Lithium iron phosphate (LFP) batteries are ideal for electrification of off-road heavy-duty vehicles with less concerns on the system weight. However, the limited battery life aggravated by the long working hours is a...
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Lithium iron phosphate (LFP) batteries are ideal for electrification of off-road heavy-duty vehicles with less concerns on the system weight. However, the limited battery life aggravated by the long working hours is a primary concern for some off-road applications such as construction equipment. Temperature is one of the main influencing factors in battery aging. Therefore, accurate prediction of temperature dynamics with fast lumped parameter models is essential and can be used for long-term analysis. This paper introduces a thermal model for pack of cells, with each cell represented by surface and core temperature states. We derived model parameters from experimental thermal cycling data, emphasizing the significance of reversible entropic heat generation for capturing faster dynamics. Furthermore, our work highlights the errors introduced by neglecting the bus bar thermal effects when extending a single-cell model to a cell pack. Our proposed solution incorporates the conduction between cell cores via the bus bar and accounts for heat dissipation through convection from the bus bar to surrounding air. Copyright (c) 2024 The Authors.
Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks toward more robust climate change pro...
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Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks toward more robust climate change projections. This study introduces a new machine-learning-based framework relying on satellite observations to improve understanding of the representation of clouds and their relevant processes in climate models. The proposed method is capable of assigning distributions of established cloud types to coarse data. It facilitates a more objective evaluation of clouds in ESMs and improves the consistency of cloud processanalysis. The method is built on satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument labeled by deep neural networks with cloud types defined by the World Meteorological Organization (WMO), using cloud-type labels from CloudSat as ground truth. The method is applicable to datasets with information about physical cloud variables comparable to MODIS satellite data and at sufficiently high temporal resolution. We apply the method to alternative satellite data from the Cloud_cci project (ESA Climate Change Initiative), coarse-grained to typical resolutions of climate models. The resulting cloud-type distributions are physically consistent and the horizontal resolutions typical of ESMs are sufficient to apply our method. We recommend outputting crucial variables required by our method for future ESM data evaluation. This will enable the use of labeled satellite data for a more systematic evaluation of clouds in climate models.
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.
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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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.
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.
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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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...
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