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Construction Progress Monitoring In Smart Cities Using Deep Alexnet

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Construction Progress Monitoring In Smart Cities Using Deep ...

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： A. Rajavel S. Karkuzhali Sarvdaman Sharma Sitesh Kumar Singh Senthilkumar P S. Jhansi Ida Electronics and Instrumentation Engineering Kamaraj College of Engineering and Technology Virudhnagar Department of Electrical and Electronics Engineering R.M.D. Engineering College Chennai Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Nekemte Oromia Ethiopia KL School of Architecture Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science and Business Systems R.M.K Engineering College India

Construction progress monitoring plays a crucial role in ensuring the timely and efficient completion of infrastructure projects within smart cities. This paper proposes a novel approach utilizing the Deep AlexNet architecture for construction progress assessment in smart cities. The Deep AlexNet model is trained on a diverse dataset of images captured from construction sites, enabling it to accurately analyze the visual cues indicative of construction advancement. By leveraging deep learning techniques, our system extracts and processes features from images, enabling automated and real-time progress evaluation. The proposed method contributes to enhancing construction management practices by providing stakeholders with a reliable tool to monitor and manage construction projects efficiently. Experimental results demonstrate the effectiveness and robustness of the Deep AlexNet-based approach in accurately assessing construction progress, thereby facilitating informed decision-making throughout the construction lifecycle.

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Formation Control of Differential-Drive Robots with Input Saturation and Constraint on Formation Size

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Formation Control of Differential-Drive Robots with Input Sa...

IEEE Conference on Decision and Control

作者： Ayush Agrawal Mukunda Bharatheesha Shishir Kolathaya Department of Mechanical Engineering Indian Institute of Technology Bombay India Robert Bosch Centre for Cyber-Physical Systems and a Senior Member of Technical Staff at ARTPARK Indian Institute of Science Bengaluru India Department of Computer Science and Automation (CSA) Robert Bosch Centre for Cyber-Physical Systems Indian Institute of Science Bengaluru India

Cooperative control involves developing control strategies for individual robots that guarantee synchronized behavior of the states of all the robots in a team in some prescribed sense. This work presents a novel controller that achieves formation control for a group of differential-drive robots. First, we propose a nonlinear feedback control law that guarantees stable tracking of a reference trajectory for a single robot without exceeding the velocity limits of the robot. Using Lyapunov analysis, we obtain the necessary conditions on the control parameters and establish ultimate boundedness on error terms. Next, we formulate the formation control problem as a trajectory tracking problem for the multi-robot system and solve it using the proposed controller. Additionally, we provide constraints on formation size for a planned reference trajectory, ensuring smooth cornering of multi-robot formation without exceeding actuation limits.

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Efficient and Reliable Information Sharing for Internet of Vehicles using Trust and Blockchain

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IEEE Transactions on Vehicular technology 2025年

作者： Yadav, Shreya Singh, Karan Yadav, Ashok Kumar Shariq, Mohd Chaudhry, Shehzad Ashraf Das, Ashok Kumar Manjul, Manisha Jawaharlal Nehru University School of Computer and Systems Sciences New Delhi 110 067 India Rajkiya Engineering College Azamgarh Department of Information Technology UP 276 201 India Sharda University Department of Computer Science and Engineering Greater Noida 201 310 India Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Nisantasi University Department of Computer Engineering Faculty of Engineering and Architecture Istanbul 34398 Turkey International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad 500 032 India Korea University Department of Computer Science and Engineering College of Informatics Seoul 02841 South Korea G.B. Pant Delhi Skill and Entrepreneurship University (DSEU) Campus-I New Delhi 110 077 India

In the Internet of Vehicles (IoV) era, connected vehicles are leveraging intelligent sensors to share sensitive and real-time information to enhance road safety, driving comfort, and traffic efficiency. Despite these advancements and benefits, security and privacy vulnerabilities remain a crucial challenge. This article proposes an enhanced trust-enabled Delegated Proof of Stake (DPoS) consensus-based consortium blockchain to address these challenges effectively for information sharing. The proposed consensus approach focuses on miner selection based on trust scores derived from vehicle transaction history analyses. The trust score calculation employs entropy information and binomial distribution, where the entropy measures the uncertainty in a vehicle's behavior, and the binomial distribution assesses the success rate of its transactions. This model ensures traceable and anonymous vehicle-to-vehicle information sharing, preventing unauthorized information transfer and improving overall system reliability. By leveraging the enhanced trust-based DPoS consensus mechanism of consortium blockchains, the proposed scheme leads to overcoming the challenges of vehicular ad hoc networks, along with fostering economic interest through enhanced traffic safety and energy conservation. The experimental results validate the proposed model, revealing significant improvements in terms of detection of malicious vehicles, communication latency, security, and efficiency of IoV applications. © 2025 IEEE.

关键词： Blockchain Consensus Information-sharing Security Trust Vehicle-to-Vehicle (V2V)

Reduced Network Cumulative Constraint Violation for Distributed Bandit Convex Optimization under Slater’s Condition

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Ding, Jinliang Cao, Ming Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201800 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater’s condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal–dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes an O(T max{c,1−c})network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the total number of iterations and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds, the network cumulative constraint violation bound is reduced to O(T 1−c). In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced to O(T1−c), and the network cumulative constraint violation bound is O(T1−c/2) and O(T1−c) without and with Slater’s condition, respectively. For the case where strongly convex parameters are known, the network regret bound is further reduced to O)(log(T)), and the network cumulative constraint violation bound is reduced to O(log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater’s condition. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2

关键词： Convex optimization

A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. The Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multi-player non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensor nodes

Dragonfly Optimization-based MPPT Algorithm for Standalone PV System under Partial Shading

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Dragonfly Optimization-based MPPT Algorithm for Standalone P...

2021 IEEE International Conference on Emergency Science and Information technology, ICESIT 2021

作者： Lodhi, Ehtisham Yang, Pu Wang, Lina Khan, M. Adil Lodhi, Zeeshan Javed, Usman Saleem, Qasim School of Automation Science and Electrical Engineering Beihang University Beijing100191 China SKL for Management and Control of Complex Systems Institute OfAutomation Chinese Academy of Sciences Beijing100190 China Chang'an University Department of Computer and Technology Xi'an710062 China COMSATS University Department of Electrical and Computer Engineering Islamabad22060 Pakistan Lahore54000 Pakistan

ISBN: (纸本)9781665435314

Currently, Photovoltaic (PV) technology has higher demand in the market and gaining momentum day by day. However, partial shading is the most significant concern as it reduces the efficiency of standalone PV systems. Partially shading is typically induced by non-uniform sunlight and uncertain temperature fluctuations. The maximum power point tracking (MPPT) algorithms play a major role in determining the overall system's efficiency. The PV system creates several peaks in its power voltage (P-V) characteristic curve under partial shadowing. To make the PV system run under partial shading at global maximum power point (GMPP), the dragonfly optimization (DFO) algorithm is put forward in this research article. The proposed DFO technique and other commonly used MPPT techniques, such as Particle swarm optimization (PSO), Perturb and Observe (PO), is investigated through theoretical analysis and simulation using Matlab/Simulink to authenticate the overall performance of the DFO approach under partial shading. Furthermore, the comparison demonstrates that the suggested DFO strategy provides very high reliability, efficiency, robustness, rapid power tracking and lower response time to trace the GMPP than PSO and PO techniques. Hence, the DFO technique's overall performance under partial shading is appropriate and satisfying. © 2021 IEEE.

关键词： Simulink

Simple and Accurate Method to Evaluate Type a Standard and Expanded Uncertainties of Measurement for the Laplace Distributed Observations

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SSRN

SSRN 2023年

作者： Dorozhovets, Mykhaylo Department of Metrology and Diagnostic Systems Faculty of Electrical and Computer Engineering Rzeszow University of Technology ul. Wincentego Pola 2A Rzeszów35-959 Poland Department of Measuring Information Technologies Institute of Computer Technologies Automation and Metrology Lviv Polytechnic National University 12 Bandera street Lviv79013 Ukraine

The article proposes simple and accurate evaluation of the standard and expanded uncertainties of the Laplace population median. The uncertainty evaluation based on the approximation of the population median distribution for n observations by the distribution of the sample median for n - 2 observations. Comparisons of the results obtained by proposed approximation with exact results shown that when 6 ≤ n ≤ 10 the differences between approximated and exact standard uncertainties are lees then 1% (0.56%), i.e. negligibly small and for a confidence level p = 0.90 and 0.95 the approximated value of expanded uncertainty differ from exact value less than 1%, and for p= 0.99 difference does not exceed ≈ 5%. When n ≥ 11 the Monte Carlo simulation results showed that the proposed method for determining both the standard and the expanded uncertainty of population median do not exceed ±1%, i.e. is very much precise. © 2023, The Authors. All rights reserved.

关键词： Monte Carlo methods

Consistent and Asymptotically Efficient Localization from Range-Difference Measurements

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arXiv 2023年

作者： Zeng, Guangyang Mu, Biqiang Shi, Ling Chen, Jiming Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen China The Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source's position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased (thus consistent) estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves 3-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services

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arXiv 2022年

作者： Du, Hongyang Liu, Jiazhen Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Junshan Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Information Renmin University of China China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of California Davis United States The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Metaverse encapsulates our expectations of the next-generation Internet, while bringing new key performance indicators (KPIs). Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse. Since the quality of experience (QoE) can be regarded as a comprehensive KPI, the URLLC is evolved towards the next generation URLLC (xURLLC) with a personalized resource allocation scheme to achieve higher QoE. To deploy Metaverse xURLLC services, we study the interaction between the Metaverse service provider (MSP) and the network infrastructure provider (InP), and provide an optimal contract design framework. Specifically, the utility of the MSP, defined as a function of Metaverse users' QoE, is to be maximized, while ensuring the incentives of the InP. To model the QoE mathematically, we propose a novel metric named Meta-Immersion that incorporates both the objective KPIs and subjective feelings of Metaverse users. Furthermore, we develop an attention-aware rendering capacity allocation scheme to improve QoE in xURLLC. Using a user-object-attention level dataset, we validate that the xURLLC can achieve an average of 20.1% QoE improvement compared to the conventional URLLC with a uniform resource allocation scheme. The code for this paper is available at https://***/HongyangDu/AttentionQoE. © 2022, CC BY.

关键词： Resource allocation