One of the challenges of the production sector of the electricity industry at present is how to meet the needs of electricity consumption in the country by using and localizing new power plant units. The main purpose ...
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One of the challenges of the production sector of the electricity industry at present is how to meet the needs of electricity consumption in the country by using and localizing new power plant units. The main purpose of the presented paper is to offer an optimum arrangement for a combined diesel generator/FC/PV system for an off-grid supply of electricity in a community in Shache, China. A multi-objective scheme of the combined system is suggested with consideration of the unpredictability in solar radiation, load, and operating reserve (OR). The multi-objective optimization has been established to make the TNPC (total net present cost) and LPSP (probability of power supply loss) minimum. In this study, the decision variables are the number of diesel generators (DG), photovoltaic (PV) panels, fuel cells (FC), electrolyzers, and hydrogen tanks. To get more authentication, the impact of several criteria on the size issue has been analyzed. The incorporation of a hydrogen production system lowers the overall expense of the combined systems, according to the modeling results. Plus, the operational reserve has a greater effect on the Pareto front in comparison with solar energy and load uncertainty.
Social media has effectively shortened the time for the distribution of information, which sometimes carry news when compared to traditional methods. The convenience and affordable instant access to data with revoluti...
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In modern days, electric vehicles are quickly industrialized as well as their penetration is also increased highly, which brings more challenges for the power system. The electric vehicle charge scheduling process is ...
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In modern days, electric vehicles are quickly industrialized as well as their penetration is also increased highly, which brings more challenges for the power system. The electric vehicle charge scheduling process is vital to encourage the daily usage of the electric vehicle. However, irregular charging methods for electric vehicles may disturb voltage security areas because of their stochastic characteristics. Moreover, an electric vehicle requires recurrent charging owing to its constrained battery capacity, but it is a time-consuming process. In this article, an effective charge scheduling model is devised using the fractional social sealionoptimization (Fr-SSLO) algorithm. At first, IoEV network is simulated along with charge station and electric vehicle location. Furthermore, multi aggregator-based charge scheduling is done for increasing the profit and amount of scheduled electric vehicles. Then, routing is performed based on developed Fr-SSLO algorithm. Moreover, several fitness measures, including distance, energy and variable energy purchase are included. Here, the devised Fr-SSLO model is designed by integrating fractional calculus (FC) and sealionoptimization (SLnO) technique along with SOA. After the completion of routing process, charge scheduling is performed based on developed Fr-SSLO approach. Moreover, various fitness functions are also considered for computing better performance.
The most significant issue with diabetes is diabetic retinopathy (DR), which is the primary cause of blindness. DR typically develops no symptoms at the beginning of the disease, thus numerous physical examinations, i...
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The most significant issue with diabetes is diabetic retinopathy (DR), which is the primary cause of blindness. DR typically develops no symptoms at the beginning of the disease, thus numerous physical examinations, including pupil dilation and a visual activity test, are necessary for DR identification. Due to the differences and challenges of DR, it is more challenging to identify it during the manual assessment. For DR patients, visual loss is prevented thanks to early detection and accurate therapy. Therefore, it is even more necessary to classify the severity levels of DR in order to provide a successful course of treatment. This study develops a deep learning method based on chronological rider sealionoptimization (CRSLO) for the classification of DR. The segmentation process divides the image into multiple subgroups, which is necessary for the appropriate detection and classification procedure. For the efficient identification of DR and classification of DR severity, the deep learning approach is used. Additionally, the CRSLO scheme is used to train the deep learning technique to achieve higher performance. With respect to testing accuracy, sensitivity, and specificity of 0.9218, 0.9304 and 0.9154, the newly introduced CRSLO-based deep learning approach outperformed other existing DR classification techniques like convolutional neural networks (CNNs), deep convolutional neural network (DCNN), synergic deep learning (SDL), HPTI-V4 and DR|GRADUATE. The Speech Enhancement Generative Adversarial Network (SEGAN) model in use also produced increased segmentation accuracy of 0.90300.
Nowadays, precise and up-to-date maps of road are of great significance in an extensive series of applications. However, it automatically extracts the road surfaces from high-resolution remote sensed images which will...
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Nowadays, precise and up-to-date maps of road are of great significance in an extensive series of applications. However, it automatically extracts the road surfaces from high-resolution remote sensed images which will remain as a demanding issue owing to the occlusion of buildings, trees, and intricate backgrounds. In order to address these issues, a robust Gradient Descent sealionoptimization-based U-Net (GDSLO-based U-Net) is developed in this research work for road outward extraction from High Resolution (HR) sensing images. The developed GDSLO algorithm is newly devised by the incorporation of Stochastic Gradient Descent (SGD) and sea lion optimization algorithm (SLnO) algorithm. Input image is pre-processed and U-Net is employed in road segmentation phase for extracting the road surfaces. Meanwhile, training data of U-Net has to be done by using the GDSLO optimizationalgorithm. Once road segmentation is done, road edge detection and road centerline detection is performed using Fully Convolutional Network (FCN). However, the developed GDSLO-based U-Net method achieved superior performance by containing the estimation criteria, including precision, recall, and F1-measure through highest rate of 0.887, 0.930, and 0.809, respectively.
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