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

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Defence Technology（防务技术） 2023年第2期20卷 187-206页

作者： Xi Zhi-fei Kou Ying-xin Li Zhan-wu Lv Yue Xu An Li You Li Shuang-qing Air Force Engineering University No.1 Baling RoadBaqiao DistrictXi'anShaanxi ProvinceChina

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver ***,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 *** 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 ***,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 ***,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 ***,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 *** 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 predi

关键词： Maneuver trajectory prediction Volterra series Transfer learning online learning Ensemble learning Robust regularization

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online Unsupervised learning Classification of Pedestrian and Vehicle for Video Surveillance

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Chinese Journal of Electronics 2017年第1期26卷 145-151页

作者： HE Yi SANG Nong GAO Changxin HAN Jun School of Automation Huazhong University of Science and Technology Air Force Early Warning Academy

This paper presents an online unsupervised learning classification of pedestrians and vehicles for video surveillance. Different from traditional methods depending on offline training, our method adopts the online label strategy based on temporal and morphological features,which saves time and labor to a large extent. It extract the moving objects with their features from the original *** online filtering procedure is adopted to label the moving objects according to certain threshold of speed and area feature. The labeled objects are sent into a SVM classifier to generate the pedestrian & vehicle classifier. Experimental results illustrate that our unsupervised learning algorithm is adapted to polymorphism of the pedestrians and diversity of the vehicles with high classification accuracy.

关键词： online learning Unsupervised Objects classification Video surveillance

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online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

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Frontiers of Environmental Science & Engineering 2023年第12期17卷 137-147页

作者： Pengxiao Zhou Zhong Li Yimei Zhang Spencer Snowling Jacob Barclay Department of Civil Engineering McMaster UniversityHamiltonOntario L8S 4L8Canada MOE Key Laboratory of Resources and Environmental System Optimization College of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing 102206China Laboratory of Environmental Remediation and Functional Material Suzhou Research Academy of North China Electric Power UniversitySuzhou 215213China Hatch Ltd. Sheridan Science&Technology ParkMississaugaOntario L5K 2R7Canada

Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants(WWTPs),as it is closely related to wastewater characteristics such as biochemical oxygen demand(BOD),total suspend solids(TSS),and *** studies have been conducted to predict influent flow rate,and it was proved that data-driven models are effective ***,most of these studies have focused on batch learning,which is inadequate for wastewater prediction in the era of COVID-19 as the influent pattern changed *** learning,which has distinct advantages of dealing with stream data,large data set,and changing data pattern,has a potential to address this *** this study,the performance of conventional batch learning models Random Forest(RF),K-Nearest Neighbors(KNN),and Multi-Layer Perceptron(MLP),and their respective online learning models Adaptive Random Forest(aRF),Adaptive K-Nearest Neighbors(aKNN),and Adaptive Multi-Layer Perceptron(aMLP),were compared for predicting influent flow rate at two Canadian *** learning models achieved the highest R2,the lowest MAPE,and the lowest RMSE compared to conventional batch learning models in all *** R2 values on testing data set for 24-h ahead prediction of the aRF,aKNN,and aMLP at Plant A were 0.90,0.73,and 0.87,respectively;these values at Plant B were 0.75,0.78,and 0.56,*** proposed online learning models are effective in making reliable predictions under changing data patterns,and they are efficient in dealing with continuous and large influent data *** can be used to provide robust decision support for wastewater treatment and management in the changing era of COVID-19 and also under other unprecedented emergencies that could change influent patterns.

关键词： Wastewater prediction Data stream online learning Batch learning Influent flow rates

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Enhancing Telemarketing Success Using Ensemble-Based online Machine learning

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Big Data Mining and Analytics 2024年第2期7卷 294-314页

作者： Shahriar Kaisar Md Mamunur Rashid Abdullahi Chowdhury Sakib Shahriar Shafin Joarder Kamruzzaman Abebe Diro Department of Information Systems and Business Analytics RMIT UniversityMelbourne 3000Australia School of Engineering and Technology Central Queensland UniversityRockhampton 4700Australia Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide 5005Australia Centre for Smart Analytics Federation University AustraliaBallarat 3350Australia School of Accounting Information Systems and Supply ChainRMIT UniversityMelbourne 3000Australia

Telemarketing is a well-established marketing approach to offering products and services to prospective *** effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested *** introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising ***,the selection of influential features and the construction of effective learning models for improved performance remain a ***,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate ***,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over *** newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the *** experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6

关键词： machine learning online learning oversampling telemarketing imbalanced dataset ensemble model

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Mill Neural Decoupling Control System Based on online learning

Mill Neural Decoupling Control System Based on Online Learni...

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The Sixth International Conference on Measurement and Control of Granular Materials

作者： Bai Yan Li Xinli Yang Guotian Dept.of Automation,North China Electrical Power University Beijing 102206,China

In this paper,a kind of online learning algorithm of the neural decoupling control system for mill,which based on MIMO cross-correlation function,is *** simulation results show that the NN decoupler can correct its ow... 详细信息

关键词： Neural network decoupling online learning mill

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Students’ Perceptions of MyMathLab:An online learning Management System

Students’ Perceptions of MyMathLab:An Online Learning Manag...

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2012 3rd International Conference on e-Education,e-Business,e-Management and e-learning（IC4E 2012）

作者： Check-Yee Law Lik-Neo Ng Wei-Wei Goh Cheng-Lan Tay Yong-Wee Sek Centre for Foundation Studies&Extension Education Multimedia University Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka

With the evolution of web-based technologies,technology-based instructions have been partially or fully incorporated into teaching and learning *** study analyzes students’perceptions of the use of MyMathLab（MML）as a supplement in a mathematics ***,students were satisfied with the use of MML in their *** students only rated the system marginally positive,it had provided them with their first hand experience in using online learning and assessment tools.

关键词： online learning perception MyMathlab mathematics pre-university

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Research on the Internal Mechanism,Model,and Effectiveness of online Deep learning

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Frontiers of Education in China 2023年第3期18卷 310-331页

作者： BU Caili LI Sa YANG Haihui WANG Limin ZHANG Tong ZHANG Si Department of Education Henan Normal UniversityXinxiang 453007China Primary School Affiliated to Henan Normal University Xinxiang 453007China Faculty of Artificial Intelligence Education Central China Normal UniversityWuhan 430079China

With the in-depth development of internet plus education and the COVID-19 pandemic,online learning has become a universal teaching method in the ***,the issue of superficial and low-quality in online learning is still *** the internal and external conditions of online deep learning,this study reveals the theory mechanism of how the online learning environment and teacher support,as external conditions,affect students’emotions and learning motivation,and then affect students’internal learning *** this basis,an online deep teaching model is constructed,which consists of three stages:online asynchronous self-study before class,online synchronous live broadcasting teaching in class,and online asynchronous expansion after ***,a one-term long online teaching practice is carried out in the course named the Instructional System *** results show that,in the learning process level,the online deep teaching model can effectively improve students’online learning engagement,promote students to use deep learning methods and deep learning motivation to learn,and enhance the depth of online interaction between *** the level of learning results,students’critical thinking and online academic performance 9 have been significantly improved.

关键词： online learning online deep learning internal mechanism teaching model teaching effectiveness

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Impact of Levels of Education on Perceived Academic Stress and Mental Wellbeing: An Investigation into online Mode of learning during Pandemic

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Journal of Psychological Research 2021年第2期3卷 12-18页

作者： Nida B.Syed Indian Institute of Psychology and Research BengaluruKarnatakaIndia

A sudden outbreak of the current pandemic COVID-19 has switches the learning to online mode which leads to an increase in perceived academic stress and a serious threat to the mental well-being of the students across the *** aim of the current study was therefore,to examine the impact of online learning on perceived academic stress and mental well being of the students with moderating effects of levels of education,during the current *** of the constructs were obtained by the online Google form which consists of the Perceptions of Academic Stress Scale(PASS)by Dalia Bedewy and Adel Gabriel(2015)and Warwick-Edinburg Mental Well-being Scale(2008),from a sample of 150 undergraduate students aged 19-25 years studying in different colleges of Bengaluru,*** well-being constituted the criterion variable whilst academic stress and levels of education were treated as predictor ***-way ANOVA were *** show that academic stress is a significant negative predictor of mental wellbeing(r=-.083;p0.05).It was concluded that combined academic stress and educational levels have an impact on mental wellbeing of students in online mode of learning during the current pandemic,but this impact is low(only 5.2%).

关键词： Academic stress Mental wellbeing online learning Levels of education

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Comparison of Students Academic Performance in Mathematics Between online and Offline learning

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Economics World 2021年第4期9卷 173-177页

作者： Serkan Kaymak Aliyeva Kalamkas Suleyman Demirel University KaskelenKazakhstan

The coronavirus has affected many areas of life,especially in the field of *** the beginning of the Pandemic,the transition to online learning began,which affected the development of students and teachers in terms of using innovative technologies and programs,such as Zoom,Webex,Discord,Google Meet,Moodle,EDX,Coursera,***,*** this regard,many teachers are wondering whether the online method of teaching is as effective as the offline *** this article,we focused on finding out whether there is a significant difference in student performance between online and offline modes of learning in the study of mathematics.58 students were in a group where they studied online and 58 students were in a group where they studied *** study involved first-year college students of Jambyl Innovative Higher College(JICH)in Taraz,*** final control work was carried out at the end of week 18,which tested all areas covered by the topic in both *** average scores of students studying offline were compared with the average of students studying *** avoid confusion,the researchers also conducted and analyzed an independent *** results showed that there is a significant difference in the academic performance of students who study online and *** offline teaching method has proven to be more effective for improving students’understanding and comprehension of mathematics topics.

关键词： online learning offline learning achievement in mathematics

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Advancing the incremental fusion of robotic sensory features using online multi-kernel extreme learning machine

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Frontiers of Computer Science 2017年第2期11卷 276-289页

作者： Lele CAO Fuchun SUN Hongbo LI Wenbing HUANG State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China Department of Computing and Information Systems The University of Melbourne Parkville 3010 VIC Australia

Robot recognition tasks usually require multiple homogeneous or heterogeneous sensors which intrinsically generate sequential, redundant, and storage demanding data with various noise pollution. Thus, online machine learning algorithms performing efficient sensory feature fusion have become a hot topic in robot recognition domain. This paper proposes an online multi-kernel extreme learning machine （OM-ELM） which assembles multiple ELM classifiers and optimizes the kernel weights with a p-norm formulation of multi-kernel learning （MKL） problem. It can be applied in feature fusion applications that require incremental learning over multiple sequential sensory readings. The performance of OM-ELM is tested towards four different robot recognition tasks. By comparing to several state-of-the-art online models for multi-kernel learning, we claim that our method achieves a superior or equivalent training accuracy and generalization ability with less training time. Practical suggestions are also given to aid effective online fusion of robot sensory features.

关键词： multi-kernel learning online learning extreme learning machine feature fusion robot recognition

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