基于数据质量评价的目标估计

针对目标估计过程需要大量人工参与、自动化程度低的问题，提出了基于数据质量评价的目标估计方法。利用目标数据质量评价方法，对不同传感器得到的目标数据质量进行科学、有效的测度和评价，并根据质量得分动态调整各数据源在目标估计过程中所占的权重，从而减少人工干预，提高目标估计效能。仿真试验结果证明了该方法的有效性。     

Numerical study of microscopic particle arrangement of suspension flow in a narrow channel for the estimation of macroscopic rheological properties

In a narrow channel, the apparent relative viscosity of a suspension with finite-size particles is strongly dependent on its microscopic particle arrangement. Relative viscosity increases when suspended particles flow near the channel wall; thus, a suspension in a narrow channel does not always exhibit the same rheological properties even if the concentration is the same. In this study, we focus on the inertia and concentration of particles in a narrow channel and consider their effects on the microscopic particle arrangement and macroscopic suspension rheology. Two-dimensional pressure-driven suspension flow simulations were performed using a two-way coupling scheme, and normalized particle density distribution (PDD) were implemented to consider their particle arrangements. The results demonstrated that the velocity profiles for the particle suspension were changed by the Reynolds number and particle concentration because of the interactions between particles according to the power-law index. These changes affected the particle equilibrium positions in the channel, and the subsequent changes in solvent layer thickness caused changes in the macroscopic apparent viscosity. The behavior of microscopic particles played important roles in determining macroscopic rheology. Thus, we have confirmed that a normalized PDD can be used to estimate and assess the macroscopic rheology of a suspension.     

The effect of rare-earth oxides on the energy storage performances in BaTiO3 based ceramics

In this work, 0.7BaTiO₃-0.3Sr_0.2Bi_0.7TiO₃ (0.7BT-0.3SBT) ceramics with 0.15 mol% various rare-earth oxides doped are designed and synthesized by the conventional solid-state route. All prepared samples exhibited a single perovskite phase and dense microstructure with fine grain size (0.2–0.5 μm) after sintering at 1180 °C. Especially, the Gd-doped 0.7BT-0.3SBT ceramics exhibited excellent energy storage performances; the corresponding recoverable energy density and efficiency were 3.2 J/cm³ and 91.5% under an electric field of 330 kV/cm, respectively. Meanwhile, doping with Gd caused the BT-based ceramics to possess excellent temperature (30–150 °C) and outstanding frequency stabilities (10–1000 Hz). Moreover, the pulsed charge-discharge experiments revealed that a high power density of 59 MW/cm³ and a fast discharge speed of 110 ns with outstanding temperature stability could be synchronously obtained in the Gd-doped composition. All these features are attractive for pulsed power applications.     

机器翻译译文质量估计综述

机器翻译译文质量估计(Quality Estimation,QE)是指在不需要人工参考译文的条件下,估计机器翻译系统产生的译文的质量,对机器翻译研究和应用具有很重要的价值。机器翻译译文质量估计经过最近几年的发展,取得了丰富的研究成果。该文首先介绍了机器翻译译文质量估计的背景与意义;然后详细介绍了句子级QE、单词级QE、文档级QE的具体任务目标、评价指标等内容,进一步概括了QE方法发展的三个阶段: 基于特征工程和机器学习的QE方法阶段,基于深度学习的QE方法阶段,融入预训练模型的QE方法阶段,并介绍了每一阶段中的代表性研究工作;最后分析了目前的研究现状及不足,并对未来QE方法的研究及发展方向进行了展望。     

Decomposition and replacement: Spatial knowledge distillation for monocular depth estimation

Knowledge distillation has become a key technique for making smart and light-weight networks through model compression and transfer learning. Unlike previous methods that applied knowledge distillation to the classification task, we propose to exploit the decomposition-and-replacement based distillation scheme for depth estimation from a single RGB color image. To do this, Laplacian pyramid-based knowledge distillation is firstly presented in this paper. The key idea of the proposed method is to transfer the rich knowledge of the scene depth, which is well encoded through the teacher network, to the student network in a structured way by decomposing it into the global context and local details. This is fairly desirable for the student network to restore the depth layout more accurately with limited resources. Moreover, we also propose a new guidance concept for knowledge distillation, so-called ReplaceBlock, which replaces blocks randomly selected in the decoded feature of the student network with those of the teacher network. Our ReplaceBlock gives a smoothing effect in learning the feature distribution of the teacher network by considering the spatial contiguity in the feature space. This process is also helpful to clearly restore the depth layout without the significant computational cost. Based on various experimental results on benchmark datasets, the effectiveness of our distillation scheme for monocular depth estimation is demonstrated in details. The code and model are publicly available at : https://github.com/tjqansthd/Lap_Rep_KD_Depth.     

Tunable BaxSr1-xTiO3 nanoparticles induced high energy storage density in layer-structured asymmetric polymer-based nanocomposites

In order to solve the problem of low charging and discharging energy density of dielectric capacitors, the structure design of layered polymer matrix composites is carried out in this paper. Ba_0.7Sr_0.3TiO₃, Ba_0.8Sr_0.2TiO₃ and Ba_0.9Sr_0.1TiO₃ nanoparticles were successfully prepared by the oxalate coprecipitation method. The surface of Ba_xSr_1-xTiO₃ was successfully coated with dopamine, which promoted the dispersion of the polymer matrix of the ceramic powder. Monolayer Ba_xSr_1-xTiO₃/PVDF composites containing Ba_xSr_1-xTiO₃ with different Ba/Sr ratios were successfully prepared by the casting method. Three-layer asymmetric composites with different fillers were successfully prepared by layer-by-layer casting. The phase and microstructure of the as-prepared materials were analyzed by XRD and SEM. The dielectric, electrical conductivity, ferroelectric and energy storage properties of the composites were tested. The effects and laws of the design of the three-layer asymmetric structure on the dielectric properties and energy storage properties of the layered composites are mainly studied. When the structure of the three-layer asymmetric composite is 1-2-3, the breakdown field strength reaches 330 kV/mm, the discharge energy density reaches 8.51 J/cm³, and the charge-discharge efficiency is 67%. This work demonstrates that layered composites with asymmetric properties can facilitate the development of electrical energy storage.     

A comparison of shear stress estimation methods for a single geobag on a rough bed

There are several methods for estimating bed shear stress in the literature, but comprehensive comparisons among them are limited and under specific conditions. This study compared these methods first on a bare smooth bed, and then for a single geobag on a rough bed in the interest of determining the stability of geobags used in riverbank protection structures. The geobag was filled with cement or sand and tested under different open channel flow conditions. The turbulent kinetic energy method appeared to best represent the local bed shear stress on the geobag when using the newly calibrated proportionality constants. The Reynolds stress method via extrapolation was relatively unaffected by changes to the geobags shape and measurement locations, suggesting this method inadequately represents the local bed shear stress. The Patel method and the universal law of the wall method failed to represent local bed shear stress in the rough bed cases due to instrument limitations and the breakdown of the law of the wall. This study highlights the impact of different methods on the bed shear stress estimation.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).