Finding the reliability expression of different substation configurations can help design a distribution system with the best overall reliability. This paper presents a computerized and implemented algorithm, based on Disjoint Sum of Product (DSOP) algorithm. The algorithm was synthesized and applied for the first time to the determination of reliability expression of a substation to determine reliability indices and costs of different substation arrangements. It deals with the implementation and synthesis of a new designed algorithm for DSOP implemented using C/C??, incorporating parallel problem solving capability and overcoming the disadvantage of Monte Carlo simulation which is the lengthy computational time to achieve satisfactory statistical convergence of reliability index values. The major highlight of this research being that the time consuming procedures of the DSOP solution generated for different substation arrangements using the proposed method is found to be significantly lower in comparison with the time consuming procedures of Monte Carlo-simulation solution or any other method used for the reliability evaluation of substations in the existing literature such as meta-heuristic and soft computing algorithms. This implementation gives the possibility of RBD simulation for different substation configurations in C/C?? using their path-set Boolean expressions mapped to probabilistic domain and result in simplest Sum of Disjoint Product which is on a one-to-one correspondence with reliability expression. This software tool is capable of handling and modeling a large, repairable system. Additionally, through its intuitive interface it can be easily used for industrial and commercial power systems. With simple Boolean expression for a configuration's RBD inputted, users can define a power system utilizing a RBD and, through a fast and efficient built-in simulation engine, the required reliability expressions and indices can be obtained. Two case studies will be analyzed in this paper. The effects of different substation configurations on the reliability are analyzed and compared. Then, the reliability of a radial distribution system will be evaluated using DSOP solution. The failure results will be combined with a load flow scenario, and indices such as SAIDI, SAIFI will be determined.

No abstract

Steel is the most important engineering and construction material in the world. It is used in all aspects of our lives. But as every metal is can be defected and then will not be useful by the consumer Steel surface inspection has seen an important attention in relation with industrial quality of products. In addition, it has been studied in different methods based on image classification in the most of time, but these can detect only such kind of defects in very limited conditions such as illumination, obvious contours, contrast and noise...etc. In this paper, we aim to try a new method to detect steel defects this last depend on artificial intelligence and artificial neural networks. We will discuss the automatic detection of steel surface defects using the convolutional neural network, which can classify the images in their specific classes. The steel we are going to use will be well-classified weather the conditions of imaging are not the same, and this is the advantage of the convolutional neural network in our work. The accuracy and the robustness of the results are so satisfying.

A serious global problem called COVID-19 has killed a great number of people and rendered many projects useless. The obtained individual's identification at the appropriate time is one of the crucial methods to reduce losses. By detecting and recognizing contaminated individuals in the early stages, artificial intelligence can help many associations in these situations. In this study, we offer a fully automated method to identify COVID-19 from a patient's chest X-ray images without the need for a clinical expert's assistance. A new dataset was released, which consists of 300 chest X-ray images from 100 healthy individuals, 100 individuals who were infected with Covid 19, and 100 images of viral pneumonitis. 100 more for testing, too. In order to attain an F1 score of 0.98, a Recall of 0.98, and also an Accuracy of 0.98 with this dataset, a classification method deep learning-based learning algorithm DenseNet-121, transfer learning, as well as data augmentation techniques were implemented. Therefore, even though there are not enough training photos, these findings are far better than other state-of-the-art.

:Substations are the weakest link between the source of supply and the customer load points in a power system, because they comprise switching arrangements that would lead to loss of load. Finding the reliability expression of different substation configurations can help design a system with the best overall reliability. This paper presents a computerized and implemented algorithm, based on disjoint sum of product (DSOP)