A Survey of Powertrain Technologies for Energy-Efficient Heavy-Duty Machinery

Quan, Zhongyi; Ge, Liu; Wei, Zhongbao; Li, Yunwei; Quan, Long

doi:10.1109/jproc.2021.3051555

Cited by 30 publications

(18 citation statements)

References 126 publications

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“…Existing tracking algorithms based on deep reinforcement learning use action space noise to improve the exploration ability of models in complex scenes [ 21 ]. However, action space noise is generated completely randomly, which is stochastic and nonreproducible.…”

Section: Deep Learning-assisted Personalized Learning Of Englishmentioning

confidence: 99%

Effectiveness of English Online Learning Based on Deep Learning

Jie

Liu

et al. 2022

Computational Intelligence and Neuroscience

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With the popularization of the Internet lifestyle and the innovation of learning methods, more and more online learning systems have emerged, allowing users to study in the system anytime and anywhere. While providing convenience to users, online learning systems also bring troubles to users, who cannot quickly find the resources they are interested in from the huge amount of learning resources. In this paper, we apply deep learning to an English online learning platform and analyze learners and learning contents by clustering algorithm and association rules. Based on this, a content organization system is developed using genetic algorithms, which is applied to the case of this paper to provide learners with personalized learning content. With the hope that the system can be extended to other online learning platforms in the future, three data mining techniques were selected to solve the problems found in the English online learning platform, and we designed how these techniques should be applied to the online learning platform. The first technique is the cluster mining technique, which is used to analyze learners’ profiles, classify learners in different categories, provide them with personalized learning content, and organize group learning. The second technique is association rules, which is used to analyze the correlation between learning contents. For the adaptive student-teacher knowledge migration strategy, the teacher model can guide the student model to track online and migrate the task-specific knowledge to the online tracking student model through the network parameters. Finally, a case study is selected and the above design is applied to this case study, and the results are analyzed in detail. The data mining technology is applied to the English online learning platform, and an innovative English learning content organization system is developed. It is hoped that the results of this study will have some practical value for promotion and provide an idea for the construction of the online learning platform, and it is also expected that the idea can improve the quality of online learning to a certain extent. Specifically, the online student model is adaptively updated by the teacher model parameters and the online student model parameters together.

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Section: Deep Learning-assisted Personalized Learning Of Englishmentioning

confidence: 99%

Effectiveness of English Online Learning Based on Deep Learning

Jie

Liu

et al. 2022

Computational Intelligence and Neuroscience

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“…is algorithm also has certain shortcomings, that is, poor explainability; related recommendations also lack a clear theoretical discussion; and the principle of the algorithm is not clear. Based on the above theory, researchers have proposed some model-based recommendation algorithms such as cloud-based models and user preference models [21].…”

Section: Collaborative Filtering Recommendations For Moviesmentioning

confidence: 99%

Construction of Mobile Screening Movie Recommendation Model Based on Artificial Immune Algorithm

Ren

2022

Scientific Programming

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The mobile screening of digital movies can fully take into account the viewing experience of scattered areas. As a public cultural service system, it is playing a pivotal role. The consistency of the film screened with the tastes of the audience in the service area of the screening team has largely affected the quality of rural public culture services. Traditional recommendation algorithms directly use raw data to make predictions, leading to deviations in predictions. This article draws on the principles of immune recognition, clone selection, immune mutation, and self-adaptation of the artificial immune system to improve the recommendation effect of single-type data, the recommendation effect of sparse data, and the recommendation effect of project cold start problems and discusses the recommendation based on artificial immunity. For the single type of data, there are only positive samples, which leads to the problem that the training results are all positive. This paper proposes a single-class recommendation algorithm based on artificial immunity. The algorithm uses the positive and negative sample addition method proposed in this paper to add positive and negative samples related to user selection, so as to effectively solve the problem of difficult definition of data negative samples. Then, the artificial immune network is used to cluster the users of various activities, reduce the size of the candidate neighbor set, calculate the user’s nearest neighbor set, and give recommendations.

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“…1,2 Compact wheel loaders are usually equipped with hydrostatic transmission (HST). 3,4 Compared with gearbox transmission, an HST provides engine optimization and smooth drive owing to its continuously variable transmission (CVT) function. 5 However, the main drawback of HST is its low efficiency due to its double energy conversion.…”

Section: Introductionmentioning

confidence: 99%

An optimal-pressure control strategy for hydrostatic wheel loader

Wang

Zhang

Sun

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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Mobile machines consume a large amount of fossil fuel each year; therefore, it is critical to reduce their fuel consumption. A wheel loader is a typical mobile machine of great research interest. Most compact wheel loaders are usually equipped with hydrostatic transmissions. To reduce the fuel consumption of the powertrain, a min-rpm strategy where the engine speed is maintained as low as possible is usually applied. It is a rule-based control strategy, however, cannot achieve minimum engine fuel consumption. Therefore, an optimal-pressure control strategy is proposed for hydrostatic wheel loaders, where the minimum fuel consumption is achieved by selecting optimal system pressure at each time step. To show the advantages of the proposed strategy, the powertrain operation and engine fuel consumption are compared with the min-rpm strategy through simulation studies. To further verify the effectiveness of the proposed strategy, comparisons are conducted using three loading cycles (30s, 57s and 90s). Results show the engine fuel savings in three loading cycles, with fuel saving up to 27% in the 90s loading cycle.

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A Survey of Powertrain Technologies for Energy-Efficient Heavy-Duty Machinery

Cited by 30 publications

References 126 publications

Effectiveness of English Online Learning Based on Deep Learning

Effectiveness of English Online Learning Based on Deep Learning

Construction of Mobile Screening Movie Recommendation Model Based on Artificial Immune Algorithm

An optimal-pressure control strategy for hydrostatic wheel loader

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