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online learning over a Decentralized Network Through ADMM

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Journal of the Operations Research Society of China 2015年第4期3卷 537-562页

作者： Hao-Feng Xu Qing Ling Alejandro Ribeiro Department of Automation University of Science and Technology of ChinaHefei 230026China Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaUSA

In this paper,we focus on the decentralized online learning problem where online data streams are separately collected and collaboratively processed by a network of decentralized *** to centralized batch learning,such a framework is faithful to the decentralized and online natures of the data *** propose an online decentralized alternating direction method of multipliers that efficiently solves the online learning problem over a decentralized *** prove its O(√T)regret bound when the instantaneous local cost functions are convex,and its O(log T)regret bound when the instantaneous local cost functions are strongly convex,where T is the number of *** regret bounds are in the same orders as those of centralized online *** experiments on decentralized online least squares and classification problems demonstrate effectiveness of the proposed algorithm.

关键词： Multi-agent network Decentralized optimization online learning

A survey on online learning and optimization for spark advance control of SI engines

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Science China(Information Sciences) 2018年第7期61卷 6-26页

作者： Yahui ZHANG Xun SHEN Tielong SHEN Department of Engineering and Applied Sciences Sophia University

One of the most important factors affecting fuel efficiency and emissions of automotive engines is combustion quality that is usually controlled by managing spark advance(SA) in spark ignition(SI) *** increasing sensing capabilities and enhancements in on-board computation capability, online learning and optimization techniques have been the subject of significant research interest. This article surveys the literature of learning and optimization algorithms with applications to combustion quality optimization and control of SI engines. In particular, this paper reviews extremum seeking control algorithms for iterative solution of online optimization problems, stochastic threshold control algorithms for iterative solution of probability control of stochastic knock event, as well as feedforward learning algorithms for iterative solution of operating-point-dependent feedforward adaptation problems. Finally, two experimental case studies including knock probabilistic constrained optimal combustion control and on-board map learning-based combustion control are carried out on an SI gasoline engine.

关键词： online learning stochastic optimization iterative solution combustion control spark ignition engine

QoE oriented intelligent online learning evaluation technology in B5G scenario

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Digital Communications and Networks 2024年第1期10卷 7-15页

作者： Mingzi Chen Xin Wei Peizhong Xie Zhe Zhang School of Communications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications) Ministry of EducationNanjing210003China Institute of Higher Education Nanjing University of Posts and TelecommunicationsNanjing210003China

Students'demand for online learning has exploded during the post-COvID-19 pandemic ***,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always *** technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the *** that affect the Quality of online learning Experience(OL-QoE)become more *** get over this dilemma,we propose a systematic scheme by integrating big data,Machine learning(ML)technologies,and educational psychology ***,we first formulate a general definition of OL-QoE by data analysis and experimental *** formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect ***,we induce an extended layer to the classic Broad learning System(BLS)to construct an Extended Broad learning System(EBLS)for the students'OL-QoE *** the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation ***,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE *** timely interventions,their OL-QoE and learning performance can be *** results verify the effectiveness oftheproposed scheme.

关键词： B5G online learning Quality of experience

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QoS-Aware Congestion Control with online learning

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China Communications 2023年第2期20卷 168-178页

作者： Lei Zhang Kewei Zhu Yong Cui Yong Jiang Zhongguancun Laboratory Beijing 100094China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

In emerging applications such as industrial control and autonomous driving,end-to-end deterministic quality of service(QoS)transmission guarantee has become an urgent problem to be *** congestion control algorithms are essential to the performance of ***,existing congestion control schemes follow the best-effort principle of data transmission without the perception of application QoS *** enable data delivery within application QoS constraints,we leverage an online learning mechanism to design Crimson,a novel congestion control algorithm in which each sender continuously observes the gap between current performance and pre-defined *** can change rates adaptively that satisfy application QoS requirements as a *** many emulation environments and real-world experiments,our proposed scheme can efficiently balance the different trade-offs between throughput,delay and loss *** also achieves consistent performance over a wide range of QoS constraints under diverse network scenarios.

关键词： congestion control quality of service online learning

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Feature-aware regularization for sparse online learning

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Science China(Information Sciences) 2014年第5期57卷 65-85页

作者： OIWA Hidekazu MATSUSHIMA Shin NAKAGAWA Hiroshi Graduate School of Information Science and Technology The University of Tokyo Information Technology Center The University of Tokyo

learning a compact predictive model in an online setting has recently gained a great deal of *** combination of online learning with sparsity-inducing regularization enables faster learning with a smaller memory space than the previous learning *** optimization methods and learning algorithms have been developed on the basis of online learning with L1-regularization.L1-regularization tends to truncate some types of parameters,such as those that rarely occur or have a small range of values,unless they are emphasized in ***,the inclusion of a pre-processing step would make it very difficult to preserve the advantages of online *** propose a new regularization framework for sparse online *** focus on regularization terms,and we enhance the state-of-the-art regularization approach by integrating information on all previous subgradients of the loss function into a regularization *** resulting algorithms enable online learning to adjust the intensity of each feature’s truncations without pre-processing and eventually eliminate the bias of *** show theoretical properties of our framework,the computational complexity and upper bound of *** demonstrated that our algorithms outperformed previous methods in many classification tasks.

关键词： online learning supervised learning sparsity-inducing regularization feature selection sentiment analysis

Safe operation of online learning data driven model predictive control of building energy systems

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Energy and AI 2023年第4期14卷 536-549页

作者： Phillip Stoffel Patrick Henkel Martin Ratz Alexander Kumpel Dirk Muller RWTH Aachen University E.ON Energy Research CenterInstitute for Energy Efficient Buildings and Indoor ClimateMathieustraße 10Aachen52074Germany

Model predictive control is a promising approach to reduce the CO 2 emissions in the building ***,the vast modeling effort hampers the widescale practical ***,data-driven process models,like artificial neural networks,are well-suited to automatize the ***,the underlying data set strongly determines the quality and reliability of artificial neural *** general,the validity domain of a machine learning model is limited to the data that was used to train *** based on system states outside that domain,so-called extrapolations,are unreliable and can negatively influence the control *** present a safe operation approach combined with online learning to deal with extrapolation in data-driven model predictive ***,the k-nearest neighbor algorithm is used to detect extrapolation to switch to a robust fallback *** continuously retraining the artificial neural networks during operation,we successively increase the validity domain of the artificial neural networks and the control *** apply the approach to control a building energy system provided by the BOPTEST *** compare controllers based on two data sets,one with extensive system excitation and one with baseline *** system is controlled to a fixed temperature set point in baseline ***,the artificial neural networks trained on this data set tend to extrapolate in other operating *** show that safe operation in combination with online learning significantly improves performance.

关键词： Data-driven model predictive control online learning Novelty detection Artificial neural networks Building energy systems

Towards a better understanding of the role of visualization in online learning:A review

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Visual Informatics 2022年第4期6卷 22-33页

作者： Gefei Zhang Zihao Zhu Sujia Zhu Ronghua Liang Guodao Sun College of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China

With the popularity of online learning in recent decades,MOOCs(Massive Open online Courses)are increasingly pervasive and widely used in many *** online learning is particularly important because it helps to analyze learner performance,evaluate the effectiveness of online learning platforms,and predict dropout *** to the large-scale,high-dimensional,and heterogeneous characteristics of the data obtained from online learning,it is difficult to find hidden *** this paper,we review and classify the existing literature for online learning to better understand the role of visualization in online *** taxonomy is based on four categorizations of online learning tasks:behavior analysis,behavior prediction,learning pattern exploration and assisted *** on our review of relevant literature over the past decade,we also identify several remaining research challenges and future research work.

关键词： Visualization in education online learning Visual analytics

online learning Effectiveness in Times of Covid-19 for Utesa University Undergraduate Students in the Dominican Republic: A Narrative Research Approach

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Online Learning Effectiveness in Times of Covid-19 for Utesa...

作者： Luis Gabriel Pérez Báez 浙江师范大学

学位级别：硕士

Effectiveness refers to the measure of to what extent goals are accomplished,this study will focus on determining how effective online learning is,more specifically how it fares during times of COVID-19 pandemic and focused on undergraduate *** studies have been previously been made regarding online learning effectiveness,however there are not many studies referring to online learning in times of pandemic and even fewer focused on developing nations like the Dominican *** purpose of this study is to determine the effectiveness of online learning in times of COVID-19 of undergraduate students in the Dominican *** research is necessary because of how the country have switched completely,albeit temporarily to a fully online teaching modality due to COVID-19,this sudden change was done without much infrastructure,teaching materials or pacification previously adapted to this particular situation,therefore assessing how effective has online education has been during this period will contribute to the country’s current situation and any future challenge that may arrive and need a similar *** order to understand this problematic as well as to get useful information from the volunteers,undergraduate students from UTESA University,interviews will be used to collect information which will later be analyzed using the qualitative methodology with a narrative inquiry *** conducting this study is expected to provide much needed information in this subject that may serve as guidance for a future reform of the higher education system in the country,putting special focus on the online learning modality.

关键词： Effectiveness online learning Undergraduate students COVID-19 Dominican Republic

A Study of Detecting Student Engagement Modes in an online learning Platform

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Frontiers of Education in China 2022年第1期17卷 23-45页

作者： XIAO Rui LIU Qianhui SHANG Junjie HUANG Win-bin learning Science Laboratory Graduate School of EducationPeking UniversityBeijing 100871China Department of Information Management Peking UniversityBeijing 100871China

In this paper,we used the platform log data to extract three features(proportion of passive video time,proportion of active video time,and proportion of assignment time)aligning with different learning activities in the Interactive-Constructive-Active-Passive(ICAP)framework,and applied hierarchical clustering to detect student engagement modes.A total of 840 learning rounds were clustered into four categories of engagement:passive(n=80),active(n=366),constructive(n=75)and resting(n=319).The results showed that there were differences in the performance of the four engagement modes,and three types of learning status were identified based on the sequences of student engagement modes:difficult,balanced and *** study indicated that based on the ICAP framework,the online learning platform log data could be used to automatically detect different engagement modes of students,which could provide useful references for online learning analysis and personalized learning.

关键词： online learning platform online learning student engagement Interactive-Constructive-Active-Passive(ICAP)framework hierarchical clustering learning analytics log analysis