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IEEE International Conference on Systems, Man and Cybernetics

作者： Zonglin Di Qi Kang Daogang Peng Mengchu Zhou Department of Control Science and Engineering Tongji University Shanghai China College of Automation Engineering Shanghai University of Electric Power Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Imbalanced classification using a support vector machine (SVM) is a normal but crucial problem in machine learning. Compared with binary classification, multiclass classification is much more complicated. Most existing studies on imbalanced classification using SVM focus on binary imbalanced classification; while only few of them look into imbalanced classification with multiple classes. Pre-clustering is a useful technique to prepare proper data from an imbalanced dataset for a classifier. It can be used to extract the feature of a dataset first and improve classification performance. Density peak based on Euclidean distance proves its effectiveness and generality in clustering. Motivated by this and the fact that the number of clusters is known in multi-class classification using a one-vs-rest strategy, we combine density peak clustering and SVM to propose a new pre-clustering method to perform effective imbalanced classification with multiple classes. Specifically, we transform a multi-class classification problem into several binary classification tasks. The results on 5 public datasets in terms of F-measure, G-mean and Area Under Curve (AUC) show its superiority over the original SVM and SVM with other methods including random under-sampling, Synthetic Minority Oversampling Technique, pre-clustering using K-Means and EasyEnsemble methods using either a one-vs-rest or one-vs-one strategy.

关键词： Support vector machines Feature extraction Nickel Testing Task analysis Euclidean distance Kernel

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A Distributed and Parallel Method of Change Detection in Remote Sensing Image Based On Fully Connected Conditional Random Field

A Distributed and Parallel Method of Change Detection in Rem...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tiantian Zhou Zebin Wu Jun Liu Jin Sun Yi Zhang Jiandong Yang Hongyi Liu Zhihui Wei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Maritime Tracking and Control Department China Satellite Jiangyin China

Change Detection in Remote sensing image is, in essence, to detect the changes of ground features with regard to time from remote sensing perspective. It is usually realized by analyzing and processing multi-temporal high resolution images. Change Detection based on fully connected conditional random field not only improves the detection accuracy of remote sensing image, but also achieves better robustness. However, with the growth of high-resolution data volumes, this algorithm consumes a huge amount of time and computational resources, and therefore needs to be improved accordingly. Spark is an open-source distributed general- purpose cluster-computing framework. It has powerful memory computing and efficient task scheduling capabilities for complex iterative calculations. Based on Spark, this paper proposes a distributed and parallel method of change detection in remote sensing image based on Fully Connected Conditional Random Field that analyzes the data input form, and proposes a multi-temporal image reading strategy on cloud platforms. This method decomposes the algorithm flow, and performs distributed parallel processing on each stage and makes full use of the processing advantages of data locality to implement a reasonable intermediate data storage. Experimental results demonstrate that this parallel method achieves a promising speedup with high scalability, while guaranteeing remarkable detection accuracy.

关键词： Image resolution Change detection algorithms Scalability Distributed databases Sparks Task analysis Remote sensing

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Signal Estimation and System Identification With Nonlinear Dynamic Sensors

Signal Estimation and System Identification With Nonlinear D...

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control Technology and Applications (CCTA),

作者： Julian Berberich Mario Sznaier Frank Allgöwer Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany Department of Electrical and Computer Engineering Northeastern University Boston MA USA

We consider the problem of estimating the output of an unknown discrete-time linear time-invariant system and identifying a model of the system, where only measurements via a nonlinear dynamic sensor with known dynamics are available. The main result of this paper is a rank-constrained semidefinite program, which provides an equivalent characterization of this identification and estimation problem. This extends existing results from Wiener system identification to the more general case that the nonlinear block exhibits dynamic behavior, which is a commonly found scenario in practical applications. Notably, the result can be applied in the presence of nonlinear sensors with general non-invertible system dynamics. Two examples are used to illustrate the applicability of our approach.

关键词： Linear systems Nonlinear dynamical systems Sensor systems Estimation Transfer functions Noise measurement

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Automatic Brain Tumor Segmentation Using 3D Architecture Based on ROI Extraction

Automatic Brain Tumor Segmentation Using 3D Architecture Bas...

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IEEE International Conference on Robotics and Biomimetics

作者： Jing Huang Minhua Zheng Peter X. Liu School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing P. R. China Department of Systems and Computer Engineering Carleton University Ottawa ON Canada

MRI (magnetic resonance image) analysis is crucial for diagnosis, monitoring, and treatment of brain tumors. Manual MRI segmentation of brain tumors requires professional knowledge and costs huge amount of time. Automatic segmentation for multimodal 3D MR images is thus very desirable for clinical applications. In this paper, we present an automatic brain tumor segmentation method based on the U-Net architecture, which is composed of ROI (region of interest) extraction and 3D segmentation networks with long and short skip connections. The introduced method is able to capture detailed features and improves the problem of the imbalanced classification of tumors' sub-regions. The method was evaluated on the BRATS 2017 dataset and the results were promising.

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Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling

arXiv

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arXiv 2021年

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui The Purple Mountain Laboratories Nanjing211111 China The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China The State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Key Laboratory of Railway Industry of Broadband Mobile Information Communications Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China Peng Cheng Laboratory Shenzhen518055 China The Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States The Department of Electrical and Information Technology Lund University Lund22100 Sweden The Department of Radio Science and Engineering Aalto University Aalto00076 Finland The School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The Institute of Information and Communication Technologies Electronics and Applied Mathematics Universite Catholique de Louvain Louvain-la-Neuve 1348 Belgium

This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. In Part I, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antenna-channel optimization, and in this part (Part II) we review state-of-the-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state-of-art, the future challenges of AI/ML-based channel data processing techniques are given as well. © 2021, CC BY-NC-ND.

关键词： Machine learning

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Discrete Fourier Transform based Virtual Constraint control of Powered Ankle-Foot Prosthesis

Discrete Fourier Transform based Virtual Constraint Control ...

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International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Awais Naeem Mohsin Rizwan Hafiz Farhan Maqbool Muhammad Ahsan Ali Raza Syed Abbas Zilqurnain Naqvi Al-Khwarizmi Institute of Computer Science UET Lahore HCR Lab National Center of Robotics and Automation Lahore Pakistan Department of Mechatronics and Control Engineering National Center of Robotics and Automation Lahore Pakistan Department of Mechatronics and Control Engineering UET Lahore Lahore Pakistan

Active prosthetic devices have been controlled using several methods such as Echo control, EMG signal based Position control and Finite State Machine (FSM) based Impedance/Compliance control. This manuscript proposes Virtual Constraint control of active prosthesis which obviates any need for the classification of EMG signals, identification of the gait phase for state switching and potentially avoids an exhaustive procedure for the tuning of impedance parameters. In this paper, a Discrete Fourier Transform (DFT) based Virtual Constraint control is presented to characterize the ankle-foot joint trajectory as a function of the human-inspired phase variable in a unified manner. An optimization-based algorithm is employed for the robust generation of continuously monotonic and linear phase variable for DFT based Virtual Constraint control. The results are generalized across various walking speeds for a specific user during the level ground walking.

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Reinforcement Learning for Adaptive Periodic Linear Quadratic control

Reinforcement Learning for Adaptive Periodic Linear Quadrati...

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IEEE Annual Conference on Decision and control

作者： Bo Pang Zhong-Ping Jiang Iven Mareels Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University Brooklyn NY USA IBM Research - Australia Melbourne Vic Australia

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

This paper presents a first solution to the problem of adaptive LQR for continuous-time linear periodic systems. Specifically, reinforcement learning and adaptive dynamic programming (ADP) techniques are used to develop two algorithms to obtain near-optimal controllers. Firstly, the policy iteration (PI) and value iteration (VI) methods are proposed when the model is known. Then, PI-based and VI-based off-policy ADP algorithms are derived to find near-optimal solutions directly from input/state data collected along the system trajectories, without the exact knowledge of system dynamics. The effectiveness of the derived algorithms is validated using the well-known lossy Mathieu equation.

关键词： Heuristic algorithms Optimal control Approximation algorithms Mathematical model Learning (artificial intelligence) System dynamics Convergence

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A Simplified Voltage Balancing control of a Modular Medium-Frequency Transformer-Based Current Source Converter

A Simplified Voltage Balancing Control of a Modular Medium-F...

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IEEE Energy Conversion Congress and Exposition

作者： Qiang Wei David Xu Bin Wu Navid R. Zargari Department of Electrical Engineering Lakehead University Thunder Bay Country Electrical and Computer Engineering Ryerson University Toronto Canada Motor Control Archtecture Rockwell Automation Canada Canada

The modular medium-frequency transformer (MFT)-based current source converter is considered a promising topology for wind energy conversion systems. The use of modular MFT-based converter, however, introduces a potential issue, that is the capacitor voltage imbalance due to mismatches among the constituent modules of the modular converter. Model predictive control (MPC) with high dynamic performance is increasingly used in high power converters, but here, it suffers high computational burden and poor control performance due to high number of switching states. To solve this issue, a simplified MPC with better control performance is proposed to ensure capacitor voltage balancing without compromising the performance of traditional MPC. The operation principle and performance of the simplified MPC are illustrated and verified by experiments.

关键词： Capacitors Voltage control Voltage measurement Power transformer insulation Generators Predictive control Switches

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Random minibatch subgradient algorithms for convex problems with functional constraints

arXiv

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arXiv 2019年

作者： Nedić, Angelia Necoara, Ion School of Electrical Computer and Energy Engineering Arizona State University Tempe United States Department of Automatic Control and Systems Engineering University Politehnica Bucharest Bucharest060042 Romania

In this paper we consider non-smooth convex optimization problems with (possibly) infinite intersection of constraints. In contrast to the classical approach, where the constraints are usually represented as intersection of simple sets, which are easy to project onto, in this paper we consider that each constraint set is given as the level set of a convex but not necessarily differentiable function. For these settings we propose subgradient iterative algorithms with random minibatch feasibility updates. At each iteration, our algorithms take a subgradient step aimed at only minimizing the objective function and then a subsequent subgradient step minimizing the feasibility violation of the observed minibatch of constraints. The feasibility updates are performed based on either parallel or sequential random observations of several constraint components. We analyze the convergence behavior of the proposed algorithms for the case when the objective function is strongly convex and with bounded subgradients, while the functional constraints are endowed with a bounded first-order black-box oracle. For a diminishing stepsize, we prove sublinear convergence rates for the expected distances of the weighted averages of the iterates from the constraint set, as well as for the expected suboptimality of the function values along the weighted averages. Our convergence rates are known to be optimal for subgradient methods on this class of problems. Moreover, the rates depend explicitly on the minibatch size and show when minibatching helps a subgradient scheme with random feasibility updates. © 2019, CC BY.

关键词： Iterative methods

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Authentication by witness functions

arXiv

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arXiv 2019年

作者： Fattahi, Jaouhar Mejri, Mohamed Pricop, Emil Department of Computer Science and Software Engineering Université Laval Québec Canada Automatic Control Computers and Electronics Department. Petroleum-Gas University of Ploiesti Romania

Witness functions have recently been introduced in cryptographic protocols’ literature as a new powerful way to prove protocol correctness with respect to secrecy. In this paper, we extend them to the property of authentication. We show how to use them safely and we run an analysis on a modified version of the Woo-Lam protocol. We show that it is correct with respect to authentication. Copyright © 2019, The Authors. All rights reserved.

关键词： Authentication

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