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International Conference on Communication Technology (ICCT)

作者： Jinhui Tang Yusun Fu Yue Qiao Junpeng Yin Haobo Huang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

The decoding success rate may be worse when classical degree distributions are used in short-packet transmission of rateless codes, especially in industrial communication scenarios. A novel Boltzmann degree distribution is proposed for Luby Transform (LT) codes. The Boltzmann degree distribution has a high decoding success rate for short-packet transmission but a lower decoding success rate in long-packet transmission. To balance the performance of rateless codes for short and long packet transmission, the Boltzmann degree distribution and the robust soliton distribution (RSD) are combined with a new joint degree distribution, called Boltzmann Robust Soliton Distribution (BRSD). Simulation results show that compared with RSD and Poisson robust soliton distribution (PRSD), the proposed joint distribution can achieve a higher decoding success rate and reduce the average degree, especially in short-packet transmission of industrial communication.

关键词： Codes Simulation Industrial communication Symbols Solitons Transforms Robustness Decoding complexity theory Physics

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Unsupervised Learning of 3D Scene Flow from Monocular Camera

arXiv

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arXiv 2022年

作者： Wang, Guangming Tian, Xiaoyu Ding, Ruiqi Wang, Hesheng Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai 200240 China

Scene flow represents the motion of points in the 3D space, which is the counterpart of the optical flow that represents the motion of pixels in the 2D image. However, it is difficult to obtain the ground truth of scene flow in the real scenes, and recent studies are based on synthetic data for training. Therefore, how to train a scene flow network with unsupervised methods based on real-world data shows crucial significance. A novel unsupervised learning method for scene flow is proposed in this paper, which utilizes the images of two consecutive frames taken by monocular camera without the ground truth of scene flow for training. Our method realizes the goal that training scene flow network with real-world data, which bridges the gap between training data and test data and broadens the scope of available data for training. Unsupervised learning of scene flow in this paper mainly consists of two parts: (i) depth estimation and camera pose estimation, and (ii) scene flow estimation based on four different loss functions. Depth estimation and camera pose estimation obtain the depth maps and camera pose between two consecutive frames, which provide further information for the next scene flow estimation. After that, we used depth consistency loss, dynamic-static consistency loss, Chamfer loss, and Laplacian regularization loss to carry out unsupervised training of the scene flow network. To our knowledge, this is the first paper that realizes the unsupervised learning of 3D scene flow from monocular camera. The experiment results on KITTI show that our method for unsupervised learning of scene flow meets great performance compared to traditional methods Iterative Closest Point (ICP) and Fast Global Registration (FGR). The source code is available at: https://***/IRMVLab/3DUnMonoFlow. Copyright © 2022, The Authors. All rights reserved.

关键词： Unsupervised learning

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Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images

arXiv

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arXiv 2025年

作者： Feng, Fuxiang Cong, Runmin Wei, Shoushui Zhang, Yipeng Li, Jun Kwong, Sam Zhang, Wei The School of Control Science and Engineering Shandong University Jinan250061 China The Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China The University of California Los Angeles United States The Hubei Key Laboratory of Intelligent Geo-Information Processing The School of Computer Science China University of Geo-sciences Wuhan430078 China The Lingnan University Hong Kong

Reconstructing Hyperspectral Images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals that local correlation and global continuity of the spectral characteristics are crucial for HSI reconstruction tasks. Therefore, we fully explore these inter-spectral relationships and propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images. For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module, which efficiently establishes spectral band similarity within a localized range. For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module, which employs memory units to recursively model the progressive variation characteristics at the global level. In order to explore the inherent complementarity of these two modules, we design the Patch-wise Adaptive Fusion (PAF) module to efficiently integrate global continuity features into the spectral features in a patch-wise adaptive manner. These innovations enhance the quality of reconstructed HSI. We perform comprehensive comparison and ablation experiments on the mainstream datasets NTIRE2022 and NTIRE2020 for the spectral reconstruction task. Compared to the current advanced spectral reconstruction algorithms, our designed algorithm achieves State-Of-The-Art (SOTA) performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Resource Allocation for Coupled Slices in Edge-Assisted Industrial IoT systems

Resource Allocation for Coupled Slices in Edge-Assisted Indu...

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IEEE International Conference on Communications in China (ICCC)

作者： Xiaojing Wen Cailian Chen Cheng Ren Ling Lyu Xinping Guan Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China School of Information Science and Technology Dalian Maritime University Dalian China

Multiple types of sensors are utilized for remote monitoring of single industrial process to compensate for the limited accuracy of a single type of sensor. Network slicing technology is considered promising for collecting data from heterogeneous sensors and meeting diverse Quality of Service (QoS) requirements. However, existing research on network slicing optimization mainly focuses on inter-slice resource constraints based on slice heterogeneity, neglecting slice coupling feature. In this regard, this paper proposes a novel network slicing framework for edge-assisted industrial IoT systems. The framework includes a slice coupling model and network resource allocation (transmission and computing) to address the problem of improving estimation performance while ensuring system convergence. The slice coupling model is primarily composed of the estimation error covariance of two slices, calculated using the Age of Task (AoT) as a bridge, which reflects the accuracy and freshness of the estimation. To reduce the complexity of the direct solution, the optimization problem is scaled down and decoupled. The iterative Particle Swarm Optimization (PSO) algorithm is proposed to determine the allocation rates of inter-slice transmission and computing resources. Simulation results demonstrate that the proposed algorithm achieves a flexible balance under heterogeneous estimation weights and exhibits near-optimal performance.

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Self-organized Criticality in C. elegans Neural Network

Self-organized Criticality in C. elegans Neural Network

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第42届中国控制会议

作者： Xiao Gu Jian Liu Ye Yuan Peng Zhao Kuankuan Xin Tao Fang Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Engineering Research Centre of Intelligent Control and Management Institute of Machine Intelligence University of Shanghai for Science and Technology Queensland Brain Institute The University of Queensland

The self-organized criticality is a fundamental property for organisms to perform normal functional *** large-scale biological neural network researches have found the existence of self-organized criticality through avalanche phenomenon, which is typical characteristic of self-organized criticality. However, such research method is not suitable for small-scale biological neural network, because number of neurons in small-scale biological neural network is not large enough to support the emergence of avalanche phenomenon. It has been demonstrated that branching parameter can also be used to describe the propagation of activity in the neural network. Here, in this paper, the C. elegans neural network composed of 302 neurons is selected to investigate the existence of self-organized criticality using branching parameter. Based on the obtained connectome of C. elegans, a directed graph model is constructed to simulate the gradient potential of neurons and the activity of C. elegans, then, the existence of self-organized criticality in the neural network of C. elegans is proved by using branching parameter method. Meanwhile, the result is further confirmed by analyzing the calcium activity data of C. elegans obtained from biological experiments.

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A depthwise separable convolution based 6D pose estimation network by efficient 2D-3D feature fusion

A depthwise separable convolution based 6D pose estimation n...

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

作者： Feng, Qi Gu, Chaochen Qin, Jiani Xu, Rui Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9781665436786

Precise 6D pose estimation of the target object is an essential prerequisite for robots to understand the real world. Previous 6D pose estimation methods based on 3D data usually have problems such as long model training time, imperfect feature extraction, redundant network model parameters, and complicated follow-up processing steps. This paper proposes a 2D-3D feature fusion module that could enhance feature extraction for the 6D pose estimation network. Furthermore, we compress the size of model parameters by adopting depthwise separable convolution to accelerate training speed and to reduce memory consumption. The experiment results on LineMOD dataset show the effectiveness of our method. Our method achieves on par or better performance than the state-of-art methods for 6D pose estimation and reduces model training time and the number of model parameters simultaneously. © 2021 IEEE.

关键词： Convolution

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Exact Noise-Robust Distributed Gradient-Tracking Algorithm for Constraint-Coupled Resource Allocation Problems

Exact Noise-Robust Distributed Gradient-Tracking Algorithm f...

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IEEE Conference on Decision and control

作者： Wenwen Wu Shanying Zhu Shuai Liu Xinping Guan Department of Automation Shanghai Jiao Tong University Shanghai China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Control Science and Engineering Shandong University Jinan China

The Industrial Internet of Things (1IoT) gradually becomes a new paradigm for information exchange in the industrial production environment. To ensure the high reliability of IloT services, an efficient resource allocation method with good robustness is urgently needed under complex industrial environments. This paper considers the distributed constraint- coupled resource allocation problem with noisy information exchange over an undirected network, where each agent holds a private cost function and obtains the solution via only local communications. Communication noise poses a challenge to gradient-tracking based algorithm as the impact of noise will accumulate and its variance tends to infinity when the noise is persistent. Adopting noise-tracing scheme, we propose an exact noise-robust distributed gradient-tracking algorithm to achieve cost-optimal distribution of resources, which can avoid noise-accumulation in the tracking step. Moreover, noise suppression parameters are introduced to further attenuate the impact of noise. With diminishing suppression parameters, it is theoretically proved that the proposed algorithm is able to achieve exact convergence to the optimal solution. Finally, a numerical example is provided for verification.

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Simulation study on control characteristics of semiconductor grade monocrystalline silicon growth process

Simulation study on control characteristics of semiconductor...

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Chinese Automation Congress (CAC)

作者： Yuyu Liu Ding Liu Zezhong Song Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xiàn University of Technology Xiàn China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xiàn University of Technology Xiàn China

The growth of Czochralski(CZ) monocrystalline silicon is a nonlinear dynamic time-varying system with complex physical changes and multi-field and multi-phase coupling. In order to meet the needs of information development, it is urgent to develop large-scale, high-quality monocrystalline silicon. However, it is extremely difficult to establish an accurate control system model. Therefore, this paper considers the complex physical changes of multi-field coupling and non-linear processes in the growth of CZ monocrystalline silicon, a lumped parameter model for the growth of monocrystalline silicon is established based on energy balance model, fluid mechanics model, and geometry model, according to the internal mechanism of the monocrystalline silicon growth and the transfer mechanism of internal material flow. According to the model, a CZ monocrystalline silicon growth simulation control system is established under the Matlab/Simulink environment, and the real-time simulation data of each state variable of crystal growth is obtained. So that the control characteristics of each state variable in the crystal growth process can be analyzed. Finally, the validity of the established model is checked by the records evaluation of the semiconductor industry production process. Finally, the validity of the established model is verified based on the data analysis of the semiconductor industry production process. The results show that the model has high accuracy and can better describe the dynamic behavior of the actual monocrystalline silicon growth system.

关键词： Semiconductor device modeling Crystal growth Analytical models Electronics industry Process control Production Crystals

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Integrated Power and Hydrogen Trading in Multimicrogrid Coupled with Offsite Hydrogen Refueling Stations

Integrated Power and Hydrogen Trading in Multimicrogrid Coup...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Longyan Li Chao Ning Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

This paper proposes a novel two-level electricity and hydrogen market framework for multi-microgrids (MMGs) coupled with offsite hydrogen refueling stations (HRSs), aiming to strengthen the synergy between electricity and hydrogen. The local hydrogen market among the MMG and HRSs is modeled by multi-leader multi-follower Stackelberg game theory, in which microgrids play the role of leaders to adjust hydrogen prices while HRSs participate as followers to determine their hydrogen purchase scheme. Moreover, to deal with economic and technical issues of the MMG in a holistic manner, Nash bargaining theory is used to model the electricity transaction among microgrids. A distributed algorithm is developed to solve such games, thus reducing the cost of information communication and the risk of privacy exposure. The case study demonstrates that this market mechanism guarantees benefits for all parties and boosts the independence of MMGs by reducing transactions with the main grid.

关键词： Privacy Renewable energy sources Uncertainty Costs Hydrogen Microgrids system integration

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LSDNet: Boosting change detection of high-resolution remote sensing images by combining convolution-involution and ensemble coordinate attention 11

LSDNet: Boosting change detection of high-resolution remote ...

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2021 11th International Workshop on Computer Science and Engineering, WCSE 2021

作者： Liu, Yifan Liu, Jingdong Raza, Asif Li, Zeng Huo, Hong Fang, Tao Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9789811817915

Change detection has always been a crucial task in remote sensing fields, and there have already been great efforts made on it for decades. However, as high resolution (HR) remote sensing images generally contain abundant ground details, it still faces a huge challenge for their change detection, especially from the aspects of change detection accuracy and speed. Concerning this issue, a novel lightweight Siamese deep network (LSDNet) is proposed, and it combines Convolution-Involution Module (CIM) and Ensemble Coordinate Attention Module (ECAM) for boosting the change detection of HR remote sensing images. CIM summarizes the context of ground objects and reweights the importance of different positions, while ECAM aggregates multiple levels of semantic features and pays different attention to different spatial information. The experiments on CNZ data set have shown that the proposed LSDNet performs better than state-of-the-art (SOTA) change detection methods, especially it improves the accuracy by 1.92% and reduces the amount of model parameters by 32.89% compared to SNUNet-CD which has the best performance currently. © 2021 11th International Workshop on Computer Science and Engineering, WCSE 2021. All Rights Reserved.

关键词： Change detection

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