Due to high maintenance costs and inaccessibility, replacing batteries regularly is a major difficulty for Wireless Sensor Nodes (WSNs) in remote locations. Harvesting energy from multiple resources like sun, wind, th...
详细信息
Due to high maintenance costs and inaccessibility, replacing batteries regularly is a major difficulty for Wireless Sensor Nodes (WSNs) in remote locations. Harvesting energy from multiple resources like sun, wind, thermal, and vibration is one option. Because of its plentiful availability, solar energy harvesting is the finest alternative among them. The battery gets charged during the day by solar energy, and while solar energy is unavailable, the system is powered by the charge stored in the battery. Hence, in this paper, a highly efficient Solar Energy Harvesting (SEH) system is proposed using Leadership Promoted wildhorse Optimizer (LPWHO). LPWHO refers to the conceptual improvement of the standard wildhorseoptimization (WHO) algorithm. This research is going to focus on overall harvesting efficiency which further depends on MPPT. MPPT is used as it extracts maximal power from the solar panels and reduces power loss. The usage of MPPT enhances the extracted power's efficiency out of the solar panel when its voltages are out of sync. At last, the supremacy of the presented approach is proved with respect to varied measures.
In this manuscript, attentive dual residual generative adversarial network optimized using wild horse optimization algorithm for brain tumor detection (ADRGAN-WHOA-BTD) is proposed. Here, the input imageries are gathe...
详细信息
In this manuscript, attentive dual residual generative adversarial network optimized using wild horse optimization algorithm for brain tumor detection (ADRGAN-WHOA-BTD) is proposed. Here, the input imageries are gathered using BraTS, RemBRANDT, and Figshare datasets. Initially, the images are preprocessed to increase the quality of images and eliminate the unwanted noises. The preprocessing is performed with dual-tree complex wavelet transform (DTCWT). The image features like geodesic data and texture features like contrasts, energy, correlations, homogeneity, and entropy are extracted using multilayer dense net methods. Then, the extracted images are given to attentive dual residual generative adversarial network (ADRGAN) classifier for classifying the brain imageries. The ADRGAN weight parameters are tuned based on wild horse optimization algorithm (WHOA). The proposed method is executed in MATLAB. For the BraTS dataset, the ADRGAN-WHOA-BTD method achieved accuracy, sensitivity, specificity, F-measure, precision, and error rates of 99.85%, 99.82%, 98.92%, 99.76%, 99.45%, and 0.15%, respectively. Then, the proposed technique demonstrated a runtime of 13 s, significantly outperforming existing methods. In this manuscript, attentive dual residual generative adversarial network optimized using wild horse optimization algorithm for brain tumor detection (ADRGAN-WHOA-BTD) is proposed. Here, the input imageries are gathered using BraTS, RemBRANDT and Figshare datasets. Initially, the images are preprocessed to enhance quality and eliminate unwanted noises. The preprocessing is performed with dual-tree complex wavelet transform (DTCWT). The image features like geodesic data and texture features like contrasts, energy, correlations, homogeneity, and entropy are extracted using multilayer dense net methods. image
Distributed energy generation increases the need for smart grid monitoring, protection, and control. Localization, classification, and fault detection are essential for addressing any problems immediately and resuming...
详细信息
Distributed energy generation increases the need for smart grid monitoring, protection, and control. Localization, classification, and fault detection are essential for addressing any problems immediately and resuming the smart grid as soon as possible. Simultaneously, the capacity to swiftly identify smart grid issues utilizing sensor data and easily accessible frequency and voltage data from PMU devices is a prerequisite of this task. Therefore, this paper proposes new methods using fuzzy logic and adaptive fuzzy neural networks as well as machine learning and meta-heuristic algorithms. First, line voltage is used by a fuzzy thresholding method to estimate when a transmission line defect would develop in less than 1.2 clock cycles. Next, features taken from frequency signals in the real-time interval are utilized to classify the type of error using machine learning systems (decision tree algorithm and random forest algorithm) optimized with wildhorse meta-heuristic algorithm. To locate the precise problem location, we finally use a neural fuzzy inference system that is capable of adapting to new data. We employ a simulated power transmission system in MATLAB to test our proposed solutions. Mean square error (MSE) and confusion matrix are used to assess the efficiency of a classifier or detector. For the decision tree algorithm method, the detector attained an acceptable MSE of 2.34e-4 and accuracy of 98.1%, and for the random forest algorithm method, an acceptable MSE of 3.54e-6 and accuracy of 100%. Furthermore, the placement error was less than 153.6 m in any direction along the line.
暂无评论