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9th computer Science and Electronic engineering Conference, CEEC 2017

作者： Acosta, Jorge A. Puerto Bottura, Celso P. Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering UNICAMP-State University of Campinas/SP/Brazil Brazil

ISBN: (纸本)9781538630075

In this work two new procedures are proposed to discretize linear parameter varying (LPV) models in the linear fractional transformation LFT representation1. In the first procedure, a Taylor series approximation is used, to obtain multivariable discrete affine models for parameter dependent sampling rates of continuous LPV systems for each sampling period. Subsequently LFT transformations of that model are made to obtain local LFT models. In the second proposal, the properties of the LFT are used to unify in a single set the varying parameters associated with the system and the varying parameters associated with the sampling period for a global representation of continuous LPV-LFT systems. Finally an application of the proposal is presented. © 2017 IEEE.

关键词： Linear systems

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Cellular Computational Networks based Voltage Contingency Ranking Regarding Power System Security

Cellular Computational Networks based Voltage Contingency Ra...

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Clemson University Power systems Conference (PSC)

作者： Lili Wu Ganesh K. Venayagamoorthy Ronald G. Harley Jinfeng Gao Real-Time Power and Intelligent Systems Laboratory Clemson University Clemson SC 29634 USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA School of Electrical Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781728103174;9781728103167

Contingency ranking in simple terms is a process deployed to select the most serious contingencies that happen to the power system in the event of unplanned component outages or topological changes. In this study, Cellular Jordan Recurrent Networks (CJRNs) based non-linear voltage prediction is used for contingency ranking. The CJRNs based predicted voltages are used in computing performance indices that give proximity to voltage collapse severity of contingencies. The problem is solved combining with concentric relaxation method to reduce computation time. In order to show the advantages of the proposed CJRNs method, single line contingency ranking is tested by CJRNs voltage prediction model trained by different time steps. A novel voltage prediction correction mechanism which is applied after the prediction process shows to be effective for all time steps based contingency ranking.

关键词： Power systems Training Generators Performance analysis Voltage measurement Predictive models Databases

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Remaining Useful Battery Life Prediction for UAVs based on Machine Learning

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IFAC-PapersOnLine 2017年第1期50卷 4727-4732页

作者： Mansouri S.S. Karvelis P. Georgoulas G. Nikolakopoulos G. Robotics Group Control Engineering Division of the Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå SE-97187 Sweden Laboratory of Knowledge and Intelligent Computing Department of Computer Engineering Technological Educational Institute of Epirus Arta Greece

Unmanned Aerial Vehicles are becoming part of many industrial applications. The advancements in battery technologies played a crucial part for this trend. However, no matter what the advancements are, all batteries have a fixed capacity and after some time drain out. In order to extend the flying time window, the prediction of the time that the battery will no longer be able to support a flying condition is crucial. This in fact can be cast as a standard Remaining Useful Life prognostic problem, similarly encountered in many fields. In this article, the problem of Remaining Useful Life estimation of a battery, under different flight conditions, is tackled using four machine learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and an advanced tree based algorithm. The efficiency of the overall proposed machine learning techniques, in the field of batteries prognostics, is evaluated based on multiple experimental data from different flight conditions. © 2017

关键词： Battery Machine Learning Prediction Remaining Useful Life UAVs

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Self-adaptation of chimera states

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Physical Review E 2019年第1期99卷 010201(R)-010201(R)页

作者： Nan Yao Zi-Gang Huang Hai-Peng Ren Celso Grebogi Ying-Cheng Lai Department of Applied Physics Xi'an University of Technology Xi'an 710048 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Institute for Complex Systems and Mathematical Biology King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of “intelligence” in achieving robustness through self-adaptation. The behavior can be exploited for the controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.

关键词： Chimera states Collective dynamics Coupled oscillators Chaos & nonlinear dynamics

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Mining and categorizing interesting topics in Twitter communities

Mining and categorizing interesting topics in Twitter commun...

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作者： Vathi, Eleni Siolas, Georgios Stafylopatis, Andreas Intelligent Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Heroon Polytechniou 9 Zografou15780 Greece

In recent years, the popularity of social networks has grown dramatically. Understanding and revealing the underlying community structure of these complex networks is an area of great interest, with a plethora of applications. In this paper, we present a methodology for identifying user communities on Twitter. Initially, Twitter features such as the shared content, the users' interactions and the following relationships between the users are utilized to define a number of similarity metrics. These metrics are then used to compute the similarity between each pair in a set of Twitter users and by extension to group these users into communities. Subsequently, we propose a novel method based on latent Dirichlet allocation to extract the topics discussed in each community and eliminate those which consist of everyday words. Additionaly, we introduce a method for automatically generating labels for the non-trivial topics. The methodology is evaluated with a real-world dataset created using the Twitter Searching API. © 2017 - IOS Press and the authors. All rights reserved.

关键词： Complex networks

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Development of a Tetris Playing Robot Controlled by KNR

Development of a Tetris Playing Robot Controlled by KNR

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IEEE International Conference on Mechatronics and Automation

作者： Ming Wang Yanlu Zhang Huifang Dong Xu Li Junzhi Yu Department of Information and Electrical Engineering University of Shandong Jianzhu University Jinan China Department of Intelligent Robotics State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Robot plays an increasingly important role in daily life. This paper presents a vision-based wheeled robot for playing Tetris. How to control the robot is elaborated at length in this paper. The robot adopted a KNR controller as its core controller. With a CCD sensor, it identified colored blocks, lines and the destination. All algorithms are programed with LabVIEW. The experiments and the contest validated the effectiveness of the proposed vision-based wheeled robot system.

关键词： Mobile robots Cameras Robot sensing systems Image color analysis Servomotors Robot kinematics

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Parallel medical imaging for intelligent medical image analysis: Concepts, methods, and applications

arXiv

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arXiv 2019年

作者： Gou, Chao Shen, Tianyu Zheng, Wenbo Xue, Huadan Yu, Hui Ji, Qiang Jin, Zhengyu Wang, Fei-Yue School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Radiology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing100730 China School of Creative Technologies University of Portsmouth PortsmouthPO1 2DJ United Kingdom Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Qingdao Academy of Intelligent Industries Qingdao266000 China

Data-driven artificial intelligence technologies have made much progress in medical image analysis in the last decades. However, it still remains challenging due to its distinctive complexity of acquiring and annotating image data, extracting medical domain knowledge, and explaining the diagnostic decision for medical image analysis. In this paper, we propose a data-knowledge-driven framework termed as Parallel Medical Imaging (PMI) for intelligent medical image analysis based on the methodology of interactive ACP-based parallel intelligence. In the PMI framework, computational experiments with predictive learning in a data-driven way are conducted to extract medical knowledge for diagnostic decision support. Artificial imaging systems are introduced to select and prescriptively generate medical image data in a knowledge-driven way to utilize medical domain knowledge. Through the closed-loop optimization based on parallel execution, our proposed PMI framework can boost the generalization ability and alleviate the limitation of medical interpretation for diagnostic decisions. Furthermore, we illustrate the preliminary implementation of PMI method through the case studies of mammogram analysis and skin lesion image analysis. Experimental results on several public medical image datasets demonstrate the effectiveness of proposed PMI. © 2019, CC BY.

关键词： Medical imaging

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Distributed Dynamic State Estimation for Smart Grid Transmission System

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IFAC-PapersOnLine 2017年第2期50卷 98-103页

作者： Rahman, Md Ashfaqur Venayagamoorthy, Ganesh Kumar Real-Time Power and Intelligent Systems Laboratory ClemsonSC29634 United States Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh School of Engineering University of Kwazulu-Natal Durban4041 South Africa

The advancement of synchrophasor technologies validates the dynamic nature of the state variables of a power system. On the other hand, the need for privacy of data in the deregulated market and speed of computation for large power systems present the importance of a fast distributed state estimation method. To serve both purposes, a distributed dynamic state estimator is developed in this study. The advantages of a predictor, the constant Jacobian method and a combining unit are utilized. The system is distributed to the bus level that makes the distributed approach a parallel estimation. The accuracy and the smoothness of the proposed method are shown to be satisfactory through simulation on the IEEE 68-bus test power system. It is also shown that the proposed method can execute faster than the fastest data rate of the phasor measurement units currently deployed in the power system. © 2017

关键词： Distributed computer systems Data privacy Deregulation Dynamics Electric power transmission Electric power transmission networks Estimation Phasor measurement units Smart power grids State estimation Computational networks distributed Distributed approaches Distributed dynamics Distributed state estimation Large power systems Parallel estimation State prediction

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New design techniques for globally convergent simultaneous localization and mapping: Analysis and implementation

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Workshop on Sensing and Control for Autonomous Vehicles: Applications to Land, Water and Air Vehicles, 2017

作者： Lourenço, Pedro Guerreiro, Bruno Batista, Pedro Oliveira, Paulo Silvestre, Carlos Laboratory for Robotics and Engineering Systems Institute for Systems and Robotics Lisbon Portugal Department of Electrical and Computer Engineering Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Mechanical Engineering Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9783319553719

This chapter presents an overview of algorithms deeply rooted in a sensor-based approach to the SLAM problem that provide global convergence guarantees and allow for the use of partially observable landmarks. The presented algorithms address the more usual range-and-bearing SLAM problem, either in 2-D using a LiDAR or in 3-D using an RGB-D camera, as well as the range-only and bearing-only SLAM problems. For each of these formulations a nonlinear system is designed, for which state and output transformations are considered together with augmented dynamics, in such a way that the underlying system structure can be regarded as linear time-varying for observability analysis and filter design purposes. This naturally allows for the design of Kalman filters with, at least, globally asymptotically stable error dynamics, for which several experimental and simulated trials are presented to highlight the performance and consistency of the obtained filters. © Springer International Publishing AG 2017.

关键词： Kalman filters

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