An improved bidirectional gate recurrent unit combined with smoothing flter algorithm for state of energy estimation of lithium-ion batteries.

The accurate estimation of state of energy (SOE) is the key to the rational energy distribution of lithium-ion battery based energy storage equipment. This paper proposes an improved bidirectional gate recursive element combined with a time-varying bounded layer based smooth variable structure filtering algorithm. First, based on the solid temporal nature of the estimated parameters, a BiGRU neural network structure is constructed to strengthen further the influence of past and future information on the current estimates. Then, based on the traditional variable structure filtering, a time-varying bounded layer smoothing mechanism with saturation restriction (TS-VBL) is proposed to smooth the output of BiGRU to obtain a more accurate estimate. Finally, the test was conducted under 15℃ hybrid pulse power characterization (HPPC) and 35℃ Beijing bus dynamic stress test (BBDST). Compared with other algorithms, the BiGRU-TSVSF algorithm has a minor maximum estimation error of 0.00495 and 0.00722, respectively. The experimental results show that the algorithm has high precision and robustness and is of great value to the energy storage research of energy storage equipment

A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution

Super-Resolution (SR) has gained increasing research attention over the past few years. With the development of Deep Neural Networks (DNNs), many super-resolution methods based on DNNs have been proposed. Although most of these methods are aimed at ordinary frames, there are few works on super-resolution of omnidirectional frames. In these works, omnidirectional frames are projected from the 3D sphere to a 2D plane by Equi-Rectangular Projection (ERP). Although ERP has been widely used for projection, it has severe projection distortion near poles. Current DNN-based SR methods use 2D convolution modules, which is more suitable for the regular grid. In this paper, we find that different projection methods have great impact on the performance of DNNs. To study this problem, a comprehensive comparison of projections in omnidirectional super-resolution is conducted. We compare the SR results of different projection methods. Experimental results show that Equi-Angular cube map projection (EAC), which has minimal distortion, achieves the best result in terms of WS-PSNR compared with other projections. Code and data will be released.Comment: Accepted to ICASSP202

CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input

With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K/8K). To reduce the computational and memory cost, current methods first split the large input into local patches and then merge the SR patches into the output. These methods adaptively allocate a subnet for each patch. Quantization is a very important technique for network acceleration and has been used to design the subnets. Current methods train an MLP bit selector to determine the propoer bit for each layer. However, they uniformly sample subnets for training, making simple subnets overfitted and complicated subnets underfitted. Therefore, the trained bit selector fails to determine the optimal bit. Apart from this, the introduced bit selector brings additional cost to each layer of the SR network. In this paper, we propose a novel method named Content-Aware Bit Mapping (CABM), which can remove the bit selector without any performance loss. CABM also learns a bit selector for each layer during training. After training, we analyze the relation between the edge information of an input patch and the bit of each layer. We observe that the edge information can be an effective metric for the selected bit. Therefore, we design a strategy to build an Edge-to-Bit lookup table that maps the edge score of a patch to the bit of each layer during inference. The bit configuration of SR network can be determined by the lookup tables of all layers. Our strategy can find better bit configuration, resulting in more efficient mixed precision networks. We conduct detailed experiments to demonstrate the generalization ability of our method. The code will be released.Comment: Accepted to CVPR202

Incremental capacity curve health-indicator extraction based on gaussian filter and improved relevance vector machine for lithium–ion battery remaining useful life estimation.

Accurate prediction of the remaining useful life (RUL) of lithium–ion batteries is the focus of lithium–ion battery health management. To achieve high–precision RUL estimation of lithium–ion batteries, a novel RUL prediction model is proposed by combining the extraction of health indicators based on incremental capacity curve (IC) and the method of improved adaptive relevance vector machine (RVM). First, the IC curve is extracted based on the charging current and voltage data. To attenuate the noise effects on the IC curve, Gaussian filtering is used and the optimal filtering window is determined to remove the noise interference. Based on this, the peak characteristics of the IC curve are analyzed and four groups of health indicators are extracted, and the strong correlation between health indicators and capacity degradation is determined using Pearson correlation analysis. Then, to optimize the traditional fixed kernel parameter RVM model, an RVM regression model whose kernel parameters are optimized by the Bayesian algorithm is established. Finally, four sets of datasets under CS2 battery in the public dataset of the University of Maryland are carried out for experimental validation. The validation results show that the improved RVM model has better short–term prediction performance and long–term prediction stability, the RUL prediction error is less than 20 cycles, and the mean absolute error is less than 0.02. The performance of the improved RVM model is better than that of the traditional RVM model

GC-MS STUDY OF THE REMOVAL OF DISSOLVED AND COLLOIDAL SUBSTANCES IN RECYCLED PAPERMAKING BY FLOCCULATION WITH NANO-SIZE TIO2 COLLOIDS

In the papermaking process, the removal and control of dissolved and colloidal substances (DCS) is a key issue for reducing the usage of fresh water. Nano-size TiO2 is an excellent capturing and flocculating agent for DCS due to its large surface area and positive charge. The composition of dissolved and colloidal substances in a system and the removal of these substances by flocculation with nano-size TiO2 colloids were determined by gas chromatography and mass spectrometry (GC-MS). The samples were obtained from non-deinked pulp (non-DIP), deinked pulp (DIP), and whitewater. The research results indicated that the removal efficiencies of the DCS, which are associated with the molecular structures, were sequenced from large to small as follows: resin acids and sterols, benzene derivatives containing carboxyl, fatty acids, and the phthalic acid esters. Then, the mechanism of flocculation removal of DCS was considered. With hydrogen bonding between the surface hydroxyl (Ti4+-OH) and the functional groups containing oxygen, the nano-size TiO2 particles can capture dissolved substances (DS), and bridge colloidal substances (CS) and complexes to induce agglomerate flocculation. The flocculating removal efficiencies were influenced by the functional groups and alkyls of the DCS. Greater numbers and polarities of functional groups produced higher removal efficiencies. Long alkyl chains shield functional groups, thereby inhibiting the formation of hydrogen bonding, which results in a decrease in removal efficiencies

Uncertainty-aware Gait Recognition via Learning from Dirichlet Distribution-based Evidence

Existing gait recognition frameworks retrieve an identity in the gallery based on the distance between a probe sample and the identities in the gallery. However, existing methods often neglect that the gallery may not contain identities corresponding to the probes, leading to recognition errors rather than raising an alarm. In this paper, we introduce a novel uncertainty-aware gait recognition method that models the uncertainty of identification based on learned evidence. Specifically, we treat our recognition model as an evidence collector to gather evidence from input samples and parameterize a Dirichlet distribution over the evidence. The Dirichlet distribution essentially represents the density of the probability assigned to the input samples. We utilize the distribution to evaluate the resultant uncertainty of each probe sample and then determine whether a probe has a counterpart in the gallery or not. To the best of our knowledge, our method is the first attempt to tackle gait recognition with uncertainty modelling. Moreover, our uncertain modeling significantly improves the robustness against out-of-distribution (OOD) queries. Extensive experiments demonstrate that our method achieves state-of-the-art performance on datasets with OOD queries, and can also generalize well to other identity-retrieval tasks. Importantly, our method outperforms the state-of-the-art by a large margin of 51.26% when the OOD query rate is around 50% on OUMVLP