This study evaluates the performance of magnetic abrasive finishing (MAF) of aluminum alloy in terms of achieving materials removal (MR). A vertical milling machine is used to perform the finishing process using a developed MAF unit that consists of an inductor made out of a 150 mm long and 20 mm diameter iron core wound with 1500 turns and 0.5 mm copper wire. The commutator and magnetic pole are attached at the top and bottom of the inductor, respectively. The required current is supplied using a DC power supply. The South Pole workpiece is a 100×50×3 mm³ plate of AA 1100 aluminum alloy, whereas the magnetic pole represented the North Pole. Pole rotational speed, applied current, and abrasive finishing time was selected as

This research deals with a shrinking method concernes with the principal components similar to that one which used in the multiple regression “Least Absolute Shrinkage and Selection: LASS”. The goal here is to make an uncorrelated linear combinations from only a subset of explanatory variables that may have a multicollinearity problem instead taking the whole number say, (K) of them. This shrinkage will force some coefficients to equal zero, after making some restriction on them by some "tuning parameter" say, (t) which balances the bias and variance amount from side, and doesn't exceed the acceptable percent explained variance of these components. This had been shown by MSE criterion in the regression case and the percent explained v

Because of the quick growth of electrical instruments used in noxious gas detection, the importance of gas sensors has increased. X-ray diffraction (XRD) can be used to examine the crystal phase structure of sensing materials, which affects the properties of gas sensing. This contributes to the study of the effect of electrochemical synthesis of titanium dioxide (TiO₂) materials with various crystal phase shapes, such as rutile TiO₂ (R-TiO₂NTs) and anatase TiO₂ (A-TiO₂NTs). In this work, we have studied the effect of voltage on preparing TiO₂ nanotube arrays via the anodization technique for gas sensor applications. The results acquired from XRD, energy dispersion spectro

This study is unique in this field. It represents a mix of three branches of technology: photometry, spectroscopy, and image processing. The work treats the image by treating each pixel in the image based on its color, where the color means a specific wavelength on the RGB line; therefore, any image will have many wavelengths from all its pixels. The results of the study are specific and identify the elements on the nucleus’s surface of a comet, not only the details but also their mapping on the nucleus. The work considered 12 elements in two comets (Temple 1 and 67P/Churyumoy-Gerasimenko). The elements have strong emission lines in the visible range, which were recognized by our MATLAB program in the treatment of the image. The percen

In this paper a refractive index sensor based on micro-structured optical fiber has been proposed using Finite Element Method (FEM). The designed fiber has a hexagonal cladding structure with six air holes rings running around its solid core.  The air holes of fiber has been infiltrated  with different liquids such as water , ethanol, methanol, and toluene then sensor characteristics like ; effective refractive index , confinement loss, beam profile of the fundamental mode, and sensor resolution are investigated by employing the FEM. This designed sensor characterized by its low confinement loss and high resolution so a small change in the analyte refractive index could be detect which is could be useful to detect the change of

This paper include the problem of segmenting an image into regions represent (objects), segment this object by define boundary between two regions using a connected component labeling. Then develop an efficient segmentation algorithm based on this method, to apply the algorithm to image segmentation using different kinds of images, this algorithm consist four steps at the first step convert the image gray level the are applied on the image, these images then in the second step convert to binary image, edge detection using Canny edge detection in third Are applie the final step is images. Best segmentation rates are (90%) obtained when using the developed algorithm compared with (77%) which are obtained using (ccl) before enhancement.

This paper proposes a new encryption method. It combines two cipher algorithms, i.e., DES and AES, to generate hybrid keys. This combination strengthens the proposed W-method by generating high randomized keys. Two points can represent the reliability of any encryption technique. Firstly, is the key generation; therefore, our approach merges 64 bits of DES with 64 bits of AES to produce 128 bits as a root key for all remaining keys that are 15. This complexity increases the level of the ciphering process. Moreover, it shifts the operation one bit only to the right. Secondly is the nature of the encryption process. It includes two keys and mixes one round of DES with one round of AES to reduce the performance time. The W-method deals with

This research depends on the relationship between the reflected spectrum, the nature of each target, area and the percentage of its presence with other targets in the unity of the target area. The changes occur in Land cover have been detected for different years using satellite images based on the Modified Spectral Angle Mapper (MSAM) processing, where Landsat satellite images are utilized using two software programming (MATLAB 7.11 and ERDAS imagine 2014). The proposed supervised classification method (MSAM) using a MATLAB program with supervised classification method (Maximum likelihood Classifier) by ERDAS imagine have been used to get farthest precise results and detect environmental changes for periods. Despite using two classificatio

    In data mining and machine learning methods, it is traditionally assumed that training data, test data, and the data that will be processed in the future, should have the same feature space distribution. This is a condition that will not happen in the real world. In order to overcome this challenge, domain adaptation-based methods are used. One of the existing challenges in domain adaptation-based methods is to select the most efficient features so that they can also show the most efficiency in the destination database. In this paper, a new feature selection method based on deep reinforcement learning is proposed. In the proposed method, in order to select the best and most appropriate features, the essential policies