Double Normalizing Flows: Flexible Bayesian Gaussian Process ODEs Learning

Recently, Gaussian processes have been utilized to model the vector field of continuous dynamical systems. Bayesian inference for such models \cite{hegde2022variational} has been extensively studied and has been applied in tasks such as time series prediction, providing uncertain estimates. However, previous Gaussian Process Ordinary Differential Equation (ODE) models may underperform on datasets with non-Gaussian process priors, as their constrained priors and mean-field posteriors may lack flexibility. To address this limitation, we incorporate normalizing flows to reparameterize the vector field of ODEs, resulting in a more flexible and expressive prior distribution. Additionally, due to the analytically tractable probability density functions of normalizing flows, we apply them to the posterior inference of GP ODEs, generating a non-Gaussian posterior. Through these dual applications of normalizing flows, our model improves accuracy and uncertainty estimates for Bayesian Gaussian Process ODEs. The effectiveness of our approach is demonstrated on simulated dynamical systems and real-world human motion data, including tasks such as time series prediction and missing data recovery. Experimental results indicate that our proposed method effectively captures model uncertainty while improving accuracy

Multi-Task Pruning for Semantic Segmentation Networks

This paper focuses on channel pruning for semantic segmentation networks. There are a large number of works to compress and accelerate deep neural networks in the classification task (e.g., ResNet-50 on ImageNet), but they cannot be straightforwardly applied to the semantic segmentation network that involves an implicit multi-task learning problem. To boost the segmentation performance, the backbone of semantic segmentation network is often pre-trained on a large scale classification dataset (e.g., ImageNet), and then optimized on the desired segmentation dataset. Hence to identify the redundancy in segmentation networks, we present a multi-task channel pruning approach. The importance of each convolution filter w.r.t the channel of an arbitrary layer will be simultaneously determined by the classification and segmentation tasks. In addition, we develop an alternative scheme for optimizing importance scores of filters in the entire network. Experimental results on several benchmarks illustrate the superiority of the proposed algorithm over the state-of-the-art pruning methods. Notably, we can obtain an about $2\times$ FLOPs reduction on DeepLabv3 with only an about $1\%$ mIoU drop on the PASCAL VOC 2012 dataset and an about $1.3\%$ mIoU drop on Cityscapes dataset, respectively

Online identification of lithium-ion battery model parameters with initial value uncertainty and measurement noise

Online parameter identification is essential for the accuracy of the battery equivalent circuit model (ECM). The traditional recursive least squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the recursive total least squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, a fast convergence rate, and also robustness against noise corruption

Analysis of Lipids in Pitaya Seed Oil by Ultra-Performance Liquid Chromatography–Time-of-Flight Tandem Mass Spectrometry

Red pitaya (Hylocereus undatus) is an essential tropical fruit in China. To make more rational use of its processing, byproducts and fruit seeds, and the type, composition, and relative content of lipids in pitaya seed oil were analyzed by UPLC-TOF-MS/MS. The results showed that the main fatty acids in pitaya seed oil were linoleic acid 42.78%, oleic acid 27.29%, and palmitic acid 16.66%. The ratio of saturated fatty acids to unsaturated fatty acids to polyunsaturated fatty acids was close to 1:1.32:1.75. The mass spectrum behavior and fracture mechanism of four lipid components, TG 54:5|TG 18:1_18:2_18:2, were analyzed. In addition, lipids are an essential indicator for evaluating the quality of oils and fats, and 152 lipids were isolated and identified from pitaya seed oil for the first time, including 136 glycerides and 16 phospholipids. The main components of glyceride and phospholipids were triglycerides and phosphatidyl ethanol, providing essential data support for pitaya seed processing and functional product development

Sensorless Temperature Estimation of Lithium-Ion Battery Based on Broadband Impedance Measurements

Temperature monitoring is of paramount importance for guaranteeing the safety and proper operation of lithium-ion batteries. Traditional temperature sensors suffer from heat transfer delay, where internal battery temperature cannot be measured directly. Motivated by this, this letter proposes a novel sensorless temperature estimation method based on broadband impedance spectroscopy. In this letter, pseudorandom sequence (PRS) with finite signal levels is utilized for impedance measurements. The measured impedance information is merged into an impedance-temperature model, which cooperates with a specially designed least-square method for temperature estimation. The proposed framework is robust against interference, whereas simple enough for online implementation. Experimental results suggest excellent estimation accuracy of the proposed method under different circumstances.</p

A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives

With the widespread use of Lithium-ion (Li-ion) batteries in Electric Vehicles (EVs), Hybrid EVs and Renewable Energy Systems (RESs), much attention has been given to Battery Management System (BMSs). By monitoring the terminal voltage, current and temperature, BMS can evaluate the status of the Li-ion batteries and manage the operation of cells in a battery pack, which is fundamental for the high efficiency operation of EVs and smart grids. Battery capacity estimation is one of the key functions in the BMS, and battery capacity indicates the maximum storage capability of a battery which is essential for the battery State-of-Charge (SOC) estimation and lifespan management. This paper mainly focusses on a review of capacity estimation methods for BMS in EVs and RES and provides practical and feasible advice for capacity estimation with onboard BMSs. In this work, the mechanisms of Li-ion batteries capacity degradation are analyzed first, and then the recent processes for capacity estimation in BMSs are reviewed, including the direct measurement method, analysis-based method, SOC-based method and data-driven method. After a comprehensive review and comparison, the future prospective of onboard capacity estimation is also discussed. This paper aims to help design and choose a suitable capacity estimation method for BMS application, which can benefit the lifespan management of Li-ion batteries in EVs and RESs

Synthesis and catalytic activity of N‐heterocyclic silylene (NHSi) iron (II) hydride for hydrosilylation of aldehydes and ketones

A novel silylene supported iron hydride [Si, C]FeH (PMe3)3 (1) was synthesized by C (sp3)-H bond activation with zero-valent iron complex Fe (PMe3)4. Complex 1 was fully characterized by spectroscopic methods and single crystal X-ray diffraction analysis. To the best of our knowledge, 1 is the first example of silylene-based hydrido chelate iron complex produced through activation of the C (sp3)H bond. It was found that complex 1 exhibited excellent catalytic activity for hydrosilylation of aldehydes and ketones. The catalytic system showed good tolerance and catalytic activity for the substrates with different functional groups on the benzene ring. It is worth mentioning that, the experimental results showed that both ketones and aldehydes could be reduced in good to excellent yields under the same catalytic conditions. Based on the experiments and literature reports, a possible catalytic mechanism was proposed