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。     

Impact of viscous dissipation and Dufour on MHD natural convective flow past an accelerated vertical plate with Hall current

The present research work concentrates on viscous dissipation, Dufour, and heat source on an unsteady magnetohydrodynamics natural convective flow of a viscous, incompressible, and electrically conducting fluid past an exponentially accelerated infinite vertical plate in the existence of a strong magnetic field. The presence of the Hall current induces a secondary flow in the problem. The distinguishing features of viscous dissipation and heat flux produced due to gradient of concentration included in the model along with heat source as they are known to arise in thermal-magnetic polymeric processing. The flow equations are discretized implicitly using the finite difference method and solved using MATLAB fsolve routine. Numerical values of the primary and secondary velocities, temperature, concentration, skin friction, Nusselt number, and Sherwood number are illustrated and presented via graphs and tables for various pertinent parametric values. The Dufour effect was observed to strengthen the velocity and temperature profile in the flow domain. In contrast, due to the impact of viscous dissipation, the local Nusselt number reduces. The study also reveals that the inclusion of the chemical reaction term augments the mass transfer rate and diminishes the heat transfer rate at the plate.     

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.     

Diet composition and overlap for adult walleye,lake whitefish,and yellow perch in Green Bay,Lake Michigan

Interspecific interactions among walleye Sander vitreus, lake whitefish Coregonus clupeaformis, and yellow perch Perca flavescens in Green Bay could influence the population status of each species, but potential trophic interactions are poorly understood. Our objectives were to determine if diet assemblages for each species and diet overlap among species varied spatially and temporally within Green Bay. Adult walleye (≥381 mm total length (TL); N = 981), lake whitefish (≥432 mm TL; N = 1507), and yellow perch (≥150 mm TL; N = 1174) were collected during May-October of 2018 and 2019 from multiple locations in southern and northern Green Bay. Diet assemblages of each species varied between northern and southern Green Bay, but walleye diets were more temporally variable (among months within zones and between years) than diets of lake whitefish or yellow perch. Lake whitefish represented a seasonally important prey item for walleye in southern Green Bay, composing 10 % and 41 % of walleye diets by weight in May and June, respectively. Yellow perch generally composed <15 % of walleye diets by weight but were consumed at a broader spatiotemporal scale than lake whitefish. Diet overlap between walleye and both lake whitefish and yellow perch was generally weak or moderate, whereas diet overlap between whitefish and perch was generally strong. Our assessment of adult trophic interactions suggests that changes in the population status of one species could influence fisheries for all three, and we identify additional research questions to address potential population-level effects of these trophic interactions.     

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).     

一种改进的特征匹配算法在碎片云测量建模中的应用

为满足超高速撞击典型Whipple防护构型的损伤评估需求,利用图像处理技术对碎片云序列阴影图像进行深入研究。使用超高速序列激光阴影成像仪得到三组不同实验条件下碎片云发展过程的高清阴影图像,分别对每组最具代表性的2帧进行图像处理分析;根据碎片云图像特点以及碎片运动特性,提出了一种改进的碎片二次特征匹配算法,该方法包含碎片粗定位、特征定义及初匹配和精确匹配三步策略;通过运用改进的匹配算法,对选取的相邻两帧图片完成碎片高效匹配,并提取匹配碎片的运动参数,进而分析碎片的速度分布和飞行角度分布,获取二次碎片云相关运动特性;得到三组实验各自的轨迹模拟图。根据得到的轨迹分析结果分别对三组实验的后板损伤进行估计,并通过与防护构型的实际损伤结果进行比较,验证了该方法的有效性。     

Preparation of faceted TaN grains by heating Ta-containing oxides in BN crucible with Na

Single-crystal grains of TaN were synthesized by heating Ta₂O₅, FeTa₂O₆, or FeTaO₄ in a BN crucible together with Na metal in an Ar atmosphere at 1100 °C. The BN crucible acted a solid source of nitrogen. Aggregates of columnar ε-TaN single crystals 10–150 μm in size were formed on the inner wall of the BN crucible when either Ta₂O₅ or FeTa₂O₆ was used. On the other hand, platelet-like single crystals of θ-TaN 1–50 μm in size were obtained from FeTaO₄. The results of wavelength-dispersive X-ray spectrometry indicated that the compositions of the ε-TaN and θ-TaN crystals were close to the stoichiometric ideal.