Feedback-based object detection for multi-person pose estimation

In this paper, a novel method is proposed for increasing the performance through coupling of top-down models adjusting the object detector based on a new loss function. Generally, object detectors and keypoint estimators are sequentially used in real-time multi-person pose estimations; however, these two models are separately trained. Therefore, the results of the object detector are not optimized for the keypoint estimator. To solve this problem, we analyze the relationship between the two models and propose a feedback-based loss optimization in the object detector, based on the estimation results of the keypoint estimator. In addition, the resulting bounding box of the object detector is readjusted to improve the accuracy of the keypoint estimation model. The experimental results demonstrate that the proposed approach can perform real-time operations with a high frame rate similar to that of the baseline model. Moreover, it achieved an accuracy of 74.2 average precision (AP), which is higher than the state-of-the-arts model including the human detector used in the experiment.     

象山隧道液压凿岩台车开挖斜孔掏槽施工技术

新建的龙岩至厦门单线铁路象山隧道断面比较小,使用液压凿岩台车开挖,斜孔掏槽方案受到很大限制。本文介绍了使用液压凿岩台车钻爆开挖象山隧道,选择斜孔掏槽方案实现快速开挖,为类似的隧道工程施工提供经验。     

Reasonable construction of proton conducting channel via biomimetic caterpillar-like alumina fiber to improve the properties of its composite proton exchange membrane

In this paper, we prepare a novel biomimetic caterpillar-like alumina fiber with the characteristic of continuous alumina backbone and fine needle whiskers spine. Then the high-performance caterpillar-like alumina fiber composite proton exchange membrane (CAPEM) is obtained by introducing the amino modified biomimetic caterpillar-like alumina fiber into sulfonated polysulfone (SPSF) matrix, which successfully reasonable construction of the proton conducting channels in both vertical and horizontal orientation. The properties of CAPEM, including proton conductivity, methanol permeability, etc. Are systematically studied. The results show that the proton conductivity of CAPEM increases with rising the temperature, which reaches the maximum of 0.263 S/cm at 80 °C and 100% RH, respectively. The excellent proton conductivity of CAPEM is attributed to the long-range continuous proton conducting channel formed by the horizontal continuous alumina skeleton in the in-plane direction and the vertical overlapped fine needle whiskers spine in the through-plane direction. In addition, the interfacial compatibility between amino modified caterpillar-like alumina fiber and SPSF matrix is enhanced through the reasonable construction of proton conducting channels, which effectively inhibits the methanol permeation of the composite membrane with 4.18 × 10^?7 cm² s^?1 and improves the comprehensive performance of the CAPEM.     

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).     

Parameter characterization of HTPEMFC using numerical simulation and genetic algorithms

This paper develops a novel approach to the parameterisation of high temperature exchange membrane fuel cells (HTPEMFC) with limited and non-invasive measurements. The proposed method allows an effective identification of electrochemical parameters for three-dimensional fuel cell models by combining computational simulation tools and genetic algorithms. To avoid each evaluation undertaken by the optimisation method involving a complete computational simulation of the 3D model, a strategy has been designed that, thanks to an iterative process, makes it possible to decouple the fluid dynamic resolution from the electrochemistry one.Two electrochemical models have been incorporated into these tools to describe the behaviour of the catalyst layer, Butler-Volmer and spherical aggregate. For each one, a case study has been carried out to validate the results by comparing them with empirical data in the first model and with data generated by numerical simulation in the second. Results show that, from a set of measured operating conditions, it is possible to identify a unique set of electrochemical parameters that fits the 3D model to the target polarisation curve. The extension of this framework can be used to systematically estimate any model parameter in order to reduce the uncertainty in 3D simulation predictions.     

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。     

MHD mixed convection of a Cu–water nanofluid flow through a channel with an open trapezoidal cavity and an elliptical obstacle

In the current work, numerical simulations are achieved to study the properties and the characteristics of fluid flow and heat transfer of (Cu–water) nanofluid under the magnetohydrodynamic effects in a horizontal rectangular canal with an open trapezoidal enclosure and an elliptical obstacle. The cavity lower wall is grooved and represents the heat source while the obstacle represents a stationary cold wall. On the other hand, the rest of the walls are considered adiabatic. The governing equations for this investigation are formulated, nondimensionalized, and then solved by Galerkin finite element approach. The numerical findings were examined across a wide range of Richardson number (0.1 ≤ Ri ≤ 10), Reynolds number (1 ≤ Re ≤ 125), Hartmann number (0 ≤ Ha ≤ 100), and volume fraction of nanofluid (0 ≤ φ ≤ 0.05). The current study's findings demonstrate that the flow strength increases inversely as the Reynolds number rises, which pushes the isotherms down to the lower part of the trapezoidal cavity. The Nu_avg rises as the Ri rise, the maximum Nu_avg = 10.345 at Ri = 10, Re = 50, ϕ = 0.05, and Ha = 0; however, it reduces with increasing Hartmann number. Also, it increase by increasing ϕ, at Ri = 10, the Nu_avg increased by 8.44% when the volume fraction of nanofluid increased from (ϕ = 0–0.05).     

Squeezed MHD tangent hyperbolic fluid flow across a sensor surface

In the present numerical study, the combined effect of temperature-dependent thermal conductivity, linear thermal radiation, and magnetic effect on shear-thinning tangent hyperbolic fluid past a sensor surface has been studied. After converting the modelled partial differential equations into ordinary differential equations by using similarity transformation, the system of equations is tackled with the aid of the shooting method. The influence of important parameters on the fluid motion and energy distribution is displayed graphically and analyzed in detail. The presented simulations depict that a significant rise in fluid velocity is noticed for an enhancement in the magnetic parameter while an opposite trend is observed for the temperature distribution. Moreover, the skin friction coefficient decreases as the squeezed flow index is increased.