The exploration of lunar regolith sampling presents challenges such as enhancing intuitive situation awareness, facilitating systematic dataanalysis, improving operational efficiency, and addressing personnel trainin...
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Measurement of teachers' digital maturity can be useful in diagnosing technology adoption in education and in supporting teachers' professional development. However, there is a lack of easily usable tools in t...
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ISBN:
(纸本)9783031723148;9783031723155
Measurement of teachers' digital maturity can be useful in diagnosing technology adoption in education and in supporting teachers' professional development. However, there is a lack of easily usable tools in the existing literature. By investigating the most effective ways to measure teachers' digital maturity, our research aims to fill this gap. The focus of this article is on a data-driven approach to the assessment of digital maturity in education. Following a process similar to teaching analytics (data collection, datamodeling, data understanding and visualization, and contextual understanding), we draw on diary data collected from 12949 French primary school teachers using a virtual learning environment (VLE) during the 2022-2023 school year. Specifically, the paper presents results related to modeling and visualizing data. To this end, we propose a critical analysis of two automatic classification techniques, one supervised rule-based and the other unsupervised (hierarchical clustering). In addition to providing different scales to measure, we have proposed a new approach to diagnose and visualize digital maturity. In this way, the method provides insight into the diversity and intensity of technology use, contributing to a better understanding of digital maturity and providing a practical tool for assessing day-to-day teaching practices.
Finite Element (FE) analysis is widely used for process simulation of advanced composites in aerospace applications. However, as the fidelity of FE models improves, the cost and time to calibrate, setup and perform si...
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ISBN:
(纸本)9780791887141
Finite Element (FE) analysis is widely used for process simulation of advanced composites in aerospace applications. However, as the fidelity of FE models improves, the cost and time to calibrate, setup and perform simulations also increases significantly. To speed-up process simulation, reduced-order FE can be used. Reduced-order FE and Machine Learning (ML) can be combined to further speed-up the process simulation and enable process optimization. However, theory-agnostic ML usually requires large datasets which may not be feasible in industry. This can be mitigated by integrating the underlying physics into ML to develop Theory-Guided Machine Learning (TGML) models. In this study, for process simulation and optimization of a composite stringer on a wing skin, three modeling approaches are compared: 1) high-fidelity FE modeling, 2) surrogate ML modeling based on high-fidelity FE results, and 3) surrogate TGML modeling based on both high-fidelity and low-fidelity FE results. It is shown that TGML, with the guidance of underlying physics of composites curing, can improve prediction accuracy and significantly reduce the amount of data needed for training compared to theory-agnostic ML.
At present, the efficient bio-liquefaction technology of dead livestock and poultry has become an important technical means for the harmless treatment of dead livestock and poultry because of its advantages of high va...
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Because data mining has made excellent achievements in customer relationship management, finance and other applications, so researchers began to explore data mining technology algorithms based on audit financial data ...
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Equal access to education without discrimination, including for students with disabilities, is guaranteed by legal and strategic frameworks at global, European, and Croatian levels. Higher education institutions estab...
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In this article, we focus on the discrete-time stochastic linear quadratic problem under the presence of process and observation noise, particularly within the framework of average cost setting, exploring the optimal ...
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ISBN:
(纸本)9798331540845;9789887581598
In this article, we focus on the discrete-time stochastic linear quadratic problem under the presence of process and observation noise, particularly within the framework of average cost setting, exploring the optimal policy based on output feedback mechanisms. This paper introduces a data-driven inverse reinforcement learning algorithm designed to reconstruct an unknown cost function and learn a near-optimal control policy solely based on observed optimal behavior trajectories (input-output pairs) in scenarios where the cost function is unknown. Initially, we present a model-based inverse reinforcement learning approach under the premise of known model parameters, followed by a proof of theoretical equivalence between this method and our proposed data-driven approach. This equivalence not only validates the theoretical soundness of the proposed data-driven method but also ensures the convergence of the algorithm through theoretical analysis. Ultimately, through carefully designed numerical simulation experiments, we demonstrate the effectiveness of the proposed algorithm, confirming its ability to successfully reconstruct the cost function and learn an effective policy based on demonstration trajectories under unknown cost function conditions.
This study presents a comprehensive analysis of the aging behavior and cycle performance of commercial lithium-ion (Li-ion) cells, with the objective of developing accurate State of Health (SoH) estimation models. Emp...
DAB converters under triple phase shift (TPS) modulation can broaden the zero voltage switching (ZVS) range to improve efficiency and noise robustness. Conventionally, the piecewise approach and harmonic approach are ...
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ISBN:
(纸本)9781665475396
DAB converters under triple phase shift (TPS) modulation can broaden the zero voltage switching (ZVS) range to improve efficiency and noise robustness. Conventionally, the piecewise approach and harmonic approach are two commonly used approaches to build analytical models for ZVS conditions under this modulation strategy. However, both two approaches fail to achieve good modeling accuracy as well as low computational cost simultaneously due to heavy human dependence. To solve this problem, this digest proposes a data-driven modeling approach for ZVS analysis (DM-ZVS) for non-resonant DAB converters under the TPS modulation strategy. The data-driven modelingprocess is conducted with the random forest algorithm automatically using ZVS performance data from simulation tools, greatly mitigating human dependence to improve accuracy and computational efficiency. With the trained data-driven classification model of ZVS, the optimal TPS modulation parameters can be found to ensure the full ZVS range. A design case is given, and 1 kW hardware experiments comprehensively validate the feasibility of the proposed DM-ZVS.
Industrial time series, as a kind of data that responds to production process information, can be analyzed and predicted for effective monitoring of industrial production processes. There are problems of data shortage...
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Industrial time series, as a kind of data that responds to production process information, can be analyzed and predicted for effective monitoring of industrial production processes. There are problems of data shortage and algorithm cold start in industrial modelingprocess caused by complex working conditions, change of data acquisition environment, and short running time of equipment. As a result, the accuracy of the existing data-driven industrial time series prediction algorithm is greatly limited. To address the aforementioned problems, we propose a new time series prediction method for industrial processes under limited data based on dynamic transfer learning in this work. This method aims to effectively use historical data of similar equipment or working conditions rather than discard them to help establish an industrial time series prediction model with limited target data. In this method, first, historical data are divided into multiple batches, and then a new multisource transfer learning framework with dynamic maximum mean difference loss is established according to the distribution distance between each batch of historical data and the limited target data at the current moment. The framework also combines multitask learning methods to establish multistep prediction model for online learning in industrial processes. Compared with other commonly used methods, experiments on two real-world datasets of solar power generation prediction and heating furnace temperature prediction demonstrate the effectiveness of the proposed method.
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