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Reinforcement learning based multi-access control and battery prediction with energy harvesting in IoT systems

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arXiv 2018年

作者： Chu, Man Li, Hang Liao, Xuewen Cui, Shuguang Department of Information and Communication Engineering Xi'an Jiaotong University Xi'an710049 China Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of California DavisCA95616 United States

Energy harvesting (EH) is a promising technique to fulfill the long-term and self-sustainable operations for Internet of things (IoT) systems. In this paper, we study the joint access control and battery prediction problems in a small-cell IoT system including multiple EH user equipments (UEs) and one base station (BS) with limited uplink access channels. Each UE has a rechargeable battery with finite capacity. The system control is modeled as a Markov decision process without complete prior knowledge assumed at the BS, which also deals with large sizes in both state and action spaces. First, to handle the access control problem assuming causal battery and channel state information, we propose a scheduling algorithm that maximizes the uplink transmission sum rate based on reinforcement learning (RL) with deep Q-network (DQN) enhancement. Second, for the battery prediction problem, with a fixed round-robin access control policy adopted, we develop a RL based algorithm to minimize the prediction loss (error) without any model knowledge about the energy source and energy arrival process. Finally, the joint access control and battery prediction problem is investigated, where we propose a two-layer RL network to simultaneously deal with maximizing the sum rate and minimizing the prediction loss: the first layer is for battery prediction, the second layer generates the access policy based on the output from the first layer. Experiment results show that the three proposed RL algorithms can achieve better performances compared with existing benchmarks. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

Enhancing stock market prediction with extended coupled Hidden Markov Model over multi-sourced data

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arXiv 2018年

作者： Zhang, Xi Li, Yixuan Wang, Senzhang Fang, Binxing Yu, Philip S. Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Institute of Electronic and Information Engineering UESTC Guangdong China Department of Computer Science University of Illinois ChicagoIL United States Institute for Data Science Tsinghua University Beijing China

Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market uctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the event data, how to leverage the joint impacts of multiple data sources still remains an open research problem. In this work, we study how to explore multiple data sources to improve the performance of the stock prediction. We introduce an Extended Coupled Hidden Markov Model incorporating the news events with the historical trading data. To address the data sparsity issue of news events for each single stock, we further study the uctuation correlations between the stocks and incorporate the correlations into the model to facilitate the prediction task. Evaluations on China A-share market data in 2016 show the superior performance of our model against previous methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Hidden Markov models

Flexible energy management protocol for cooperative EV-to-EV charging

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arXiv 2018年

作者： Zhang, Rongqing Cheng, Xiang Yang, Liuqing State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing China Department of Electrical and Computer Engineering Colorado State University CO United States Shenzhen Research Institute of Big Data Shenzhen Guangdong China

In this paper, we investigate flexible power transfer among electric vehicles (EVs) from a cooperative perspective in an EV system. First, the concept of cooperative EV-to- EV (V2V) charging is introduced, which enables active cooperation via charging/discharging operations between EVs as energy consumers and EVs as energy providers. Then, based on the cooperative V2V charging concept, a flexible energy management protocol with different V2V matching algorithms is proposed, which can help the EVs achieve more flexible and smarter charging/discharging behaviors. In the proposed energy management protocol, we define the utilities of the EVs based on the cost and profit through cooperative V2V charging and employ the bipartite graph to model the charging/discharging cooperation between EVs as energy consumers and EVs as energy providers. Based on the constructed bipartite graph, a max-weight V2V matching algorithm is proposed in order to optimize the network social welfare. Moreover, taking individual rationality into consideration, we further introduce the stable matching concepts and propose two stable V2V matching algorithms, which can yield the EV-consumer-optimal and EVprovider- optimal stable V2V matchings, respectively. Simulation results verify the efficiency of our proposed cooperative V2V charging based energy management protocol in improving the EV utilities and the network social welfare as well as reducing the energy consumption of the EVs. Copyright © 2018, The Authors. All rights reserved.

关键词： Electric vehicles

Generation and detection of coherent longitudinal acoustic waves in ultrathin 1T'-MoTe2

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arXiv 2019年

作者： Rivas, Nicolas Zhong, Shazhou Dekker, Tina Cheng, Meixin Gicala, Patrick Chen, Fangchu Luo, Xuan Sun, Yuping Petruk, Ariel A. Pichugin, Kostyantyn Tsen, Adam W. Sciaini, Germán Ultrafast Electron Imaging Lab Department of Chemistry Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L3G1 Canada Institute for Quantum Computing Department of Physics and Astronomy Department of Electrical and Computer Engineering Department of Chemistry University of Waterloo WaterlooONN2L3G1 Canada Key Laboratory of Materials Physics Institute of Solid-State Physics Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China High Magnetic Field Laboratory Chinese Academy of Sciences Hefei230031 Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China

Layered transition metal dichalcogenides have attracted substantial attention owing to their versatile functionalities and compatibility with current nanofabrication technologies. Thus, noninvasive means to determine the mechanical properties of nanometer (nm) thick specimens are of increasing importance. Here, we report on the detection of coherent longitudinal acoustic phonon modes generated by impulsive femtosecond (fs) optical excitation. Broadband fs-transient absorption experiments in 1T-f-MoTe2 flakes as a function of thickness (7 nm . 30 nm) yield a longitudinal sound speed of vl = (2990± 90) m s-1. In addition, temperature dependent measurements unveil a linear decrease of the normalized Young-fs modulus El/El,295Kwith a slope of δ(***,%' ))/δT = (.2.0± 0.1) 10-3K-1and no noticeable change caused by the Td-1T-f structural phase transition or variations in film thickness. Copyright © 2019, The Authors. All rights reserved.

关键词： Elastic moduli

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Preface

CEUR Workshop Proceedings 2021年 3010卷

作者： Zwiggelaar, Reyer Ganesan, Geetha Cheng, Qiang Du, Ke-Lin Satti, Srinivasa Rao Goyal, Ayush Ciufudean, Calin Saba, Djamel Goi, Bok Min Srivastava, Hari Mohan Al Marzouqi, Hasan Cheng, Haw Su Skliarova, Iouliia Lin, Jerry Chun-Wei Yeh, Jyh-Haw Ali, Liaqat Pekar, Libor Meribout, Mahmoud Fouladirad, Mitra Mukherjee, Partha Panda, Priyadarshini Litoriya, Ratnesh Thomas, Reji Turaev, Sherzod Bagui, Subhash C. Shang, Yilun Farhaoui, Yousef Jaroucheh, Zakwan School of Computer Science Aberystwyth University AberystwythSY23 3DB United Kingdom Advanced Computing Research Society Tamilnadu Chennai India College of Engineering University of Kentucky LexingtonKY40506-0503 United States Department of Electrical and Computer Engineering Concordia University MontrealH3G1M8 Canada Department of Computer Science Norwegian Institute of Science and Technology Trondheim Norway Texas A&M University Kingsville United States Stefan cel Mare University Romania Renewable Energy Research Unit Algeria Universiti Tunku Abdul Rahman Malaysia University of Victoria Canada Khalifa University United Arab Emirates Multimedia University Malaysia University of Aveiro Portugal Western Norway University of Applied Sciences Norway Boise State University Idaho United States University of Science and Technology of Fujairah United Arab Emirates Tomas Bata University in Zlin Czech Republic University of Technology of Troyes France Penn State Great Valley MalvernPA United States Yale University United States Jaypee Institute of Engineering and Technology India Lovely Professional University India UAE University United Arab Emirates University of West Florida United States Northumbria University United Kingdom University of Moulay Ismail Morocco Edinburgh Napier University Edinburgh United Kingdom

Spectral structure of the Neumann–Poincaré operator on tori

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arXiv 2018年

作者： Ando, Kazunori Ji, Yong-Gwan Kang, Hyeonbae Kawagoe, Daisuke Miyanishi, Yoshihisa Department of Electrical and Electronic Engineering and Computer Science Ehime University Ehime790-8577 Japan Department of Mathematics Institute of Applied Mathematics Inha University Incheon22212 Korea Republic of Center for Mathematical Modeling and Data Science Osaka University Osaka560-8531 Japan

We address the question whether there is a three-dimensional bounded domain such that the Neumann–Poincaré operator defined on its boundary has infinitely many negative eigenvalues. It is proved in this paper that tori have such a property. It is done by decomposing the Neumann–Poincaré operator on tori into infinitely many self-adjoint compact operators on a Hilbert space defined on the circle using the toroidal coordinate system and the Fourier basis, and then by proving that the numerical range of infinitely many operators in the decomposition has both positive and negative values.47A45, 31B25 Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

Author Correction: Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort

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Nature genetics 2024年第3期56卷 554页

作者： Youwen Qin Aki S Havulinna Yang Liu Pekka Jousilahti Scott C Ritchie Alex Tokolyi Jon G Sanders Liisa Valsta Marta Brożyńska Qiyun Zhu Anupriya Tripathi Yoshiki Vázquez-Baeza Rohit Loomba Susan Cheng Mohit Jain Teemu Niiranen Leo Lahti Rob Knight Veikko Salomaa Michael Inouye Guillaume Méric Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. School of BioSciences The University of Melbourne Melbourne Victoria Australia. Department of Public Health and Welfare Finnish Institute for Health and Welfare Helsinki Finland. Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland. Department of Clinical Pathology Melbourne Medical School The University of Melbourne Melbourne Victoria Australia. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK. Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK. Department of Ecology and Evolutionary Biology Cornell University Ithaca NY USA. Cornell Institute for Host-Microbe Interaction and Disease Cornell University Ithaca NY USA. Department of Pediatrics School of Medicine University of California San Diego La Jolla CA USA. Division of Biological Sciences University of California San Diego La Jolla CA USA. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. Department of Computer Science & Engineering Jacobs School of Engineering University of California San Diego La Jolla CA USA. NAFLD Research Center Department of Medicine University of California San Diego La Jolla CA USA. Smidt Heart Institute Cedars-Sinai Medical Center Los Angeles CA USA. Department of Medicine Turku University Hospital and University of Turku Turku Finland. Department of Computing University of Turku Turku Finland. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. mi336@medschl.cam.ac.uk. School of BioSciences The University of Melbourne Melbourne Victoria Australia. mi336@medschl.cam.ac.uk. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care Unive

Nanorobots-assisted detection of multifocal cancer: A multimodal optimization perspective

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arXiv 2018年

作者： Shi, Shaolong Chen, Yifan Yao, Xin Harbin Institute of Technology Harbin150001 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Faculty of Science and Engineering Faculty of Computing and Mathematical Sciences University of Waikato Hamilton3240 New Zealand Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

We propose a new framework of computing-inspired multifocal cancer detection procedure (MCDP). Under the rubric of MCDP, the tumor foci to be detected are regarded as solutions of the objective function, the tissue region around the cancer areas represents the parameter space, and the nanorobots loaded with contrast medium molecules for cancer detection correspond to the optimization agents. The process that the nanorobots detect tumors by swimming in the high-risk tissue region can be regarded as the process that the agents search for the solutions of an objective function in the parameter space with some constraints. For multimodal optimization (MMO) aiming to locate multiple optimal solutions in a single simulation run, niche technology has been widely used. Specifically, the Niche Genetic Algorithm (NGA) has been shown to be particularly effective in solving MMO. It can be used to identify the global optima of multiple hump functions in a running, keep effectively the diversity of the population, and avoid premature of the genetic algorithm. Learning from the optimization procedure of NGA, we propose the NGA-inspired MCDP in order to locate the tumor targets efficiently while taking into account realistic in vivo propagation and controlling of nanorobots, which is different from the use scenario of the standard NGA. To improve the performance of the MCDP, we also modify the crossover operator of the original NGA from crossing within a population to crossing between two populations. Finally, we present comprehensive numerical examples to demonstrate the effectiveness of the NGA-inspired MCDP when the biological objective function is associated with the blood flow velocity profile caused by tumor-induced angiogenesis. Copyright © 2018, The Authors. All rights reserved.

关键词： Medical imaging

Adversarial Text Generation for Google's Perspective API

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Adversarial Text Generation for Google's Perspective API

International Conference on Computational science and Computational Intelligence (CSCI)

作者： Edwin Jain Stephan Brown Jeffery Chen Erin Neaton Mohammad Baidas Ziqian Dong Huanying Gu Nabi Sertac Artan Department of Computer Science Tufts University Medford MA USA College of Engineering and Computing Sciences New York Institute of Technology New York NY USA Department of Electrical Engineering University of California Berkeley CA USA Department of Biopsychology University of Michigan Ann Arbor MI USA

With the preponderance of harassment and abuse, social media platforms and online discussion platforms seek to curb toxic comments. Google's Perspective aims to help platforms classify toxic comments. We have created a pipeline to modify toxic comments to evade Perspective. This pipeline uses existing adversarial machine learning attacks to find the optimal perturbation which will evade the model. Since these attacks typically target images, as opposed to discrete text data, we include a process to generate text candidates from perturbed features and select candidates to retain syntactic similarity. We demonstrated that using a model with just 10,000 queries, changing three words in each comment evades Perspective 25% of the time, suggesting that building a surrogate model may not require many queries and a more robust approach is needed to improve the toxic comment classifier accuracy.

关键词： Google Adversarial machine learning Syntactics Task analysis Semantics Perturbation methods