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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

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 *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** 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 *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Adaptive Uniform Performance Control of Strict-Feedback Nonlinear systems With Time-Varying Control Gain

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song Frank L.Lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

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A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 533-546页

作者： Hao Wu Xin Luo MengChu Zhou Muhyaddin J.Rawa Khaled Sedraoui Aiiad Albeshri Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714and also with the University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA IEEE Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia College of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A large-scale dynamically weighted directed network(DWDN)involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications,like in a terminal interaction pattern analysis system(TIPAS).It can be represented by a high-dimensional and incomplete(HDI)tensor whose entries are mostly *** such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN.A latent factorization-of-tensors(LFT)model proves to be highly efficient in extracting such knowledge from an HDI tensor,which is commonly achieved via a stochastic gradient descent(SGD)***,an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale *** address this issue,this work proposes a proportional-integralderivative(PID)-incorporated LFT *** constructs an adjusted instance error based on the PID control principle,and then substitutes it into an SGD solver to improve the convergence *** studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models,the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

关键词： Big data high dimensional and incomplete(HDI)tensor latent factorization-of-tensors(LFT) machine learning missing data optimization proportional-integral-derivative(PID)controller

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Customizing Textile and Tactile Skins for Interactive Industrial Robots

Customizing Textile and Tactile Skins for Interactive Indust...

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作者： Su, Bo Ying Wei, Zhongqi McCann, James Yuan, Wenzhen Liu, Changliu Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Mechanical Engineering Carnegie Mellon University PittsburghPA15213 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

Tactile skins made from textiles enhance the robot–human interaction by localizing contact points and measuring contact forces. This paper presents a solution for rapidly fabricating, calibrating, and deploying these skins on industrial robot arms. The novel automated skin calibration procedure maps skin locations to robot geometry and calibrates contact force. Through experiments on a FANUC LR Mate 200id/7L industrial robot, we demonstrate that tactile skins made from textiles can be effectively used for human–robot interaction in industrial environments, and can provide unique opportunities in robot control and learning, making them a promising technology for enhancing robot perception and interaction. © 2023 by ASME.

关键词： Calibration

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Organoid intelligence:Integration of organoid technology and artificial intelligence in the new era of in vitro models

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Medicine in Novel Technology and Devices 2024年第1期21卷 39-44页

作者： Huaiyu Shi Andrew Kowalczewski Danny Vu Xiyuan Liu Asif Salekin Huaxiao Yang Zhen Ma Department of Biomedical&Chemical Engineering Syracuse UniversitySyracuseNYUSA BioInspired Institute for Material and Living Systems Syracuse UniversitySyracuseNYUSA Department of Mechanical&Aerospace Engineering Syracuse UniversitySyracuseNYUSA Department of Electrical Engineering&Computer Science Syracuse UniversitySyracuseNYUSA Department of Biomedical Engineering University of North TexasDentonTXUSA

Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial ***,three-dimensional miniature organ-like structures cultivated from stem cells,offer an unparalleled opportunity to simulate complex human organ systems in *** the convergence of organoid technology and AI,researchers gain the means to accelerate discoveries and insights across various *** intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors,intricate cellular interactions,and dynamic responses to *** synergy empowers the development of predictive models,precise disease simulations,and personalized medicine approaches,revolutionizing our understanding of human development,disease mechanisms,and therapeutic *** Intelligence holds the promise of reshaping how we perceive in vitro modeling,propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.

关键词： Organoids Machinelearning Deep learning Artificial intelligence Stem cells

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Fourier Imager Network(FIN):A deep neural network for hologram reconstruction with superior external generalization

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Light(Science & Applications) 2022年第9期11卷 2225-2234页

作者： HANLONG CHEN LUZHE HUANG TAIRAN LIU AYDOGAN OZCAN Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California Nano Systems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USAElectrical and Computer Engineering DepartmentUniversity of CaliforniaLos AngelesCA 90095USA David Geffen School of Medicine University of California Los AngelesLos AngelesCA 90095USA

Deep learning-based image reconstruction methods have achieved remarkable success in phase recovery and holographic ***,the generalization of their image reconstruction performance to new types of samples never seen by the network remains a *** we introduce a deep learning framework,termed Fourier Imager Network(FIN),that can perform end-to-end phase recovery and image reconstruction from raw holograms of new types of samples,exhibiting unprecedented success in external *** architecture is based on spatial Fourier transform modules that process the spatial frequencies of its inputs using learnable filters and a global receptive *** with existing convolutional deep neural networks used for hologram reconstruction,FIN exhibits superior generalization to new types of samples,while also being much faster in its image inference speed,completing the hologram reconstruction task in~0.04 s per 1 mm^(2) of the sample *** experimentally validated the performance of FIN by training it using human lung tissue samples and blindly testing it on human prostate,salivary gland tissue and Pap smear samples,proving its superior external generalization and image reconstruction *** holographic microscopy and quantitative phase imaging,FIN and the underlying neural network architecture might open up various new opportunities to design broadly generalizable deep learning models in computational imaging and machine vision fields.

关键词： field. generalization holographic

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Safe-By-Design Digital Twins for Human-Robot Interaction: A Use Case for Humanoid Service Robots

Safe-By-Design Digital Twins for Human-Robot Interaction: A ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jon Škerlj Mazin Hamad Jean Elsner Abdeldjallil Naceri Sami Haddadin Department of Computer Engineering School of Computation Information and Technology Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence Technical University of Munich (TUM)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Integrating humanoid service mobile robots into human environments presents numerous challenges, primarily concerning the safety of interactions between robots and humans. To address these safety concerns, we propose a novel approach that leverages the capabilities of digital twin technology by tailoring it to incorporate comprehensive and robust safety concepts. This paper introduces a "safe-by-design" digital twin that operates alongside the real twin robot in the loop, engaging real-time safety framework during physical interactions with the surrounding environment, including *** validate the effectiveness of our proposed safe-by-design digital twin framework, we conducted experiments using a humanoid service mobile robot alongside simulated human counterparts. Our results demonstrate the capability of the integrated impact safety module within the proposed digital twin approach to limit the velocities of both the robot’s base and arms, adhering to injury biomechanics-based safety thresholds. These findings emphasize the promise of our proposed approach for ensuring the physical safety of humanoid service mobile robots operating in dynamic human environments. It enables the digital twin to preemptively identify potential safety hazards and formulate safe intervention actions to ensure the robot’s compliance with safety regulations, paving the way for safer and more widespread adoption of robotic systems in various service domains.

关键词： Service robots Tracking Humanoid robots Human-robot interaction Robot sensing systems Safety Digital twins

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Impact of UAV Body Dynamics on Coverage Probability in 5G FR2

Impact of UAV Body Dynamics on Coverage Probability in 5G FR...

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IEEE Conference on Wireless Communications and Networking

作者： Pawan Srivastava Rajesh Kumar Samantaray M. P. R. S. Kiran Department of Electrical Engineering Connected Intelligence Lab IDRP on Robotics and Mobility Systems Indian Institute of Technology Jodhpur Rajasthan India

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The next-generation wireless communication technologies, such as 5G Frequency Range 2 (FR2), WLANs, etc., are utilizing the millimeter-wave (mmWave) spectrum for increased data rates. For example, applications involving unmanned aerial vehicles (UAVs) for coverage extension in 5G comprise a huge amount of user data to be transmitted to the gNodeB (gNB). Hence, utilizing mmWave communication technologies such as 5G FR2 to establish the link between the UAV and gNB can be a suitable option. However, one of the critical technologies of 5G FR2 is the utilization of directional beamforming, and the impact of the axial unsteadiness of UAV s on the coverage needs to be carefully analyzed. This paper studies and models the effect of beam misalignment manifested due to axial disturbances in UAVs while hovering at a fixed altitude on the network performance. Firstly, a stochastic model for beam misalignment is developed by collecting real-time inertial data from a hovering UAV over multiple flights. Second, an analytical formulation of the coverage probability in a directional UAV to gNB mmWave communication link is formulated. The performance analysis shows that the analytical model matches the simulation outcomes with less than 2% error.

关键词： Wireless communication 5G mobile communication Millimeter wave technology Stochastic processes Autonomous aerial vehicles Real-time systems Performance analysis

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A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1952-1966页

作者： Meiji Cui Li Li MengChu Zhou Jiankai Li Abdullah Abusorrah Khaled Sedraoui IEEE the School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China the Department of Electronics and Information Engineering Tongji UniversityShanghai 201804China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction *** search operation conducted in this low space facilitates the population with fast convergence towards the *** strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary ***,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence *** proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to *** indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base *** with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

关键词： Autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization

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