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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitudes. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic programming

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IRL-PM: An Inverse Reinforcement Learning-based Power Minimization in Multi-User MISO networks 8

IRL-PM: An Inverse Reinforcement Learning-based Power Minimi...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Tian, Xingcong Gao, Bo Liu, Ming Xiong, Ke Fan, Pingyi Letaief, Khaled Ben Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China The Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781665456128

This paper aims to efficiently save energy consumption of multiple-input-single-output (MISO) networks. To this end, an optimization problem is formulated to minimize the transmit power of the base station (BS) while ensuring the minimal required information rate of each user. Reinforcement learning (RL) is a significant solution to tackle this non-convex problems. Nevertheless, designing efficient reward functions that accurately describe the involved optimization problems for RL is a challenging problem, which may result in low learning efficiency and even inaccurate learning. To cope with such an issue, this paper proposes an inverse reinforcement learning (IRL)-based approach to minimize the transmit power of MISO networks, in which the generative adversarial imitation learning (GAIL) is employed as the IRL framework and the reward function is represented by an artificial neural network (ANN) model, whose parameters are learned automatically in terms of an expert policy based on the semidefinite relaxation (SDR)-based approach. Since SDR is able to achieve the near optimal solution, the learned reward function is more accurate than traditional artificially designed one. Simulations show that, the proposed IRL-based approach reaches similar energy consumption to the SDR-based approach, and reduces about 50% transmit power compared to traditional RL-based one. © 2023 IEEE.

关键词： Reinforcement learning

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Robust Co-Optimization of Distribution network Hardening and Mobile Resource Scheduling with Decision-Dependent Uncertainty

arXiv

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arXiv 2025年

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Chen, Chen Zhai, Qiaozhu Jia, Qing-Shan Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resources (MHERs) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with min − max resilience constraint and discrete recourse structure, which helps achieve the load survivability target considering endogenous uncertainties. Different from the traditional model with a fixed uncertainty set, we adopt a dynamic representation that explicitly captures the endogenous uncertainties of network contingency as well as the available hydrogen storage levels of MHERs, which induces a decision-dependent uncertainty (DDU) set. Also, the multi-period adaptive routing and energy scheduling of MHERs are modeled as a mixed-integer recourse problem for further decreasing the resilience cost. Then, a nested parametric column-and-constraint generation (N-PC&CG) algorithm is customized and developed to solve this challenging formulation. By leveraging the structural property of the DDU set as well as the combination of discrete recourse decisions and the corresponding extreme points, we derive a strengthened solution scheme with nontrivial enhancement strategies to realize efficient and exact computation. Numerical results on 14-bus test system and 56-bus real-world distribution network demonstrate the resilience benefits and economical feasibility of the proposed method under different damage severity levels. Moreover, the enhanced NPC&CG shows a superior solution capability to support prompt decisions for resilient planning with DDU models. Copyright © 2025, The Authors. All rights reserved.

关键词： Hardening

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Privacy-Preserving Reinforcement Learning Beyond Expectation

Privacy-Preserving Reinforcement Learning Beyond Expectation

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IEEE Conference on Decision and Control

作者： Arezoo Rajabi Bhaskar Ramasubramanian Abdullah Al Maruf Radha Poovendran Department of Electrical and Computer Engineering Network Security Lab University of Washington Seattle WA USA Electrical and Computer Engineering Western Washington University Bellingham WA USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Cyber and cyber-physical systems equipped with machine learning algorithms such as autonomous cars share environments with humans. In such a setting, it is important to align system (or agent) behaviors with the preferences of one or more human users. We consider the case when an agent has to learn behaviors in an unknown environment. Our goal is to capture two defining characteristics of humans: i) a tendency to assess and quantify risk, and ii) a desire to keep decision making hidden from external parties. We incorporate cumulative prospect theory (CPT) into the objective of a reinforcement learning (RL) problem for the former. For the latter, we use differential privacy. We design an algorithm to enable an RL agent to learn policies to maximize a CPT-based objective in a privacy-preserving manner and establish guarantees on the privacy of value functions learned by the algorithm when rewards are sufficiently close. This is accomplished through adding a calibrated noise using a Gaussian process mechanism at each step. Through empirical evaluations, we highlight a privacy-utility tradeoff and demonstrate that the RL agent is able to learn behaviors that are aligned with that of a human user in the same environment in a privacy-preserving manner.

关键词： Privacy Differential privacy Machine learning algorithms Decision making Reinforcement learning Gaussian processes Cyber-physical systems

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Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social networks

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Big Data Mining and Analytics 2021年第4期4卷 242-251页

作者： Xueting Liao Danyang Zheng Xiaojun Cao Department of Computer Science Georgia State UniversityAtlantaGA 30302USA Suzhou Key Laboratory of Advanced Optical Communication Network Technology School of Electronic and Information EngineeringSoochow UniversitySuzhou 215006China

The COVID-19 pandemic has hit the world *** reaction to the pandemic related issues has been pouring into social platforms,such as *** public officials and governments use Twitter to make policy *** keep close track of the related information and express their concerns about the policies on *** is beneficial yet challenging to derive important information or knowledge out of such Twitter *** this paper,we propose a Tripartite Graph Clustering for Pandemic Data Analysis(TGC-PDA)framework that builds on the proposed models and analysis:(1)tripartite graph representation,(2)non-negative matrix factorization with regularization,and(3)sentiment *** collect the tweets containing a set of keywords related to coronavirus pandemic as the ground truth *** framework can detect the communities of Twitter users and analyze the topics that are discussed in the *** extensive experiments show that our TGC-PDA framework can effectively and efficiently identify the topics and correlations within the Twitter data for monitoring and understanding public opinions,which would provide policy makers useful information and statistics for decision making.

关键词： COVID-19 clustering online social network Twitter

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DTMR: A Decision Tree-based Multimetric Routing Protocol for Vehicular Ad Hoc networks 18

DTMR: A Decision Tree-based Multimetric Routing Protocol for...

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18th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous networks, PE-WASUN 2021

作者： Cárdenas, Leticia Lemus Mezher, Ahmad Mohamad León, Juan Pablo Astudillo Igartua, Mónica Aguilar University of Guadalajara Knowledge Foundations Department Colotlán Jalisco Mexico University of New Brunswick Electrical and Computer Engineering Department Fredericton Canada Universitat Politècnica de Catalunya Department of Network Engineering Barcelona Spain

ISBN: (纸本)9781450390781

The emerging application of machine learning (ML) in different areas and the good results obtained have motivated its inclusion in the intelligent transport system (ITS) with smart cities and also in vehicular ad hoc networks (VANETs). In this sense, the main contribution of this work is the proposal of a decision tree-based multimetric routing protocol to make more intelligent forwarding decisions in the selection of the best next-hop neighbour node to transmit packets to the destination. To the best of our knowledge, most of the available datasets regarding vehicular networks are related to mobility patterns. Thus, we have collected our targeted dataset from several simulations runs over different urban vanet scenarios. Besides, we have included the evaluation of the importance of each routing metric by applying regularization. The goal here is to include the more relevant metrics to support the ML in the routing decisions. The performance evaluation shows significant improvements in terms of packet losses and end-to-end delay. © 2021 ACM.

关键词： Routing protocols

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AI-based Flexible Online Laboratory Learning System for Post-COVID-19 Era: Requirements and Design

AI-based Flexible Online Laboratory Learning System for Post...

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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Elmesalawy, Mahmoud M. Atia, Ayman Yousef, Ahmed Mohamed Fahmy El-Haleem, Ahmed M. Abd Anany, Mohamed G. Elmosilhy, Noha A. Salama, Abdullah I. Hamdy, Alaa Zoghby, Helmy M. El Din, Eman Serag El Helwan University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligent Computer Science Department Cairo Egypt Giza Egypt Fayoum University Faculty of Specific Education Education Technology Department Fayoum Egypt Electronics and Communications Engineering Department Cairo Egypt Helwan University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Helwan University Faculty of Engineering Electrical Power and Machines Engineering Department Cairo Egypt Network Planning Department Cairo Egypt

ISBN: (纸本)9781665412438

The worldwide outbreak due to COVID-19 pandemic has led to a great interest in e-learning. However, the lack of suitable online laboratory management systems has posed a particular challenge for sectors that need laboratory activities such as engineering, science and technology. In this paper, the requirements and design for a flexible AI-based laboratory learning system (LLS) that can support online laboratory experimentations are presented. The elicitation of the LLS design requirements is decided based on a conducted survey for a set of LLS features. The LLS is designed with the flexibility to support various types of online experimentations such as virtual or remote controlled experiments using either desktop or web applications. The virtualization technique is used to manage the laboratory resources and allow multiple users to access the LLS. Moreover, the proposed LLS introduces the use of AI techniques to provide efficient virtual lab assistant and adaptive assessment process. © 2021 IEEE.

关键词： COVID-19

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A Novel Steganography Algorithm Based on Instance Segmentation

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computers, Materials & Continua 2020年第4期63卷 183-196页

作者： Ruohan Meng Qi Cui Zhili Zhou Chengsheng Yuan Xingming Sun School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Engineering Centre of Network Monitoring Nanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorCanada

Information hiding tends to hide secret information in image area where is rich texture or high frequency,so as to transmit secret information to the recipient without affecting the visual quality of the image and arousing *** take advantage of the complexity of the object texture and consider that under certain circumstances,the object texture is more complex than the background of the image,so the foreground object is more suitable for steganography than the *** the basis of instance segmentation,such as Mask R-CNN,the proposed method hides secret information into each object's region by using the masks of instance segmentation,thus realizing the information hiding of the foreground object without *** method not only makes it more efficient for the receiver to extract information,but also proves to be more secure and robust by experiments.

关键词： Steganography object detection mask R-CNN irregular region

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Navigation Design to Improve Interaction Technique in Multi-Browser Virtual Environment 15

Navigation Design to Improve Interaction Technique in Multi-...

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15th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2024

作者： Ichsan, Mochammad Hannats Hanafi Sik-Lanyi, Cecilia Guzsvinecz, Tibor Department of Electrical Engineering and Information Systems Faculty of Information Technology University of Pannonia Egyetem u. 10 Veszprem8200 Hungary Department of Informatics Faculty of Computer Science Brawijaya University Malang Indonesia Hungarian Research Network Piarista u. 4 Budapest1052 Hungary Department of Information Technology and its Applications Faculty of Information Technology University of Pannonia Gasparich M. u. 18/A Zalaegerszeg8900 Hungary

ISBN: (纸本)9798350378245

Efficient, precise, and intuitive navigation in a Virtual Environment (VE) or in desktop Virtual Reality (VR) is critical to improving user experience. This research introduces three navigation designs for physical embodiment inside VE using a computer mouse for multi-browser VE in desktop VR based on navigation possibilities. Navigation is intended for the First-Person Shooter (FPS) perspective: Intuitive Navigation (IntuNav), Edge Shift Navigation (EdgeNav), and Twist Shift Navigation (TwistNav). IntuNav uses mouse x and y coordinate movements to move and rotate the camera. EdgeNav relies on the cursor positioning at the edge of the screen for camera rotation and movement. TwistNav uses a middle click and right mouse click for camera movement and rotation. The current navigation design has been implemented in Unity with C#. This research proposed three model designs based on feasible navigation possibilities, which shows that each navigation model has unique advantages that can be adapted to user needs and preferences. Based on the test results using the System Usability Scale, IntuNav and EdgeNav obtained good results and can be used for further development. © 2024 IEEE.

关键词： Virtual environments

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Numerical analysis of unsteady free convection of Al₂O₃ inside a tubular reactor under the influences of exothermic reaction, and inclined MHD as an application to chemical reactor

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Results in Physics 2024年 65卷

作者： Alaydamee, Hussein H. Alomari, Mohammed Azeez Al-Salami, Qusay H. Alyousuf, Farah Q.A. Alqurashi, Faris Flayyih, Mujtaba A. Department of Chemical Engineering University of Al-Qadisiyah College of Engineering Al-Qadisiyah 58002 Iraq Department of Mechanical Engineering University of Al-Qadisiyah Ad-Diwaniyah 58001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Business Administration College of Administrative and Financial Sciences Cihan University-Erbil Iraq Department of Computer Network College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq Mechanical Engineering Dept. College of Engineering University of Bisha Bisha Saudi Arabia Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Hillah Iraq

This paper delves into the numerical investigation of aluminum oxide–water nanofluid thermal and dynamic performances under the influences of magnetic field application and chemical reaction, utilizing the Finite Element Method within a circular enclosure containing three inner tubes, as an application to the heat exchanging phenomenon between the reactive shell of the cavity and the surface of the triple tubes. Various governing parameters were studied for their interaction on the nanofluid flow and heat transmission rate within the proposed geometry, including the Rayleigh parameter (10³≤Ra≤10⁵), Hartmann parameter (0≤Ha≤61), nanoparticles concentration (0≤∅≤6×10^-2), magnetic rotational angle (0^o≤γ≤90^o), and Frank-Kamenetskii parameter (0≤F_k≤3). The results indicated that raising Ra from 10³ to 10⁵ results in expediting the nanofluid velocity by 10.62 % and 100 % respectively as well as raising the total heat transfer efficiency. The nanofluid speed was also increased by 28.57 % when F_k has to further increase to a value of 3. When there was no exothermic activity present, the rate of heat transmission was at its lowest, and it was greater when the F_k value was 3. Similarly, there were discernible impacts in various areas of the geometry as the Ha number intensified and the Nu_avg decreased. Improvements in local and mean Nusselt parameters are observed when the concentration of nanoparticles is increased, suggesting better heat transfer, achieving an increase by 7 % in the average Nusselt number. This research emphasizes the importance of nanoparticle concentration in raising the medium's rates of heat transmission, contributing to advancements in energy storage development. © 2024 The Author(s)

关键词： Aluminum oxide Circular enclosure Exothermic reaction MHD Nanofluid Natural convection Numerical analysis

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