Experimental Study on the Chemical Reaction Characteristics in the Calcination Process of an 811 Ternary Cathode Material.

The research on 811 ternary cathode materials is mainly based on synthesis and modification. However, the preparation process of these materials is accompanied by complex chemical reactions, and the reaction process and corresponding kinetic analysis have not been widely explored. Under different oxygen concentrations, this study analyzed the chemical reaction mechanism of the raw material's (namely, Ni0.8Co0. 1Mn0. 1(OH)2 and LiOH·H2O mixture, which is referred to as the raw material hereinafter) calcination process by non-isothermal thermogravimetry, differential scanning calorimetry, and in situ X-ray diffraction. Based on the obtained data, multiple heating rate methods were used to calculate the reaction mechanism functions and kinetic parameters at each stage as well as the corresponding activation energy and pre-exponential factor. Results showed that four chemical reactions occurred successively during the calcination process of the raw materials with each corresponding to a different kinetic function, pre-exponential factor, and activation energy. Comparing the calcination characteristics under different oxygen concentrations showed that the activation energy was the smallest when the oxygen concentration was 60%.

[Identification of heat shock protein hsp70 family genes from Rana amurensis and its expression profiles upon infection].

Heat shock proteins (HSPs) widely exist in all organisms, the structures of which are usually extraordinarily conservative. They are also well-known stress proteins that are involved in response to physical, chemical and biological stresses. HSP70 is an important member of the HSPs family. In order to study the roles of amphibians HSP70 during infection, the cDNA sequence of Rana amurensis hsp70 family genes were cloned by homologous cloning method. The sequence characteristics, three-dimensional structure and genetic relationship of Ra-hsp70s were analyzed by bioinformatics methods. The expression profiles under bacterial infection were also analyzed by real-time quantitative PCR (qRT-PCR). Expression and localization of HSP70 protein were tested by immunohistochemical techniques. The results showed that three conservative tag sequences of HSP70 family, HSPA5, HSPA8 and HSPA13, were found in HSP70. Phylogenetic tree analysis indicated four members are distributed in four different branches, and members with the same subcellular localization motif are distributed in the same branch. The relative expression levels of the mRNA of four members were all significantly upregulated (P < 0.01) upon infection, but the time for up-regulating the expression levels were diverse in different tissues. The immunohistochemical analysis showed that HSP70 was expressed to different degrees in the cytoplasm of liver, kidney, skin and stomach tissue. The four members of Ra-hsp70 family have ability to respond bacterial infection to varying degrees. Therefore, it was proposed that they are involved in biological processes against pathogen and play different biological functions. The study provides a theoretical basis for functional studies of HSP70 gene in amphibians.

Building roof extraction from ASTIL echo images applying OSA-YOLOv5s.

Light detection and ranging (LiDAR) is a type of essential tool for urban planning and geoinformation extraction. Airborne streak tube imaging LiDAR (ASTIL) is a new system with great advantages in the rapid collection of remote sensing data. To the best of our knowledge, a new method to extract a building roof from the echo images of ASTIL is proposed. We improve YOLOv5s with a one-shot aggregation (OSA) module to improve efficiency. The experimental results show that the mean average precision of the OSA-YOLOv5s algorithm can reach 95.2%, and the frames per second can reach 11.74 using a CPU and 39.39 using a GPU. The method proposed can extract building objects efficiently from the echo images of ASTIL and acquire the building roof point cloud.

A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks.

The rapid development of deep neural networks (DNNs) in recent years can be attributed to the various techniques that address gradient explosion and vanishing. In order to understand the principle behind these techniques and develop new methods, plenty of metrics have been proposed to identify networks that are free of gradient explosion and vanishing. However, due to the diversity of network components and complex serial-parallel hybrid connections in modern DNNs, the evaluation of existing metrics usually requires strong assumptions, complex statistical analysis, or has limited application fields, which constraints their spread in the community. In this paper, inspired by the Gradient Norm Equality and dynamical isometry, we first propose a novel metric called Block Dynamical Isometry, which measures the change of gradient norm in individual blocks. Because our Block Dynamical Isometry is norm-based, its evaluation needs weaker assumptions compared with the original dynamical isometry. To mitigate challenging derivation, we propose a highly modularized statistical framework based on free probability. Our framework includes several key theorems to handle complex serial-parallel hybrid connections and a library to cover the diversity of network components. Besides, several sufficient conditions for prerequisites are provided. Powered by our metric and framework, we analyze extensive initialization, normalization, and network structures. We find that our Block Dynamical Isometry is a universal philosophy behind them. Then, we improve some existing methods based on our analysis, including an activation function selection strategy for initialization techniques, a new configuration for weight normalization, a depth-aware way to derive coefficients in SeLU, and initialization/weight normalization in DenseNet. Moreover, we propose a novel normalization technique named second moment normalization, which has 30 percent fewer computation overhead than batch normalization without accuracy loss and has better performance under micro batch size. Last but not least, our conclusions and methods are evidenced by extensive experiments on multiple models over CIFAR-10 and ImageNet.

Effective and Efficient Batch Normalization Using a Few Uncorrelated Data for Statistics Estimation.

Deep neural networks (DNNs) thrive in recent years, wherein batch normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the huge reduction and elementwise operations that are hard to be executed in parallel, which heavily reduces the training speed. To address this issue, in this article, we propose a methodology to alleviate the BN's cost by using only a few sampled or generated data for mean and variance estimation at each iteration. The key challenge to reach this goal is how to achieve a satisfactory balance between normalization effectiveness and execution efficiency. We identify that the effectiveness expects less data correlation in sampling while the efficiency expects more regular execution patterns. To this end, we design two categories of approach: sampling or creating a few uncorrelated data for statistics' estimation with certain strategy constraints. The former includes "batch sampling (BS)" that randomly selects a few samples from each batch and "feature sampling (FS)" that randomly selects a small patch from each feature map of all samples, and the latter is "virtual data set normalization (VDN)" that generates a few synthetic random samples to directly create uncorrelated data for statistics' estimation. Accordingly, multiway strategies are designed to reduce the data correlation for accurate estimation and optimize the execution pattern for running acceleration in the meantime. The proposed methods are comprehensively evaluated on various DNN models, where the loss of model accuracy and the convergence rate are negligible. Without the support of any specialized libraries, 1.98× BN layer acceleration and 23.2% overall training speedup can be practically achieved on modern GPUs. Furthermore, our methods demonstrate powerful performance when solving the well-known "micro-BN" problem in the case of a tiny batch size. This article provides a promising solution for the efficient training of high-performance DNNs.

Voxel-based spatial elongation filtering method for airborne single-photon LiDAR data.

A novel voxel-based spatial elongation filtering method is proposed, to reduce noise in airborne single-photon lidar (SPL) data. In this method, six additional points are generated adjacent to each point of interest in the SPL data. Then, we count the number of points within each voxel and discriminate signals from noise via a predefined threshold. A filter performance evaluation index (taking into account the false alarm and signal loss rates, and the average distance between the residual noise points and their nearest signal points) is introduced. We compare the proposed and voxel-based spatial filtering method. The average false alarm rate found with our method (3.5%) is 18.6% smaller than that of the voxel-based spatial filtering method (4.3%).

Deblurring streak image of streak tube imaging lidar using Wiener deconvolution filter.

We use a Wiener deconvolution filter to deblur the streak image of streak tube imaging lidar to improve the spatial resolution of the system and reduce the edge blurring effect in point clouds. Experiments were performed to investigate the performance of the deconvolution method. Results show that the spatial resolution improved from 9 to 4.5 mm, and the root-mean-square errors of the edge regions are effectively reduced. Additionally, the transition section decreases from 14 to 5.6 mm when the target is 5 m away from the receiver.

Linearly frequency-tuned LD-pumped Nd:YVO4 laser with an 18-GHz broadband tuning range.

A continuously frequency-tuned laser diode end-pumped Nd:YVO4 laser at 1064 nm is demonstrated. A coated etalon and a piezoelectric-transducer (PZT) are utilized for coarse and fine frequency tuning, respectively. Broadband and linear frequency tuning without mode hops is conducted by the synchronous adjustment of the etalon and the PZT. Dependence of the frequency excursion on the displacement of the PZT and the tilting angle of the etalon are theoretically and experimentally investigated. A linear frequency tuning range up to 18 GHz without mode hops or frequency overlaps in a one-way non-stopped scanning is obtained. The maximum output power is 930 mW at 1064 nm, and the average frequency tuning speed is 1.24 GHz/s. Standard deviation of the frequency variation to a linear frequency tuning is estimated to be 186 MHz, indicating a high tuning linearity.