Under the skin effect caused by the alternating electromagnetic field, the transmission line parameters changes with frequency, and different line resistivity, surge impedance, wave speed are presented in one line und...
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This paper analyzes the general process of power flow transferring and divides it into four parts: 1) the process of first phase selection after fault occurs;2) the process of selective action at the first time;3) the...
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This paper studies the fault current frequency spectrum characteristics of single phase ground fault in ungrounded distribution networks, and the fault location method based on it. Firstly, it analyzes the production ...
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Travelling-wave starting element is an integral part of the ultra high-speed protection. Existing design method depends on engineering experience, low accuracy in travelling-wave signal recognition, easy to trip frequ...
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Fault locator for power distribution system with neutral non-effectively grounded should locate phase-to-phase fault and single-phase-to-ground fault respectively. For phase-to-phase fault, the point can be located by...
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Charging electric vehicles (EVs) from smart microgrids fueled by renewable energy resources is becoming a popular green approach. Although some works have been done about renewable energy sources and EVs in smart micr...
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In this paper, the fixed-time stabilization of Takagi-Sugeno (T-S) fuzzy system with discrete time delays and external disturbances is investigated via sliding-mode control. Firstly, a suitable controller and sliding-...
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With high speed data sampling technology development, fault generated high-frequency signals can be got, in which fault generated traveling waves reflects fault location, fault type and faulted equipment so on from di...
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Power grid and communication network, which depends on each other to provide proper functionality, are becoming more and more coupled together. Failure of a fraction of nodes in communication networks may lead to a fa...
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Neurodegenerative disease especially dementia are reported as disease that leads to death, Alzheimer's Disease (AD) is kind dementia that cause progressive and irreversible brain disorder loss which leads to death...
Neurodegenerative disease especially dementia are reported as disease that leads to death, Alzheimer's Disease (AD) is kind dementia that cause progressive and irreversible brain disorder loss which leads to death. AD shows no symptoms in its early stages which makes diagnosing it its beginning a challenge and helpful for doctors as they can slow down its progress in its early stages. Computer-aided approaches such as machine learning which come up with several techniques to detect AD by extracting features from the given image data and use them to build a classifier. Recently, a subcategory of machine learning called deep learning has widely been employed to enhance the medical diagnosis by attempting notable performance. In fact, these approaches avoid the tricky manual feature extraction using Convolutional Neural Network (CNN) considered as a reference in the field of computer vision. This paper proposes a combination of machine-deep learning technics for early diagnosis of AD from positron emission tomography (PET). We first train our CNN on PET images to extract the most relevant features, then we select the most appropriate CNN's level from where the features will be extracted, which will be feed in a second step as input to a Support Vector Machine based classifier (SVM). The proposed approach achieves notable results that exceeded the performance obtained by various existing approaches.
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