Tuning parameters in model predictive control (MPC) presents significant
challenges, particularly when there is a notable discrepancy between the
controller's predictions and the actual behavior of the closed-loop plant. This
mismatch may stem from factors like substantial model-plant differences,
limited prediction horizons that do not cover the entire time of interest, or
unforeseen system disturbances. Such mismatches can jeopardize both performance
and safety, including constraint satisfaction. Traditional methods address this
issue by modifying the finite horizon cost function to better reflect the
overall operational cost, learning parts of the prediction model from data, or
implementing robust MPC strategies, which might be either computationally
intensive or overly cautious. As an alternative, directly optimizing or
learning the controller parameters to enhance closed-loop performance has been
proposed. We apply Bayesian optimization for efficient learning of unknown
model parameters and parameterized constraint backoff terms, aiming to improve
closed-loop performance of battery fast charging. This approach establishes a
hierarchical control framework where Bayesian optimization directly fine-tunes
closed-loop behavior towards a global and long-term objective, while MPC
handles lower-level, short-term control tasks. For lithium-ion battery fast
charging, we show that the learning approach not only ensures safe operation
but also maximizes closed-loop performance. This includes maintaining the
battery's operation below its maximum terminal voltage and reducing charging
times, all achieved using a standard nominal MPC model with a short horizon and
notable initial model-plant mismatch.

通过贝叶斯优化方法，针对模型预测控制中的参数调整问题，提出一种直接优化学习控制器参数的方法，以提高闭环性能。该方法在锂离子电池快速充电系统中应用，实现了安全充电和最大化闭环性能的目标。

通过贝叶斯优化学习模型预测控制参数实现电池快速充电

Learning Model Predictive Control Parameters via Bayesian Optimization  for Battery Fast Charging

The SdSv challenge Task 2 provided an opportunity to assess efficiency and
robustness of modern text-independent speaker verification systems. But it also
made it possible to test new approaches, capable of taking into account the
main issues of this challenge (duration, language, ...). This paper describes
the contributions of our laboratory to the speaker recognition field. These
contributions highlight two other challenges in addition to short-duration and
language: the mismatch between enrollment and test data and the one between
subsets of the evaluation trial dataset. The proposed approaches experimentally
show their relevance and efficiency on the SdSv evaluation, and could be of
interest in many real-life applications.

本研究描述了我们实验室在发言者识别领域的贡献，突出了除了短时长和语言之外，注册和测试数据之间以及评估数据集子集之间的不匹配问题。提出的方法在 SdSv 评估中实验性地展示了它们的相关性和效率，并可能在许多实际应用中具有重要意义。

非对称和试验依赖性建模：LIA 对 SdSV 挑战任务 2 的贡献

Asymmetric and trial-dependent modeling: the contribution of LIA to SdSV  Challenge Task 2

A major hurdle on the road to conversational interfaces is the difficulty in
collecting data that maps language utterances to logical forms. One prominent
approach for data collection has been to automatically generate pseudo-language
paired with logical forms, and paraphrase the pseudo-language to natural
language through crowdsourcing (Wang et al., 2015). However, this data
collection procedure often leads to low performance on real data, due to a
mismatch between the true distribution of examples and the distribution induced
by the data collection procedure. In this paper, we thoroughly analyze two
sources of mismatch in this process: the mismatch in logical form distribution
and the mismatch in language distribution between the true and induced
distributions. We quantify the effects of these mismatches, and propose a new
data collection approach that mitigates them. Assuming access to unlabeled
utterances from the true distribution, we combine crowdsourcing with a
paraphrase model to detect correct logical forms for the unlabeled utterances.
On two datasets, our method leads to 70.6 accuracy on average on the true
distribution, compared to 51.3 in paraphrasing-based data collection.

本文针对自然语言和逻辑形式之间的映射、基于众包的数据收集方式存在的问题进行了深入分析，提出了一种结合模型和众包技术的数据收集方法，以减轻众包方法带来的偏差，实验结果表明，该方法对于自然语言和逻辑形式解析的准确度相对更高。