Compensation of defects in printing process with histogram methods

A method of image preparation for printing reproduction is suggested. This method allows to automatically compensate transformations that occur during reproduction, by analyzing a histogram of test chart image and based on it, creating a compensation pre-correction function. It also takes into consideration the visual perception of images. Pre-correction function is applied to images at the prepress stage after all other corrections. It is aimed to compensate defects, occurring at the printing stage, caused by the process of tone value increase and restriction of tonal range reproduction. It is suggested to use a test chart, which is a gradient with an even increase of lightness in the range from 0 to 255. After printing the test chart its digital image is created by scanning. Then Gaussian filter is applied to the image with parameters according to the visual perception, and lightness distribution histogram is calculated. This histogram will have changes in lightness distribution in comparison with the original digital image. These changes will correspond to the influence of tone value increasing process during printing. The cumulative sum is calculated from the received histogram, and the pre-correction is being formed. And this precorrection applies to an image, prepared for printing in similar conditions as test chart. The algorithm was written on Python and allows to create a pre-correction using a press sheet with the test chart. It is shown that the use of the suggested method gives a positive result and doesn’t require expensive measurement equipment. Having a scanner calibrated for linear transmission of lightness and developed programming module is enough. This method was tested on electrographic printing equipment on three different types of paper. Statistic parameters of a histogram, such as mean, standard deviation and the Skewness, were used for evaluation. It is shown that the suggested method can be used as part of an automatized system based on histogram methods for image preparation before printing.

Are Large Particles of Iron Detrimental to Properties of Powder Metallurgy Steels?

It is experimentally shown that a removal of particles exceeding 100 microns in size from iron powders typically used in the fabrication of medium density powder metallurgy steels has a weak effect on apparent density, flowability and compressibility of blends as well as on density and strength of green bodies. An elimination of such particles, i.e., cutting off a heavy tail of a size distribution histogram at the 100 μm threshold, improves a compositional uniformity of sintered materials, but has no noticeable beneficial effect upon the strength of a final product, which is likely be determined by a fraction of pores and their shapes. A presence of soft pearlitic inclusions hardly matters unless their number density becomes so large that a 3D continuity (integrity) of a hard martensitic matrix is lost. This finding suggests that such an expensive preparatory step as sieving away large particles from as-received mixtures would bear no technological advantages. It was experimentally found that an attempt to lower the threshold below 100 μm noticeably worsened apparent density, flowability and compressibility.

Algorithm for measuring fiber length distributions of raw cotton and combed wool using dual-beard image method

The dual-beard image method, which has been developed in recent years as a fast and economical method for fiber length measurement, consists of dual-beard specimen preparation, image processing, fibrogram extraction, and length parameter calculations. However, one of the shortcomings of this method is that it can only produce extremely limited length parameters such as mean length, coefficient of variation, modal length, and quality length (UHML, upper half mean length). This study introduces a new algorithm for converting the dual-beard fibrogram into a length distribution histogram which can be used to calculate most of the current length parameters. The algorithm is based on the short fiber content formulae but modified by theoretical analysis and experimental comparison. The length distributions of 24 cotton samples and 12 wool samples are measured by dual-beard image method with the new algorithm, and Advanced Fiber Information System (AFIS) and Almeter are employed for comparison. Comparative analysis shows that the peaks and ranges of the distribution histograms using the dual-beard method are similar to those from the reference methods, and the shapes of histograms from difference methods match well with one another. In addition, five length parameters calculated from the dual-beard distributions are verified to be consistent with those measured by AFIS and Almeter. The new algorithm employed in the dual-beard image method avoids the differential operation which amplifies the curve error, giving the dual-beard image method the ability to output more comprehensive length information.

The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

Identifikasi Citra Daging Ayam Berformalin Menggunakan Metode Fitur Tekstur dan K-Nearest Neighbor (K-NN)

The research aimed to create a fresh chicken meat identification system to detect differences between formalin and non-formalin chicken meat based on the image of raw chicken meat. Feature extraction method used is the Feature Texture method which is included in the statistical method where the statistical calculation uses a gray degree distribution (histogram) by measuring the level of contrast, granularity, and roughness of an area from the neighboring relationships between pixels in the image then feature extraction, results feature extraction is then classified by K-NN. With the classification using K-NN results obtained high classification accuracy. The K-NN method is a very good method of dealing with the problem of recognizing complex patterns in the form of data training and processing calibration, based on very fast and high accurate literature methods more than other methods. Observation images will be carried out at various distances between the smartphone camera and chicken meat samples.

Optimal Modes for the Fabrication of Aluminum Nanopowders by the Electrical Explosion of Wires

The paper is aimed at studying the impact of initial conditions of electrical explosion of wires on energy characteristics of the explosion and some other properties of the obtained aluminum powders. Explosion modes where the energy input into the wire has the maximal level were found. These modes are optimal for fabrication of powders with the best properties. The powders have the highest value of the specific surface of 14.5 m2/g, a narrow histogram of the particle size distribution, and a narrow distribution histogram with a high polydispersity coefficient of 0.7.