sadegh kalantari

Grinding in a ball mill is a process with high energy consumption; therefore, a slight improvement in its performance can lead to great economic benefit in the industry. The softness of the product of the grinding circuits prevents loss of energy in the subsequent processes. In addition, controlling the performance of a ball mill is a challenging issue due to its complex dynamic characteristics. The main purpose of this article is to use the ground particle size diagram and acoustic signal in ball mill control, and model their relationship based on least squares method. As a result, by extracting useful data from the the acoustic signal, the optimal condition of the ball mill_ in terms of ground particle size and ball mill load (normal, low, high)_ can be achieved. In doing so, this goal, in this article, innovative ideas such as adaptive quantum basis, sparse representation, SVD and PCA-based methods were used. The proposed method has been practically implemented on the ball mill of Lakan lead-zinc processing plant. Also, a prototype of the device was built. The test results show that the optimal load for the studied ball mill is 10t/h. In this case, the ground particle size is 110-120 microns which is ideal for the purposes of this plant. Also, the power spectrum is in the middle frequency band (frequency range of 300-700 Hz). According to the analysis and results, the proposed method will increase the efficiency of the studied ball mill.

The same size of the helix steps in a traveling wave tube has a direct relationship with the efficiency of the system. In this article, the goal is to build a device that measures helix steps automatically and with proper accuracy by using image processing. For this purpose, the proposed algorithm was presented along with innovations in the fields of noise removal and threshold determination for the automatic measurement of helix steps, and the prototype of the device was made. This device has the ability to work in two modes of manual and automatic measurement, and it has no domestic equivalent, and it is much cheaper in terms of cost and use in measuring helix steps than the sample made abroad. In automatic mode, this device is able to measure all helix steps at once. In the case of a similar sample made abroad the steps must be measured manually and separately. The selection of the beginning and end points of steps in this device is done adaptively, which increases the speed of measurement and increases the repeatability in measuring steps. In the sample made abroad, the selection of the start and end points of the steps is done by the user, which reduces the repeatability of the measurement. The obtained results show that the measurement accuracy of the manufactured device is acceptable compared to the sample made abroad.

Reconstruction of geological images using partial measurement is one of the most important topics in geosciences. In many methods, this is done using training images and very complex models which increase the computational complexity. In the first part of the article, a simple method based on spatial domain filters such as median and mean filter has been presented to reconstruct geological images. One of the most significant characteristics of this method is that it does not need the training image; moreover, its computational complexity is less than the other advanced methods. Via this method, it is easy to reconstruct binary, continuous, and three-dimensional images. The results show that the reconstruction accuracy of the proposed method is also acceptable. In the second part of the article, to introduce quantum computing to geosciences and encourage researchers to work on this issue, a quantum median filter is proposed to reconstruct geological images. According to the results, this method has much less computational complexity than classical methods such as DS. Also, its results are acceptable in terms of reconstruction rate. Due to the high speed of quantum algorithms and the widespread use of quantum computers in the near future, researchers in this field must become more familiar with quantum computing.

This paper aimed to formulate image noise reduction as an optimization problem and denoise the target image using matrix low rank approximation. Considering the fact that the smaller pieces of an image are more similar (more dependent) in natural images; therefore, it is more logical to use low rank approximation on smaller pieces of the image. In the proposed method, the image corrupted with AWGN (Additive White Gaussian Noise) is locally denoised, and the optimization problem of low rank approximation is solved on all fixed-size patches (Windows with pixels needing to be processed). This method can be implemented in parallelly for practical purposes, because it can simultaneously handle different image patches. This is one of the advantages of this method. In all noise reduction methods, the two factors, namely the amount of the noise removed from the image and the preservation of the edges (vital details), are very important. In the proposed method, all the new ideas including the use of TI image (Training Image) and SVD adaptive basis, iterability of the algorithm and patch labeling have all been proved efficient in producing sharper images, good edge preservation and acceptable speed compared to the state-of-the-art denoising methods.

Assuming having only one low-resolution image, the study aims to obtain an equivalent image with a higher resolution. This problem is usually referred to as “Super-resolution”. Since the number of unknown target values is far more than that of known values given in the input image, the super-resolution is a severely ill-posed problem. In this paper, a model is developed in order to narrow down the solution space. The underlying assumption in the modeling is that the image blocks, in small-enough dimensions, usually contain only one linear edge. To model this behavior, an analytical model with five parameters is presented which greatly reduces the efficient dimensionality of the unknowns. Simple and analytical formulation of this model makes it possible to convert the image super-resolution to a convex optimization problem and effectively solve the problem using the well-known gradient descent optimization method. Experiments on standard test images indicate that the proposed algorithm increases PSNRabout 0.5 to 4 dB, in comparison with other interpolation-based methods.