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IEEE International Conference on Communications (ICC) / workshop on Integrating UAVs into 5G

作者： Wang, Ling Zhang, Hongwei Ren, Pengfei Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)9781538631805

Mission-critical IoT applications such as wireless-networked industrial control require reliable wireless communication. Due to co-channel interference and wireless channel dynamics (e.g., multi-path fading), however, wireless communication is inherently dynamic and subject to complex uncertainties. Joint scheduling and power control has been explored for reliable wireless communication, but existing solutions are mostly centralized or do not consider real-world challenges such as fast channel fading. Towards a foundation for mission-critical IoT communication, we develop a distributed, field-deployable approach to joint scheduling and power control that adaptively regulates co-channel interference and ensures predictable IoT communication reliability in the presence of wireless communication dynamics and uncertainties. Our approach effectively leverages the Perron-Frobenius theory, physical-ratio-K (PRK) interference model, and feedback control for PRK model adaptation and transmission power update. Through simulation analysis, we have shown that our approach improves concurrency by 70% than state-of-art fixed scheduling while ensuring successful SINR tracking over time. To the best of our knowledge, our approach is the first distributed scheduling and power control scheme that ensures predictable wireless communication reliability while considering real-world challenges such as fast channel fading, and it is expected to serve as a foundation for real-world deployment of mission-critical IoT systems.

关键词： Power control Interference Signal to noise ratio Reliability Optimal scheduling Scheduling Wireless communication"

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Robot manipulator control using neural networks: A survey

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NEUROCOMPUTING 2018年 285卷 23-34页

作者： Jin, Long Li, Shuai Yu, Jiguo He, Jinbo Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Qufu Normal Univ Sch Informat Sci & Engn Rizhao Peoples R China

Robot manipulators are playing increasingly significant roles in scientific researches and engineering applications in recent years. Using manipulators to save labors and increase accuracies are becoming common practices in industry. Neural networks, which feature high-speed parallel distributed processing, and can be readily implemented by hardware, have been recognized as a powerful tool for real-time processing and successfully applied widely in various control systems. Particularly, using neural networks for the control of robot manipulators have attracted much attention and various related schemes and methods have been proposed and investigated. In this paper, we make a review of research progress about controlling manipulators by means of neural networks. The problem foundation of manipulator control and the theoretical ideas on using neural network to solve this problem are first analyzed and then the latest progresses on this topic in recent years are described and reviewed in detail. Finally, toward practical applications, some potential directions possibly deserving investigation in controlling manipulators by neural networks are pointed out and discussed. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Robot manipulators Robot arms Neural network Winner-take-all parallel manipulators Cable-driven manipulators Feedforward neural network Dual neural network Recurrent neural network

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distributed Algorithms for Fully Personalized PageRank on Large Graphs

arXiv

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arXiv 2019年

作者： Lin, Wenqing Interactive Entertainment Group Tencent Inc

Personalized PageRank (PPR) has enormous applications, such as link prediction and recommendation systems for social networks, which often require the fully PPR to be known. Besides, most of real-life graphs are edge-weighted, e.g., the interaction between users on the Facebook network. However, it is computationally difficult to compute the fully PPR, especially on large graphs, not to mention that most existing approaches do not consider the weights of edges. In particular, the existing approach cannot handle graphs with billion edges on a moderate-size cluster. To address this problem, this paper presents a novel study on the computation of fully edge-weighted PPR on large graphs using the distributed computing framework. Specifically, we employ the Monte Carlo approximation that performs a large number of random walks from each node of the graph, and exploits the parallel pipeline framework to reduce the overall running time of the fully PPR. Based on that, we develop several optimization techniques which (i) alleviate the issue of large nodes that could explode the memory space, (ii) pre-compute short walks for small nodes that largely speedup the computation of random walks, and (iii) optimize the amount of random walks to compute in each pipeline that significantly reduces the overhead. With extensive experiments on a variety of real-life graph datasets, we demonstrate that our solution is several orders of magnitude faster than the state-of-the-arts, and meanwhile, largely outperforms the baseline algorithms in terms of accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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time- and Cost- Efficient Task Scheduling across Geo-distributed Data Centers

Time- and Cost- Efficient Task Scheduling across Geo-Distrib...

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作者： Hu, Zhiming Li, Baochun Luo, Jun Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 2E4 Canada School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Typically called big data processing, analyzing large volumes of data from geographically distributed regions with machine learning algorithms has emerged as an important analytical tool for governments and multinational corporations. The traditional wisdom calls for the collection of all the data across the world to a central data center location, to be processed using data-parallel applications. This is neither efficient nor practical as the volume of data grows exponentially. Rather than transferring data, we believe that computation tasks should be scheduled near the data, while data should be processed with a minimum amount of transfers across data centers. In this paper, we design and implement Flutter, a new task scheduling algorithm that reduces both the completion times and the network costs of big data processing jobs across geographically distributed data centers. To cater to the specific characteristics of data-parallel applications, in the case of optimizing the job completion times only, we first formulate our problem as a lexicographical min-max integer linear programming (ILP) problem, and then transform the ILP problem into a nonlinear program problem with a separable convex objective function and a totally unimodular constraint matrix, which can be further solved using a standard linear programming solver efficiently in an online fashion. In the case of improving both time- and cost- efficiency, we formulate the general problem as an ILP problem and we find out that solving an LP problem can achieve the same goal in the real practice. Our implementation of Flutter is based on Apache Spark, a modern framework popular for big data processing. Our experimental results have shown convincing evidence that Flutter can shorten both job completion times and network costs by a substantial margin. © 2017 IEEE.

关键词： Big data

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Rethinking PLCs: Industrial ethernet for large-scale real-time distributed control applications 12

Rethinking PLCs: Industrial ethernet for large-scale real-ti...

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12th International workshop on Emerging Technologies and Scientific Facilities Controls, PCaPAC 2018

作者： Ploetzeneder, B. Janda, O. Krucenko, A. Trdlicka, J. Pivonka, P. Bastl, P. ELI Beamlines Institute of Physics of the ASCR Prague Czech Republic

ISBN: (纸本)9783954502004

Many research facilities rely on PLCs to automate large slow systems like vacuum or HVAC, where price, availability and reliability matter. The dominant architecture consists of local units of controllers/modules (programmed in IEC61131-3 languages), which operate mostly autonomously from a SCADA layer. While some vendors provide low-level stacks to encourage growth of their ecosystems, PLC programming remains largely within a closed, proprietary world. In this paper, we introduce a different way of thinking about PLC hardware. Working with the open stacks intended for the design of new EtherCAT (Beckhoff)/Powerlink (B&R) modules, we built an abstract C++- API to control the existing ones. These industrial Ethernet busses can be propagated using standard network hardware, so any RT-Linux system can now control any PLC module from anywhere in our facility using high-level languages (C++, LabVIEW). This way, PLC modules are seamlessly integrated into our distributed TANGO-based control system. PC-PLC interfaces are no longer needed;or in the case of traditionally implemented subsystems, trivial. Copyright © 2019 by JACoW — cc Creative Commons Attribution 3.0.

关键词： Computer operating systems

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Decentralized Reinforcement Learning of Robot Behaviors

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ARTIFICIAL INTELLIGENCE 2018年 256卷 130-159页

作者： Leottau, David L. Ruiz-del-Solar, Javier Babuska, Robert Univ Chile Adv Min Technol Ctr Dept Elect Engn Av Tupper 2007 Santiago Chile Delft Univ Technol Fac 3mE Cognit Robot Dept NL-2628 CD Delft Netherlands Czech Tech Univ CIIRC Prague Czech Republic

A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual behaviors in problems where multi-dimensional action spaces are involved. When using this methodology, sub-tasks are learned in parallel by individual agents working toward a common goal. In addition to proposing this methodology, three specific multi agent DRL approaches are considered: DRL-Independent, DRL Cooperative Adaptive (CA), and DRL-Lenient. These approaches are validated and analyzed with an extensive empirical study using four different problems: 3D Mountain Car, SCARA real-time Trajectory Generation, Ball-Dribbling in humanoid soccer robotics, and Ball Pushing using differential drive robots. The experimental validation provides evidence that DRL implementations show better performances and faster learning times than their centralized counterparts, while using less computational resources. DRL-Lenient and DRL-CA algorithms achieve the best final performances for the four tested problems, outperforming their DRL-Independent counterparts. Furthermore, the benefits of the DRLLenient and DRL-CA are more noticeable when the problem complexity increases and the centralized scheme becomes intractable given the available computational resources and training time. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Reinforcement learning Multi-agent systems Decentralized control Autonomous robots distributed artificial intelligence

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distributed C++-Python embedding for fast predictions and fast prototyping 1

Distributed C++-Python embedding for fast predictions and fa...

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1st workshop on distributed Infrastructures for Deep Learning, DIDL 2018, co-located with the International Middleware Conference 2018

作者： Varisteas, Georgios Avanesov, Tigran State, Radu University of Luxembourg Luxembourg OlaMobile Luxembourg

ISBN: (纸本)9781450361194

Python has evolved to become the most popular language for data science. It sports state-of-the-art libraries for analytics and machine learning, like Sci-Kit Learn. However, Python lacks the computational performance that a industrial system requires for high frequency real time predictions. Building upon a year long research project heavily based on SciKit Learn (sklearn), we faced performance issues in deploying to production. Replacing sklearn with a better performing framework would require re-evaluating and tuning hyperparameters from scratch. Instead we developed a python embedding in a C++ based server application that increased performance by up to 20x, achieving linear scalability up to a point of convergence. Our implementation was done for mainstream cost effective hardware, which means we observed similar performance gains on small as well as large systems, from a laptop to an Amazon EC2 instance to a high-end server. © 2018 Copyright held by the owner/author(s).

关键词： Python

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Short-Term Rainfall Estimation by Machine Learning Methods

Short-Term Rainfall Estimation by Machine Learning Methods

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IEEE International Scientific Conference on Informatics

作者： Peter Krammer Marcel Kvassay Ondrej Habala Ladislav Hluchý Department of Parallel and Distributed Information Processing Institute of Informatics Slovak Academy of Sciences Bratislava Slovakia

ISBN: (数字)9781728131801

ISBN: (纸本)9781728131818

Modeling stochastic systems is a real challenge in many areas. Even the meteorology domain is not an exception; the modeling of precipitation activity is markedly stochastic and is influenced by a number of related physical variables (temperature, pressure, humidity, wind). Accurate precipitation estimation is thus highly non-trivial. Today's technical capability, automated data measuring (whether using Radar or automatic meteorological stations), as well as subsequent large-scale data processing and regression model training, allow the meteorological estimations and predictions with increasing accuracy. This paper demonstrates selected uses of artificial neural networks in the field of meteorology, as well as solving problems with pre-processing and integrating time-spatial meteorological data.

关键词： Temperature measurement Atmospheric modeling Meteorology Meteorological radar Sea measurements Reflectivity

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Markdown architectural decision records: Format and tool support 10

Markdown architectural decision records: Format and tool sup...

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10th Central European workshop on Services and their Composition, ZEUS 2018

作者： Kopp, Oliver Armbruster, Anita Zimmermann, Olaf Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Rapperswil Switzerland

Architectural decision records answer "why" questions about designs and make tacit knowledge explicit. Many architectural decisions are made during development iterations because they have a close connection to the code. It is challenging to come up with task-specific decision capturing practices and supporting tools that are not perceived as time wasters;context switches and media breaks that harm the productivity of coding architects and developers involved in the decision making have to be avoided. To integrate existing architect-centric approaches into the developer toolchain, this paper presents a Markdown-based decision capturing template that we derived from previous work to enhance an existing decision capturing tool for developers. Our early validation results in the context of an open source project suggest that format and tool promise to contribute to an integrated decision capturing practice, with further enhancements being required. Tool and template are available in public GitHub repositories. © 2018 CEUR-WS. All rights reserved.

关键词： Decision making

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M4IOT 2018 - Proceedings of the 2018 workshop on Middleware and Applications for the Internet of Things, Part of Middleware 2018 Conference

M4IOT 2018 - Proceedings of the 2018 Workshop on Middleware ...

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2018 workshop on Middleware and Applications for the Internet of Things, M4IOT 2018, Part of Middleware 2018 Conference

ISBN: (纸本)9781450361187

The proceedings contain 9 papers. The topics discussed include: supporting the evaluation of fog-based IoT applications during the design phase;xpect the unexpected: towards a middleware for policy adaptation in IoT platforms;public video surveillance: using the fog to increase privacy;towards automated privacy risk assessments in IoT systems;real-time distributed in-situ benchmarking of energy harvesting IoT devices;towards an intelligent user-oriented middleware for opportunistic composition of services in ambient spaces;cross-layer QoS-aware resource allocation for IoT-enabled service choreographies;towards end-to-end privacy for publish/subscribe architectures in the Internet of things;and a middleware environment for developing Internet of things applications.

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