an upper bound of the required bits with this quantizer is provided. Moreover, we prove the linear convergence of the proposed algorithm with compression, and numerical examples are provided to verify the performance of the proposed algorithm.

关键词： Upper bound Heuristic algorithms Directed graphs Production Compressors Convex functions Optimization

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SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds

arXiv

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arXiv 2024年

作者： Wang, Yanbo Zhao, Wentao Cao, Chuan Deng, Tianchen Wang, Jingchuan Chen, Weidong Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

Although LiDAR semantic segmentation advances rapidly, state-of-the-art methods often incorporate specifically designed inductive bias derived from benchmarks originating from mechanical spinning LiDAR. This can limit model generalizability to other kinds of LiDAR technologies and make hyperparameter tuning more complex. To tackle these issues, we propose a generalized framework to accommodate various types of LiDAR prevalent in the market by replacing window-attention with our sparse focal point modulation. Our SFPNet is capable of extracting multi-level contexts and dynamically aggregating them using a gate mechanism. By implementing a channel-wise information query, features that incorporate both local and global contexts are encoded. We also introduce a novel large-scale hybrid-solid LiDAR semantic segmentation dataset for robotic applications. SFPNet demonstrates competitive performance on conventional benchmarks derived from mechanical spinning LiDAR, while achieving state-of-the-art results on benchmark derived from solid-state LiDAR. Additionally, it outperforms existing methods on our novel dataset sourced from hybrid-solid LiDAR. Code and dataset are available at https://***/Cavendish518/SFPNet and https://***. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Model Predictive control for T-S Fuzzy Markovian Jump systems Using Dynamic Prediction Optimization

arXiv

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arXiv 2024年

作者： Zhang, Bin Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

In this paper, the model predictive control (MPC) problem is investigated for the constrained discrete-time Takagi-Sugeno fuzzy Markovian jump systems (FMJSs) under imperfect premise matching rules. To strike a balance between initial feasible region, control performance, and online computation burden, a set of mode-dependent state feedback fuzzy controllers within the frame of dynamic prediction optimizing (DPO)-MPC is delicately designed with the perturbation variables produced by the predictive dynamics. The DPO-MPC controllers are implemented via two stages: at the first stage, terminal constraints sets companied with feedback gain are obtained by solving a "min-max" problem;at the second stage, and a set of perturbations is designed felicitously to enlarge the feasible region. Here, dynamic feedback gains are designed for off-line using matrix factorization technique, while the dynamic controller state is determined for online over a moving horizon to gradually guide the system state from the initial feasible region to the terminal constraint set. Sufficient conditions are provided to rigorously ensure the recursive feasibility of the proposed DPO-MPC scheme and the mean-square stability of the underlying FMJS. Finally, the efficacy of the proposed methods is demonstrated through a robot arm system example. © 2024, CC BY.

关键词： Predictive control systems

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Carrier Frequency Offset Estimation for MIMO OCDM Underwater Acoustic Communications Based on Null Symbols

Carrier Frequency Offset Estimation for MIMO OCDM Underwater...

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第43届中国控制会议

作者： Buyiyi Wang Peishuo Huang Yichen Li Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of information and Communication Engineering Hainan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Orthogonal chirp division multiplexing(OCDM) has emerged as a promising approach for underwater acoustic(UWA)*** the constraints of limited bandwidth resources in the underwater environment,enhancing the data transmission rate necessitates leveraging spatial resources ***,we should integrate multiple-input multipleoutput(MIMO) technology with OCDM UWA communication ***,existing MIMO OCDM systems ignore the interference caused by carrier frequency offset(CFO),which is a primary challenge faced by co-located MIMO systems in underwater *** this paper,we propose a CFO estimator based on the null symbols of the Fresnel domain tailored for MIMO OCDM *** estimator regards the energy leakage on null symbols as the objective function to identify the optimal CFO ***,we design pilot allocation strategies for different transmitters to mitigate co-channel interference in MIMO *** enables the estimation of the channel transfer matrix and simultaneous transmission of information,thereby enhancing adaptability to dynamic UWA ***,we derive a low-complexity equalizer *** simulations,the proposed algorithm demonstrates superior communication performance compared to existing algorithms with and without CFO interference.

关键词： Orthogonal chirp division multiplexing(OCDM) carrier frequency offset(CFO) multiple-input multiple-output(MIMO) underwater communications

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