Effects of Different Short-Term UV-B Radiation Intensities on Metabolic Characteristics of Porphyra haitanensis

The effects of ultraviolet (UV) radiation, particularly UV-B on algae, have become an important issue as human-caused depletion of the protecting ozone layer has been reported. In this study, the effects of different short-term UV-B radiation on the growth, physiology, and metabolism of Porphyra haitanensis were examined. The growth of P. haitanensis decreased, and the bleaching phenomenon occurred in the thalli. The contents of total amino acids, soluble sugar, total protein, and mycosporine-like amino acids (MAAs) increased under different UV-B radiation intensities. The metabolic profiles of P. haitanensis differed between the control and UV-B radiation-treated groups. Most of the differential metabolites in P. haitanensis were significantly upregulated under UV-B exposure. Short-term enhanced UV-B irradiation significantly affected amino acid metabolism, carbohydrate metabolism, glutathione metabolism, and phenylpropane biosynthesis. The contents of phenylalanine, tyrosine, threonine, and serine were increased, suggesting that amino acid metabolism can promote the synthesis of UV-absorbing substances (such as phenols and MAAs) by providing precursor substances. The contents of sucrose, D-glucose-6-phosphate, and beta-D-fructose-6-phosphate were increased, suggesting that carbohydrate metabolism contributes to maintain energy supply for metabolic activity in response to UV-B exposure. Meanwhile, dehydroascorbic acid (DHA) was also significantly upregulated, denoting effective activation of the antioxidant system. To some extent, these results provide metabolic insights into the adaptive response mechanism of P. haitanensis to short-term enhanced UV-B radiation.     

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.     

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。     

火电机组一次调频性能实时监测系统

为了实时监测和评价火电机组一次调频性能，开发了火电机组一次调频性能实测监测系统。该系统具有一次调频参数异动感知、一次调频性能量化预测及自适应校正等关键功能。某机组的实际应用表明，该系统能够实现火电机组一次调频关键参数的实时监测和调频性能的准确预测。     

基于自适应LS-SVM的柴油机废气再循环冷却控制系统设计

针对当前柴油机废气再循环冷却控制系统受到时滞问题影响,导致系统开度走势与设定走势不一致、废气再循环效果差的问题,提出基于自适应LS-SVM的柴油机废气再循环冷却控制系统;以AT89C51单片机40针作为电控单元核心部件,利用位置传感器向系统发送负载信号,利用温度传感器来传输信号并反馈至D/A转换器;在D/A转换器中,反馈信号以串行或并行方式进入寄存器,并在转换后输出电流电压,控制系统阀门在排气再循环时的开启;伺服泵采用冷却系统,为系统提供液压动力源;利用自适应LS-SVM原理计算柴油机废气再循环冷却控制目标边界值,在最小二乘支持向量机基础上,利用求解线性方程组进行优化,消除了控制误差;根据控制差的大小,调整控制系统的输出值;由试验结果可知,该系统开度走势与设定开度走势一致,且与设定开度误差为1开度,说明使用该系统具有良好稳态特性,有效克服了系统中时滞问题,废气再循环效果较好。     

Real time power management strategy for fuel cell hybrid electric bus based on Lyapunov stability theorem

The fuel cell/battery durability and hybrid system stability are major considerations for the power management of fuel cell hybrid electric bus (FCHEB) operating on complicated driving conditions. In this paper, a real time nonlinear adaptive control (NAC) with stability analyze is formulated for power management of FCHEB. Firstly, the mathematical model of hybrid power system is analyzed, which is established for control-oriented design. Furthermore, the NAC-based strategy with quadratic Lyapunov function is set up to guarantee the stability of closed-loop power system, and the power split between fuel cell and battery is controlled with the durability consideration. Finally, two real-time power management strategies, state machine control (SMC) and fuzzy logic control (FLC), are implemented to evaluate the performance of NAC-based strategy, and the simulation results suggest that the guaranteed stability of NAC-based strategy can efficiently prolong fuel cell/battery lifespan and provide better fuel consumption economy for FCHEB.