The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries

It is important to accurately estimate the capacity of the battery in order to extend the service life of the battery and ensure the reliable operation of the battery energy storage system. As entropy can quantify the regularity of a dataset, it can serve as a feature to estimate the capacity of batteries. In order to analyze the effect of voltage dataset selection on the accuracy of entropy-based estimation methods, six voltage datasets were collected, considering the current direction (i.e., charging or discharging) and the state of charge level. Furthermore, three kinds of entropies (approximate entropy, sample entropy, and multiscale entropy) were introduced, and the relationship between the entropies and the battery capacity was established by using first-order polynomial fitting. Finally, the interaction between the test conditions, entropy features, and estimation accuracy was analyzed. Moreover, the results can be used to select the correct voltage dataset and improve the estimation accuracy

CSPRD: A Financial Policy Retrieval Dataset for Chinese Stock Market

In recent years, great advances in pre-trained language models (PLMs) have sparked considerable research focus and achieved promising performance on the approach of dense passage retrieval, which aims at retrieving relative passages from massive corpus with given questions. However, most of existing datasets mainly benchmark the models with factoid queries of general commonsense, while specialised fields such as finance and economics remain unexplored due to the deficiency of large-scale and high-quality datasets with expert annotations. In this work, we propose a new task, policy retrieval, by introducing the Chinese Stock Policy Retrieval Dataset (CSPRD), which provides 700+ prospectus passages labeled by experienced experts with relevant articles from 10k+ entries in our collected Chinese policy corpus. Experiments on lexical, embedding and fine-tuned bi-encoder models show the effectiveness of our proposed CSPRD yet also suggests ample potential for improvement. Our best performing baseline achieves 56.1% MRR@10, 28.5% NDCG@10, 37.5% Recall@10 and 80.6% Precision@10 on dev set