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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Dan Li Yuhao Cao Wenmao Liu College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Electrical Engineering Tsinghua University Beijing China

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

In response to challenges arising from the large-scale integration of new energy sources into power systems, issues such as frequency and voltage stability, peak shaving capability, transient stability, and dynamic stability of the power grid have garnered considerable attention. The research on key equipment, namely the doubly-excited synchronous generator (DESG), characterized by robust stability and the capability for deep-phase operation, has become a focal point. However, addressing the diverse operational requirements under various conditions, the design of rotor topology and thermal balance of the DESG remain unresolved. This study presents a theoretical derivation of the mathematical model for the synchronous axis system of the DESG. Building upon this, an investigation into rotor topology design is conducted, considering factors such as rotor slot pitch, rotor excitation winding arrangements (symmetric and asymmetric, single-layer and double-layer, orthogonal and non-orthogonal), and air gap length. Additionally, a finite volume method is employed to explore the thermal balance issues and optimize the cooling structure of the DESG under stringent operating conditions. The research outcomes contribute theoretical foundations and technical support for the development of high-power doubly-excited synchronous generators by large-scale power generation equipment manufacturers.

关键词： Cooling Windings Rotors Voltage Power system stability Synchronous generators Stability analysis

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Adaptive Stochastic Predictive control from Noisy Data: A Sampling-based Approach

Adaptive Stochastic Predictive Control from Noisy Data: A Sa...

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IEEE Conference on Decision and control

作者： Johannes Teutsch Christopher Narr Sebastian Kerz Dirk Wollherr Marion Leibold Department of Computer Engineering Chair of Automatic Control Engineering (LSR) Technical University of Munich Theresienstr. 90 Munich Germany

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this work, an adaptive predictive control scheme for linear systems with unknown parameters and bounded additive disturbances is proposed. In contrast to related adaptive control approaches that robustly consider the parametric uncertainty, the proposed method handles all uncertainties stochastically by employing an online adaptive sampling-based approximation of chance constraints. The approach requires initial data in the form of a short input-output trajectory and distributional knowledge of the disturbances. This prior knowledge is used to construct an initial set of dataconsistent system parameters and a distribution that allows for sample generation. As new data stream in online, the set of consistent system parameters is adapted by exploiting set membership identification. Consequently, chance constraints are deterministically approximated using a probabilistic scaling approach by sampling from the set of system parameters. In combination with a robust constraint on the first predicted step, recursive feasibility of the proposed predictive controller and closed-loop constraint satisfaction are guaranteed. A numerical example demonstrates the efficacy of the proposed method.

关键词： Robust control Linear systems Uncertainty Shape Probabilistic logic Trajectory Noise measurement Synthetic aperture sonar Predictive control Convergence

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A Collaborative and Distributed Learning-Based Solution to Autonomously Plan computer Networks

A Collaborative and Distributed Learning-Based Solution to A...

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International Conference on Intelligence in Next Generation Networks (ICIN)

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

The high programmability provided by Software Defined Networking (SDN) paradigm facilitated the integration of Machine Learning (ML) methods to design a new family of network management schemes. Among them, we can cite self-driving networks, where ML is used to analyze data and define strategies that are then translated into network configurations by the SDN controllers, making the networks autonomous and capable of auto-scaling decisions based on the network’s needs. Despite their attractiveness, the centralized design of the majority of proposed solutions cannot keep up with the increasing size of the network. To this end, this paper investigates the use of a multi-agent reinforcement learning (MARL) model for auto-scaling decisions in an SDN environment. In particular, we study two possible alternatives for distributing operations: a collaborative one, where controllers share the same observations, and an individual one, where controllers make decisions according to their own logic and share only some basic information, such as the network topology. After an experimental campaign performed both on Mininet and GENI, results showed that both approaches can guarantee high throughput while minimizing the set of active resources.

关键词： Technological innovation computer aided instruction Network topology Distance learning Design methodology Conferences Collaboration

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A 3d-Pointcloud Based Semi-Automatic Private Dataset Generation Method for Specific 6d Pose Estimation Tasks

SSRN

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SSRN 2024年

作者： Lou, Yuanlong Fu, Dongxiang Tian, Ying Department of control science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Deep learning algorithms often require high-quality datasets, but public datasets can be less effective for specific applications like visual robotic arm grasping. Generating private datasets traditionally involves time-consuming manual work or is limited by equipment constraints. To address this problem, a 3D-pointcloud based semi-automatic private dataset generation method for specific object pose detection task is proposed, which has broadly adaptive performance that can be done with only one binocular camera. Firstly, the RGBD data is used to automatically generate labeled point clouds. Then, the labels, which contain the target category and position information as well as the pose information are automatically generated from the point cloud. Only the denoising need simple manual processing, the rest of the steps are operated by the algorithm, so it is called semi-automatic method. Compared to manual labeling, experiments show this semi-automatic method significantly reduces time while maintaining accuracy. © 2024, The Authors. All rights reserved.

关键词： Object detection

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RLVNA: a Platform for Experimenting with Virtual Networks Adaptations over Public Testbeds

RLVNA: a Platform for Experimenting with Virtual Networks Ad...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

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

Network emulators and simulation environments traditionally support computer networking and distributed system research. The continued use of multiple approaches highlights both the value and inadequacy of each approach. To this end, several large-scale virtual networks testbeds, such as GENI and CloudLab, have emerged, allowing testing of a networked system in controlled yet realistic environments, focusing in particular on facilitating the test of network management schema in Software-Defined Network (SDN) scenarios. Nevertheless, setting up those experiments first and integrating machine learning models later in these deployments is challenging. In this paper, we propose designing and implementing a web-based platform that integrates Reinforcement Learning (RL)-based models with a virtual network experiment using resources acquired within a real-world testbed, e.g., GENI. Users are able to reserve the network resources (links, switches, and hosts) and configure them through our intuitive interface with little effort. The RL algorithm is then launched to learn how to steer traffic dynamically and according to diverse traffic network conditions. Such a model can be easily customized by the user, while our architecture enables fast reprogramming of the Open Virtual Switches via the SDN controller instantiated. We experimented with trace-based traffic to validate this user-friendly platform and evaluated how centralized and decentralized RL algorithms can effectively lead to self-driving networks. While in this paper, the system focuses on the deployment of experiments for virtual network adaptation, the platform can be easily extended to other network management mechanisms and machine learning algorithms.

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Preprocessing Techniques for Rectal Cancer Diagnosis using MR Images 24

Preprocessing Techniques for Rectal Cancer Diagnosis using M...

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13th International Conference on Software and computer Applications, ICSCA 2024

作者： D'Almeida, Sonal Patreena Kamath, Surekha Rajagopal, K.V. Manjunath, K.N. Department Of Instrumentation And Control Engineering Manipal Institute Of Technology Manipal Academy Of Higher Education Manipal Karnataka576104 India Department Of Radiodiagnosis And Imaging Kasturba Medical College Manipal Academy Of Higher Education Manipal Karnataka576104 India Department Of Computer Science And Engineering Manipal Institute Of Technology Manipal Academy Of Higher Education Manipal Karnataka576104 India

ISBN: (纸本)9798400708329

The use of magnetic resonance (MR) Image has become more significant when treating rectal cancer. Rectal cancer can be staged more accurately with MRI, which serves as a great tool for choosing the most suitable course of action. Image pre-processing is used to improve the quality of image and making it better for analysis and further processing. The study carried out in this paper is concentrated on pre-processing methods like Gaussian and Sobel filtering. The filtering techniques were implemented on Pelvic MR images obtained from Kasturba Medical College (KMC), Manipal. The findings suggest that Sobel filtering had a superior output image quality, indicating its potential as a preferred preprocessing technique for enhancing Pelvic MR images in rectal cancer diagnosis and treatment planning. The output image efficiency is calculated by Peak to Signal Noise Ratio (PSNR) and Mean Square Error (MSE). © 2024 Owner/Author.

关键词： Mean square error

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Convergent Wheeled Robot Navigation Based on an Interpolated Potential Function and Gradient

SSRN

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SSRN 2023年

作者： Seder, Marija Klančar, Gregor Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Croatia Department of System Control and Cybernetics Faculty of Electrical Engineering University of Ljubljana Slovenia

The article presents a novel idea to construct a smooth navigation function based on the grid-based search that enables replanning in dynamic environments. Since the dynamic constraints of the robot are also considered, the navigation function is combined with the model predicted control (MPC) to guide the robot safely to the defined goal location. The main novelty of this work is the definition of this navigation function and its MPC application with guaranteed closed-loop convergence in finite time for a non-holonomic robot with speed and acceleration constraints. The navigation function consists of an interpolated potential function derived from the grid-based search and a term that guides the orientation of the robot on continuous gradients. The navigation function guarantees convergent trajectories to the desired goal, results in smooth motion between obstacles, has no local minima, and is computationally efficient. The proposed navigation is also suitable in dynamic environments, as confirmed by experiments with a Husky mobile robot. © 2023, The Authors. All rights reserved.

关键词： Model predictive control

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HINT: Supporting Congestion control Decisions with P4-driven In-Band Network Telemetry

HINT: Supporting Congestion Control Decisions with P4-driven...

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IEEE Workshop on High Performance Switching and Routing

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

Years of research on congestion controls have highlighted how end-to-end and in-network protocols might perform poorly in some contexts. Recent advances in data plane network programmability could also bring advantages in transport protocols, enabling mining and processing in-network congestion signals. However, the new machine learning-based congestion control class has only partially used data from the network, favoring a more sophisticated model design but neglecting possibly precious pieces of data. In this paper, we present HINT, an in-band network telemetry architecture designed to provide insights into network congestion to the end-host TCP algorithm during the learning process. In particular, the key idea is to adapt switches’ behavior via P4 and instruct them to insert simple device information, such as processing delay and queue occupancy, directly into transferred packets. Initial experimental results show that this approach comes with a little network overhead but can improve the visibility and, consequently, the accuracy of TCP decisions of the end-host. At the same time, the programmability of both switches and hosts also enables customization of the default behavior as the user’s needs change.

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SRG Analysis of Lur’e Systems and the Generalized Circle Criterion

arXiv

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arXiv 2024年

作者： Krebbekx, Julius P.J. Tóth, Roland Das, Amritam Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Lab HUN-REN Institute for Computer Science and Control Budapest Hungary

Scaled Relative Graphs (SRGs) provide a novel graphical frequency-domain method for the analysis of nonlinear systems. However, we show that the current SRG analysis suffers from some pitfalls that limit its applicability in analysing practical nonlinear systems. We overcome these pitfalls by modifying the SRG of a linear time invariant operator, combining the SRG with the Nyquist criterion, and apply our result to Lur’e systems. We thereby obtain a generalization of the celebrated circle criterion, which deals with broader class of nonlinearities, and provides (incremental) L2-gain performance bounds. We illustrate the power of the new approach on the analysis of the controlled Duffing oscillator. © 2024, CC BY.

关键词： Frequency domain analysis

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Hide & Seek: Traffic Matrix Completion and Inference Using Hidden Information

Hide & Seek: Traffic Matrix Completion and Inference Using H...

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Consumer Communications and Networking Conference, CCNC IEEE

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

Traffic matrices are used for many network management operations, from planning to repairing. Despite years of research on the topic, their estimation and inference on the Internet are still challenging and error-prone. For example, missing values are unavoidable due to flaws in the measurement systems and possible failure in data collection systems. It is thus helpful for many network operators to recover the missing data from the partial direct measurements. Some existing matrix completion methods do not fully consider network traffic behavior and hidden traffic characteristics, showing the inability to adapt to multiple scenarios. Others instead make assumptions on the matrix structure that may be invalid or impractical, curtailing the applicability. In this paper, we propose Hide & Seek, a novel matrix completion and prediction algorithm based on a combination of generative autoencoders and Hidden Markov Models. We demonstrate with an extensive experimental evaluation on real-world datasets how our algorithm can accurately reconstruct missing values while predicting their short-term evolution.

关键词： Hidden Markov models Estimation Telecommunication traffic Data collection Prediction algorithms Inference algorithms Encoding

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