Electric vehicles are prone to torsional resonance when traveling across rough terrain, which can subject the drive shaft to undue mechanical stress. Conventional time-series prediction methods often fall short in predicting such resonance phenomena accurately because they primarily concentrate on the central tendencies of a distribution and neglect the tails. In this study, we propose a probabilistic forecasting framework employing quantile regression to predict multiple percentile levels for the amplitude of torsional resonances. To assess the efficacy of our framework, we constructed a specialized dataset comprising 2,000 simulator-generated vibration signals that are representative of realistic driving conditions on uneven roads. Our findings substantiate that the proposed quantile-based forecasting model can predict long-term resonance with considerable accuracy compared to conventional methods. Additionally, our results reveal that temporal convolutional networks augmented with seasonal and trend decomposition achieved superior performance across a selection of five baseline models applied to our dataset.

The synthesis process is essential for achieving computational experiment design in the field of inorganic materials chemistry. In this work, we present a novel corpus of the synthesis process for all-solid-state batteries and an automated machine reading system for extracting the synthesis processes buried in the scientific literature. We define the representation of the synthesis processes using flow graphs, and create a corpus from the experimental sections of 243 papers. The automated machine-reading system is developed by a deep learning-based sequence tagger and simple heuristic rule-based relation extractor. Our experimental results demonstrate that the sequence tagger with the optimal setting can detect the entities with a macro-averaged F1 score of 0.826, while the rule-based relation extractor can achieve high performance with a macro-averaged F1 score of 0.887.

Contact imaging is a compact lensless microscopy technique that allows observation of living cells placed directly on a charge-coupled device sensor. However, captured images exhibit low visibility in general. This work is aimed at integrating computational refocusing and high-frequency illumination into contact imaging to enhance the visibility of the captured images. Refocusing contributes to the synthesis of shallow depth-of-field images from the light field captured by moving a point light source. The visibility may be enhanced via high-frequency illumination. Naïve integration of both techniques requires several observations. Therefore, we propose an efficient integration from reduced observations by reformulating the computational refocusing applied in contact imaging. Furthermore, we developed a prototype system that demonstrates the effectiveness of the proposed method.