In this study, we focus on the development and implementation of a comprehensive ensemble of numerical time series forecasting models, collectively referred to as the Group of Numerical Time Series Prediction Model (G...
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In this paper, we extend the ElGamal cryptosystem to the third group of units of the ring n, which we prove to be more secure than the previous extensions. We describe the arithmetic needed in the new setting. We also...
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Deepfakes are a form of synthetic media that uses deep-learning technology to create fake images, video, and audio. The emergence of this technology has inspired much commentary and speculation from academics across a...
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The Industrial Internet of Things (IIoT) promises to provide an expanded awareness of field assets and equipment, access to data from across locations, and actionable insights for maximizing operational performance an...
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ISBN:
(纸本)9781450397148
The Industrial Internet of Things (IIoT) promises to provide an expanded awareness of field assets and equipment, access to data from across locations, and actionable insights for maximizing operational performance and safety of the oil and gas industry. Using automation and machine learning, with the application of predictive maintenance, efficiencies can be boosted and problems can be mitigated sooner and more effectively. The proposed system is mainly based on the data collection, processing, analysis, and modeling of an enormous number of historical and real-time data generated during the operation of the equipment on the edge side. The data-driven predictive maintenance used machine learning models and deep learning models to predict the remaining useful life (RUL). Bi-LSTM based prediction model has been trained on the cloud, and deployed onto the edge devices. The predictive maintenance process includes data acquisition, data processing, training of machine learning model, equipment health assessment, remaining useful life prediction, strategy formulation, and strategy execution. The predictive maintenance solution driven by the IIoT helps oil and gas companies make predictions before equipment failures have a significant impact on their company's safety level and profits to improve asset reliability and promote cost savings.
Let Ω ∈ C be a domain such that K := C\Ω is compact and non-polar. Let (qk)k>0 be a sequence of polynomials with nk, the degree of qk satisfying nk → ∞, and let (qk(m))k denote the sequence of m-th derivatives. ...
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We consider a retrial queueing system with limited processor sharing which can be used for modeling the operation of a cell of fixed capacity in a wireless cellular network with two types of customers (handover and ne...
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We propose a virtual clinical trial for assessing the safety and efficacy of closed-loop diabetes treatments prior to an actual clinical trial. Such virtual trials enable rapid and risk-free pretrial testing of algori...
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We propose a virtual clinical trial for assessing the safety and efficacy of closed-loop diabetes treatments prior to an actual clinical trial. Such virtual trials enable rapid and risk-free pretrial testing of algorithms, and they can be used to compare different treatment variations for large and diverse populations. The participants are represented by multiple mathematical models, consisting of stochastic differential equations, and we use Monte Carlo closed-loop simulations to compute detailed statistics of the closed-loop treatments. We implement the virtual clinical trial using high-performance software and hardware, and we present an example trial with two mathematical models of one million participants over 52 weeks (i.e., two million simulations), which can be completed in 2 h 9 min.
The most important factor influencing the percentage of deaths due to road accident injuries is the time between the occurrence of the accident and the arrival of emergency responders at the scene of the accident. Kol...
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In this paper, we introduce StochGradAdam, a novel optimizer designed as an extension of the Adam algorithm, incorporating stochastic gradient sampling techniques to improve computational efficiency while maintaining ...
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Diabetic Retinopathy (DR) is a common complication of diabetes that, in severe cases, can result in blindness. Accurate clinical treatment is imperative to prevent these cases and relies considerably on an exact diagn...
Diabetic Retinopathy (DR) is a common complication of diabetes that, in severe cases, can result in blindness. Accurate clinical treatment is imperative to prevent these cases and relies considerably on an exact diagnosis of the various symptoms of DR. We aim to advance DR diagnosis by providing a practical tool to automatically classify Optical Coherence Tomography (OCT) scans for DR and to identify and localize DR-related morphological features within the scans. Our system obtains raw OCT input and only sparse clinical annotations at the volume level, which can be obtained automatically from routine electronic medical *** developed a novel neural network architecture, OCT-Transformer, that obtains state-of-the-art classification results compared to previous models and does so with limited training data. We base our architecture on an attention mechanism and show this to be the driving factor for the boost in performance. We additionally use our model to locate pixels within the input scans that explain its classification.
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