Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 5 Articles
The rate of growth of mining copper industry in Chile requires higher consumption of water, which is a resource limited in quality and quantity and a major point of concern in present times. In addition, the efficient use of water is restricted due to high levels of evaporation (10 to 15 (l/m2) per day), in particular at the north highland mining sites (Chile). On the contrary, the final disposal of tailings is mainly on pond, which loses water by evaporation and in some cases by percolation. An alternative are the paste thickeners, which generate stable paste (70% solids), reducing evaporation and percolation and therefore reducing water make up.Water is a resource with more demand as the industries are expanding, making the water recovery processes more of a necessity than a simple upgrade in efficiency. This technology was developed in Canada (early 80s) and it has widely been used in Australia (arid zones with similar weather conditions to Chile), although few plants are using this technology. The tendency in the near future is to move from open ponds to paste thickeners. One of the examples of this is Minera El Tesoro. This scenario requires\ndeveloping technical capacity in both paste flow characterization and rheology modifiers (fluidity enhancer) in order to make possible the final disposal of this paste. In this context, a new technique is introduced and experimental results of fluidity modifiers are discussed. This study describes how water content affects the flow behavior and depositional geometry of tailings and silica flour pastes. The depositional angle determined from the flume tests, and the yield stresses is determined from slump test and a rheological model. Both techniques incorporate digital video and image analysis. The results indicate that the new technique can be incorporated in order to determine the proper solid content and modifiers to a given fluidity requirement. In addition, the experimental results showed that the pH controls strongly the fluid paste behavior....
Digital cameras with a single sensor use a color filter array (CFA) that captures only one color component in each pixel. Therefore,\nnoise and artifacts will be generated when reconstructing the color image, which reduces the resolution of the image. In this paper,\nwe proposed an image demosaicing method based on generative adversarial network (GAN) to obtain high-quality color images.\nThe proposed network does not need any initial interpolation process in the data preparation phase, which can greatly reduce the\ncomputational complexity.Thegenerator of the GAN is designed using the U-net to directly generate the demosaicing images.The\ndense residual network is used for the discriminator to improve the discriminant ability of the network. We compared the\nproposed method with several interpolation-based algorithms and the DnCNN. Results from the comparative experiments proved\nthat the proposed method can more effectively eliminate the image artifacts and can better recover the color image....
In the era of big data, images and videos are one of the main means of information dissemination. In recent years, research on the\nproblem of image and video reorganization and integration has become a hot topic in digital image processing technology. Using a\ncomputer for image processing, complicated programming is unavoidable. Therefore, it is necessary to optimize the interactive\nalgorithms for image processing. In this paper, the content of image processing experiment is screened and integrated, and an\nimage processing experiment system based on Matlab GUI platform is established for different levels of image processing\nknowledge modules. In order to verify the effectiveness and practicability of the optimization algorithm proposed in this paper,\nexperimental simulations were performed on complex natural images and complex human eye images. The speed of the USB\ncamera is generally between 15 frames/second and 25 frames/second, and in a.....................
With the development of remote sensing technology, the application of hyperspectral images is becoming more and more\nwidespread. The accurate classification of ground features through hyperspectral images is an important research content and\nhas attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral\nimages. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification,\nsemisupervised classification, and unsupervised classification....
Due to the importance of underwater exploration in the development and utilization of deep-sea resources, underwater\nautonomous operation is more and more important to avoid the dangerous high-pressure deep-sea environment. For\nunderwater autonomous operation, the intelligent computer vision is the most important technology. In an underwater\nenvironment, weak illumination and low-quality image enhancement, as a preprocessing procedure, is necessary for underwater\nvision. In this paper, a combination of max-RGB method and shades of gray method is applied to achieve the enhancement of\nunderwater vision, and then a CNN (Convolutional Neutral Network) method for solving the weakly illuminated problem for\nunderwater images is proposed to train the mapping relationship to obtain the illumination map. After the image processing, a\ndeep CNN method is proposed to perform the underwater detection and classification, according to the characteristics of\nunderwater vision, two improved schemes are applied to modify the deep CNN structure. In the first scheme, a.....................
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