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2018 CoNLL-SIGMORPHON Shared Task: Universal Morphological Reinflection, CoNLL 2018

作者： Ács, Judit Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Hungarian Academy of Sciences Hungary

This paper presents an encoder-decoder neural network based solution for both subtasks of the CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection. All of our models are sequence-to-sequence neural... 详细信息

ISBN: (纸本)9781948087834

关键词： Decoding

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Distributed delay model of the McKeithan’s network

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IFAC-PapersOnLine 2019年第7期52卷 33-38页

作者： György Lipták Katalin M. Hangos Process Control Research Group Systems and Control Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences P.O. Box 63 H-1518 Budapest Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary

In this paper CRNs containing linear reaction chains with multiple joint complexes were considered in order to obtain an equivalent reduced order delayed CRN model with distributed time delays. For this purpose, our earlier method (Lipták and Hangos (2018)) for decomposing the chains of linear reactions with multiple joint complexes was used together with the "linear chain trick". An analytical expression for the kernel function of the distributed delay was also derived from the reaction rate coefficients of the linear reaction chains. Our approach was demonstrated using the example of the well known McKeithan’s network model of kinetic proofreading.

关键词： Process control Delay Chemical Reaction Networks

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Secure Distributed Filtering for Unstable Dynamics Under Compromised Observations

Secure Distributed Filtering for Unstable Dynamics Under Com...

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IEEE Annual Conference on Decision and control

作者： Xingkang He Xiaoqiang Ren Henrik Sandberg Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Automation Shanghai University Shanghai China

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

In this paper, we consider a secure distributed filtering problem for linear time-invariant systems with bounded noises and unstable dynamics under compromised observations. A malicious attacker is able to compromise a subset of the agents and manipulate the observations arbitrarily. We first propose a recursive distributed filter consisting of two parts at each time. The first part employs a saturation-like scheme, which gives a small gain if the innovation is too large. The second part is a consensus operation of state estimates among neighboring agents. A sufficient condition is then established for the boundedness of estimation error, which is with respect to network topology, system structure, and the maximal compromised agent subset. We further provide an equivalent statement, which connects to 2s-sparse observability in the centralized framework in certain scenarios, such that the sufficient condition is feasible. Numerical simulations are finally provided to illustrate the developed results.

关键词： Observability Estimation error Technological innovation control systems State estimation Robustness

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Mobile Energy Transfer in Internet of Things

arXiv

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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

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

arXiv

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arXiv 2020年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Chai, Tianyou Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Department of Electrical Engineering University of North Texas DentonTX76203 United States The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is considered. This problem is an important component of many machine learning techniques with data parallelism, such as deep learning and federated learning. We propose a distributed primal–dual stochastic gradient descent (SGD) algorithm, suitable for arbitrarily connected communication networks and any smooth (possibly nonconvex) cost functions. We show that the proposed algorithm achieves the linear speedup convergence rate O(1/√nT) for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak–Lojasiewicz (P–L) condition, where T is the total number of iterations. We also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global optimum. We demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Cost functions

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Network Selection in 5G Networks Based on Markov Games and Friend-or-Foe Reinforcement Learning

Network Selection in 5G Networks Based on Markov Games and F...

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IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

作者： Alessandro Giuseppi Emanuele De Santis Francesco Delli Priscoli Seok Ho Won Taesang Choi Antonio Pietrabissa Department of Computer Control and Management Engineering “Antonio Ruberti” University of Rome La Sapienza Rome Italy Space Research Group of the Consortium for the Research in Automation and Telecommunication (CRAT) Rome Italy University of Rome La Sapienza Rome Italy Future Mobile Communication Lab and the Intelligent Network Research Lab of ETRI Daejoen Republic of Korea

ISBN: (数字)9781728151786

ISBN: (纸本)9781728151793

This paper presents a control solution for the optimal network selection problem in 5G heterogeneous networks. The control logic proposed is based on multi-agent Friend-or-Foe Q-Learning, allowing the design of a distributed control architecture that sees the various access points compete for the allocation of the connection requests. Numerical simulations validate conceptually the approach, developed in the scope of the EU-Korea project 5G-ALLSTAR.

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Monitoring the Moisture Content in Pharmaceutical Batch Fluidized Bed Dryers Using Observer-Based Soft Sensors

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IFAC-PapersOnLine 2020年第2期53卷 12056-12061页

作者： Marc-Olivier Roseberry Francis Gagnon André Desbiens Jocelyn Bouchard Pierre-Philippe Lapointe-Garant Department of Electrical and Computer Engineering LOOP Université Laval Québec City Québec G1V 6A6 Canada Department of Chemical Engineering LOOP Université Laval Québec City Québec G1V 6A6 Canada Process Monitoring Automation and Control Global Engineering Pfizer Montréal Québec H4R 1J6 Canada

Tablet manufacturing in the pharmaceutical industry involves batch fluidized bed drying for particle moisture removal. This paper introduces five approaches for moisture content monitoring, relying either on a complex phenomenological model or its simplified version. The first two soft sensors consist of open-loop estimators, i.e. they simply simulate the models fed by the manipulated variables. Three closed-loop moving horizon estimators based on the simplified model are also proposed for improved robustness. In the first one, the measurements of the inlet gas and particle temperatures feed back the soft sensor. The last two closed-loop observers additionally can take into account infrequent delayed moisture content measurements, such as at-line loss on drying analysis. A validation of the soft sensors is performed with experimental data collected on a pilot scale fluidized bed dryer. Results show that the closed-loop observer with the delayed moisture content measurements still has an accuracy that is equivalent (and sometimes better) than the complex phenomenological model.

关键词： state estimation batch fluidized bed dryer moving horizon estimator offline measurement measurement delay

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Spherical interpolated convolutional network with distance-feature density for 3D semantic segmentation of point clouds

arXiv

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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Design of Face Recognition Access Entrance Guard System with Mask Based on Embedded Development

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Journal of Physics: Conference Series 2021年第1期1883卷

作者： Lin Ruiqin Tan Wenan Chen Zhenyu Qinqin Faculty of Environmental and Materials Engineering Shanghai Polytechnic University Shanghai 201209 China Computer Engineering Shanghai Polytechnic University Shanghai 201209 China Department of Measurement and Control and Automation Shanghai Polytechnic University Shanghai 201209 China

This paper investigates all kinds of access entrance guard control systems at home and abroad and analyzes their advantages and disadvantages. COVID-19 epidemic has a serious impact on life, travel must wear a mask, so that the access entrance guard control system can not carry out face recognition under the mask. In view of this kind of situation, an embedded face recognition access entrance guard control system with mask based on EAIDK-310 development board is designed. The system can complete face recognition and body temperature measurement without contact, and drive the motor to open the gate. After testing, the system is suitable for deployment in companies, communities, campuses and other small application scenarios. It is convenient to travel and can reduce the impact of the epidemic.

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Affinely Parametrized State-space Models: Ways to Maximize the Likelihood Function

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IFAC-PapersOnLine 2018年第15期51卷 718-723页

作者： Wills, Adrian Yu, Chengpu Ljung, Lennart Verhaegen, Michel School of Electrical Engineering and Computer Science University of Newcastle Australia School iof Automation Beijing Institute of Technology China Division of Automatic Control Department of Electrical Engineering Linkoping University Sweden Delft Center for Systems and Control Delft University Delft2628CD Netherlands

Using Maximum Likelihood (or Prediction Error) methods to identify linear state space model is a prime technique. The likelihood function is a nonconvex function and care must be exercised in the numerical maximization. Here the focus will be on affine parameterizations which allow some special techniques and algorithms. Three approaches to formulate and perform the maximization are described in this contribution: (1) The standard and well known Gauss-Newton iterative search, (2) a scheme based on the EM (expectation-maximization) technique, which becomes especially simple in the affine parameterization case, and (3) a new approach based on lifting the problem to a higher dimension in the parameter space and introducing rank constraints. © 2018

关键词： Maximum likelihood estimation

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