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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Qin, Shujin Yang, Xinyi Wang, Jiacun Liu, Shixin Guo, Xiwang Qi, Liang Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal University Shangqiu476000 China Monmouth University Department of Computer Science and Software Engineering West Long BranchNJ07764 United States College of Information Science and Engineering Northeast University Shenyang110819 China College of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9798350337020

This paper classifies and summarises the historical literature on carousel systems in automated storage and retrieval systems in recent years. As an automated storage and retrieval system for distribution centers and production facilities, carousels facilitate the storage and dispatching of goods, significantly improving warehouse turnover efficiency. Their performance have been investigated by many scholars and experts. As carousels evolve and upgrade, more and more innovative algorithms have been used to improve the efficiency of outbound carousel storage. In this paper, we collate articles investigating how the carousel system is stored inbound versus retrieved outbound. We then discuss articles on the dual-command model of automatic storage retrieval systems as a whole. By reviewing over 50 papers, we summarise research on how to store and unload goods, focusing on the performance of automatic storage retrieval systems under dual-command conditions. On this basis, we review the current research's limitations and suggest future research directions. © 2023 IEEE.

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Low-disturbance Nonlinear control of TBM Cutterhead Speed in Coal Mine Tunneling 50

Low-disturbance Nonlinear Control of TBM Cutterhead Speed in...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Liu, Nannan Wang, Tong Wang, Jianmin Fan, Yingda School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Department of Computer Science University of Pittsburgh Pittsburgh United States

ISBN: (纸本)9781665464543

To minimize the disturbance of the Tunnel Boring Machine (TBM) cutterhead on the surrounding rock during the coal mine roadway excavation process and ensure that the cutterhead rotation speed achieves fast tracking performance with minimal overshoot, we propose a direct adaptive robust control method for the cutterhead rotation speed hydraulic system based on inversion design. This method considers the strong disturbances such as loads and motion affecting the cutterhead hydraulic drive system, as well as the uncertainties in the cutterhead model. We establish the nonlinear model of the cutterhead hydraulic drive system and employ virtual control to reduce the model order. Using Lyapunov functions, we ensure the stability of the entire system and derive the control law for the cutterhead rotation speed controller, along with parameter-adaptive laws acting as parameter estimators. We validate the effectiveness of the proposed control strategy through joint simulation using AMESim and Simulink. The results show that the designed cutterhead rotation speed controller achieves high tracking accuracy and good adaptability. © 2024 IEEE.

关键词： Hydraulic drives

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Inferring Visibility of Internet Traffic Matrices Using eXplainable AI

Inferring Visibility of Internet Traffic Matrices Using eXpl...

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IEEE Symposium on Network Operations and Management

作者： Cristian Zilli Alessio Sacco Doriana Monaco Okwudilichukwu Okafor Flavio Esposito Guido Marchetto Department of Control and Computer Engineering Politecnico di Torino Italy Computer Science Department Saint Louis University USA

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

A large fraction of recent network management tasks rely on Internet traffic matrices, ranging from planning and troubleshooting to routing and anomaly detection. Despite extensive research efforts over the years, acquiring a comprehensive overview of network traffic remains a difficult and error-prone task. While the literature has mostly proposed increasingly accurate and complex Machine Learning (ML) models to reconstruct missing information, in this paper we propose an alternative approach to further enhance this process: combining the ML model with eXplainable AI (XAI) to analyze the model behavior, detect most significant features, and limit the reconstruction process to such reduced input. With this methodology, not only we simplify the problem, but the entire solution finds greater deployability as the data acquisition phase is also simplified. Numerical results demonstrate that, with our solution on a Convolution Neural Network model, the error during completion can be lowered by 80% for a network telemetry traffic reduction of 75%.

关键词： Analytical models Accuracy Explainable AI Telecommunication traffic Routing Numerical models Internet

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Routing with ART: Adaptive Routing for P4 Switches With In-Network Decision Trees

Routing with ART: Adaptive Routing for P4 Switches With In-N...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Antonino Angi Alessio Sacco Flavio Esposito Guido Marchetto Department of Control and Computer Engineering Politecnico di Torino Italy Computer Science Department Saint Louis University USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Recent advances in Machine Learning (ML) brought several advantages also within computer network management. For programmable data planes, however, it is more challenging to benefit from these advantages, given their limited resource capabilities colliding with the complexity of ML models. In this paper, we propose ART, an attempt to simplify ML-based solutions for routing, so that they can "fit", i.e., be executed, on P4 switches. To provide such model simplification, ART relies on efficient knowledge distillation techniques, converting, in particular, Deep Reinforcement Learning (DRL) models into a simpler Decision Tree (DT). Our evaluation results validate the accuracy of the extracted model and the application of the model logic directly into switches with little impact, paving the way for a more reactive data plane programmability via machine learning integration.

关键词： Adaptation models Accuracy Subspace constraints Packet loss Routing Deep reinforcement learning Throughput Data models Decision trees Load modeling

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ResCue: Inferring Fine-Grained Traffic Matrices via Distributed Deep Residual Networks

ResCue: Inferring Fine-Grained Traffic Matrices via Distribu...

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International Conference on Network and Service Management

作者： Lorenzo Pappone Cristian Zilli Alessio Sacco Flavio Esposito Department of Computer Science Saint Louis University USA Department of Control and Computer Engineering Politecnico di Torino Italy

ISBN: (数字)9783903176669

ISBN: (纸本)9798331505158

Network measurement and telemetry techniques are central to the management of modern computer networks. Internet traffic matrix estimation is a popular technique employed for network management and telemetry to reconstruct missing information. Existing approaches use statistical methods, which often make impractical assumptions about the structure of the Internet traffic matrix. Data-driven methods, instead, heavily rely on the assumption of full knowledge of network topology data, that may be unavailable or impractical to collect. In this work, we propose ResCue, a deep residual networks technique to infer fine-grained Internet network traffic starting from spatial coarse-grained measurements. To address scenarios with network visibility constraints, we design a federated learning approach for fine-grained traffic prediction with partial network knowledge. Our evaluation across real-world traffic data shows that our proposed approach outperforms existing interpolation techniques and that our federated learning design achieves similar accuracy with respect to its centralized counterpart while requiring only partial knowledge of the network.

关键词： Knowledge engineering Training Interpolation Federated learning Superresolution Telecommunication traffic computer architecture Internet Telemetry Residual neural networks

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ROAR: Routing Packets in P4 Switches With Multi-Agent Decisions Logic

ROAR: Routing Packets in P4 Switches With Multi-Agent Decisi...

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Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Antonino Angi Alessio Sacco Flavio Esposito Guido Marchetto Department of Control and Computer Engineering Politecnico di Torino Italy Computer Science Department Saint Louis University USA

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

The soaring complexity of networks has led to more complex methods to efficiently manage and orchestrate the multitude of network environments. Recent advances in machine learning (ML) have opened new opportunities for network management automation, exploiting existing advances in software-defined infrastructures. Advanced routing strategies have been proposed to accommodate the traffic demand of interactive systems, where the common architecture is composed of a data-driven network management schema collecting network data that feed a reinforcement learning (RL) algorithm. However, the overhead introduced by the SDN controller and its operations can be mitigated if the networking architecture is redesigned. In this paper, we propose ROAR, a novel architectural solution that implements Deep Reinforcement Learning (DRL) inside P4 programmable switches to perform adaptive routing policies based on network conditions and traffic patterns. The network devices act independently in a multi-agent reinforcement learning (MARL) framework but are able to learn cooperative behaviors to reduce the queuing time of transmitting packets. Experimental results show that for an increasing amount of traffic in the network, there is both a throughput and delay improvement in the transmission compared to traditional approaches.

关键词： Performance evaluation Interactive systems computer architecture Traffic control Routing Throughput Deep reinforcement learning

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Design and Implementation of a Wide-Swing CMOS Multiplier for AC Source Signal Tracking and Modulation

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Circuits, Systems, and Signal Processing 2024年第9期43卷 5377-5400页

作者： Wang, Wei Xu, Sendren Sheng-Dong Chen, Chi-Lin Chiu, Huang-Jen Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei 106335 Taiwan Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei 106335 Taiwan Advanced Manufacturing Research Center National Taiwan University of Science and Technology Taipei 106335 Taiwan Himax Technologies Inc. Taipei 100507 Taiwan

The multiplier is a pervasive critical component today. However, most of them are difficult to be integrated into an integrated circuit. In this study, we proposed a novel analog multiplier for AC power application, using CMOS process to realize the analog voltage multiplication function of double-ended input and single-ended output. Under the working voltage of 3.3 V, the multiplier can operate with a wide input voltage range of 0–1.6 V (about 48.8% of the working voltage) and a wide output voltage range of 0–2.56 V (about 77.5% of the working voltage). The multiplier is implemented through the current difference between two NMOSs operating in the linear region and the saturation region, respectively. Compared with the existing technique, the proposed analog multiplier has a wider input and output range, and better linearity. It is suitable for AC power signal tracking and modulation. Finally, the United Microelectronics Corporation 0.18 μm 1P4M CMOS standard process was used for physical implementation. Moreover, there is not any BJT or special device used. It was measured that the analog multiplier circuit consumes about 0.56 mW at a working voltage of 3.3 V. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： AC power signal modulation AC source signal tracking Analog integrated circuit (IC) design Analog multipliers Circuits and systems

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Crysformer:An attention-based graph neural network for properties prediction of crystals

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Chinese Physics B 2023年第9期32卷 15-20页

作者：王田陈家辉滕婧史金钢曾新华 Hichem Snoussi Institute of Artificial Intelligence SKLSDEBeihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100191China School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Software Engineering Xi'an Jiaotong UniversityXi'an 710049China Academy for Engineering and Technology Fudan UniversityShanghai 200433China Charles Delaunay Institute University of Technology of TroyesTroyes Cedex 10004France

We present a novel approach for the prediction of crystal material properties that is distinct from the computationally complex and expensive density functional theory(DFT)-based ***,we utilize an attention-based graph neural network that yields high-accuracy *** approach employs two attention mechanisms that allow for message passing on the crystal graphs,which in turn enable the model to selectively attend to pertinent atoms and their local environments,thereby improving *** conduct comprehensive experiments to validate our approach,which demonstrates that our method surpasses existing methods in terms of predictive *** results suggest that deep learning,particularly attention-based networks,holds significant promise for predicting crystal material properties,with implications for material discovery and the refined intelligent systems.

关键词： deep learning property prediction crystal attention networks

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Bayesian hypergame approach to equilibrium stability and robustness in moving target defense 63

Bayesian hypergame approach to equilibrium stability and rob...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zhang, Hanzheng Cheng, Zhaoyang Chen, Guanpu Johansson, Karl Henrik Shanghai Research Institute for Autonomous Intelligent Systems Tongji University Shanghai China University of Chinese Academy of Science Academy of Mathematics and System Science Chinese Academy of Science Beijing China Kth Royal Institute of Technology and the Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350316339

We investigate the equilibrium stability and robustness in a class of moving target defense problems, in which players have both incomplete information and asymmetric cognition. We first establish a Bayesian Stackelberg game model for incomplete information and then employ a hypergame reformulation to address asymmetric cognition. With the core concept of the hyper Bayesian Nash equilibrium (HBNE), a condition for achieving both the strategic and cognitive stability in equilibria can be realized by solving linear equations. Moreover, to deal with players' underlying perturbed knowledge, we study the equilibrium robustness by presenting a condition of robust HBNE under the given configuration. Experiments evaluate our theoretical results. © 2024 IEEE.

关键词： Hierarchical systems

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Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects

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Machine Intelligence Research 2023年第3期20卷 335-369页

作者： Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou College of Information Engineering Shanghai Maritime UniversityShanghai201306China School of Computer Science and Technology East China Normal UniversityShanghai200062China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China

Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.

关键词： Moving object segmentation(MOS) change detection background subtraction deep learning(DL) video understanding

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