篇一：3000字英文翻译
Bolt Supporting of Large-Span Soft Rockway in
Shaqu Colliery
Abstract The instability of trapezoidal I-steel support is analysed for the compound roof of main coal seam in Shaqu Colliery, and the mechanism of bolt supporting is studied. A scheme of bolt supporting has been given and put into practice,remarkable technical and economic benefits have been got.
Key words :large-span,compound roof, bolt supporting, mechanism
1.Introduction
In shaqu colliery a large coal mine mining rare coking coal in China, most roadways are laid out in main coal seam
roof of coal seam .The soft compound ,which is composed of mudstone and coal seam
contains aboundant beddings and joints. The strength of the roof is so low that its uniaxial-saturated compressive strength is only 10.7 Mpa.RQD value of coal seam and is zero ,and that of mudstone is lower than 10%. There is clay minerals in mudstone, main compositions are interbedded strata of illite and montmorillonite which will swell when soaked by water, The span of preperation roadways and gateways is wider than 4m, and that of some main roadways is over 5m. In shaqu colliery , preperation roadways and gateways were supported by trapezoidal I-steel support, the beams of which were bent and damaged, and the roadways were destroyed seriously within a short period just after excavated. Roof
controlling of Large-Span Soft Rockways in the coal seam became the key to the production and construction of shaqu colliery.
2.supporting status and instability analysis of trapezoidal I-steel supports
trapezoidal I-steel supports were used in drawing roadways,which roof span is 4.0m, floor span is 4.9m, and hight 2.95m and spacing 0.5m. Initial resistance of the supports was almost zero because it was difficult to the support beams contact the roof, even if with high quality of installation. The trapezoidal I-steel supports would not carry load until the displacement of surrounding rock excceded 80-100 mm because the supports increased very slowly. Therefore, right after excavation, the roof would bend and subside severly. Eight hours after excavation, the roof strata would break completely, and then form rock cavity. The weight of caving rock would act on the beams of supports, which forms loose rock pressure.
By calculating, the ultimate load-bearing capacity is smaller than roof pressure whether it is uniformal or concentrated, Based on the in-situ observation, inflection value of most roof reached 200-300mm. When paired supports were used, paired beams were still bent and damaged; then midprops were added, they were also destroyed. Many roof beams were stabilized only if 2-3 props had been added. The supports were damaged completely, and most of them could not be reused. The part
section of roadways had become inverted trapezoid, and the available section was far smaller than the designed section. Part of roadways was out of use because it was in the danger of serious caving.
3.Mechanism of bolt supporting
Its mechanism is to make full use of the self-load-bearing capacity of surrounding rock by bolting, and then make the surrounding rock stabilize by itself. The stability of surrounding rock depends on the equilibrium status of ground pressure, self-load-bearing capacity of surrounding rock and anchoring force of bolts. Ground pressure is to make surrounding rock deform and break; self-load-bearing capacity is the main factor to stablize surrounding rock. Anchoring force of bolts can not change the equilibrium status of the three because it is very small, compared with ground pressure and self-load-bearing capacity. And its function is to change the decreasing regularity of self-load-bearing capacity versus the deformation of surrounding rock, and balance self-load-bearing capacity against ground pressure early.
Roof pressure is the pressure acting on the roof beams when I-steel supports are used to control the roof. When roof is supported by bolts, the roof pressure change to be the pressure acting on the rock within the bolting range because this part of rock is change into self-bearing body. According to the characteristics of the roof of coal seams
can be divided into six substrata. , bolts strata
When the value of roof subsidence is zero, roof pressure is in-situ stress; then roof pressure decreases with the increase of roof subsidence. The variation of roof pressure is analyzed by FLAC, The results are shown as curve 1 in Fig.1. Wheoof subsidence reaches 19 mm, the first roof substratum begins to bearing tensile stress, then losts self-load-bearing capacity, and roof pressure decreases to 0.67Mpa. When roof subsidence reaches 40 mm, the second substratum loses self-load-bearing capacity, and roof pressure decreases to 0.16Mpa. When roof subsidence reaches 100 mm, the fourth substratum loses self-load-bearing capacity, and roof pressure decreases to0.08Mpa. In the initial stage of roof subsidence, roof pressure decreases rapidly, and in the later stage of roof subsidence, roof pressure decreases slowly and then has an increasing trend.
The self-load-bearing capacity of the roof without bolting is calculated upon the theory of laminated beam, the result are shown as curve 2 in Fig.1. When roof subsidence is zero, the self-load-bearing capacity is at its utmost value 0.0625Mpa; when roof subsidence is 100mm,roof strata have broken, most of self-load-bearing capacity has lost, and the residual self-load-bearing capacity is only 0.0375Mpa.The self-load-bearing capacity of the roof with bolting is calculated upon the theory of combined beam, the result are shown as curve 3 in Fig.1. When roof subsidence is zero, the self-load-bearing capacity is at its utmost
value 0.4Mpa; when the roof subsidence reaches 40mm the self-load-bearing capacity decreases to 0.225Mpa,and when roof subsidence reaches 100mm, the self-load-bearing capacity decreases to 0.1Mpa .
As shown in Fin. 1, the self-load-bearing capacity of roof strata without bolting is lower than roof pressure during the whole course of roof subsiding, so roof strata cave inevitably. When bolted, roof strata is changed from laminated beam into combined beam ,and the selr-load-bearing capacity increases markedly. When roof subsidence reaches 44mm, the self-load-bearing capacity exceeds roof pressure, then roof strata stabilized by itself.
4 Anchoring technology
Based on the above study of bolting mechanism, large setting resistance, high speed of resistance and high final resistance are the key technology to the large-spon soft rock roadway before roof strata detaching, which includes: (1)to improve the setting resistance increasing and achieve high speed of resistance increasing, to make the real working properties of bolts coordinate self-load-bearing properties of roof strata , which enables to make full use of the self-load-bearing capacity of roof strata; (2)to raise bolting reliability, and solve the difficult problems that anchoring force between bolts and soft rock is small and easy to lose.
4.1 Bloting scheme
篇二：本科论文 3000字外文翻译
附录A
3 Image Enhancement in the Spatial Domain
The principal objective of enhancement is to process an image so that the result is more suitable than the original image for a specific application. The word specific is important, because it establishes at the outset than the techniques discussed in this chapter are very much problem oriented. Thus, for example, a method that is quite useful for enhancing X-ray images may not necessarily be the best approach for enhancing pictures of Mars transmitted by a space probe. Regardless of the method used .However, image enhancement is one of the most interesting and visually appealing areas of image processing.
Image enhancement approaches fall into two broad categories: spatial domain methods and frequency domain methods. The term spatial domain refers to the image plane itself, and approaches in this category are based on direct manipulation of pixels in an image. Fourier transform of an image. Spatial methods are covered in this chapter, and frequency domain enhancement is discussed in Chapter 4.Enhancement techniques based on various combinations of methods from these two categories are not unusual. We note also that many of the fundamental techniques introduced in this chapter in the context of enhancement are used in subsequent chapters for a variety of other image processing applications.
There is no general theory of image enhancement. When an image is processed for visual interpretation, the viewer is the ultimate judge of how well a particular method works. Visual evaluation of image quality is a highly is highly subjective process, thus making the definition of a “good image” an elusive standard by which to compare algorithm performance. When the problem is one of processing images for machine perception, the evaluation task is somewhat easier. For example, in dealing with a character recognition application, and leaving aside other issues such as computational requirements, the best image processing method would be the one yielding the best machine recognition results. However, even in situations when a
clear-cut criterion of performance can be imposed on the problem, a certain amount of trial and error usually is required before a particular image enhancement approach is selected.
3.1 Background
As indicated previously, the term spatial domain refers to the aggregate of pixels composing an image. Spatial domain methods are procedures that operate directly on these pixels. Spatial domain processes will be denotes by the expression
g?x,y??T?f(x,y)? (3.1-1)
where f(x, y) is the input image, g(x, y) is the processed image, and T is an operator on f, defined over some neighborhood of (x, y). In addition, T can operate on a set of input images, such as performing the pixel-by-pixel sum of K images for noise reduction, as discussed in Section 3.4.2.
The principal approach in defining a neighborhood about a point (x, y) is to use a square or rectangular subimage area centered at (x, y).The center of the subimage is moved from pixel to starting, say, at the top left corner. The operator T is applied at each location (x, y) to yield the output, g, at that location. The process utilizes only the pixels in the area of the image spanned by the neighborhood. Although other neighborhood shapes, such as approximations to a circle, sometimes are used, square and rectangular arrays are by far the most predominant because of their ease of implementation.
The simplest from of T is when the neighborhood is of size 1×1 (that is, a single pixel). In this case, g depends only on the value of f at (x, y), and T becomes a gray-level (also called an intensity or mapping) transformation function of the form
s?T(r) (3.1-2)
where, for simplicity in notation, r and s are variables denoting, respectively, the grey level of f(x, y) and g(x, y)at any point (x, y).Some fairly simple, yet powerful, processing approaches can be formulates with gray-level transformations. Because enhancement at any point in an image depends only on the grey level at that point, techniques in this category often are referred to as point processing.
Larger neighborhoods allow considerably more flexibility. The general approach is to use a function of the values of f in a predefined neighborhood of (x, y) to determine the value of g at (x, y). One of the principal approaches in this formulation is based on the use of so-called masks (also referred to as filters, kernels, templates, or windows). Basically, a mask is a small (say, 3×3) 2-Darray, in which the values of the mask coefficients determine the nature of the type of approach often are referred to as mask processing or filtering. These concepts are discussed in Section 3.5.
3.2 Some Basic Gray Level Transformations
We begin the study of image enhancement techniques by discussing gray-level transformation functions. These are among the simplest of all image enhancement techniques. The values of pixels, before and after processing, will be denoted by r and s, respectively. As indicated in the previous section, these values are related by an expression of the from s = T(r), where T is a transformation that maps a pixel value r into a pixel value s. Since we are dealing with digital quantities, values of the transformation function typically are stored in a one-dimensional array and the mappings from r to s are implemented via table lookups. For an 8-bit environment, a lookup table containing the values of T will have 256 entries.
As an introduction to gray-level transformations, which shows three basic types of functions used frequently for image enhancement: linear (negative and identity transformations), logarithmic (log and inverse-log transformations), and power-law (nth power and nth root transformations). The identity function is the trivial case in which out put intensities are identical to input intensities. It is included in the graph only for completeness.
3.2.1 Image Negatives
The negative of an image with gray levels in the range [0, L-1]is obtained by using the negative transformation show shown, which is given by the expression
s?L?1?r(3.2-1)
Reversing the intensity levels of an image in this manner produces the equivalent of a photographic negative. This type of processing is particularly suited for enhancing white or grey detail embedded in dark regions of an image, especially
when the black areas are dominant in size.
3.2.2 Log Transformations
The general from of the log transformation is
s?clog(1?r) (3.2-2)
Where c is a constant, and it is assumed that r ≥0 .The shape of the log curve transformation maps a narrow range of low gray-level values in the input image into a wider range of output levels. The opposite is true of higher values of input levels. We would use a transformation of this type to expand the values of dark pixels in an image while compressing the higher-level values. The opposite is true of the inverse log transformation.
Any curve having the general shape of the log functions would accomplish this spreading/compressing of gray levels in an image. In fact, the power-law transformations discussed in the next section are much more versatile for this purpose than the log transformation. However, the log function has the important characteristic that it compresses the dynamic range of image characteristics of spectra. It is not unusual to encounter spectrum values that range from 0 to 106 or higher. While processing numbers such as these presents no problems for a computer, image display systems generally will not be able to reproduce faithfully such a wide range of intensity values .The net effect is that a significant degree of detail will be lost in the display of a typical Fourier spectrum.
3.2.3 Power-Law Transformations
Power-Law transformations have the basic from
s?cr? (3.2-3)
Where c and y are positive constants .Sometimes Eq. (3.2-3) is written asto account for an offset (that is, a measurable output when the input is zero). However, offsets typically are an issue of display calibration and as a result they are normally ignored in Eq. (3.2-3). Plots of s versus r for various values of y are shown in Fig.3.6. As in the case of the log transformation, power-law curves with fractional values of y map a narrow range of dark input values into a wider range of output values, with the
opposite being true for higher values of input levels. Unlike the log function, however, we notice here a family of possible transformation curves obtained simply by varying y. As expected, we see in Fig.3.6 that curves generated with values of y＞1 have exactly the opposite effect as those generated with values of y＜1. Finally, we note that Eq.(3.2-3) reduces to the identity transformation when c = y = 1.
A variety of devices used for image capture, printing, and display respond according to as gamma[hence our use of this symbol in Eq.(3.2-3)].The process used to correct this power-law response phenomena is called gamma correction.
Gamma correction is important if displaying an image accurately on a computer screen is of concern. Images that are not corrected properly can look either bleached out, or, what is more likely, too dark. Trying to reproduce colors accurately also requires some knowledge of gamma correction because varying the value of gamma correcting changes not only the brightness, but also the ratios of red to green to blue. Gamma correction has become increasingly important in the past few years, as use of digital images for commercial purposes over the Internet has increased. It is not Internet has increased. It is not unusual that images created for a popular Web site will be viewed by millions of people, the majority of whom will have different monitors and/or monitor settings. Some computer systems even have partial gamma correction built in. Also, current image standards do not contain the value of gamma with which an image was created, thus complicating the issue further. Given these constraints, a reasonable approach when storing images in a Web site is to preprocess the images with a gamma that represents in a Web site is to preprocess the images with a gamma that represents an “average” of the types of monitors and computer systems that one expects in the open market at any given point in time.
3.2.4 Piecewise-Linear Transformation Functions
A complementary approach to the methods discussed in the previous three sections is to use piecewise linear functions. The principal advantage of piecewise linear functions over the types of functions we have discussed thus far is that the form of piecewise functions can be arbitrarily complex. In fact, as we will see shortly, a practical implementation of some important transformations can be formulated only
篇三：3000字外文文献翻译模板
学 年 论 文
（外文文献翻译）
英文题目 Financial structure and development 译文题目 金融结构与金融发展
姓 名
学 号
所在学院管 理 学 院
专业班级
指导教师
日 期 2014 年 6 月 28 日
目 录
文献名：
作者：
文献来源：
原文………………………………………………………………………………1 译文………………………………………………………………………………10
原文：（可以保留文献的原有格式）
译文：（需要有页眉为：2011级会计专业学年论文）
免费论文网友情提示：本网站所有内容为共享上传提供，不涉及任何商业利益，本站对此不承担任何保证责任！
Copyright © www.csmayi.cn Corporation, All Rights Reserved 共享时代 共享你我他 版权所有