Behavior of LSSVM and SVM channel equalizers in non-Gaussian noise

Kobaiz, Ahmed R.; Ghrare, Seddeq E.

doi:10.1109/i4ct.2014.6914215

Cited by 1 publication

(1 citation statement)

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“…By identifying the hyperplane with the largest possible margin between the nearest data points of different classes, SVMs exhibit good generalization performance. This makes them suitable for applications where robust classification is desired, even in the presence of noisy or overlapping data [37].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines

Ward,

Miller,

Engel

et al. 2024

Machines

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Traditionally, condition monitoring of wind turbines has been performed manually by certified rope teams. This method of inspection can be dangerous for the personnel involved, and the resulting downtime can be expensive. Wind turbine inspection can be performed using autonomous drones to achieve lower downtime, cost, and health risks. To enable autonomy, the field of drone path planning can be assisted by this research, namely machine learning that detects wind turbines present in aerial RGB images taken by the drone before performing the maneuvering for turbine inspection. For this task, the effectiveness of two deep learning architectures is evaluated in this paper both without and with a proposed fuzzy contrast enhancement (FCE) image preprocessing algorithm. Efforts are focused on two convolutional neural network (CNN) variants: VGG19 and Xception. A more traditional approach involving support vector machines (SVM) is also included to contrast a machine learning approach with our deep learning networks. The authors created a novel dataset of 4500 RGB images of size 210×210 to train and evaluate the performance of these networks on wind turbine detection. The dataset is captured in an environment mimicking that of a wind turbine farm, and consists of two classes of images: with and without a small-scale wind turbine (12V Primus Air Max) assembled at Utah Valley University. The images were used to describe in detail the analysis and implementation of the VGG19, Xception, and SVM algorithms using different optimization, model training, and hyperparameter tuning technologies. The performances of these three algorithms are compared in depth alongside those augmented using the proposed FCE image preprocessing technique.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%