检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Wang, Cong Pedrycz, Witold Li, ZhiWu Zhou, MengChu School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Institute of Systems Engineering Macau University of Science and Technology Macau999078 China Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Although spatial information of images can usually enhance the robustness of the conventional Fuzzy C-Means (FCM) algorithm, it greatly increases the computational costs for image segmentation. To achieve a sound trade-off between the segmentation performance and the speed of clustering, we come up with a Kullback-Leibler divergence-based FCM algorithm by incorporating a tight wavelet frame transform and a morphological reconstruction operation. To enhance FCM’s robustness, an observed image is first filtered by using the morphological reconstruction. A tight wavelet frame system is employed to decompose the observed and filtered images so as to form their feature sets. Considering these feature sets as data of clustering, an modified FCM algorithm is proposed, which introduces a Kullback-Leibler divergence term in the partition matrix into its objective function. The Kullback-Leibler divergence term aims to make membership degrees of each image pixel closer to those of its neighbors, which brings that the membership partition becomes more suitable and the parameter setting of FCM becomes simplified. On the basis of the obtained partition matrix and prototypes, the segmented feature set is reconstructed by minimizing the inverse process of the modified objective function. To modify abnormal features produced in the reconstruction process, each reconstructed feature is reassigned to the closest prototype. As a result, the segmentation accuracy of Kullback-Leibler divergence-based FCM is further improved. What’s more, the segmented image is reconstructed by using a tight wavelet frame reconstruction operation. Finally, supporting experiments coping with synthetic, medical and color images are reported. Experimental results exhibit that the proposed algorithm works well and comes with better segmentation performance than other comparative algorithms. Moreover, the proposed algorithm requires less time than most of the FCM-related *** codes 62H30 Copyright ©

关键词： Image segmentation

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Framing image registration as a landmark detection problem for label-noise-aware task representation (HitR)

arXiv

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arXiv 2023年

作者： Waldmannstetter, Diana Ezhov, Ivan Wiestler, Benedikt Campi, Francesco Kukuljan, Ivan Ehrlich, Stefan Vinayahalingam, Shankeeth Baheti, Bhakti Chakrabarty, Satrajit Baid, Ujjwal Bakas, Spyridon Schwarting, Julian Metz, Marie Kirschke, Jan S. Rueckert, Daniel Heckemann, Rolf A. Piraud, Marie Menze, Bjoern H. Kofler, Florian Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany Helmholtz AI Helmholtz Munich Neuherberg Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Healthcare Technologies SETLabs Research Munich Germany Department of Oral and Maxillofacial Surgery Radboud University Nijmegen Medical Centre Nijmegen Netherlands Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St.Louis St. LouisMO United States Artificial Intelligence in Healthcare and Medicine Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Department of Medical Radiation Sciences University of Gothenburg Gothenburg Sweden

Accurate image registration is pivotal in bio-medical image analysis, where selecting suitable registration algorithms demands careful consideration. While numerous algorithms are available, the evaluation metrics to assess their performance have remained relatively static. This study addresses this challenge by introducing a novel evaluation metric termed Landmark Hit Rate (HitR), which focuses on the clinical relevance of image registration accuracy. Unlike traditional metrics such as Target Registration Error, which emphasize sub-resolution differences, HitR considers whether registration algorithms successfully position landmarks within defined confidence zones. This paradigm shift acknowledges the inherent annotation noise in medical images, allowing for more meaningful assessments. To equip HitR with label-noise-awareness, we propose defining these confidence zones based on an Inter-rater Variance analysis. Consequently, hit rate curves are computed for varying landmark zone sizes, enabling performance measurement for a task-specific level of accuracy. Our approach offers a more realistic and meaningful assessment of image registration algorithms, reflecting their suitability for clinical and biomedical applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

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Gauss-Newton unrolled neural networks and data-driven priors for regularized PSSE with robustness

arXiv

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arXiv 2020年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is timely and of major importance to maintain a comprehensive view of the system in real time. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares cost using e.g., the Gauss-Newton method. Those iterative solvers however, are sensitive to initialization, and may converge to local minima. To overcome these hurdles, the present paper adapts and leverages recent advances on image denoising to introduce a PSSE approach with a regularizer capturing a deep neural network (DNN) prior. For the resultant regularized PSSE objective, a "Gauss-Newton-type" alternating minimization solver is developed first. To accommodate real-time monitoring, a novel end-to-end DNN is constructed subsequently by unrolling the proposed alternating minimization solver. The deep PSSE architecture can further account for the power network topology through a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is put forth. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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Toolbox for discovering dynamic system relations via TAG guided genetic programming

arXiv

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arXiv 2020年

作者： Nechita, Stefan-Cristian Tóth, Roland Khandelwal, Dhruv Schoukens, Maarten Department of Electrical Engineering Eindhoven University of Technology Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 BudapestH-1111 Hungary

Data-driven modeling of nonlinear dynamical systems often require an expert user to take critical decisions a priori to the identification procedure. Recently an automated strategy for data driven modeling of single-input single-output (SISO) nonlinear dynamical systems based on Genetic Programming (GP) and Tree Adjoining Grammars (TAG) has been introduced. The current paper extends these latest findings by proposing a multi-input multi-output (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modelling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models. © 2020, CC BY-NC-ND.

关键词： Dynamical systems

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Existence and uniqueness of global Koopman eigenfunctions for stable fixed points and periodic orbits

arXiv

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arXiv 2019年

作者： Kvalheim, Matthew D. Revzen, Shai Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical Engineering and Computer Science Ecology and Evolutionary Biology Department Robotics Institute University of Michigan Ann ArborMI48109 United States

We consider C1 dynamical systems having an attracting hyperbolic fixed point or periodic orbit and prove existence and uniqueness results for Ck (actually Clock,α) linearizing semiconjugacies-of which Koopman eigenfunctions are a special case-defined on the entire basin of attraction. Our main results both generalize and sharpen Sternberg's Ck linearization theorem for hyperbolic sinks, and in particular our corollaries include uniqueness statements for Sternberg linearizations and Floquet normal forms. Using our main results we also prove new existence and uniqueness statements for Ck Koopman eigenfunctions, including a complete classification of C∞ eigenfunctions assuming a C∞ dynamical system with semisimple and nonresonant linearization. We give an intrinsic definition of "principal Koopman eigenfunctions" which generalizes the definition of Mohr and Mezić for linear systems, and which includes the notions of "isostables" and "isostable coordinates" appearing in work by Ermentrout, Mauroy, Mezić, Moehlis, Wilson, and others. Our main results yield existence and uniqueness theorems for the principal eigenfunctions and isostable coordinates and also show, e.g., that the (a priori non-unique) "pullback algebra" defined in [MM16b] is unique under certain conditions. We also discuss the limit used to define the "faster" isostable coordinates in [WE18, MWMM19] in light of our main *** Codes 37C05, 37C10, 37C15, 37C27, 34C15, 34C20, 37D99 Copyright © 2019, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Channels, learning, queueing and remote estimation systems with a utilization-dependent component

arXiv

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arXiv 2019年

作者： Jog, Varun La, Richard J. Martins, Nuno C. Electrical and Computer Engineering Department UW-Madison Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College ParkMD20742 United States

In this article, we survey the main models, techniques, concepts, and results centered on the design and performance evaluation of engineered systems that rely on a utilization-dependent component (UDC) whose operation may depend on its usage history or assigned workload. More specifically, we report on research themes concentrating on the characterization of the capacity of channels and the design with performance guarantees of learning algorithms, queueing and remote estimation systems. Causes for the dependency of a UDC on past utilization include the use of replenishable energy sources to power the transmission of information among the sub-components of a networked system, the influence of the dynamics of optimization iterates on the convergence of learning mechanisms and the assistance of a human operator for servicing a queue. Our analysis unveils the similarity of the UDC models typically adopted in each of the research themes, and it reveals the differences in the objectives and technical approaches employed. We also identify new challenges and future research directions inspired by the cross-pollination among the central concepts, techniques and problem formulations of the research themes discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Queueing theory

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Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

arXiv

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arXiv 2020年

作者： Mansouri, Sina Sharif Pourkamali-Anaraki, Farhad Arranz, Miguel Castao Agha-mohammadi, Ali-akbar Burdick, Joel Nikolakopoulos, George Robotics and AI Team Department of Computer Electrical and Space Engineering Luleå University of Technology LuleåSE-97187 Sweden Department of Computer Science University of Massachusetts Lowell MA United States Department of Civil Environmental and Natural Resources Engineering Luleå University of Technology LuleåSE-97187 Sweden Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Division of Engineering and Applied Sciences California Institute of Technology PasadenaCA United States

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology. Copyright © 2020, The Authors. All rights reserved.

关键词： Antennas

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Reconfigurable intelligent surface assisted mobile edge computing with heterogeneous learning tasks

arXiv

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arXiv 2020年

作者： Huang, Shanfeng Wang, Shuai Wang, Rui Wen, Miaowen Huang, Kaibin The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The Department of Electrical and Electronic Engineering The Department of Computer Science and Engineering The Sifakis Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China The Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since it is with rich computation resources to train machine learning (ML) models, as well as low-latency access to the data generated by mobile and internet of things (IoT) devices. In this paper, we present an infrastructure to perform ML tasks at an MEC server with the assistance of a reconfigurable intelligent surface (RIS). In contrast to conventional communication systems where the principal criterions are to maximize the throughput, we aim at maximizing the learning performance. Specifically, we minimize the maximum learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station (BS), and the phase-shift matrix of the RIS. An alternating optimization (AO)-based framework is proposed to optimize the three terms iteratively, where a successive convex approximation (SCA)-based algorithm is developed to solve the power allocation problem, closed-form expressions of the beamforming vectors are derived, and an alternating direction method of multipliers (ADMM)-based algorithm is designed together with an error level searching (ELS) framework to effectively solve the challenging nonconvex optimization problem of the phase-shift matrix. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks. Lastly, a unified communication-training-inference platform is developed based on the CARLA platform and the SECOND network, and a use case (3D object detection in autonomous driving) for the proposed scheme is demonstrated on the developed platform. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Residual-sparse fuzzy c-means clustering incorporating morphological reconstruction and wavelet frames

arXiv

引用

arXiv 2020年

作者： Wang, Cong Pedrycz, Witold Li, ZhiWu Zhou, MengChu Zhao, Jun School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Institute of Systems Engineering Macau University of Science and Technology Macau China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States School of Computer Science and Engineering Nanyang Technological University Singapore

Instead of directly utilizing an observed image including some outliers, noise or intensity inhomogeneity, the use of its ideal value (e.g. noise-free image) has a favorable impact on clustering. Hence, the accurate estimation of the residual (e.g. unknown noise) between the observed image and its ideal value is an important task. To do so, we propose an 0 regularization-based Fuzzy C-Means (FCM) algorithm incorporating a morphological reconstruction operation and a tight wavelet frame transform. To achieve a sound trade-off between detail preservation and noise suppression, morphological reconstruction is used to filter an observed image. By combining the observed and filtered images, a weighted sum image is generated. Since a tight wavelet frame system has sparse representations of an image, it is employed to decompose the weighted sum image, thus forming its corresponding feature set. Taking it as data for clustering, we present an improved FCM algorithm by imposing an 0 regularization term on the residual between the feature set and its ideal value, which implies that the favorable estimation of the residual is obtained and the ideal value participates in clustering. Spatial information is also introduced into clustering since it is naturally encountered in image segmentation. Furthermore, it makes the estimation of the residual more reliable. To further enhance the segmentation effects of the improved FCM algorithm, we also employ the morphological reconstruction to smoothen the labels generated by clustering. Finally, based on the prototypes and smoothed labels, the segmented image is reconstructed by using a tight wavelet frame reconstruction operation. Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other *** codes 62H30 Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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