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Blockchain in Environmental Sustainability Measures: a Survey

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arXiv 2024年

作者： Vladucu, Maria-Victoria Wu, Hailun Medina, Jorge Salehin, Khondaker M. Dong, Ziqian Rojas-Cessa, Roberto The Network and Innovation Laboratory Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Computer Science University of Portland PortlandOR97203 United States The Networking and Intelligent Systems Laboratory Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Real and effective regulation of contributions to greenhouse gas emissions and pollutants requires unbiased and truthful monitoring. Blockchain has emerged not only as an approach that provides verifiable economical interactions but also as a mechanism to keep the measurement, monitoring, incentivation of environmental conservationist practices and enforcement of policy. Here, we present a survey of areas in what blockchain has been considered as a response to concerns on keeping an accurate recording of environmental practices to monitor levels of pollution and management of environmental practices. We classify the applications of blockchain into different segments of concerns, such as greenhouse gas emissions, solid waste, water, plastics, food waste, and circular economy, and show the objectives for the addressed concerns. We also classify the different blockchains and the explored and designed properties as identified for the proposed solutions. At the end, we provide a discussion about the niches and challenges that remain for future research. © 2024, CC BY-NC-ND.

关键词： Circular economy

A Bilevel Optimization Method for Inverse Mean-Field Games

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arXiv 2024年

作者： Yu, Jiajia Xiao, Quan Chen, Tianyi Lai, Rongjie The Department of Mathematics Duke University United States The Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States The Department of Mathematics Purdue University United States

In this paper, we introduce a bilevel optimization framework for addressing inverse mean-field games, alongside an exploration of numerical methods tailored for this bilevel problem. The primary benefit of our bilevel formulation lies in maintaining the convexity of the objective function and the linearity of constraints in the forward problem. Our paper focuses on inverse mean-field games characterized by unknown obstacles and metrics. We show numerical stability for these two types of inverse problems. More importantly, we, for the first time, establish the identifiability of the inverse mean-field game with unknown obstacles via the solution of the resultant bilevel problem. The bilevel approach enables us to employ an alternating gradient-based optimization algorithm with a provable convergence guarantee. To validate the effectiveness of our methods in solving the inverse problems, we have designed comprehensive numerical experiments, providing empirical evidence of its efficacy. © 2024, CC BY-NC-ND.

关键词： Inverse problems

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

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Measurement of Absorption Current of Polyester Coated Enamel...

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

Learning Agent Interactions from Density Evolution in 3D Regions With Obstacles

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arXiv 2023年

作者： Tirumalai, Amoolya Mavridis, Christos N. Baras, John S. Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland College ParkMD20742 United States

In this work, we study the inverse problem of identifying complex flocking dynamics in a domain cluttered with obstacles. We get inspiration from animal flocks moving in complex ways with capabilities far beyond what current robots can do. Owing to the difficulty of observing and recovering the trajectories of the agents, we focus on the dynamics of their probability densities, which are governed by partial differential equations (PDEs), namely compressible Euler equations subject to non-local forces. We formulate the inverse problem of learning interactions as a PDE-constrained optimization problem of minimizing the squared Hellinger distance between the histogram of the flock and the distribution associated to our PDEs. The numerical methods used to efficiently solve the PDE-constrained optimization problem are described. Realistic flocking data are simulated using the Boids model of flocking agents, which differs in nature from the reconstruction models used in our PDEs. Our analysis and simulated experiments show that the behavior of cohesive flocks can be recovered accurately with approximate PDE solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

Optimal Deep Learning Enabled Statistical Analysis Model for Traffic Prediction

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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

Combinatorial Multi-armed Bandits: Arm Selection via Group Testing

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arXiv 2024年

作者： Mukherjee, Arpan Ubaru, Shashanka Murugesan, Keerthiram Shanmugam, Karthikeyan Tajer, Ali Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States IBM T.J. Watson Research Center United States

This paper considers the problem of combinatorial multi-armed bandits with semi-bandit feedback and a cardinality constraint on the super-arm size. Existing algorithms for solving this problem typically involve two key sub-routines: (1) a parameter estimation routine that sequentially estimates a set of base-arm parameters, and (2) a super-arm selection policy for selecting a subset of base arms deemed optimal based on these parameters. State-of-the-art algorithms assume access to an exact oracle for super-arm selection with unbounded computational power. At each instance, this oracle evaluates a list of score functions, the number of which grows as low as linearly and as high as exponentially with the number of arms. This can be prohibitive in the regime of a large number of arms. This paper introduces a novel realistic alternative to the perfect oracle. This algorithm uses a combination of group-testing for selecting the super arms and quantized Thompson sampling for parameter estimation. Under a general separability assumption on the reward function, the proposed algorithm reduces the complexity of the super-arm-selection oracle to be logarithmic in the number of base arms while achieving the same regret order as the state-of-the-art algorithms that use exact oracles. This translates to at least an exponential reduction in complexity compared to the oracle-based approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Parameter estimation

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

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

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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Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective

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arXiv 2023年

作者： Abu-Rasheed, Hasan Weber, Christian Fathi, Madjid Department of Electrical Engineering and Computer Science Institute of Knowledge Based Systems and Knowledge Management University of Siegen Siegen Germany

Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner’s current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context-aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. During the last decade, several approaches have emerged in the literature to define the learning context and the factors that may capture it. Those approaches led to different definitions of contextualized learner-profiles. In this paper, we review the state-of-the-art approaches for defining a user’s learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view.

关键词： Recommender systems

Implementation and Validation of the CautiousBug Algorithm in ROS Noetic

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Implementation and Validation of the CautiousBug Algorithm i...

Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Roman Mishenin Alexander Eryomin Tatyana Tsoy Evgeni Magid Department of Computer Engineering (DCE) Moscow Institute of Electronics and Mathematics Higher School of Economics Moscow Russian Federation Laboratory of Intelligent Robotics Systems (LIRS) Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russian Federation

ISBN: (数字)9798331517564

ISBN: (纸本)9798331517571

In this paper, we present an implementation of the CautiousBug algorithm within the Noetic distribution of the Robot Operating System (ROS). Bug algorithms address a challenge of robot navigation in unknown environments without relying on pre-existing maps or constructing new ones. These algorithms utilize odometry data, operate without a map, require minimal computational resources, and can be implemented on relatively simple hardware. A C++ software application was created to simulate a behavior of the CautiousBug algorithm in various environments within the Gazebo simulator. This application allows for an analysis of key metrics, including accumulated yaw, distance traversed and algorithm's runtime. We conducted a set of virtual experiments in the Gazebo to evaluate the CautiousBug performance.

关键词： Runtime Navigation Software architecture Working environment noise Operating systems Software algorithms Software Robustness Path planning Robots