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Proceedings of the 2021 5th International Conference on Video and Image Processing

作者： Sheng Ji Qijun Gu Ye Yuan Peng Zhao Tao Fang Hong Huo Xin Niu Department of Automation_School of Electronic Information and Electrical Engineering_Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China College of Computer Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (纸本)9781450385893

SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. The additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. The experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes.

关键词： Additive Spiking Timing Dependent Plasticity Learning Primary Visual Pathway Image Classification Spiking Neural Network

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Integrated Simulation of Process of Steel Casting on the Continuous Steel Casting Unit 2

Integrated Simulation of Process of Steel Casting on the Con...

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2nd International Conference on control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2020

作者： Galkin, Alexander Pimenov, Vladimer Saraev, Pavel Tyrin, Dmitry Lipetsk State Technical University Faculty of Automation and Computer Science Lipetsk Russia Novolipetsk Steel Corporation Lipetsk Russia Lipetsk State Technical University Department of Automated Control Systems Lipetsk Russia

ISBN: (纸本)9781728188409

Studying technological processes on real production equipment is associated with a large costs of resources: financial, time, etc. Therefore, various types of modeling are actively used to analyze and optimize these processes. With the development of digital technologies, it is possible to collect data about the process directly during the production. This data can be used both for direct control of the process, and for building (adjusting) the model of the process, which allows to get a "digital twin"repeating both the main properties of the modeled object and its current state. Many complex manufacturing processes involve a set of different interconnected models. In this case, the "digital twin"should contain information about the object characteristics, individual models of its functioning, the connections between these models, and the current state. These processes include many stages of metallurgical production. The paper considers complex modeling of the casting process in continuous steel casting unit. The set of models includes a model of the thermal state of the ingot during casting, a model of the stress-strain state, a model of the melt hydrodynamics, structural and concentration models of the ingot. Mathematical models of thermal and stress-strain states and the connections between them are presented. Examples of temperature fields and deformations calculation during the process of casting are given. © 2020 IEEE.

关键词： Steel ingots

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Maneuver Classification for Road Vehicles with Constrained Filtering Techniques ⁎

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IFAC-PapersOnLine 2020年第2期53卷 15495-15500页

作者： Olivér Törö Tamás Bécsi Szilárd Aradi Máté Kolat Péter Gáspár Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences

Environment perception and situation awareness are keystones for autonomous road vehicles. The problem of maneuver classification for road vehicles in the context of multi-model state estimation under model uncertainty is addressed in this paper. The conventional approach is to define different motion models that match the desired type of movements. In this work we used a single motion model as a starting point and applied constraints to construct such filters that are fine tuned for the predefined maneuvers. The estimation is carried out in the interacting multiple model framework, where the elemental filters are constrained Kalman filters. To capture the characteristics of the considered maneuvers linear equality and non-equality state constraints were used. The performance of the proposed method is demonstrated in a simulation environment participating an observer and a maneuvering vehicle.

关键词： Manoeuvring target Road traffic Constraints Estimation algorithms Simulation Filtering

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Simultaneously Calibration of Multi Hand-Eye Robot System Based on Graph

arXiv

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arXiv 2023年

作者： Zhou, Zishun Ma, Liping Liu, Xilong Cao, Zhiqiang Yu, Junzhi The Institute of Automation Chinese Academy of Sciences Beijing100190 China The College of Electrical Engineering and Computer Science Australian National University Canberra2601 Australia The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. Systems with multiple eyes (cameras) and multiple hands (robots) are particularly sensitive to calibration errors, such as micro-assembly systems. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the relative pose between each unit, but the serialized incremental calibration strategy cannot avoid the problem of error accumulation in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method. © 2023, CC BY-SA.

关键词： Multipurpose robots

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Label Cluster Chains for Multi-Label Classification

arXiv

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arXiv 2024年

作者： Gatto, Elaine Cecília Nakano, Felipe Kenji Read, Jesse Ferrandin, Mauri Cerri, Ricardo Vens, Celine Department of Computer Science Federal University of São Carlos SP São Carlos13565-905 Brazil Faculty of Medicine Katholieke Universiteit Leuven West Flanders Kortrijk8500 Belgium École Polytechnique Polytechnic Institute of Paris Palaiseau Paris91120 France Department of Control Automation and Computing Engineering Federal University of Santa Catarina SC Blumenau89036-002 Brazil Institute of Mathematical and Computing Sciences University of São Paulo SP São Carlos13566-590 Brazil

Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (local approach), others learn all labels at once (global approach), and others combine several classifiers (ensemble approach). Regardless of the approach used, exploring and learning label correlations is important to improve the classifier predictions. Ensemble of Classifier Chains (ECC) is a well-known multi-label method that considers label correlations and can achieve good overall performance on several multi-label datasets and evaluation measures. However, one of the challenges when working with ECC is the high dimensionality of the label space, which can impose limitations for fully-cascaded chains as the complexity increases regarding feature space expansion. To improve classifier chains, we propose a method to chain disjoint correlated label clusters obtained by applying a partition method in the label space. During the training phase, the ground truth labels of each cluster are used as new features for all of the following clusters. During the test phase, the predicted labels of clusters are used as new features for all the following clusters. Our proposal, called Label Cluster Chains for Multi-Label Classification (LCC-ML), uses multi-label Random Forests as base classifiers in each cluster, combining their predictions to obtain a final multi-label classification. Our proposal obtained better results compared to the original ECC. This shows that learning and chaining disjoint correlated label clusters can better explore and learn label correlations. © 2024, CC BY.

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Optical Data Center Implementation within Data Center Networks Context Based on Mininet

Optical Data Center Implementation within Data Center Networ...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Esraa Ali Abdulmahdi Adnan Hussein Ali Raed Khalid Ibrahim Ali Al-Askery Hayder Jasim Alhamdane Amal Ibrahim Mahmood Student at Dept. of Computer Engineering Techniques Electrical Engineering College Middle Technical University Baghdad Iraq Electrical Techniques Department Institute of Technology Middle Technical University Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Department of Control and Automation Technical Engineering Electrical Engineering College Middle Technical University Baghdad Iraq College of Applied Arts Middle Technical University Baghdad Iraq Dept. of Computer Engineering Techniques Electrical Engineering College Middle Technical University Baghdad Iraq

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The technology of reconfigurable optical networks (RONs) is growing to be a promising solution to effectively cater to the rapidly increasing traffic generated by the digital society. Optical technology emerges as a viable alternative because to its capacity to offer a substantial bandwidth, little latency, and enhanced overall performance. Nevertheless, it is imperative to effectively manage the physical resources comprising a data center. This study provides an experimental demonstration of the RONs, which is enhanced by a control plane. The implementation relies on the monitoring of real-time network statistics. This enables the execution of various activities, including as network slice provisioning and reconfiguration, assignment of packet priority classes, and dynamic operations. These actions are performed to ensure the attainment of the desired Quality of Service level. The experimental results show the successfully implementation of RONs using Mininet Emulator and sFlow traffic monitoring technology. The results approve that optical datacenter has minimum delay with low OSNR even with long distance and fast switching time.

关键词： Data centers Optical switches Optical fiber networks Traffic control Real-time systems Service-oriented architecture Monitoring

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Quantized Distributed Nonconvex Optimization with Linear Convergence

Quantized Distributed Nonconvex Optimization with Linear Con...

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IEEE Conference on Decision and control

作者： Lei Xu Xinlei Yi Jiayue Sun Yang Shi Karl Henrik Johansson Tao Yang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper considers distributed nonconvex op-timization for minimizing the average of local cost functions, by using local information exchange over undirected communication networks. Since the communication channels often have limited bandwidth or capacity, we first introduce a quantization rule and an encoder/decoder scheme to reduce the transmission bits. By integrating them with a distributed algorithm, we then propose a distributed quantized nonconvex optimization algorithm. Assuming the global cost function satisfies the Polyak– Łojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that the proposed algorithm linearly converges to a global optimal point. Moreover, a low data rate is shown to be sufficient to ensure linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical simulation examples.

关键词： Quantization (signal) Communication channels Bandwidth Cost function Numerical simulation Communication networks Distributed algorithms

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$H_{\infty }$ PID control for Discrete-Time Fuzzy Systems With Infinite-Distributed Delays Under Round-Robin Communication Protocol

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IEEE Transactions on Fuzzy Systems 2021年第6期30卷 1875-1888页

作者： Yezheng Wang Zidong Wang Lei Zou Hongli Dong College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University London Uxbridge U.K. Institute of Complex Systems and Advanced Control and the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University Daqing China

This article is concerned with the $H_{\infty }$ proportional–integral–derivative (PID) control problem for class of discrete-time Takagi–Sugeno fuzzy systems subject to infinite-distributed time delays and round-robin (RR) protocol scheduling effects. The information exchange between the sensors and the controller is conducted through a shared communication network. For the purpose of alleviating possible data collision, the well-known RR communication protocol is deployed to schedule the data transmissions. To stabilize the target system with guaranteed $H_{\infty }$ performance index, a novel yet easy-to-implement fuzzy PID controller is developed whose integral term is calculated based on the past measurements defined in a limited time window with hope to improve computational efficiency and reduce accumulation error. Based on the Lyapunov stability theory and the convex optimization technique, sufficient conditions are derived to ensure the exponential stability as well as the $H_{\infty }$ disturbance attenuation/rejection capacity of the underlying system. Furthermore, by utilizing the cone complementarity linearization algorithm, the nonconvex controller design problem is transformed into an iterative optimization one that facilitates the controller implementation. Finally, simulation examples are given to show the effectiveness and correctness of the developed control method.

关键词： PD control Protocols PI control control systems Symmetric matrices Communication networks Optimization

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Multi-Agent Consensus with Event-Triggered Communication and Relative Output Sensing

Multi-Agent Consensus with Event-Triggered Communication and...

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第40届中国控制会议

作者： Xianwei Li Shaoyuan Li Wei Xing Zheng Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Computer Data and Mathematical Sciences Western Sydney University

This paper investigates the consensus problem of linear multi-agent systems with event-triggered(ET) communication on undirected graphs. More practically, it is assumed that each agent can only access the relative output about its ***, the existing event-triggered(ET) protocols based on relative state sensing or absolute output sensing are not *** by this, an ET output-feedback protocol based on relative output sensing is developed. By a new Lyapunov function with more augmented states, it is proved that the resulting closed-loop system can reach consensus and the protocol states converge to zero. It is also shown that Zeno behavior is excluded from the triggering process. Furthermore, a method for reconstructing the continuous-time relative output from sampled data is formulated, based on which, the protocol can be implemented without continuous monitoring of neighbors. Finally, the effectiveness of the theoretic developments is illustrated by a numerical example.

关键词： Multi-agent system(MAS) Consensus Relative information Output feedback Event-triggered(ET) communication

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