Improving RF-Based Partial Discharge Localization via Machine Learning Ensemble Method
نویسندگان
چکیده
منابع مشابه
Machine Learning Based Localization
A vast majority of localization techniques proposed for sensor networks are based on triangulation methods in Euclidean geometry. They utilize the geometrical properties of the sensor network to infer the sensor locations. A fundamentally different approach is presented in this chapter. This approach is based on machine learning, in which we work directly on the natural (non-Euclidean) coordina...
متن کاملHypertension Prediction in Primary School Students Using an Ensemble Machine Learning Method
Introduction: The prevalence of hypertension in children is increasing, and this complication is considered the most important risk factor for cardiovascular diseases in older age. Early detection and control of hypertension can prevent its progress and reduce its consequences. Machine learning methods can help predict this complication promptly and reduce cost and time. This study aimed to pro...
متن کاملHypertension Prediction in Primary School Students Using an Ensemble Machine Learning Method
Introduction: The prevalence of hypertension in children is increasing, and this complication is considered the most important risk factor for cardiovascular diseases in older age. Early detection and control of hypertension can prevent its progress and reduce its consequences. Machine learning methods can help predict this complication promptly and reduce cost and time. This study aimed to pro...
متن کاملMobile Robot Localization via Machine Learning
An appearance-based robot self-localization problem is considered in the machine learning framework. The appearance space is composed of all possible images, which can be captured by a robot’s visual system under all robot localizations. Using recent manifold learning and deep learning techniques, we propose a new geometrically motivated solution based on training data consisting of a finite se...
متن کاملECE544NA Final Project: Robust Machine Learning Hardware via Classifier Ensemble
In this paper, we propose to use classifier ensemble (CE) as a method to enhance the robustness of machine learning (ML) kernels in presence of hardware error. Different ensemble methods (Bagging and Adaboost) are explored with decision tree (C4.5) and artificial neural network (ANN) as base classifiers. Simulation results show that ANN is inherently tolerant to hardware errors with up to 10% h...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Power Delivery
سال: 2019
ISSN: 0885-8977,1937-4208
DOI: 10.1109/tpwrd.2019.2907154