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Optimal Deep Learning Enabled Statistical Analysis Model for Traffic Prediction

computers, Materials & Continua 2022年第9期72卷 5563-5576页

作者： Ashit Kumar Dutta S.Srinivasan S.N.Kumar T.S.Balaji Won Il Lee Gyanendra Prasad Joshi Sung Won Kim Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India Department of Electrical&Electronics Engineering Amal Jyothi College of EngineeringKanjirappallyKerala686518India Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India Department of Electronics and Communication Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Business Administration Hanbat National National UniversityDaejeon34158Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control,navigation,route mapping,*** traffic prediction model aims to predict the traffic conditions based on the past traffic *** more accurate traffic prediction,this study proposes an optimal deep learning-enabled statistical analysis *** study offers the design of optimal convolutional neural network with attention long short term memory(OCNN-ALSTM)model for traffic *** proposed OCNN-ALSTM technique primarily preprocesses the traffic data by the use of min-max normalization ***,OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time *** enhancing the predictive outcomes of the OCNN-ALSTM technique,the bird swarm algorithm(BSA)is employed to it and thereby overall efficacy of the network gets *** design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the *** experimental validation of the OCNNALSTM technique is performed using benchmark datasets and the results are examined under several *** simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions.

关键词： Statistical analysis predictive models deep learning traffic prediction bird swarm algorithm

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Consensus-Based Leader-Follower Formation Tracking for Control-Affine Nonlinear Multiagent systems

arXiv

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arXiv 2023年

作者： Enwerem, Clinton Baras, John S. The Department of Electrical & Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

In the typical multiagent formation tracking problem centered on consensus, the prevailing assumption in the literature is that the agents’ nonlinear models can be approximated by integrator systems, by their feedback-linearized equivalents, or by dynamics composed of deterministic linear and nonlinear terms. The resulting approaches associated with such assumptions, however, are hardly applicable to general nonlinear systems. To this end, we present consensus-based control laws for multiagent formation tracking in finite-dimensional state space, with the agents represented by a more general class of dynamics: control-affine nonlinear systems. The agents also exchange information via a leader-follower communication topology modeled as an undirected and connected graph with a single leader node. By leveraging standard tools from algebraic graph theory and Lyapunov analysis, we first derive a locally asymptotically stabilizing formation tracking law. Next, to demonstrate the effectiveness of our approach, we present results from numerical simulations of an example in robotics. These results — together with a comparison of the formation errors obtained with our approach and those realized via an optimization-based method — further validate our theoretical propositions. © 2023, CC BY.

关键词： Multi agent systems

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Graph-Based Visual-Kinematic Fusion and Monte Carlo Initialization for Fast-Deployable Cable-Driven Robots

Graph-Based Visual-Kinematic Fusion and Monte Carlo Initiali...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： R. Khorrambakht H. Damirchi M.R. Dindarloo A. Saki S.A. Khalilpour Hamid D. Taghirad Stephan Weiss Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY School of Computer Science University of Adelaide Australia Faculty of Electrical Engineering University of Technology Tehran Iran Department of Smart Systems Technologies University of Klagenfurt Austria

Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework that presents a unified localization and calibration system which requires no initial values for the kinematic parameters. This is achieved by formulating a Monte-Carlo algorithm that initializes a factor-graph representation of the calibration and localization problem. With this, we are able to jointly identify both the kinematic parameters and the visual odometry scale alongside their corresponding uncertainties. We demonstrate the practical applicability of the framework using our state-estimation dataset recorded with the ARAS-CAM suspended cable driven parallel robot, and published as part of this manuscript.

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Resource Efficiency vs Performance Isolation Tradeoff in Network Slicing

arXiv

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arXiv 2023年

作者： Nikolaidis, Panagiotis Zoulkarni, Asim Baras, John Department of Electrical & Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

We consider the tradeoff between resource efficiency and performance isolation that emerges when multiplexing the resource demands of Network Slices (NSs). On the one hand, multiplexing allows the use of idle resources, which increases resource efficiency. On the other hand, the performance of each NS becomes susceptible to traffic surges in other NSs, which degrades performance isolation. The analysis of this tradeoff enables network operators to determine the effect of performance isolation on the operating cost of each NS. To study the tradeoff, we solve an optimization problem where we find the multiplexing policy that requires the least provisioned resources to honor the Service Level Agreements (SLAs) of all NSs. The SLA of each NS i states that its resource demand should be met for PiH fraction of time, and for PiL ≤ PiH fraction of time, it should be met regardless of the demands of other NSs. For resource demands that follow ergodic Markov chains, we show that the well-known Max-Weight scheduler is an optimal multiplexing policy. Since the Max-Weight scheduler does not require any knowledge of the statistics of the resource demands, we also propose its use in non-markovian settings. For resource demands obtained in the LTE module of ns-3, we show that the Max-Weight scheduler reduces the provisioned bandwidth by 36.2% when no performance isolation is required. Lastly, for these non-markovian resource demands, the Max-Weight scheduler maintains its optimality since it requires as much provisioned bandwidth as the best non-causal scheduler. © 2023, CC BY.

关键词： Bandwidth

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Shared Information for a Markov Chain on a Tree

arXiv

引用

arXiv 2023年

作者： Bhattacharya, Sagnik Narayan, Prakash Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

Shared information is a measure of mutual dependence among multiple jointly distributed random variables with finite alphabets. For a Markov chain on a tree with a given joint distribution, we give a new proof of an explicit characterization of shared information. The Markov chain on a tree is shown to possess a global Markov property based on graph separation;this property plays a key role in our proofs. When the underlying joint distribution is not known, we exploit the special form of this characterization to provide a multiarmed bandit algorithm for estimating shared information, and analyze its error performance. © 2023, CC BY.

关键词： Trees (mathematics)

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Differential Equation Approximations for Population Games using Elementary Probability

arXiv

引用

arXiv 2023年

作者： Kara, Semih Martins, Nuno C. The Electrical and Computer Engineering Department The Institute for Systems Research University of Maryland College ParkMD20742 United States

Population games model the evolution of strategic interactions among a large number of uniform agents. Due to the agents' uniformity and quantity, their aggregate strategic choices can be approximated by the solutions of a class of ordinary differential equations. This mean-field approach has found to be an effective tool of analysis. However its current proofs rely on advanced mathematical techniques, making them less accessible. In this article, we present a simpler derivation, using only undergraduate-level probability. Copyright © 2023, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Towards Data Auctions with Externalities

SSRN

引用

SSRN 2023年

作者： Agarwal, Anish Dahleh, Munther Horel, Thibaut Rui, Maryann Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science

The design of data markets has gained importance as firms increasingly use machine learning models fueled by externally acquired training data. A key consideration is the externalities firms face when data, though inherently freely replicable, is allocated to competing firms. In this setting, we demonstrate that a data seller’s optimal revenue increases as firms can pay to prevent allocations to others. To do so, we first reduce the combinatorial problem of allocating and pricing multiple datasets to the auction of a single digital good by modeling utility for data through the increase in prediction accuracy it provides. We then derive welfare and revenue maximizing mechanisms, highlighting how the form of firms’ private information– whether the externalities one exerts on others is known, or vice-versa– affects the resulting structures. In all cases, the optimal allocation rule is a single threshold per firm, where either all data is allocated or none is. © 2023, The Authors. All rights reserved.

关键词： Machine design

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Online Distributed Learning with Quantized Finite-Time Coordination

arXiv

引用

arXiv 2023年

作者： Bastianello, Nicola Rikos, Apostolos I. Johansson, Karl H. The School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Sweden The Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA02215 United States

In this paper we consider online distributed learning problems. Online distributed learning refers to the process of training learning models on distributed data sources. In our setting a set of agents need to cooperatively train a learning model from streaming data. Differently from federated learning, the proposed approach does not rely on a central server but only on peer-to-peer communications among the agents. This approach is often used in scenarios where data cannot be moved to a centralized location due to privacy, security, or cost reasons. In order to overcome the absence of a central server, we propose a distributed algorithm that relies on a quantized, finite-time coordination protocol to aggregate the locally trained models. Furthermore, our algorithm allows for the use of stochastic gradients during local training. Stochastic gradients are computed using a randomly sampled subset of the local training data, which makes the proposed algorithm more efficient and scalable than traditional gradient descent. In our paper, we analyze the performance of the proposed algorithm in terms of the mean distance from the online solution. Finally, we present numerical results for a logistic regression task. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Measurement of Absorption Current of Polyester Coated Enameled Wires with Q(t) meter

Measurement of Absorption Current of Polyester Coated Enamel...

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2023 International Symposium on electrical Insulating Materials, ISEIM 2023

作者： Watanabe, Akinao Fukuma, Masumi Sekiguchi, Yoitsu Advanced Electronic and Information Systems Course National Institute of Technology Matsue College Matsue Japan Department of Electrical Engineering and Computer Science National Institute of Technology Matsue College Matsue Japan Energy and Electronics Materials R&D Laboratories Sumitomo Electric Industries Osaka Japan

ISBN: (纸本)9784886864376

A Q(t) meter is used to measure electrical properties in insulating materials. Here, Q(t) is the integral value of circuit current with time t. It is possible to measure the Q(t) of the insulation material of a capacitor and a cable by using Q(t) meter. The current through an insulating material is divided into an instantaneous charging current, an absorption current and an equilibrium leakage current. It is considered that the absorption current is related to charge accumulation in the insulating materials. In this paper, an enameled wire (1.6 PEW) is used to prepare a cable sample, the Q(t) of the coated polyester (PEs) is measured, and extraction of absorption current components from measured Q(t) are reported. © 2023 The institute of electrical Engineers of Japan.

关键词： Insulating materials

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Data-informed Healthy Cell Clustering Technique for Second-life Applications of Retired Electric Vehicle Batteries

Data-informed Healthy Cell Clustering Technique for Second-l...

引用

IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Akash Samanta Dominic Karnehm Antje Neve Sheldon Williamson Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada Institute of Intelligent Distributed Systems University of the Bundeswehr Munich Neubiberg Germany

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

The electric vehicles (EVs) adoption rate has been significant in the last decade and keeps on increasing. Recently, the retired EV battery packs which no longer provide satisfactory performance to power an EV started appearing in the market. Typically, EV batteries are declared unfit for EV applications when the battery capacity is reduced to 70-80% of its nominal capacity. Various end-of-life (EOL) options are proposed by researchers before sending the retired batteries to recycle such as low-demanding e-mobility, grid-tired energy storage systems, storage of renewable energy, and home emergency power supplies. It is predicted that this may create new value pools in the energy and transportation sectors shortly. It is noticed in lithium-ion battery packs that all cells do not age similarly, thus clustering of healthy cells among the retired batteries is very crucial for safe and reliable operation in second-life applications. Thus, an efficient cell clustering method utilizing voltage relaxation curves powered by a data-driven BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) algorithm is proposed in this paper. The efficiency and efficacy of the proposed algorithm compared to the state-of-the-art cell sorting technique are also demonstrated in the paper.

关键词： Renewable energy sources Accuracy Clustering methods Clustering algorithms Transportation Voltage Electric vehicles

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