Results 31 to 40 of about 22,071 (254)
Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis
Sensors, 2020 Anomaly detection systems can accurately identify malicious network traffic, providing network security. With the development of internet technology, network attacks are becoming more and more sourced and complicated, making it difficult for traditional ...
Minghui Gao +5 more
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IEEE Access, 2021 In this paper, we propose two deep-learning based uplink channel estimation approaches that can utilize not only high-resolution-ADC-quantized but also low-resolution-ADC-quantized received pilot signals to improve estimation performance for mixed analog-
Jin Zicheng +4 more
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Deep learning-based modeling method for probabilistic LCF life prediction of turbine blisk
Propulsion and Power ResearchTurbine blisk is one of the typical components of gas turbine engines. The fatigue life of turbine blisk directly affects the reliability and safety of both turbine blisk and aeroengine whole-body. To monitor the performance degradation of an aeroengine,
Cheng-Wei Fei +5 more
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Arthritis Care &Research, EarlyView.Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos +2 more
wiley +1 more source
Bayesian Deep Neural Network to Compensate for Current Transformer Saturation
IEEE Access, 2021 Current transformer saturation has a negative effect on the operation of IEDs, resulting in their malfunction. Here, we present a technique to compensate for saturated waveforms using Bayesian Deep Neural Network (BDNN) comprising Deep Neural Network ...
Sopheap Key +3 more
doaj +1 more source
Advanced Functional Materials, EarlyView.An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone +11 more
wiley +1 more source
Brazilian Archives of Biology and TechnologyEfficient and exact traffic flow forecasting is critical for intelligent transportation systems. The number of model generations increases the computational complexity of deep neural network (DNN) models.
Rajalakshmi Gurusamy +1 more
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Deep Neural Network DNN Applied to the Analysis of Student Dropout
Investigación e Innovación en Ingenierías, 2022 This paper reports the design of a deep neural network (DNN) focused to predict patterns of student dropout at the UPTC University of Colombia. We apply specialized artificial intelligence (AI) algorithms for the implementation of the DNN specifically were using a learning machine for classification and clustering task.
Marco Javier Suarez Baron +2 more
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Memristive‐Gated RC‐Delay Synaptic Transistors for Time‐Encoded Analog in‐Memory Computing
Advanced Functional Materials, EarlyView.A Memristive‐Gated Transistor for Time‐Encoded Analog In‐Memory Computing — By exploiting the RC delay of a self‐rectifying interface‐type memristor, nonlinear I–V distortion is structurally bypassed, enabling 3‐bit nonvolatile memory, spike‐timing‐based analog encoding, and hardware‐calibrated reservoir‐computing validation within a unified device ...
Yun‐Seo Shin +7 more
wiley +1 more source
Electricity load forecasting using a deep neuralnetwork
Engineering and Applied Science Research, 2019 Forecasting the daily load demandofan electric utility provideris a complex problem as it is nonlinear andinfluenced byexternal factors.
Chawalit Jeenanunta
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