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Research on control Optimization Method of Mine Ventilation system Based on Intelligent Optimization Algorithm

Journal of Computers (Taiwan) 2023年第1期34卷 239-248页

作者： Lian, Wen-Li Qi, Jing-Jing Key Laboratory of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Hebei Province Tangshan City063600 China Key Laboratory of Intelligent Motion Control System of Tangshan City Tangshan University Hebei Province Tangshan City063600 China Tangshan University Hebei Province Tangshan City063600 China

In view of the complex ventilation network in the mine, many influencing factors, and the complex establishment of the air demand model, this paper establishes an air volume optimization model with the minimum energy consumption of the entire ventilation network as the optimization objective, and adds the air volume balance law conditions, the air pressure balance law conditions, the branch air demand law conditions, the air resistance regulation law conditions, and the fan conditions as constraints to the model. Then, the intelligent algorithm is used to solve the problem. In order to make the intelligent algorithm converge faster and solve the problem optimally, the Grey Wolf algorithm is improved. Finally, the model and the improved intelligent algorithm are used to optimize the ventilation regulation of the actual operating coal mine, and the air volume control scheme is given. © 2023 Authors. All rights reserved.

关键词： Coal mines

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Data-Driven Distributionally Robust Planning of Energy Hub Coupled with Carbon Capture Utilization and Storage Facilities

Data-Driven Distributionally Robust Planning of Energy Hub C...

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Chinese control Conference (CCC)

作者： Xutao Ma Chao Ning Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China

Toward carbon peaking and carbon neutrality goals in China, a transition from traditional fossil power to renewable energy becomes an imperative need. This paper aims to address the planning problem of a highly renewable-energy penetrated energy hub system, coupled with carbon capture facilities and downstream production processes of hydrogen and methanol, under the uncertainty of renewable power output. To this end, we propose a two-stage distributionally robust optimal planning model, where investment decisions are the first-stage variables and operation variables are determined at the second stage after the realization of uncertainty. In the proposed model, we utilize historical data samples to construct a strengthened ambiguity set that leverages both Wasserstein distance and moment information for a better characterization of renewable generation uncertainty. Moreover, to efficiently solve the proposed planning problem, we develop a simplified affine decision rule method in a general setting with both moment and Wasserstein ambiguity, and then apply it to reformulate the distributionally robust planning problem. The derived reformulation possesses the favorable property that its scale is free from the sample size, thus greatly reducing the computational complexity. Computational results validate the effectiveness of the proposed planning model and the computational efficiency of the solution methodology.

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LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement

arXiv

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arXiv 2024年

作者： Yang, Haodong Xu, Jisheng Lin, Zhiliang He, Jianping The Department of Computer Science Shanghai Jiao Tong University China The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Ocean and Civil Engineering Shanghai Jiao Tong University State Key Laboratory of Ocean Engineering Shanghai200240 China

Computer vision techniques have empowered underwater robots to effectively undertake a multitude of tasks, including object tracking and path planning. However, underwater optical factors like light refraction and absorption present challenges to underwater vision, which cause degradation of underwater images. A variety of underwater image enhancement methods have been proposed to improve the effectiveness of underwater vision perception. Nevertheless, for real-time vision tasks on underwater robots, it is necessary to overcome the challenges associated with algorithmic efficiency and real-time capabilities. In this paper, we introduce Lightweight Underwater Unet (LU2Net), a novel U-shape network designed specifically for real-time enhancement of underwater images. The proposed model incorporates axial depthwise convolution and the channel attention module, enabling it to significantly reduce computational demands and model parameters, thereby improving processing speed. The extensive experiments conducted on the dataset and real-world underwater robots demonstrate the exceptional performance and speed of proposed model. It is capable of providing well-enhanced underwater images at a speed 8 times faster than the current state-of-the-art underwater image enhancement method. Moreover, LU2Net is able to handle real-time underwater video enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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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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RHNet: A Real-Time High-Resolution Network for Surface Defect Detection

RHNet: A Real-Time High-Resolution Network for Surface Defec...

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第43届中国控制会议

作者： Yijie Wang Zhigang Ren Jianpu Cai Zongze Wu Shengli Xie School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the industrial domain, surface defect detection after multiple processing steps is crucial for improving the outgoing quality of products. However, due to the characteristics of surface defects, such as low contrast, irregular shapes, small and elongated features, and significant noise. To address these issues, this paper proposes a novel real-time high-resolution network(RHNet). Unlike previous models, RHNet emphasizes the importance of modeling spatial information to capture small ***, the output feature maps of each module are maintained at 1/4 resolution of the original image. We introduce a short-term dual-branch module(SDBM) to handle real-time processing of high-resolution feature maps. Additionally, FPAPPM is proposed for swift extraction of deep information and multi-scale context. Through extensive experiments, we demonstrate that our proposed model exhibits robust defect modeling capabilities and detection performance while still meeting real-time requirements.

关键词： Surface defect inspection Anomaly detection Semantic segmentation Deep learning Pattern recognition

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Model predictive control of event triggering in silicon single crystal growth processes

Model predictive control of event triggering in silicon sing...

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Chinese Automation Congress (CAC)

作者： Yankai Li Biao Ren Ding Liu Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

In this paper, the control issue of the semiconductor silicon monocrystalline growth process is studied through an event triggered model prediction approach. Firstly, a state-space model of silicon monocrystalline growth process is developed to describe the energy conversion and the dynamics and the geometry of the isodiametric stage silicon monocrystalline via the Czochralski method. Secondly, a small perturbation linearization is used to obtain a linear continuous model, which is translated into a linear discrete model under zero-order retainers. Finally, an event triggered model prediction controller is established to decrease the calculation of model prediction in the rolling optimization process. Based on experimental results, the event-triggered model prediction control algorithm is confirmed to be an effectively control scheme for the silicon monocrystalline growth process.

关键词： Crystal growth Semiconductor device modeling Computational modeling Photonic crystals Predictive models Prediction algorithms Silicon

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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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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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Direction-Constrained control for Efficient Physical Human-Robot Interaction under Hierarchical Tasks

arXiv

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arXiv 2024年

作者： Xu, Mengxin Wan, Weiwei Wang, Hesheng Harada, Kensuke Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of System Innovation Graduate School of Engineering Science Osaka University Toyonaka Osaka560-0043 Japan Department of Automation the Key Laboratory of System Control and Information Processing of Ministry of Education the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

This paper proposes a control method to address the physical Human-Robot Interaction (pHRI) challenge in the context of hierarchical tasks. A common approach to managing hierarchical tasks is Hierarchical Quadratic Programming (HQP), which, however, cannot be directly applied to human interaction due to its allowance of arbitrary velocity direction adjustments. To resolve this limitation, we introduce the concept of directional constraints and develop a direction-constrained optimization algorithm to handle the nonlinearities induced by these constraints. The algorithm solves two sub-problems, minimizing the error and minimizing the deviation angle, in parallel, and combines the results of the two sub-problems to produce a final optimal outcome. The mutual influence between these two sub-problems is analyzed to determine the best parameter for combination. Additionally, the velocity objective in our control framework is computed using a variable admittance controller. Traditional admittance control does not account for constraints. To address this issue, we propose a variable admittance control method to adjust control objectives dynamically. The method helps reduce the deviation between robot velocity and human intention at the constraint boundaries, thereby enhancing interaction efficiency. We evaluate the proposed method in scenarios where a human operator physically interacts with a 7-degree-of-freedom robotic arm. The results highlight the importance of incorporating directional constraints in pHRI for hierarchical tasks. Compared to existing methods, our approach generates smoother robotic trajectories during interaction while avoiding interaction delays at the constraint boundaries. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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A Study on Citation Impact with Age Diversity between Disciplines

SSRN

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SSRN 2024年

作者： Sheng, Jingran Liang, Bo Wang, Lin Wang, Xiaofan Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

With the ongoing evolution of scientific research collaboration in depth and breadth, the age distribution among paper collaborators is becoming increasingly varied. Consequently, the question of whether this age distribution affects the quality of collaborative papers has emerged as a significant topic in recent research. In this study, we have compiled and established a comprehensive scientific database, comprising more than 3.5 million papers. These papers, featuring multiple attribute information, originate from over 180 countries. We define the age diversity of a paper based on the academic ages of its co-authors and examine the relationship between age diversity and citation impact using regression analyses. Our findings reveal a clear upward trend in the age diversity of authors over time. The results indicate that the average age diversity of highly cited papers is consistently higher than that of general papers across the entire timeline, spanning four disciplines: economics, engineering, computer science, and physics. Moreover, we demonstrate a significantly positive correlation between age diversity and citation impact. In the aforementioned four disciplines, an increase in the collaborators’ age diversity is associated with a corresponding rise in the paper’s citation impact. This study contributes to a deeper understanding of age-related dynamics in scientific collaboration. It also offers insights into the age distribution of teams, providing practical suggestions for researchers engaged in collaborative endeavors. © 2024, The Authors. All rights reserved.

关键词： Regression analysis

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