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Mobile Energy Transfer in Internet of Things

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arXiv 2020年

作者： Zhang, Qingqing Wang, Gang Giannakis, Georgios B. Liu, Qingwen Chen, Jie College of Electronic and Information Engineering Tongji University Shanghai201800 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN 55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200092 China

—Internet of things (IoT) is powering up smart cities by connecting all kinds of electronic devices. The power supply problem of IoT devices constitutes a major challenge in current IoT development, due to the poor battery endurance as well as the troublesome cable deployment. The wireless power transfer (WPT) technology has recently emerged as a promising solution. Yet, existing WPT advances cannot support free and mobile charging like Wi-Fi communications. To this end, the concept of mobile energy transfer (MET) is proposed, which relies critically on an resonant beam charging (RBC) technology. The adaptive (A) RBC technology builds on RBC, but aims at improving the charging efficiency by charging devices at device preferred current and voltage levels adaptively. A mobile ARBC scheme is developed relying on an adaptive source power control. Extensive numerical simulations using a 1,000mAh Li-ion battery show that the mobile ARBC outperforms simple charging schemes such as the constant power charging, the profile-adaptive charging, and the distance-adaptive charging in saving energy. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy transfer

Time series classification for locating forced oscillation sources

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arXiv 2020年

作者： Meng, Yao Yu, Zhe Lu, Ning Shi, Di Electrical and Computer Engineering Department North Carolina State University RaleighNC27606 United States Geiri North America San JoseCA95134 United States China Electric Power Planning and Engineering Institute Beijing China Systems Center North Carolina State University RaleighNC27606 United States

Forced oscillations are caused by sustained cyclic disturbances. This paper presents a machine learning (ML) based time-series classification method that uses the synchrophasor measurements to locate the sources of forced oscillations for fast disturbance removal. Sequential feature selection is used to identify the most informative measurements of each power plant so that multivariate time series (MTS) can be constructed. By training the Mahalanobis matrix, we measure and compare the distance between the MTSs. Templates for representing each class is constructed to reduce the size of training datasets and improve the online matching efficiency. Dynamic time warping (DTW) algorithm is used to align the out-of-sync MTSs to account for oscillation detection errors. The algorithm is validated on two test systems: the IEEE 39-bus system and the WECC 179-bus system. When a forced oscillation occurs, MTSs will be constructed by designated PMU measurements. Then, the MTSs will be classified by the trained classifiers, the class membership of which corresponds to the location of each oscillation source. Simulation results show that the proposed method can be used online to identify the forced oscillation sources with high accuracy. The robustness of the proposed algorithm in the presence of oscillation detection errors is also quantified. Copyright © 2020, The Authors. All rights reserved.

关键词： Phasor measurement units

Asymptotic degree distributions in random threshold graphs

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arXiv 2019年

作者： Makowski, Armand M. Pal, Siddharth Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College ParkMD20742 United States BBN Technologies

We discuss several limiting degree distributions for a class of random threshold graphs in the many node regime. This analysis is carried out under a weak assumption on the distribution of the underlying fitness variable. This assumption, which is satisfied by the exponential distribution, determines a natural scaling under which the following limiting results are shown: The nodal degree distribution, i.e., the distribution of any node, converges in distribution to a limiting pmf. However, for each d = 0, 1, . . ., the fraction of nodes with given degree d converges only in distribution to a non-degenerate random variable Π(d) (whose distribution depends on d), and not in probability to the aforementioned limiting nodal pmf as is customarily expected. The distribution of Π(d) is identified only through its characteristic function. Implications of this result include: (i) The empirical node distribution may not be used as a proxy for or as an estimate to the limiting nodal pmf;(ii) Even in homogeneous graphs, the network-wide degree distribution and the nodal degree distribution may capture vastly different information;and (iii) Random threshold graphs with exponential distributed fitness do not provide an alternative scale-free model to the Barabási-Albert model as was argued by some authors;the two models cannot be meaningfully compared in terms of their degree distributions! Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

Deep image reconstruction using unregistered measurements without groundtruth

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arXiv 2020年

作者： Gan, Weijie Sun, Yu Eldeniz, Cihat Liu, Jiaming An, Hongyu Kamilov, Ulugbek S. Department of Computer Science and Engineering Washington University in St. Louis St. LouisMO United States Mallinckrodt Institute of Radiology Washington University in St. Louis St. LouisMO United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States

One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images. This paper addresses this limitation by proposing a novel unsupervised deep registration-augmented reconstruction method (U-Dream) for training deep neural nets to reconstruct high-quality images by directly mapping pairs of unregistered and artifact-corrupted images. The ability of U-Dream to circumvent the need for accurately registered data makes it widely applicable to many biomedical image reconstruction tasks. We validate it in accelerated magnetic resonance imaging (MRI) by training an image reconstruction model directly on pairs of undersampled measurements from images that have undergone nonrigid deformations. Copyright © 2020, The Authors. All rights reserved.

关键词： Image reconstruction

Developing future human-centered smart cities: Critical analysis of smart city security, data management, and ethical challenges

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arXiv 2020年

作者： Ahmad, Kashif Maabreh, Majdi Ghaly, Mohamed Khan, Khalil Qadir, Junaid Al-Fuqaha, Ala Hamad Bin Khalifa University Doha Qatar Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdullah Ii for Information Technology The Hashemite University Zarqa13133 Jordan Research Center for Islamic Legislation & Ethics College of Islamic Studies Hamad Bin Khalifa University Doha Qatar Department of Information Technology and Computer Science Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology KPK Haripur Pakistan Department of Computer Science and Engineering Faculty of Engineering Qatar University Doha Qatar Department of Electrical Engineering Information Technology University Lahore Pakistan

As the globally increasing population drives rapid urbanisation in various parts of the world, there is a great need to deliberate on the future of the cities worth living. In particular, as modern smart cities embrace more and more data-driven artificial intelligence services, it is worth remembering that (1) technology can facilitate prosperity, wellbeing, urban livability, or social justice, but only when it has the right analog complements (such as well-thought out policies, mature institutions, responsible governance);and (2) the ultimate objective of these smart cities is to facilitate and enhance human welfare and social flourishing. Researchers have shown that various technological business models and features can in fact contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In the light of these observations, addressing the philosophical and ethical questions involved in ensuring the security, safety, and interpretability of such AI algorithms that will form the technological bedrock of future cities assumes paramount importance. Globally there are calls for technology to be made more humane and human-centered. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical (data and algorithmic) challenges to a successful deployment of AI in human-centric applications, with a particular emphasis on the convergence of these concepts/challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions. We believe such rigorous analysis will provide a baseline for future research in the domain. © 2020, CC BY.

关键词： Information management

Decentralized submodular maximization: Bridging discrete and continuous settings 35

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Decentralized submodular maximization: Bridging discrete and...

35th International Conference on Machine Learning, ICML 2018

作者： Mokhtari, Aryan Hassani, Hamed Karbasi, Amin Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States Department of Electrical and Systems Engineering University of Pennsylvania United States Department of Electrical Engineering and Computer Science Yale University United States

ISBN: (纸本)9781510867963

In this paper, we showcase the interplay between discrete and continuous optimization in network-structured settings. We propose the first fully decentralized optimization method for a wide class of non-convex objective functions that possess a diminishing returns property. More specifically, given an arbitrary connected network and a global continuous submodular function, formed by a sum of local functions, we develop Decentralized Continuous Greedy (DCG), a message passing algorithm that converges to the tight (1 - 1/e) approximation factor of the optimum global solution using only local computation and communication. We also provide strong convergence bounds as a function of network size and spectral characteristics of the underlying topology. Interestingly, DCG readily provides a simple recipe for decentralized discrete submodular maximization through the means of continuous relaxations. Formally, we demonstrate that by lifting the local discrete functions to continuous domains and using DCG as an interface we can develop a consensus algorithm that also achieves the tight (1 - 1/e) approximation guarantee of the global discrete solution once a proper rounding scheme is applied. © The Author(s) 2018.

关键词： Optimization

A workload adaptive haptic shared control scheme for semi-autonomous driving

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arXiv 2020年

作者： Luo, Ruikun Weng, Yifan Wang, Yifan Jayakumar, Paramsothy Brudnak, Mark J. Paul, Victor Desaraju, Vishnu R. Stein, Jeffrey L. Ersal, Tulga Jessie Yang, X. Robotics Institute University of Michigan Ann ArborMI United States Department of Mechanical Engineering University of Michigan Ann ArborMI United States Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States U.S. Army Ground Vehicles System Center WarrenMI United States Toyota Research Institute Ann ArborMI United States Department of Industrial and Operations Engineering University of Michigan Ann ArborMI United States

Haptic shared control is used to manage the control authority allocation between a human and an autonomous agent in semi-autonomous driving. Existing haptic shared control schemes, however, do not take full consideration of the human agent. To fill this research gap, this study presents a haptic shared control scheme that adapts to a human operator’s workload, eyes on road and input torque in real-time. We conducted human-in-the-loop experiments with 24 participants. In the experiment, a human operator and an autonomy module for navigation shared the control of a simulated notional High Mobility Multipurpose Wheeled Vehicle (HMMWV) at a fixed speed. At the same time, the human operator performed a target detection task for surveillance. The autonomy could be either adaptive or non-adaptive to the above-mentioned human factors. Results indicate that the adaptive haptic control scheme resulted in significantly lower workload, higher trust in autonomy, better driving task performance and smaller control effort. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

CNN-based Broad Learning with Efficient Incremental Reconstruction Model for Facial Emotion Recognition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10236-10241页

作者： Luefeng Chen Min Li Xuzhi Lai Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition in deep learning. Feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotion recognition. When the network needs to expand, the network is dynamically updated by incremental learning algorithm. We verified the experimental performance through k -fold cross validation. According to the recognition results, the accuracy on JAFFE database of our proposal is greater than that of the state of the art, such as the Local Binary Patterns with Softmax and Deep Attentive Multi-path convolutional neural network.

关键词： Convolution Neural Networks Broad Learning Emotion Recognition Human-Robot Interaction

Exponentially Weighted Regularization Strategy in Constructing Reinforced Second-order Fuzzy Rule-based Model

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arXiv 2020年

作者： Zhang, Congcong Oh, Sung-Kwun Pedrycz, Witold Fu, Zunwei Lu, Shanzhen Department of Computer University of Suwon San 2-2 Wau-ri Bongdam-eup Hwaseong-si Gyeonggi-do445-743 Korea Republic of Research Center for Big Data and Artificial Intelligence Linyi University Linyi276005 China School of Electrical & Electronic Engineering University of Suwon Hwaseong-si Gyeonggi-do18323 Korea Republic of Department of Electrical & Computer Engineering University of Alberta EdmontonT6R 2V4 AB Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw Poland School of Mathematical Sciences Beijing Normal University Beijing100875 China

In the conventional Takagi-Sugeno-Kang (TSK)-type fuzzy models, constant or linear functions are usually utilized as the consequent parts of the fuzzy rules, but they cannot effectively describe the behavior within local regions defined by the antecedent parts. In this article, a theoretical and practical design methodology is developed to address this problem. First, the information granulation (Fuzzy C-Means) method is applied to capture the structure in the data and split the input space into subspaces, as well as form the antecedent parts. Second, the quadratic polynomials (QPs) are employed as the consequent parts. Compared with constant and linear functions, QPs can describe the input-output behavior within the local regions (subspaces) by refining the relationship between input and output variables. However, although QP can improve the approximation ability of the model, it could lead to the deterioration of the prediction ability of the model (e.g., overfitting). To handle this issue, we introduce an exponential weight approach inspired by the weight function theory encountered in harmonic analysis. More specifically, we adopt the exponential functions as the targeted penalty terms, which are equipped with 2 regularization (2) (i.e., exponential weighted 2 , 2 ) to match the proposed reinforced second-order fuzzy rule-based model (RSFRM) properly. The advantage o2 compared to ordinary 2 lies in separately identifying and penalizing different types of polynomial terms in the coefficient estimation, and its results not only alleviate the overfitting and prevent the deterioration of generalization ability but also effectively release the prediction potential of the model. The effectiveness of RSFRM is evaluated through 23 machine learning datasets. A comparative analysis shows that the proposed RSFRM results in better performance as well as sound interpretability when compared to the state-of-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Deterioration