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IEEE Symposium Series on Computational Intelligence

作者： Jie Tian Ying Tan Chaoli Sun Jianchao Zeng Haibo Yu Yaochu Jin College of Mechanical Engineering Shandong Womens University Jinan China Department of Computer Science and Technology Northeastern University Shenyang China College of Mechanical Engineering Taiyuan University of Science and Technology Taiyuan China Division of Industrial and System Engineering Taiyuan University of Science and Technology Taiyuan China School of Computer Science and Control Engineering North University of China Taiyuan China Department of Computer Science Taiyuan University of Science and Technology Taiyuan China

Surrogate-assisted evolutionary algorithms (SAEAs) have received increasing attention in recent years. Kriging is one of the most popular surrogate models due to its ability to provide approximation uncertainty as well as the approximation value without additional computational cost. The kernel function used in the Kriging model plays a very important role in Kriging, as it has considerable influence on the approximation performance of Kriging models. However, little work has been dedicated to the efficiency of training the Kriging model and the influence of the kernel function on the performance of SAEAs. In this paper, we conduct extensive empirical experiments to examine the performance of Kriging-assisted SAEAs on a widely used test suite of benchmark problems of a dimension of 20 or 30 using different kernel functions. Detailed analyses of the training efficiency and the performance of Kriging-assisted SAEAs with different kernel functions are given.

关键词： Expensive optimization Kriging Kernel function Fitness approximation

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An Event-Triggered Approach to State Estimation for Neural Networks with Individual Triggering Thresholds

An Event-Triggered Approach to State Estimation for Neural N...

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第35届中国控制会议

作者： Licheng Wang Zidong Wang Lei Zou Guoliang Wei Shanghai Key Lab of Modern Optical System Department of Control Science and EngineeringUniversity of Shanghai for Science and Technology Department of Computer Science Brunel University LondonUxbridge Research Institute of Intelligent Control and Systems Harbin Institute of Technology

ISBN: (纸本)9781509009107

In this paper, the state estimation problem is investigated for a class of discrete-time stochastic neural networks with event-triggered transmission(ETT) mechanism. Different from the traditional periodic communication mechanism, the ETT mechanism employed in this paper possesses the advantage of mitigating the network traf c resulting from the unnecessary sending and receiving data between the sensors and the estimators. Moreover, the proposed ETT mechanism is implemented individually for each neuron. That is, the measurement output of each neuron is transmitted to the estimator only when its corresponding triggering condition with the speci ed triggering threshold is satis ed, thereby re ecting the engineering practice in a more realistic way. The aim of the addressed problem is to design an estimator for the neural networks subject to the ETT mechanism such that the estimation error is asymptotically bounded in the mean square. Furthermore, the estimator gain matrix is explicitly parameterized in the sense of minimizing such an asymptotic error upper bound. Finally, a numerical example is presented to demonstrate the effectiveness of the results.

关键词： Event-triggered scheme Neural networks Individual triggering threshold State estimation Asymptotic boundedness

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Observability-driven Sensor Deployment in Smart Academic Environments: Demo Abstract 16

Observability-driven Sensor Deployment in Smart Academic Env...

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Proceedings of the 14th ACM Conference on Embedded Network Sensor systems CD-ROM

作者： A. Agarwal K. Jaiswal U. Gudhaka V. Munigala Krithi Ramamritham Gopinath Karmakar Department of Computer Science & Engineering Indian Institute of Technology Bombay Reactor Control System Design Section Bhabha Atomic Research Centre

ISBN: (纸本)9781450342636

Saving energy without causing discomfort and without demanding human intervention is the need of the day. It is important to develop sensor systems which not only satisfy user requirements, but also take energy consumption into consideration. In this demo, we present the sensing subsystems of a Smart Academic Environment, based upon the core concept of 'Observability' [1] which -- while satisfying user needs -- helps to reduce the number of sensors required.

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Stellar masses from granulation and oscillations of 23 bright red giants observed by BRITE - Constellation

arXiv

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arXiv 2019年

作者： Kallinger, T. Beck, P.G. Hekker, S. Huber, D. Kuschnig, R. Rockenbauer, M. Winter, P.M. Weiss, W.W. Handler, G. Moffat, A.F.J. Pigulski, A. Popowicz, A. Wade, G.A. Zwintz, K. Institut für Astrophysik Universität Wien Türkenschanzstrasse 17 Vienna1180 Austria Instituto de Astrofísica de Canarias La Laguna TenerifeE-38200 Spain Departamento de Astrofísica Universidad de La Laguna La Laguna TenerifeE-38206 Spain Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen37077 Germany Stellar Astrophysics Centre Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus C8000 Denmark Institute for Astronomy University of Hawai'i 2680 Woodlawn Drive HonoluluHI96822 United States Institut für Kommunnikationsnetze und Satellitenkommunikation Technical University Graz Inffeldgasse 12 Graz8010 Austria Institute for Machine Learning Johannes Kepler University Computer Science Building Altenberger Str. 69 Linz4040 Austria Nicolaus Copernicus Astronomical Center ul. Bartycka 18 Warsaw00-716 Poland Université de Montréal CP 6128 Succ. Centre-Ville MontréalQCH3C 3J7 Canada Instytut Astronomiczny Uniwersytet Wroclawski Kopernika 11 Wroclaw51-622 Poland Institute of Automatic Control Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Department of Physics Royal Military College of Canada PO Box 17000 Station Forces KingstonONK7K 0C6 Canada Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstrasse 25 InnsbruckA-6020 Austria

Context. The study of stellar structure and evolution depends crucially on accurate stellar parameters. The photometry from space telescopes has provided superb data that allowed asteroseismic characterisation of thousands of stars. However, typical targets of space telescopes are rather faint and complementary measurements are difficult to obtain. On the other hand, the brightest, otherwise well-studied stars, are lacking seismic characterization. Aims. Our goal is to use the granulation and/or oscillation time scales measured from photometric time series of bright red giants (1.6 ≤ V mag ≤ 5.3) observed with BRITE - Constellation to determine stellar surface gravities and masses. Methods. We use probabilistic methods to characterize the granulation and/or oscillation signal in the power density spectra and the autocorrelation function of the BRITE - Constellation time series. Results. We detect a clear granulation and/or oscillation signal in 23 red giant stars and extract the corresponding time scales from the power density spectra as well as the autocorrelation function of the BRITE - Constellation time series. To account for the recently discovered non-linearity of the classical seismic scaling relations, we use parameters from a large sample of Kepler stars to re-calibrate the scalings of the high- and low-frequency components of the granulation signal. We develop a method to identify which component is measured if only one granulation component is statistically significant in the data. We then use the new scalings to determine the surface gravity of our sample stars, finding them to be consistent with those determined from the autocorrelation signal of the time series. We further use radius estimates from the literature to determine the stellar masses of our sample stars from the measured surface gravities. We also define a statistical measure for the evolutionary stage of the stars. Conclusions. Our sample of stars covers low-mass stars on the lower giant br

关键词： Granulation

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$\mathcal{L}_{2}$-Gain Analysis for a Class of Hybrid systems With Applications to Reset and Event-Triggered control: A Lifting Approach

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IEEE Transactions on Automatic control 2016年第10期61卷 2766-2781页

作者： W. P. M. H. Heemels G. E. Dullerud A. R. Teel Control System Technology Group Department of Mechanical Engineering Eindhoven University of Technology Eindhoven The Netherlands Mechanical Science and Engineering Department and Coordinated Science Laboratory University of Illinois Urbana IL USA Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

In this paper we study the stability and L 2 -gain properties of a class of hybrid systems that exhibit linear flow dynamics, periodic time-triggered jumps and arbitrary nonlinear jump maps. This class of hybrid systems is relevant for a broad range of applications including periodic event-triggered control, sampled-data reset control, sampled-data saturated control, and certain networked control systems with scheduling protocols. For this class of continuous-time hybrid systems we provide new stability and L 2 -gain analysis methods. Inspired by ideas from lifting we show that the stability and the contractivity in L 2 -sense (meaning that the L 2 -gain is smaller than 1) of the continuous-time hybrid system is equivalent to the stability and the contractivity in L 2 -sense (meaning that the ℓ 2 -gain is smaller than 1) of an appropriate discrete-time nonlinear system. These new characterizations generalize earlier (more conservative) conditions provided in the literature. We show via a reset control example and an event-triggered control application, for which stability and contractivity in L 2 -sense is the same as stability and contractivity in ℓ 2 -sense of a discrete-time piecewise linear system, that the new conditions are significantly less conservative than the existing ones in the literature. Moreover, we show that the existing conditions can be reinterpreted as a conservative ℓ 2 -gain analysis of a discretetime piecewise linear system based on common quadratic storage/Lyapunov functions. These new insights are obtained by the adopted lifting-based perspective on this problem, which leads to computable L 2 -gain (and thus L 2 -gain) conditions, despite the fact that the linearity assumption, which is usually needed in the lifting literature, is not satisfied.

关键词： Stability analysis control systems Numerical stability Nonlinear systems Linear systems Linearity Electronic mail

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Visual tracking via an ensemble of random classifiers

Visual tracking via an ensemble of random classifiers

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Yichun Shi Hesheng Wang Department of Computer Science Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (纸本)9781467389600

One largest problem for tracking-by-detection methods is the incomplete and noisy training set. Occlusion, illumination and many other problems could lead to this problem. Models that are not adaptive enough would fail to track the target when drastic appearance change takes place. Adaptive ones, although keep tracking at first, could lose the target because of learning too many incorrect samples. In this paper, we present an ensemble model of random classifiers updated on different dataset. When the appearance of the target changes drastically and some sub-models are confused, the others could help correct the tracking result. A latent variable is added for choosing sub-models and it naturally leads to predicting the new samples with a weighted sum of sub-models. To calculate the weight, we add a generative model to each random classifier. Experiments show that our method could track the target robustly and accurately.

关键词： Target tracking Training Visualization Noise measurement Robustness Lighting Adaptation models

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Robust stability of spacecraft traffic control system using Lyapunov functions

Robust stability of spacecraft traffic control system using ...

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International Conference on control, Automation and systems ( ICCAS)

作者： Gulzhan Uskenbayeva Mamyrbek Beisenbi Dana Satybaldina Vasyl Martsenyuk Aigul Shaikhanova Department of System analysis and control L.N. Gumilyov Eurasian National University Astana Kazakhstan Department of Computer Science and Automatics University of Bielsko-Biala Bielsko-Biala Poland Department of Automatic Control Shakarim State University of Semey Semey Kazakhstan

ISBN: (纸本)9781467390583

The article presents a new approach to the construction control systems for objects with uncertain parameters in the form of one-parametric structurally stable maps from catastrophe theory. This method allows synthesizing the highly effective control systems, possessing in the extremely wide area of robust stability. Research of control systems robust stability based on a new approach to A.M Lyapunov's function construction.

关键词： Lyapunov methods control systems Robust stability Stationary state Steady-state Thermal stability Mathematical model

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Design of distributed H∞-optimized controllers considering stochastic communication link failures

Design of distributed H∞-optimized controllers considering ...

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2015 American control Conference, ACC 2015

作者： Jilg, Martin Tonne, Jens Stursberg, Olaf Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Germany

ISBN: (纸本)9781479986842

This paper proposes an approach for the design of distributed state-feedback controllers for interconnected discrete-time LTI systems with uncertain communication links. These links may be subject to intermittent or persistent failure. In particular, we assume that the availability of the communication links over time can be modeled by a Markov chain. For the control design, it is shown in a first step that the combination of the LTI system dynamics and the stochastic model of the time-varying communication topology is a special case of Jump Markov Linear systems (JMLS). Next, constraints on the information structure are introduced, restricting the subsystems' local knowledge about the global network state. This assumes that each subsystem can only detect the availability of incoming communication links. Finally, the synthesis of a stabilizing distributed feedback control law respecting the given information structure and a guaranteed bound on the expected H∞-performance is formulated and solved as a semidefinite program. © 2015 American Automatic control Council.

关键词： Stochastic systems

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F-CNN: An FPGA-based framework for training Convolutional Neural Networks

F-CNN: An FPGA-based framework for training Convolutional Ne...

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International Conference on Application Specific systems (ASAP), Architectures and Processors

作者： Wenlai Zhao Haohuan Fu Wayne Luk Teng Yu Shaojun Wang Bo Feng Yuchun Ma Guangwen Yang Ministry of Education Key Laboratory for Earth System Modeling and Center for Earth System Science Tsinghua University China Tsinghua National Laboratory for Information Science and Technology China Department of Computing Imperial college London UK Department of Automatic Test and Control Harbin Institute of Technology China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781509015047

This paper presents a novel reconfigurable framework for training Convolutional Neural Networks (CNNs). The proposed framework is based on reconfiguring a streaming datapath at runtime to cover the training cycle for the various layers in a CNN. The streaming datapath can support various parameterized modules which can be customized to produce implementations with different trade-offs in performance and resource usage. The modules follow the same input and output data layout, simplifying configuration scheduling. For different layers, instances of the modules contain different computation kernels in parallel, which can be customized with different layer configurations and data precision. The associated models on performance, resource and bandwidth can be used in deriving parameters for the datapath to guide the analysis of design trade-offs to meet application requirements or platform constraints. They enable estimation of the implementation specifications given different layer configurations, to maximize performance under the constraints on bandwidth and hardware resources. Experimental results indicate that the proposed module design targeting Maxeler technology can achieve a performance of 62.06 GFLOPS for 32-bit floating-point arithmetic, outperforming existing accelerators. Further evaluation based on training LeNet-5 shows that the proposed framework achieves about 4 times faster than CPU implementation of Caffe and about 7.5 times more energy efficient than the GPU implementation of Caffe.

关键词： Training Field programmable gate arrays Convolution Computational modeling Bandwidth Neural networks Runtime

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