The grounding grid is a key part of substation protection, which provides safety to personnel and equipment under normal as well as fault conditions. Currently, the topology of a grounding grid is determined by assuming that its orientation is parallel to the plane of earth. However, in practical scenarios, the assumed orientation may not coincide with the actual orientation of the grounding grid. Hence, currently employed methods for topology detection fails to produce the desired results. Therefore, accurate detection of grounding grid orientation is mandatory for measuring its topology accurately. In this paper, we propose a derivative method for orientation detection of grounding grid in high voltage substations. The proposed method is applicable to both equally and unequally spaced grounding grids. Furthermore, our method can also determine the orientation of grounding grid in the challenging case when a diagonal branch is present in the mesh. The proposed method is based on the fact that the distribution of magnetic flux density is perpendicular to the surface of the earth when a current is injected into the grid through a vertical conductor. Taking the third order derivative of the magnetic flux density, the main peak coinciding with the position of underground conductor is accurately obtained. Thus, the main peak describes the orientation of buried conductor of grounding grid. Simulations are performed using Comsol Multiphysics 5.0 to demonstrate the accuracy of the proposed method. Our results demonstrate that the proposed method calculate the orientation of grounding grid with high accuracy. We also investigate the effect of varying critical parameters of our method.

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An outbreak of coronavirus caused by a novel virus called SARS-CoV-2 occurred at the end of 2019. The unexpected outbreak and unchecked global spread of COVID-19 indicate that the current global healthcare networks have limitations in addressing the crises for public safety. The detection of infected or tested patients globally is challenging when patients travel abroad. As such, innovative technology such as blockchain has emerged as a potential approach for addressing coronavirus patient tracking. Blockchain technology can tackle pandemics by allowing early detection of outbreaks, protecting individual privacy while maintaining data security by using smart contracts. Motivated by these facts, we propose a novel blockchain-based framework that integrates intercountry for COVID-19 to track infected or tested patients globally using the methodology of design science research. The proposed framework could help governments, aviation industries, health authorities, and residents make essential decisions on infection identification, infection prediction, and infection prevention.

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Super-pixels represent perceptually similar visual feature vectors of the image. Super-pixels are the meaningful group of pixels of the image, bunched together based on the color and proximity of singular pixel. Computation of super-pixels is highly affected in terms of accuracy if the image has high pixel intensities, i.e., a semi-dark image is observed. For computation of super-pixels, a widely used method is SLIC (Simple Linear Iterative Clustering), due to its simplistic approach. The SLIC is considerably faster than other state-of-the-art methods. However, it lacks in functionality to retain the content-aware information of the image due to constrained underlying clustering technique. Moreover, the efficiency of SLIC on semi-dark images is lower than bright images. We extend the functionality of SLIC to several computational distance measures to identify potential substitutes resulting in regular and accurate image segments. We propose a novel SLIC extension, namely, SLIC++ based on hybrid distance measure to retain content-aware information (lacking in SLIC). This makes SLIC++ more efficient than SLIC. The proposed SLIC++ does not only hold efficiency for normal images but also for semi-dark images. The hybrid content-aware distance measure effectively integrates the Euclidean super-pixel calculation features with Geodesic distance calculations to retain the angular movements of the components present in the visual image exclusively targeting semi-dark images. The proposed method is quantitively and qualitatively analyzed using the Berkeley dataset. We not only visually illustrate the benchmarking results, but also report on the associated accuracies against the ground-truth image segments in terms of boundary precision. SLIC++ attains high accuracy and creates content-aware super-pixels even if the images are semi-dark in nature. Our findings show that SLIC++ achieves precision of 39.7%, outperforming the precision of SLIC by a substantial margin of up to 8.1%.

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