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英语小论文翻译

英语小论文翻译
英语小论文翻译

一、英文原文

Compensation of temperature drift of MEMS

gyroscope using BP neural network

Qintuo Zhang,Zhenfan Tan,Lidong Guo

College f Automation

Harbin Engineering university

Nantong Street 145,Harbin,Heilongjiang,China Abstract:In order to solve the temperature change on the impact of MEMS gyroscope output, through self-development MEMS IMU temperature test, we used BP neural network to predict the temperature drift of MEMS gyroscope and compensation it. After using BP neural network to compensate three gyroscopes, we compared the results with the traditional least-squares method that the output error variance respectively reduced from 0.4242, 0.3506 and 0.4335 to 0.0758, 0.1024 and 0.1122. The results indicate the correctness and validity of the method, so as to offer a new method to improve the performance of MEMS gyroscope .

Keywords:MEMS Gyroscope; BP Neural Network; Temperature Drift

I. INTRODUCTION

In recent years, a promising technology, micro-electromechanical systems (MEMS) based inertial sensors has been developed[9], however, due to the small size and weight of it, the performance characteristics are highly dependent on the temperature variations[1]. Due to the production process and other reasons, the gyroscope frequency or AGC are tested to simulate MEMS gyroscope temperature changes, which inevitably bring additional error [2-3]. We researched on ADXL150 MEMS gyroscope made by Analog Devices Inc, and which can give the temperature information of it, the temperature coefficient is 8.4mv/°, while its sensitivity is only 12.5mv/°/s. Therefore, it’s necessary and practical to compensate temperature drift through temperature test. Some researchers focuses on the impact of temperature on the MEMS gyroscope zero bias, in which the zero bias voltage is converted to angular rate value, but it is not clear how to select the scale factor with temperature changes[4]; Reference[5] shows temperature impact on scale factor is not as important as on zero bias of gyroscope; Reference[7] presents the system identification of MEMS vibratory gyroscope designed by JPL, and analyze gyroscope error through changing structural resonant frequency made by temperature. In this paper, BP neural network was used to predict temperature drift of gyroscope, through in-hand MEMS IMU temperature test, the results show that the method is obviously superior to the traditional least-squares method.

II. METHODOLOGY

A. Temperature Character of MEMS Gyroscope

First, with the change of temperature, changes of microstructure of gyroscope mainly two kinds: the change in size and change in material elastic modulus. Because change in size with temperature has little effect on the performance of gyroscope, here

mainly discuss the elastic modulus changes.

Changes in elastic modulus will change the system stiffness, the resonant frequency of the gyroscope will change with the system stiffness. Therefore, temperature changes will lead to resonant frequency drift.

Elastic modulus varies with temperature linearly as follows[8]:

Where E(T) , E0 is elastic modulus for the silicon material at temperature of T and 0 T respectively.

System stiffness is proportional to elastic modulus:

Where K , K0 is system stiffness at temperature of T and T0 respectively.

We can get the formula between resonant frequency of gyroscope with temperature as follows:

Where Wn(T)is resonant frequency of gyroscope at temperatureT .

Change of resonant frequency of gyroscope impact on the driven mode and test mode of it, so we can get the output of driven axis:

Where y(t) is output of driven axis and F is driven force[5].

B. BP Neural Networkthe

Minsky and Papert showed a two layer feed-forward network which can overcome many restrictions, but did not present a solution to the problem of how to adjust the weights from input to hidden units in 1969. An answer to this question was presented by Rumelhart, Hinton and Williams in 1986, and similar solutions appeared to have been published earlier. The central idea behind this solution is that the errors for the units of the hidden layer are determined by back-propagating the errors of the units of the output layer. For this reason the method is often called the back-propagation learning rule. Back-propagation can also be considered as a generalization of the delta rule for non-linear activation functions and multilayer networks.

A feed-forward network has a layered structure. Each layer consists of units which receive their input from units from a layer directly below and send their output to units in a layer directly above the unit. There are no connections within a layer.

Multi-layer neural network model using BP algorithm is commonly known as the BP algorithm. Multi-layer model topology is shown as Fig.1. Network consists of input layer, hidden layer and output layer nodes. Hidden layer can also be multi-layer, the former layer after layer of nodes to connect through the connection weights, that is, topological structure of directed acyclic graph feed-forward network.

III. TEST SETUP

Before In this paper, a self-development MEMS IMU(Fig.2) was chosen for the trial, in which gyroscope is ADXRS150, its performance shows as Table I. Three gyroscopes were installed orthogonally as shown in Fig.3. As temperature of gyroscope is not the temperature of environment, so temperature in this paper is the internal temperature of gyroscope, which was got by data acquisition circuit. At the same time, impact of temperature on scale factor of every gyroscope was negligible. We obtained scale factors of MEMS gyroscopes installed in each axis through calibration tests , which are x k = 11.9665mv/ ° / s, y k = 11.8281 mv / ° / s and z k = 12.1239 mv / ° / s. We put the entire MEMS IMU into temperature chamber with the environment temperature was changed from -20 ° C to 20 °C[6]. In order to make the gyroscopes temperature close to environment temperature, we began to record data 30 minutes after the system started.

Figure 1. Multi-layer Forward BP Network

TABLE I Specifications for ADXRS150 gyro

Figure 2. MEMS IMU

Figure 3.Installment of MEMS gyroscopes

IV. TEST RESULTS

Before using BP neural network, Here we first give three gyroscopes output varies time as show in Fig.4temperature versus time curve as shown in Fig.5 and Fig.6 shows gyroscope’s output varies with temperature.

Figure 4. Gyroscope output

Figure 5.Temperature vs.time

Temprature/degC

Figure 6. Gyroscope output vs. Temperature

We used a single hidden layer design in this article. In the MATLAB neural network toolbox, we apply the function newff to establish BP network. Network transfer function of hidden layer neurons using symmetric tangent function tansig, and the output layer neurons use a linear function of the transfer function purelin, this is because we must first return a handle, and a linear function of the network just to meet the output requirements. Levenberg-Marquardt algorithm for network training is corresponds to trainlm function in MATLAB toolbox. Here are some parameters we chose in BP neural network prediction.

a) Largest training times 1000

b) 3 training targets 0.001

c) Shows number of train travel stack 100

d) The number of hidden layer neurons 6

BP neural network prediction is shown below as Fig .7, where the blue line represents the gyroscopes origin output, while green line represents prediction of it. The three gyroscopes forecast error variances are as follows: 0.0758, 0.1024 and 0.1122.

To illustrate the superiority of this method, here we give the results forecasted by traditional least-squares method as Fig.8 shows. Here we chose second –degree polynomial. In Fig.8, blue line represents outputs of gyroscopes and green line represents prediction of least-squares. Three gyroscopes forecast error variance are 0.4242, 0.3506 and 0.4335 respectively.

Temperature/degC

Figure 7. Forecast curve of BP neural network

Temprature/degC

Figure8. Least-squares curve

V. CONCLUSIONS

Temperature has a great impact on zero-bias of MEMS gyroscope, and is showing a non-linear characteristic. This paper presents a BP neural network to forecasting the temperature drift. Test proved that the neural network can compensate temperature drift effectively, with the use of neural network training data, gyroscope and temperature data was directly input to the neural network, and then output data would be gotten. Through the neural network model, high precision simulation models could be out. The whole process is stable, relatively fast speed, and finally the results compared with traditional least-squares method, the estimation error variance from the gyroscope reduced from 0.4242, 0.3506 and 0.4335 to 0.0758, 0.1024 and 0.1122. Through the software compensation method, the method can successfully reduce the temperature drift of the gyroscope. Deficiency is that the text does not consider the scale factor affected by temperature as well as gyro temperature drift characteristics in dynamic cases.

ACKNOWLEDGMENT

The authors would like to thank Beijing Aerospace Control Instrument Research Institute for providing temperature chamber as well as hardware interfaces to collect test data given in this paper.

二、英文翻译

基于BP神经网络算法的MEMS陀螺仪的温度漂移补偿

谭振藩,张勤拓,郭立冬

自动化学院

哈尔滨工程大学

南通街145号,哈尔滨,黑龙江,中国

摘要:为了解决温度变化对MEMS陀螺仪输出的影响,通过对自主开发的MEMS IMU的温度测试,我们使用BP神经网络对MEMS陀螺仪的温度漂移进行补偿。利用BP神经网络补偿三个陀螺仪后,我们将对三个陀螺仪补偿后与用传统的最小二乘法补偿的输出误差的方差相比,分别从0.4242,0.3506和0.4335减少为0.0758,0.1024和0.1122。结果表明该方法的正确性和有效性,从而提供了一种新方法来提高MEMS陀螺仪的性能。

关键词:MEMS陀螺仪;BP神经网络;温度漂移

1 引言

近年来,一种有前景的技术,以微机电系统(MEMS)为基础的惯性传感器已被开发的[9],但是,由于它的小尺寸和重量、性能特征是高度依赖于温度的变化[1]。由于生产工艺过程等原因,对陀螺仪的频率或AGC进行测试来以模拟MEMS陀螺仪的温度变化,这不可避免地带来另外的误差[2][3]。我们研究由ADI 公司生产的ADXL150 MEMS陀螺仪,它可以给它的温度信息,温度系数为8.4mv /度,而它的灵敏度只有12.5mV/°/ s的。因此,通过温度测试进行温度漂移的补偿是必要和实际的。

一些研究人员集中于MEMS陀螺仪的零偏置(零偏置电压被转换为角速度值)随温度的影响,但随温度的变化它并不清楚如何选择比例因子[4],参考文献[5]表明陀螺仪零偏比比例因子更容易受温度影响,重要文献[7]提出了系统鉴定中由喷气推进实验室设计的MEMS振动陀螺仪,对由于温度的变化改变结构,共振频率的陀螺仪进行误差分析。在本文中,通过手持MEMS IMU的温度测试,用BP神经网络来补偿陀螺仪的温度漂移,结果表明,该方法明显优于传统的最小二乘法。

2 方法论

A. MEMS陀螺的温度特性

首先,随着温度的变化,陀螺仪的微观结构的变化主要是两种:材料尺寸的变化和弹性模量的变化。因为尺寸随温度的变化对陀螺仪的性能影响不大,这里主要讨论弹性模量的变化。

弹性模量的变化将改变系统的刚度,陀螺仪的谐振频率会随系统刚度而变化。因此,温度变化会导致谐振频率漂移。弹性模量随温度变化的线性如下[8]:

其中,E(T),E0是分别为硅材料在温度T和T0为的弹性模量。

系统刚度与弹性模量成比例:

其中K,K0系统分别在温度为T和T0的刚度。

我们可以得到陀螺仪谐振频率随温度的变化,如下所示:

Wn(T)是陀螺仪在温度为T时的谐振频率。

陀螺仪的谐振频率影响它的驱动模式和测试模式,所以我们可以得到驱动轴的输出:

其中,y(t)是驱动轴输出和F是驱动力[5]。

B. BP神经网络

明斯基和帕尔特于1969年显示出可以克服许多限制但从输入到隐藏单元并没有提出一个如何调整权重的解决方案的两层前馈网络。由鲁梅哈特,韩丁和威廉姆斯在1986年提出了这个问题的答案,似乎和早些时候发表的解决方案类似。此解决方案背后的核心思想是为单位隐藏层单元的误差由反向传播的输出层单元的误差传来决定。出于这个原因,该方法通常被称为反向传播学习规则。由于非线性激活功能和多层网络的的泛化,反向传播也可以归纳为delta规则的概括。

前馈网络具有层状结构。每层是由一些神经元构成的,这些神经元直接接收前一层神经元的输入和发送后一层神经元的输出。具有分层结构。

采用BP算法的多层神经网络模型通常被称为BP算法。多层模型拓扑结构如图1所示。网络由输入层,隐含层和输出层节点构成。隐层也可以是多层,前后层的节点通过网络权值来连接,即前馈网络的拓扑结构为有向无环图。

3 测试设置

在本文的前面,选用自己设计的MEMS IMU(图2)做试验,ADXRS150陀螺仪的性能参数如表一,三个陀螺仪被垂直安装,如图3所示。由于陀螺仪的温度不是环境温度,所以在本文中的温度是陀螺仪的内部温度,由数据采集电路得到。同时,温度对每一个陀螺仪标度因子的的影响是可以忽略不计的。在每次的校准实验中我们得到了每个轴的标度因子,其中,K X=11.9665mv/°/s,K Y=11.8281 mv/°/s和K Z=12.1239 mv /°/s。我们把整个MEMS IMU放在温度室,让环境温度从-20°C变化20°C[6]。为了使陀螺仪温度接近环境温度,我们将在系统启动30分钟后开始记录数据。

图1 多层正向BP网络

图2 MEMS 测量单元

图3 MEMS陀螺仪的安装

4 测试结果

现我们首先给出在利用BP神经网络之前三个陀螺仪的输出随时间的关系如图4所示,图5显示了温度随时间变化的曲线,图6显示了陀螺仪的输出随温度变化。

图4 陀螺仪的输出随时间的关系

图5 温度与时间

图6 陀螺仪输出与温度

本文使用了一个单隐层设计。在MATLAB神经网络工具箱中,我们应用newff 函数建立BP网络。隐藏层神经元的网络传递函数使用对称正切函数tansig,输出层神经元采用传递函数purelin的线性函数,这是因为我们必须首先返回网络的句柄和一个线性函数来满足输出要求。Levenberg-Marquardt网络训练算法与MATLAB工具箱中的trainlm函数对应。在BP神经网络预测中,一些参数的选择如下:

a) 最大的培训时间1000

b)3次训练目标目标0.001

c)显示火车旅行堆栈数100

d)在隐层神经元数6

BP神经网络预测如图7所示,蓝线代表陀螺仪的原始输出,绿线代表预测输

出。如下三个陀螺仪预测误差的方差分贝为:0.0758,0.1024和0.1122。

为了说明该方法的优越性,这里我们给出传统最小二乘法预测的结果,如图8所示。在这里,我们选择了二次多项式拟合。在图8中,蓝线代表的陀螺仪的输出和绿线代表最小二乘预测输出。三个陀螺仪预测误差的方差分别0.4242,0.3506和0.4335。

图7 BP神经网络预测曲线

图8 最小二乘曲线

5 结论

温度对MEMS陀螺仪的零偏有很大的影响,并且温度对零偏的影响是非线性。本文提出了用BP神经网络预测陀螺仪的温度漂移。试验证明,神经网络可以有效地补偿温度漂移。陀螺仪数据和温度直接被输入到神经网络,利用神经网络训练数据,然后将得到输出数据。通过神经网络模型,高精度仿真模型出现了。整个过程是稳定的,速度也比较快,陀螺仪最后的结果与传统的最小二乘法相比较,估计误差的方差从0.4242,0.3506和0.4335分别减少为0.0758,0.1024和0.1122。通过软件补偿的方法可以明显地减少陀螺仪的温度漂移。本文的不足之处是没有考虑标度因子受温度的影响以及在动态情况下陀螺仪的温度漂移特性。

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英文论文及中文翻译

International Journal of Minerals, Metallurgy and Materials Volume 17, Number 4, August 2010, Page 500 DOI: 10.1007/s12613-010-0348-y Corresponding author: Zhuan Li E-mail: li_zhuan@https://www.wendangku.net/doc/b61099818.html, ? University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg 2010 Preparation and properties of C/C-SiC brake composites fabricated by warm compacted-in situ reaction Zhuan Li, Peng Xiao, and Xiang Xiong State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China (Received: 12 August 2009; revised: 28 August 2009; accepted: 2 September 2009) Abstract: Carbon fibre reinforced carbon and silicon carbide dual matrix composites (C/C-SiC) were fabricated by the warm compacted-in situ reaction. The microstructure, mechanical properties, tribological properties, and wear mechanism of C/C-SiC composites at different brake speeds were investigated. The results indicate that the composites are composed of 58wt% C, 37wt% SiC, and 5wt% Si. The density and open porosity are 2.0 g·cm–3 and 10%, respectively. The C/C-SiC brake composites exhibit good mechanical properties. The flexural strength can reach up to 160 MPa, and the impact strength can reach 2.5 kJ·m–2. The C/C-SiC brake composites show excellent tribological performances. The friction coefficient is between 0.57 and 0.67 at the brake speeds from 8 to 24 m·s?1. The brake is stable, and the wear rate is less than 2.02×10?6 cm3·J?1. These results show that the C/C-SiC brake composites are the promising candidates for advanced brake and clutch systems. Keywords: C/C-SiC; ceramic matrix composites; tribological properties; microstructure [This work was financially supported by the National High-Tech Research and Development Program of China (No.2006AA03Z560) and the Graduate Degree Thesis Innovation Foundation of Central South University (No.2008yb019).] 温压-原位反应法制备C / C-SiC刹车复合材料的工艺和性能 李专,肖鹏,熊翔 粉末冶金国家重点实验室,中南大学,湖南长沙410083,中国(收稿日期:2009年8月12日修订:2009年8月28日;接受日期:2009年9月2日) 摘要:采用温压?原位反应法制备炭纤维增强炭和碳化硅双基体(C/C-SiC)复合材

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英语翻译论文开题报告 时间:2015-03- 12Bell.?Translation?and?Translating:?Theory?and?Practice.?Beijing:?Forei gn?Language?Teaching?and?Research?Press,?2006. 崔长青,?张碧竹.?翻译的要素[M].?苏州:?苏州大学出版社,?2007. 李琏.?英式显性词性转换与英语写作[J].?新疆教育学学 报,?2003,?19(1):?85-89. 李连生.?英汉互译中的词性转换[J].?武汉交通管理干部学院学 报,?1996,?(1):102-107. 项伙珍.?谈翻译中的转性译法[J].?长江职工大学学报,?2000,?17(3):?46-48. 叶海燕.?翻译中的词性转换及换形[J].?安徽工业大学学报(社会科学 版),?2005,?22(3):60-61. (责任编辑:1025) 三、对英文翻译中词类转换的引入 从语言的角度来分析,对一门语言中的词性以及在语句中的成分进行分析,?从转喻理论的相关知识出发,来探讨英汉语言翻译中词类转换的概念和知识,并就其异同和特性进行对比,不仅是当前国内外学者研究的重点问题,也是大学英语教学中始终关注的热点。?为?此?,从英文翻译的实践中,?通过例证或典型问题的互译,从语言结构及表达习惯上进行探讨英文翻译中的转换方法,以增强语言翻译的可读性和准确性,并从中探讨出词类转换的规律,帮助更多的学生从中获得有益的指

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