Effect of Perma-Zyme on Properties of Tabia Used to Protect Ancient Earthen Ruins

2011 ◽  
Vol 243-249 ◽  
pp. 327-334
Author(s):  
Hong Tao Peng ◽  
Qi Zhang ◽  
Nai Sheng Li ◽  
De Fa Wang

Perma-Zyme is an enzymatic stabilizer. To test the feasibility of adding Perma-Zyme to tabia used to protect ancient earthen ruins, the 28d unconfined compression strength, permeability and color-difference were tested to determine the difference between the tabia and tabia treated with Perma-Zyme. The experimental results show that the 28d unconfined compression strengths of specimens treated with Perma-Zyme are higher than those without Perma-Zyme, and the color-difference(DE*) between the tabia and tabia mixed Perma-zyme is larger than 3(suggested color tolerance Chinese standardized GB/T 15608-2006), and the permeability coefficient of tabia mixed Perma-Zyme is smaller than that without Perma-Zyme, i.e., has better impermeability. Box dimension values of SEM images(with different scale bars) of samples were computed by box-counting method. Box dimension values of SEM images of the same sample are different on different scale bars. The analysis shows that SEM microstructure of the tabia sample treated with Perma-Zyme is finer and denser than the one without Perma-Zyme.

2011 ◽  
Vol 280 ◽  
pp. 5-8
Author(s):  
Hong Tao Peng ◽  
Qi Zhang ◽  
Nai Sheng Li ◽  
De Fa Wang

The lime-stabilized soil was mixed with glutinous rice paste in proper proportion to determine the difference in compressive strength caused by introduction of glutinous rice paste. The experimental results show that the unconfined compressive strengths of lime-stabilized soil specimens treated with glutinous rice paste are all higher than those without treated at different curing times (7d, 28d, 40d, and 60d). The calculated fractal box dimension value of SEM image of lime stabilized soil sample is close to and slightly less than the one treated with glutinous rice paste. The SEM images show that the microstructure of lime-stabilized soil treated with glutinous rice paste is denser than that without treated. This kind of denser microstructure should be the basis of higher unconfined compressive strengths of the specimens treated with glutinous rice paste.


2003 ◽  
Vol 40 (4) ◽  
pp. 409-415 ◽  
Author(s):  
Jack C. Yu ◽  
Ronald L. Wright ◽  
Matthew A. Williamson ◽  
James P. Braselton ◽  
Martha L. Abell

Objectives Many biological structures are products of repeated iteration functions. As such, they demonstrate characteristic, scale-invariant features. Fractal analysis of these features elucidates the mechanism of their formation. The objectives of this project were to determine whether human cranial sutures demonstrate self-similarity and measure their exponents of similarity (fractal dimensions). Design One hundred three documented human skulls from the Terry Collection of the Smithsonian Institution were used. Their sagittal sutures were digitized and the data converted to bitmap images for analysis using box-counting method of fractal software. Results The log-log plots of the number of boxes containing the sutural pattern, Nr, and the size of the boxes, r, were all linear, indicating that human sagittal sutures possess scale-invariant features and thus are fractals. The linear portion of these log-log plots has limits because of the finite resolution used for data acquisition. The mean box dimension, Db, was 1.29289 ± 0.078457 with a 95% confidence interval of 1.27634 to 1.30944. Conclusions Human sagittal sutures are self-similar and have a fractal dimension of 1.29 by the box-counting method. The significance of these findings includes: sutural morphogenesis can be described as a repeated iteration function, and mathematical models can be constructed to produce self-similar curves with such Db. This elucidates the mechanism of actual pattern formation. Whatever the mechanisms at the cellular and molecular levels, human sagittal suture follows the equation log Nr = 1.29 log 1/r, where Nr is the number of square boxes with sides r that are needed to contain the sutural pattern and r equals the length of the sides of the boxes.


2001 ◽  
Vol 38 (6) ◽  
pp. 1201-1212 ◽  
Author(s):  
Zon-Yee Yang ◽  
Jian-Liang Juo

In fractal theory, the fractal dimension (D) is a measure of the complexity of particle distribution in nature. It can provide a description of how much space a particle set fills. The box-counting method uses squared grids of various sizes to cover the particles to obtain a box dimension. This sequential counting concept is analogous to the sieve analysis test using stacked sieves. In this paper the box-counting method is applied to describe the particle-size distribution of gravelly cobbles. Three approaches to obtain the fractal dimension are presented. In the first approach the data obtained from a classic laboratory sieve analysis are rearranged into a double-logarithmic plot, according to a fractal model, to obtain the fractal dimension of the particle collection. In addition, an equivalent number of covered grids on each sieve during the sieve analysis are counted to produce the box dimension. According to the box-counting method concept, a photo-sieving technique used in scanning electron microscope microstructure analysis is adopted for use on gravelly cobbles in the field. The box-counting method concept is capable of explaining the sieve analysis data to clarify the information on the particle-size distribution. Using photo-sieving to produce the fractal dimension from field photographs can provide another approach for understanding the particle-size distribution. However, the representative cross-profile should be chosen carefully. The composition of the particle-size distribution for gravelly cobbles with higher D values is more complicated than those at sites with smaller D values.Key words: sieve analysis, box-counting method, fractal dimension, particle-size distribution, gravelly cobbles.


Fractals ◽  
1996 ◽  
Vol 04 (04) ◽  
pp. 463-468 ◽  
Author(s):  
TAKASHI SATO ◽  
MAKOTO MATSUOKA ◽  
HIDEKI TAKAYASU

We construct color map images of fractal dimension distribution from natural scenes and medical images by applying the box-counting method locally. The map images clearly show the difference between clouds and rocks, as well as between cancer parts and normal tissue in the colon. The method is simple and may be expected to be applicable to a real-time video-data processing.


Arena Tekstil ◽  
2018 ◽  
Vol 33 (2) ◽  
Author(s):  
Andrian Wijayono ◽  
Valentinus Galih Vidia Putra

Stitch per inch is defined as the one of many quality parameters which evaluated in the garment industry. The strength of the stitch will be determined by stitch per inch, both on the clothes and fabrics. Conventionally, stitch per inch determined by using a traditionaly visual method. Many researchers have been developed the image processing technology which applied into various textile fields. In this reserach, it has been developed a new method and a new software which could determine stitch per inch on fabrics using the image processing techniques. Stitch per inch measurement has been done using the box counting method (pixel) on image processing software. Stitch per inch in several fabrics (with different color and structures) has been measured, which shows that the value of the each methods are equal


2020 ◽  
Author(s):  
Hee Sook Woo ◽  
Kwang Seok Kwon ◽  
Byung Guk Kim

<p>Coastline extraction and decisions have important implications for efficient land management and national policy formulation. Therefore, shorelines should be determined in a reasonable manner, and consistent results should be produced for the same area. This must be calculated efficiently. For example, simple shoreline areas should be constructed using relatively large vertex intervals (point-to-point distances) for efficiency, while complex shoreline areas should be constructed using small vertex intervals, thus improving accuracy. In this study, we suggest an optimum vertex interval that can represent more than 99.7% (3σ) of the original shoreline data using a grid generated by applying a box-counting method. All coastline areas were gridded using 11 grid sizes. Generalization was performed on the shorelines contained within each grid, and the sum of the generalized shoreline lengths was calculated. As the grid size used increases, the shoreline will become more simplified, and the difference from the original data will increase. As the grid size decreases, the more precisely the shoreline will be represented, and the sum will be similar to the original value. As a result of regression analysis, using the sum of the generalized shoreline length, we could predict the vertex interval that would represent more than 99.7% (3σ) of the original data. For the experiment, three regions with distinct coastline characteristics were selected. The grid was generated by the box-counting method, a representative fractal technique, and the vertex interval was estimated. From this, the fractal dimension was then calculated. As a result of the experiment, it was confirmed that the area A had a vertex interval of 0.7m, and the areas B and C had vertex intervals of 1m. These optimal vertex interval values mean that when the coastline was reconstructed, it was the closest, efficient representation of the actual coastline. Furthermore, these interval values suggest that the area A has a more complex coastline, and therefore the coastline should be constructed with a smaller vertex interval than the other areas. Using fractal dimensions, we also found that the area B has a more complex coastline than the area C. Overall, we confirmed that the optimal vertex interval for the accurate and efficient construction of the shoreline is able to be calculated by the approach presented in this paper. This research is expected to contribute to efficient land management and national policy establishment and progress. </p>


2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Nemanja Rajković ◽  
Bojana Krstonošić ◽  
Nebojša Milošević

This study calls attention to the difference between traditional box-counting method and its modification. The appropriate scaling factor, influence on image size and resolution, and image rotation, as well as different image presentation, are showed on the sample of asymmetrical neurons from the monkey dentate nucleus. The standard BC method and its modification were evaluated on the sample of 2D neuronal images from the human neostriatum. In addition, three box dimensions (which estimate the space-filling property, the shape, complexity, and the irregularity of dendritic tree) were used to evaluate differences in the morphology of type III aspiny neurons between two parts of the neostriatum.


2021 ◽  
Author(s):  
Nicholas Dudu ◽  
Arturo Rodriguez ◽  
Gael Moran ◽  
Jose Terrazas ◽  
Richard Adansi ◽  
...  

Abstract Atmospheric turbulence studies indicate the presence of self-similar scaling structures over a range of scales from the inertial outer scale to the dissipative inner scale. A measure of this self-similar structure has been obtained by computing the fractal dimension of images visualizing the turbulence using the widely used box-counting method. If applied blindly, the box-counting method can lead to misleading results in which the edges of the scaling range, corresponding to the upper and lower length scales referred to above are incorporated in an incorrect way. Furthermore, certain structures arising in turbulent flows that are not self-similar can deliver spurious contributions to the box-counting dimension. An appropriately trained Convolutional Neural Network can take account of both the above features in an appropriate way, using as inputs more detailed information than just the number of boxes covering the putative fractal set. To give a particular example, how the shape of clusters of covering boxes covering the object changes with box size could be analyzed. We will create a data set of decaying isotropic turbulence scenarios for atmospheric turbulence using Large-Eddy Simulations (LES) and analyze characteristic structures arising from these. These could include contours of velocity magnitude, as well as of levels of a passive scalar introduced into the simulated flows. We will then identify features of the structures that can be used to train the networks to obtain the most appropriate fractal dimension describing the scaling range, even when this range is of limited extent, down to a minimum of one order of magnitude.


2016 ◽  
Author(s):  
Kexue Lai ◽  
Tao He ◽  
Cancan Li ◽  
Weisong Zhou ◽  
Liangen Yang

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