Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial int...
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The main objective of this work is to develop novel fault diagnosis techniques using ensemble learning and multivariate statistical techniques. The proposed methods are capable of identifying and classifying PV faults...
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This article investigates four issues, background (BKG) suppression (BS), anomaly detectability, noise effect, and interband correlation reduction (IBCR), which have significant impacts on its performance. Despite tha...
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Gaussian pyramid (GP) is a commonly used image coding technique that encodes an image as a pyramid that is stacked by a set of images with Gaussian window-reduced sizes and multiple spatial resolutions. Associated wit...
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This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolution of swept-source optical coherence tomography (SS-OCT) images. ...
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This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to ...
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ISBN:
(数字)9798350373974
ISBN:
(纸本)9798350373981
This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. These classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.
Anomaly detection in spacecraft telemetry channels is of great importance, especially considering the extremeness of the spacecraft operating environment. These anomalies often function as precursors for system failur...
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Anomaly detection in spacecraft telemetry channels is of great importance, especially considering the extremeness of the spacecraft operating environment. These anomalies often function as precursors for system failure. Currently, domain experts manually monitor telemetry channels, which is time-consuming and limited in scope. An automated approach to anomaly detection would be ideal, considering that each satellite system has thousands of channels to monitor. Deep learning models have been shown to be effective at capturing the normal behavior of the channels and flagging any abnormalities. However, each channel needs a unique model trained on it, and high performing models have been shown to require an increased training time. We instead propose training deep learning models in an online manner to quickly understand the behavior of a given channel and identify anomalies in real-time. This greatly reduces the amount of training time required to obtain a model for each channel. We present the results of our approach to show that we can achieve performance comparable to state-of-the-art spacecraft anomaly detection methods with minimal training time.
Anomaly detection plays a vital role in the early identification of brain tumors in MRI scans, and it directly impacts diagnostic accuracy and patient outcomes. Despite its importance, current methods often fall short...
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ISBN:
(数字)9798350388152
ISBN:
(纸本)9798350388169
Anomaly detection plays a vital role in the early identification of brain tumors in MRI scans, and it directly impacts diagnostic accuracy and patient outcomes. Despite its importance, current methods often fall short in handling sparse labeled data and precisely localizing anomalies. In this study, an innovative method that integrates Generative Adversarial Networks (GANs) with few-shot learning and transfer learning techniques, offering a new perspective in handling scarce labeled data in medical image analysis. At the core of the proposed method is a Vision Transformer-based generator, showcasing the dedication to advancing medical diagnostics technology. This generator, paired with a uniquely adapted discriminator benefiting from a pre-trained VGG16 network, enhances the model’s efficiency and accuracy in anomaly detection. The efficacy of the proposed method is demonstrated through its ability to distinguish between normal and patho- logical brain images obtained from a public dataset. In our study, the model achieved anomaly scores of approximately $0.14 \pm 0.18$ for normal images and $0.23 \pm 0.76$ for abnormal images. The enhanced precision achieved in anomaly detection and localization signifies a notable advancement beyond current methodologies. These outcomes pave the way for the creation of increasingly sophisticated and dependable diagnostic instruments, thereby facilitating more precise detection of brain tumors.
In this paper, a stochastic-parametric model is developed for simulating the temporal and spectral nonstationary characteristics of ground motion sequences. In the proposed model, after extracting the wavelet coeffici...
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Detection and localization of small objects is crucial in many applications, such as surveillance, microscopy, and astronomy. In many space-based imaging applications, the spatial resolution of the imaging system is n...
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Detection and localization of small objects is crucial in many applications, such as surveillance, microscopy, and astronomy. In many space-based imaging applications, the spatial resolution of the imaging system is not enough to localize a small object (point source). Low signal-to-noise ratio (SNR) increases the difficulty such as when the objects are dim. We can model the noise in several ways and consider two cases in this paper. Noise is modeled as additive spatially invariant Gaussian distributed noise and spatially varying Poisson distributed noise. We assume that we have a coarse estimate for the spatial location of an object in an image and that a 2D symmetric Gaussian function approximates the point spread function of the imaging system and, thus, the gray-level distribution of an object. In this paper, we describe a machine learning method that minimizes a cost function derived from the maximum likelihood estimation of the observed image to determine an object's sub-pixel spatial location and amplitude. We call the proposed method Sub-Pixel Location Estimator for Small Objects (SPLEO). We compare the variance of SPLEO (both spatial location and object amplitude estimates) with the Cramer-Rao lower bound (CRLB).
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