Support Vector Mechanism Research Articles

A New Voltage Based Fault Detection Technique for Distribution Network Connected to Photovoltaic Sources Using Variational Mode Decomposition Integrated Ensemble Bagged Trees Approach

The increasing integration of renewable sources into distributed networks results in multiple protection challenges that would be insufficient for conventional protection strategies to tackle because of the characteristics and functionality of distributed generation. These challenges include changes in fault current throughout various operating modes, different distribution network topologies, and high-impedance faults. Therefore, the protection and reliability of a photovoltaic distributed network relies heavily on accurate and adequate fault detection. The proposed strategy utilizes the Variational Mode Decomposition (VMD) and ensemble bagged trees method to tackle these problems in distributed networks. Primarily, VMD is used to extract intrinsic mode functions from zero-, positive-, and negative-sequence components of a three-phase voltage signal. Next, the acquired intrinsic mode functions are supplied into the ensemble bagged trees mechanism for detecting fault events in a distributed network. Under both radial and mesh-soft normally open-point (SNOP) topologies, the outcomes are investigated and compared in the customarily connected and the island modes. Compared to four machine learning mechanisms, including linear discriminant, linear support vector mechanism (SVM), cubic SVM and ensemble boosted tree, the ensemble bagged trees mechanism (EBTM) has superior accuracy. Furthermore, the suggested method relies mainly on local variables and has no communication latency requirements. Therefore, fault detection using the proposed strategy is reasonable. The simulation outcomes show that the proposed strategy provides 100 percent accurate symmetrical and asymmetrical fault diagnosis within 1.25 ms. Moreover, this approach accurately identifies high- and low-impedance faults.

COVID-19 Hotspot Trend Prediction Using Hybrid Cellular Automata in India

The coronavirus disease 2019 (COVID-19) is an infectious disease identified at Wuhan, China, in December 2019 caused by new Coronavirus. The Indian government has taken many initiatives to mitigate the effect of COVID by encouraging the standard mechanisms of social distancing, the use of masks, and various safety parameters. COVID-19 hotspot identifies regions in India where COVID-19 severity is very high. We propose a novel hybrid cellular automata classifier for predicting the trend of various Hotspots in India, processing different parameters including infection control, virus reproduction rate, critical correlation, safety parameters, and social distancing. The proposed classifier was named Hybrid Cellular Automata-Hotspot (HCA-HS), predicts the number of hotspots in various districts of states, and also gives the status of each city marked either as Totally Safe or Marginally Safe or Unsafe. This will alert the state authorities to take necessary action to mitigate the COVID effect and help the people for possibly refraining from going to the infected areas, i.e., hotspots. The data sets were collected from Kaggle and the local Indian database for more adaptability. The accuracy of the predictions of Hotspots is reported as 91.58%, which is considerable at this moment. The developed classifier is compared with Support Vector Mechanism (SVM), K-Means, Decision Tree, and HCA-HS has reported an accuracy of 10.69% higher than the existing literature.

Fusion of Multiscale Convolutional Neural Networks for Building Extraction in Very High-Resolution Images

Extracting buildings from very high resolution (VHR) images has attracted much attention but is still challenging due to their large varieties in appearance and scale. Convolutional neural networks (CNNs) have shown effective and superior performance in automatically learning high-level and discriminative features in extracting buildings. However, the fixed receptive fields make conventional CNNs insufficient to tolerate large scale changes. Multiscale CNN (MCNN) is a promising structure to meet this challenge. Unfortunately, the multiscale features extracted by MCNN are always stacked and fed into one classifier, which make it difficult to recognize objects with different scales. Besides, the repeated sub-sampling processes lead to a blurred boundary of the extracted features. In this study, we proposed a novel parallel support vector mechanism (SVM)-based fusion strategy to take full use of deep features at different scales as extracted by the MCNN structure. We firstly designed a MCNN structure with different sizes of input patches and kernels, to learn multiscale deep features. After that, features at different scales were individually fed into different support vector machine (SVM) classifiers to produce rule images for pre-classification. A decision fusion strategy is then applied on the pre-classification results based on another SVM classifier. Finally, superpixels are applied to refine the boundary of the fused results using region-based maximum voting. For performance evaluation, the well-known International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam dataset was used in comparison with several state-of-the-art algorithms. Experimental results have demonstrated the superior performance of the proposed methodology in extracting complex buildings in urban districts.