Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

QoE oriented intelligent online learning evaluation technology in B5G scenario

Students'demand for online learning has exploded during the post-COvID-19 pandemic era.However,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always satisfactory.The 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 users.Factors that affect the Quality of Online Learning Experience(OL-QoE)become more complicated.To get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology theory.Specifically,we first formulate a general definition of OL-QoE by data analysis and experimental verification.This formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect OLQoE.Then,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 prediction.Since 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 complexity.Finally,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE values.Through timely interventions,their OL-QoE and learning performance can be improved.Experimental results verify the effectiveness oftheproposed scheme.

How Presence Affects College Students’Online Learning Outcomes:Unveiling Its Underlying Mechanism

Over the past few years,China’s higher education institutions have experienced remarkable growth in online teaching.However,it remains uncertain whether and how the sense of presence perceived by students affects their online learning outcomes when teachers use online teaching media for communication.This sense specifically pertains to the extent to which students perceive themselves as“real persons”and establish connections with others.Therefore,this study constructs a conceptual model elucidating the impact of presence on students’online learning outcomes and empirically examines the mechanism through which three types of presence influence students’online learning.The test results of the structural equation modeling(SEM)indicate that:(a)teaching presence,social presence,and cognitive presence all exhibit significantly positive outcomes on students’online learning outcomes;(b)these three types of presence can also indirectly and positively influence students’online learning outcomes through the mediating effect of flow experience and learning satisfaction;and(c)flow experience and learning satisfaction play a sequential mediating role in the process by which presence impacts students’online learning outcomes.We hope that the relevant research findings may contribute to unveiling the“black box”of the impact of presence on students’online learning outcomes and offer valuable insights for college educators to overcome online teaching constraints and enhance online teaching quality.

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

Computational intelligence interception guidance law using online off-policy integral reinforcement learning

Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.

The Influence of Academic Self-Efficacy on Learning Engagement in Online Education in China:The Mediating Role of Effort

The shift towards online intelligent learning has become the norm in education and is now a fundamental part of modern educational activities.However,this new model can influence students’learning behavior and lead to changes in their approach to learning.Based on online intelligent learning,we investigated how the academic self-efficacy of nursing students affects their engagement with learning and explored the role of academic attribution as a mediator.Five hundred fifty-three nursing college students from Hebei and Hunan provinces in China participated in the online questionnaire.The results revealed that effort plays a mediating role in the relationship between academic self-efficacy and learning engagement.

Efficacy of Online Learning and Influence on Skin and Eyes Among College Students

To evaluate the efficacy of online learning and explore the impact of long-term use of electronic products on facial skin as well as eyes.A cross-sectional survey was conducted to 180 sophomores in Xi′an Jiaotong University by cluster random sampling from September to October 2021.The questionnaire covering study condition,skin lesion and Ocular Surface Disease Index.χ_(2) test was used to compare the facial skin condition among different groups,and spearman correlation test was used to test the correlation of rank data.During online education,students′learning pressure is reduced,their autonomy is improved,and the learning efficiency is reduced.There were differences in the incidence of facial itching and papules among different groups.Duration of use of electronic products was positively correlated with the facial itching,with an r value of 0.231(P<0.05);the proportion of pigmentation in non-blue light protection groups(12.8%)was higher than that in blue light protection groups(1.7%),the difference was statistically significant(χ_(2)=8.384,P<0.05).The prevalence of dry eye among college students is 66.7%,and the proportion of moderate to severe dry eye is 34.5%.The study autonomy has been improved during online teaching.Long-term use of electronic products and no blue light protection have an impact on facial skin.Students should enhance the knowledge of skin-care and eye-care and develop better habits.

Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.

Air combat target maneuver trajectory prediction based on robust regularized Volterra series and adaptive ensemble online transfer learning

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making.However,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be solved.To solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is proposed.Firstly,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory data.Secondly,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori error.Finally,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of models.Compared with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current prediction.The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.

QoS-Aware Congestion Control with Online Learning

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 solved.Internet congestion control algorithms are essential to the performance of applications.However,existing congestion control schemes follow the best-effort principle of data transmission without the perception of application QoS requirements.To 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 QoS.Crimson can change rates adaptively that satisfy application QoS requirements as a result.Across many emulation environments and real-world experiments,our proposed scheme can efficiently balance the different trade-offs between throughput,delay and loss rate.Crimson also achieves consistent performance over a wide range of QoS constraints under diverse network scenarios.

Surviving the shift: College student satisfaction with emergency online learning during COVID-19 pandemic

BACKGROUND The coronavirus disease 2019(COVID-19)epidemic disrupted education systems by forcing systems to shift to emergency online leaning.Online learning satisfaction affects academic achievement.Many factors affect online learning satisfaction.However there is little study focused on personal characteristics,mental status,and coping style when college students participated in emergency online courses.regression analyses were performed to identify factors that affected online learning satisfaction.RESULTS Descriptive findings indicated that 62.9%(994/1580)of students were satisfied with online learning.Factors that had significant positive effects on online learning satisfaction were online learning at scheduled times,strong exercise intensity,good health,regular schedule,focusing on the epidemic less than one hour a day,and maintaining emotional stability.Positive coping styles were protective factors of online learning satisfaction.Risk factors for poor satisfaction were depression,neurasthenia,and negative coping style.CONCLUSION College students with different personal characteristics,mental status,and coping style exhibited different degrees of online learning satisfaction.Our findings provide reference for educators,psychologists,and school adminis-trators to conduct health education intervention of college students during emergency online learning.

A Study of Multimodal Intelligent Adaptive Learning System and Its Pattern of Promoting Learners’Online Learning Engagement

As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalization of educational resources,the intellectualization of educational methods,and the modernization of educational reform,among other benefits.This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data.It provides a detailed explanation of the system’s working principles and patterns,which aim to enhance learners’online engagement in behavior,emotion,and cognition.The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’learning behavior based solely on learning achievement,to improve learners’online engagement,enable them to master more required knowledge,and ultimately achieve better learning outcomes.

Influence of Online Learning Attitude in the Blended Teaching Model on the Final Grades

The combination of online teaching and traditional offline teaching can maximize the advantages of both.Based on the blended teaching of English Reading course,39 students were selected as the research subjects to study the relationship between their online learning attitudes and their grades in the final examination.Judged from the number of times for each student to download teaching resources,the number of assignments submitted online,and the quality of the submitted assignments,each student’s attitude toward online learning was examined comprehensively,and a correlation analysis was conducted through SPSS Statistics 21.0 to explore the influence of online learning attitude on English reading performance.Through data collection and analysis of the online learning attitudes over a 16-week period,a significant positive correlation was found between the online learning attitudes and the English reading grades,indicating that the online learning attitude in the blended learning model plays a crucial role in improving the English reading skill,and students should maintain a positive attitude toward online teaching in blended learning.

Semi-supervised learning based hybrid beamforming under time-varying propagation environments

Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.

An Empirical Study of the Influence of Online Learning Behaviors on Learning Outcomes in Blended Teaching Mode—Taking College English Listening as an Example

Blended teaching, which integrates the advantages of online and offline teaching, has become the main direction of higher education teaching reform. In the era of education big data, research on the online learners’ behavior based on data mining has attracted more and more attention from higher education researchers. However, in the field of foreign language teaching, research on the relationship between online learning behaviors and learning outcomes in the blended teaching mode is still at an early stage. Taking the course College English Listening in Zhejiang Yuexiu University (ZYU) as an example, this study conducted a comprehensive data analysis of online learning behaviors of 152 students of ZYU to explore the influence of online learning behaviors on learning outcomes in the blended teaching mode by utilizing Microsoft Excel and SPSS.20 statistic software. The result shows that the number of course login, the quantity and the quality of forum replies, the number of note submission, the quality of the notes, the average score of vocabulary tests, the number of the times of taking listening tests and the average score of listening tests are all significantly and positively correlated with students’ learning outcomes, while the study does not find a correlation between students’ learning outcomes and the number of the times of taking vocabulary tests, the total length of online learning and the length of video viewing. Based on the study results, implications are put forward to give reference for the teaching design and the management of the foreign language blended courses.

Ontology and metadata for online learning resource repository management based on semantic web

An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontology and user ontology, and evaluated from its function and implementation; then the corresponding class diagram, resource description framework （RDF） schema and extensible markup language （XML） schema are given. Secondly, the metadata for online learning resource repository management is proposed based on the Dublin Core Metadata Initiative and the IEEE Learning Technologies Standards Committee Learning Object Metadata Working Group. Finally, the inference instance is shown, which proves the validity of ontology and metadata in online learning resource repository management.

Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components.Shunt active filters(SAF) with current controlled voltage source inverters(CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents.However,CCVSI with traditional controllers have a limited transient and steady state performance.In this paper,we propose an adaptive dynamic programming(ADP) controller with online learning capability to improve transient response and harmonics.The proposed controller works alongside existing proportional integral(PI) controllers to efficiently track the reference currents in the d-q domain.It can generate adaptive control actions to compensate the PI controller.The proposed system was simulated under different nonlinear(three-phase full wave rectifier) load conditions.The performance of the proposed approach was compared with the traditional approach.We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison.The online learning based ADP controller not only reduced average total harmonic distortion by 18.41%,but also outperformed traditional PI controllers during transients.

Topic Sentiment Analysis in Online Learning Community from College Students

Purpose:Opinion mining and sentiment analysis in Online Learning Community can truly reflect the students’learning situation,which provides the necessary theoretical basis for following revision of teaching plans.To improve the accuracy of topic-sentiment analysis,a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models.Methodology/approach:We aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularitylevels.The proposed method comprised data preprocessing,topic detection,sentiment analysis,and visualization.Findings:The proposed model can effectively perceive students’sentiment tendencies on different topics,which provides powerful practical reference for improving the quality of information services in teaching practice.Research limitations:The model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach,without considering the intensity of students’sentiments and their evolutionary trends.Practical implications:The implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint,which can be utilized as a reference for enhancing the accuracy of learning content recommendation,and evaluating the quality of their services.Originality/value:The topic-sentiment analysis model can clarify the hierarchical dependencies between different topics,which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.

Quality Models for Open, Flexible, and Online Learning

This article is based on research conducted for the European CommissionEducation & Training 2020 working group on digital and online learning(ET2020 WG-DOL) specifically regarding policy challenges, such as thefollowing: 1) Targeted policy guidance on innovative and open learningenvironments under outcome;2) Proposal for a quality assurance modelfor open and innovative learning environments, its impact on specificassessment frameworks and its implication for EU recognition and transparencyinstruments. The article aims to define quality in open, flexible,and online learning, particularly in open education, open educationalresources (OER), and massive open online courses (MOOC). Hence,quality domains, characteristics, and criteria are outlined and discussed,as well as how they contribute to quality and personal learning so thatlearners can orchestrate and take responsibility for their own learningpathways. An additional goal is to identify the major stakeholders directlyinvolved in open online education and to describe their visions, communalities,and conflicts regarding quality in open, flexible, and online learning.The article also focuses on quality in periods of crisis, such as duringthe pandemic in 2020. Finally, the article discusses the rationale and needfor a model of quality in open, flexible, and online learning based on threemajor criteria for quality: excellence, impact, and implementation fromthe learner’s perspective.

An Empirical Study on Self-Efficacy of Online Learning of Overseas Chinese Children in Europe

In the post-Covid-19 pandemic era,it is more difficult for some Chinese schools in Europe to provide online extra classes for overseas Chinese children after school hours,as they did previously.To meet students'multifaceted learning needs,online extra classes teaching,including online Chinese language classes and some online art classes,is increasingly being offered as a supplement to the diversity of teaching activities in Chinese schools in Europe,with the ultimate goal of improving the learning abilities of overseas Chinese children while relieving pressure on teaching resources in schools.Children’s learning self-efficacy in online extracurricular courses has its own uniqueness,which can be considered from three dimensions,including learning confidence,learning ability,and self-assessment ability.This study aims to examine the factors influencing the self-efficacy of overseas Chinese children and to make optimization suggestions for better teaching methods.In search of that,an online questionnaire survey with 127 participants from overseas Chinese children aged􀀘to􀀔􀀘was collected.The findings indicate that the role of learning confidence in overseas Chinese children outweighs their learning ability and self-assessment ability.Gender and age have a negligible effect on self-efficacy but have an impact on learning confidence.Chinese schools in Europe do not need to show gender differences when conducting classroom activities in online teaching to improve the online self-efficacy of Chinese children,and efforts should also be made to keep the courage of older students to trial and error.Teachers are expected to investigate more aspects of their students'personalities in future classrooms rather than sticking to a consistent and unchanging teaching model.