检索结果-内蒙古大学图书馆

Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

IEEE/ACM Transactions on Networking 2023年第5期31卷 2086-2100页

作者： Sahay, Rajeev Nicoll, Serena Zhang, Minjun Yang, Tsung-Yen Joe-Wong, Carlee Douglas, Kerrie A. Brinton, Christopher G. Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Autonomy and Underwater Systems Division Saab Inc West LafayetteIN47907 United States STR WoburnMA01801 United States GoForward Inc. San FransiscoCA94104 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States School of Engineering Education Purdue University West LafayetteIN47907 United States

We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured interactions. While link prediction has been studied for general types of social networks, the evolution of SLNs over their lifetimes coupled with their dependence on which topics are being discussed presents new challenges for this type of network. To address these challenges, we develop a series of autonomous link prediction methodologies that utilize spatial and time-evolving network architectures to pass network state between space and time periods, and that models over three types of SLN features updated in each period: neighborhood-based (e.g., resource allocation), path-based (e.g., shortest path), and post-based (e.g., topic similarity). Through evaluation on six real-world datasets from Massive Open Online Course (MOOC) discussion forums and from Purdue University, we find that our method obtains substantial improvements over Bayesian models, linear classifiers, and graph neural networks, with AUCs typically above 0.91 and reaching 0.99 depending on the dataset. Our feature importance analysis shows that while neighborhood and path-based features contribute the most to the results, post-based features add additional information that may not always be relevant for link prediction. The code and four of the datasets used in this work are available at https://***/Jess-jpg-txt/sln-learning. © 2023 IEEE.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

Neuromimetic Dynamic Networks with Hebbian Learning

Neuromimetic Dynamic Networks with Hebbian Learning

引用

American control Conference (ACC)

作者： Zexin Sun John Baillieul Division of Systems Engineering Boston University Boston MA Department of Mechanical Engineering Electrical and Computer Engineering Division of Systems Engineering at Boston University Boston MA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Continuing work on what we have called neuromimetic control system designs is reported. The focus here is on control system models in which the dynamics of networks of neuron-like states are governed by hybrid con-tinuous/discrete, linear/nonlinear models. The models studied support Hebbian-like learning of network structure, and formal analysis grounded in graph theory and classical control allows us to prove that the biological model exhibits boundedness, stability, and structural controllability. The results make contact with previous results involving sym-cactus graphs. Simulations using a 14-node generalized sym-cactus network with two input types validate the model's effectiveness in capturing key neural dynamics.

关键词： Analytical models Biological system modeling controllability Stability analysis Graph theory Hebbian theory

来源：评论

学校读者我要写书评

暂无评论

The ChatGPT After: Building Knowledge Factories for Knowledge Workers with Knowledge Automation

引用

IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2041-2044页

作者： Yutong Wang Xiao Wang Xingxia Wang Jing Yang Oliver Kwan Lingxi Li Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence Anhui UniversityHefei 230039China Motion G Inc068898Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndiana 46202-5132 USA IEEE

Briefing: This perspective introduces the concept and framework of knowledge factories with knowledge machines for knowledge workers to achieve knowledge automation for Industry 5.0 and intelligent *** The big hit of Chat GPT makes it imperative to contemplate the practical applications of big or foundation models [1]-[5]. However, as compared to conventional models, there is now an increasingly urgent need for foundation intelligence of foundation models for real-world industrial applications.

关键词： Automation template Chat GPT

来源：评论

学校读者我要写书评

暂无评论

Dynamic Curing and Network Design in SIS Epidemic Processes 63

Dynamic Curing and Network Design in SIS Epidemic Processes

引用

63rd IEEE Conference on Decision and control, CDC 2024

作者： Yi, Yuhao Shan, Liren Pare, Philip E. Johansson, Karl H. Sichuan University Institute of Clinical Pathology West China Hospital College of Computer Science Chengdu China Toyota Technological Institute at Chicago United States Purdue University School of Electrical and Computer Engineering West LafayetteIN United States Kth Royal Institute of Technology and They Are Also Affiliated with Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden

ISBN: (纸本)9798350316339

This paper studies efficient algorithms for dynamic curing policies and the corresponding network design problems to guarantee fast extinction of epidemic spread in a Markov process-based susceptible-infected-susceptible (SIS) model. We provide a computationally efficient curing algorithm based on the curing policy proposed by Drakopoulos, Ozdaglar, and Tsitsiklis (2014). Since the corresponding optimization problem is NP-hard, finding optimal policies is intractable for large graphs. We provide approximation guarantees on the curing budget of the proposed dynamic curing algorithm. To avoid the waiting period included in the original curing policy, we study network design problems to reduce the total infection rate by deleting edges or reducing the weight of edges. To this end, we provide algorithms with provable guarantees. In summary, the proposed curing and network design algorithms together provide an effective and computationally efficient approach that mitigates SIS epidemic spread in networks. © 2024 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Distributionally Robust control for Chance-Constrained Signal Temporal Logic Specifications 63

Distributionally Robust Control for Chance-Constrained Signa...

引用

63rd IEEE Conference on Decision and control, CDC 2024

作者： Kordabad, Arash Bahari Vlahakis, Eleftherios E. Lindemann, Lars Dimarogonas, Dimos V. Soudjani, Sadegh Max Planck Institute for Software Systems Kaiserslautern Germany Kth Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden University of Southern California Viterbi School of Engineering Thomas Lord Department of Computer Science Los Angeles United States

ISBN: (纸本)9798350316339

We consider distributionally robust optimal control of stochastic linear systems under signal temporal logic (STL) chance constraints when the disturbance distribution is unknown. By assuming that the underlying predicate functions are Lipschitz continuous and the noise realizations are drawn from a distribution having a concentration of measure property, we first formulate the underlying chance-constrained control problem as stochastic programming with constraints on expectations and propose a solution using a distributionally robust approach based on the Wasserstein metric. We show that by choosing a proper Wasserstein radius, the original chance-constrained optimization can be satisfied with a user-defined confidence level. A numerical example illustrates the efficacy of the method. © 2024 IEEE.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

MaxCUCL: Max−Consensus with Deterministic Convergence in Networks with Unreliable Communication Links

arXiv

引用

arXiv 2024年

作者： Rikos, Apostolos I. Charalambous, Themistoklis Johansson, Karl H. Department of Electrical and Computer Engineering Division of Systems Engineering Boston University Boston United States Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Division of Decision and Control Systems KTH Royal Institute of Technology also affiliated with Digital Futures StockholmSE-100 44 Sweden

In this paper, we present a novel distributed algorithm (herein called MaxCUCL) designed to guarantee that max−consensus is reached in networks characterized by unreliable communication links (i.e., links suffering from packet drops). Our proposed algorithm is the first algorithm that achieves max−consensus in a deterministic manner (i.e., nodes always calculate the maximum of their states regardless of the nature of the probability distribution of the packet drops). Furthermore, it allows nodes to determine whether convergence has been achieved (enabling them to transition to subsequent tasks). The operation of MaxCUCL relies on the deployment of narrowband error-free feedback channels used for acknowledging whether a packet transmission between nodes was successful. We analyze the operation of our algorithm and show that it converges after a finite number of time steps. Finally, we demonstrate our algorithm’s effectiveness and practical applicability by applying it to a sensor network deployed for environmental monitoring. © 2024, CC BY.

关键词： Probability distributions

来源：评论

学校读者我要写书评

暂无评论

Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part II)

引用

IFAC-PapersOnLine 2024年第15期58卷 497-501页

作者： Donatello Materassi Sean Warnick Cristian Rojas Maarten Schoukens Elizabeth Cross Department of Electrical and Computer Engineering University of Minnesota US Department of Homeland Security Science and Technology Directorate EECS/Division of Decision and Control Systems KTH Royal Institute of Technology Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands Department of Mechanical Engineering University of Sheffield

In the second segment of the tutorial, we transition from the granularity of local interpretability to a broader exploration of eXplainable AI (XAI) methods. Building on the specific focus of the first part, which delved into Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), this section takes a more expansive approach. We will navigate through various XAI techniques of more global nature, covering counterfactual explanations, equation discovery, and the integration of physics-informed AI. Unlike the initial part, which concentrated on two specific methods, this section offers a general overview of these broader classes of techniques for explanation. The objective is to provide participants with a comprehensive understanding of the diverse strategies available for making complex machine learning models interpretable on a more global scale.

关键词： eXplainable AI

来源：评论

学校读者我要写书评

暂无评论

Nonlinear Bandwidth and Bode Diagrams based on Scaled Relative Graphs

arXiv

引用

arXiv 2025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. In this paper, we use the restriction of the SRG to particular input spaces to compute frequency-dependent gain bounds for incrementally stable nonlinear systems. This leads to a nonlinear (NL) generalization of the Bode diagram, where the sinusoidal, harmonic, and subharmonic inputs are considered separately. When applied to the analysis of the NL loop transfer and sensitivity, we define a notion of bandwidth for both the open-loop and closed-loop, compatible with the LTI definitions. We illustrate the power of our method on the analysis of a DC motor with a parasitic nonlinearity, verifying our results in simulations. © 2025, CC BY.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Distributed model predictive control without terminal cost under inexact distributed optimization

arXiv

引用

arXiv 2025年

作者： Liu, Xiaoyu Dimarogonas, Dimos V. Liu, Changxin Dabiri, Azita De Schutter, Bart Delft Center for Systems and Control Delft University of Technology Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper presents a novel distributed model predictive control (MPC) formulation without terminal cost and a corresponding distributed synthesis approach for distributed linear discrete-time systems with coupled constraints. The proposed control scheme introduces an explicit stability condition as an additional constraint based on relaxed dynamic programming. As a result, contrary to other related approaches, system stability with the developed controller does not rely on designing a terminal cost. A distributed synthesis approach is then introduced to handle the stability constraint locally within each local agent. To solve the underlying optimization problem for distributed MPC, a violation-free distributed optimization approach is developed, using constraint tightening to ensure feasibility throughout iterations. A numerical example demonstrates that the proposed distributed MPC approach ensures closed-loop stability for each feasible control sequence, with each agent computing its control input in parallel. © 2025, CC BY.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning-based Receding Horizon control using Adaptive control Barrier Functions for Safety-Critical systems

arXiv

引用

arXiv 2024年

作者： Sabouni, Ehsan Sabbir Ahmad, H.M. Giammarino, Vittorio Cassandras, Christos G. Paschalidis, Ioannis Ch. Li, Wenchao Division of Systems Engineering Boston University BostonMA02215 United States The Department of Electrical and Computer Engineering The Division of Systems Engineering Boston University BostonMA02215 United States The Department of Electrical and Computer Engineering Division of Systems Engineering Department of Biomedical Engineering Boston University BostonMA02215 United States

Optimal control methods provide solutions to safety-critical problems but easily become intractable. control Barrier Functions (CBFs) have emerged as a popular technique that facilitates their solution by provably guaranteeing safety, through their forward invariance property, at the expense of some performance *** approach involves defining a performance objective alongside CBF-based safety constraints that must always be enforced. Unfortunately, both performance and solution feasibility can be significantly impacted by two key factors such as the selection of the cost function and associated parameters, and the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness. To address these challenges, we propose a Reinforcement Learning (RL)-based Receding Horizon control (RHC) approach leveraging Model Predictive control (MPC) with CBFs (MPC-CBF). In particular, we parameterize our controller and use bilevel optimization, where RL is used to learn the optimal parameters while MPC computes the optimal control input. We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles (CAVs) at conflicting roadways. Results demonstrate improved performance and a significant reduction in the number of infeasible cases compared to traditional heuristic approaches used for tuning CBF-based controllers, showcasing the effectiveness of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：