Neural network-based software tool for predicting magnetic performance of strip-wound magnetic cores at medium to high frequency

2004 ◽  
Vol 151 (3) ◽  
pp. 181-187 ◽  
Author(s):  
G.K. Miti ◽  
A.J. Moses
Keyword(s):  
Neural Network ◽  
High Frequency ◽  
Software Tool ◽  
Magnetic Performance ◽  
Magnetic Cores

scholarly journals HF-SNN: High-frequency Spiking Neural Network

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jing Su ◽  
Jing Li
Keyword(s):  
Neural Network ◽  
High Frequency ◽  
Spiking Neural Network

An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals

2008 ◽  
Vol 147 (2) ◽  
pp. 372-383 ◽  
Author(s):  
Marcelo C. Medeiros ◽  
Michael McAleer ◽  
Daniel Slottje ◽  
Vicente Ramos ◽  
Javier Rey-Maquieira
Keyword(s):  
Neural Network ◽  
High Frequency ◽  
Conditional Volatility ◽  
Alternative Approach

scholarly journals Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

2018 ◽  
Vol 8 (8) ◽  
pp. 1258 ◽  
Author(s):  
Shuming Jiao ◽  
Zhi Jin ◽  
Chenliang Chang ◽  
Changyuan Zhou ◽  
Wenbin Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Frequency ◽  
Critical Issue ◽  
Compression Process ◽  
Compression Scheme ◽  
Quality Degradation ◽  
Digital Format ◽  
Enormous Amount ◽  
Frequency Features

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed “JPEG + deep learning” hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression.

scholarly journals hicap:in silicoserotyping of theHaemophilus influenzaecapsule locus

2019 ◽  
Author(s):  
Stephen C. Watts ◽  
Kathryn E. Holt
Keyword(s):  
High Frequency ◽  
Respiratory Infections ◽  
Software Tool ◽  
Type B ◽  
Dominant Group ◽  
Genome Sequences ◽  
Vaccination Programs ◽  
Genomic Epidemiology ◽  
Invasive Diseases ◽  
General Association

AbstractHaemophilus influenzaeexclusively colonises the human nasopharynx and can cause a variety of respiratory infections as well as invasive diseases including meningitis and sepsis. A key virulence determinant ofH. influenzaeis the polysaccharide capsule of which six serotypes are known, each encoded by a distinct variation of the capsule biosynthesis locus (cap-a tocap-f).H. influenzaetype b (Hib) was historically responsible for the majority of invasiveH. influenzaedisease and prevalence has been markedly reduced in countries that have implemented vaccination programs targeting this serotype. In the postvaccine era, non-typeableH. influenzaeemerged as the most dominant group causing disease but in recent years a resurgence of encapsulatedH. influenzaestrains has also been observed, most notably serotype a. Given the increasing incidence of encapsulated strains and the high frequency of Hib in countries without vaccination programs, there is growing interest in genomic epidemiology ofH. influenzae. Here we present hicap, a software tool for rapid in silico serotype prediction fromH. influenzaegenome sequences. hicap is written using Python3 and is freely available at github.com/scwatts/hicap under a GPLv3 license. To demonstrate the utility of hicap, we used it to investigate the cap locus diversity and distribution in 691 high-qualityH. influenzaegenomes from GenBank. These analyses identifiedcaploci in 95 genomes and confirmed the general association of each serotype with a unique clonal lineage and also identified occasional recombination between lineages giving rise to hybridcaploci (2% of encapsulated strains).

scholarly journals Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Cailing Hao
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Image Analysis ◽  
Frequency Band ◽  
High Frequency ◽  
Principal Component ◽  
Speech Signals ◽  
College English ◽  
Neural Network Algorithm ◽  
Component Image

With the development of information technology, band expansion technology is gradually applied to college English listening teaching. This technology aims to recover broadband speech signals from narrowband speech signals with a limited frequency band. However, due to the limitations of current voice equipment and channel conditions, the existing voice band expansion technology often ignores the high-frequency and low-frequency correlation of the audio, resulting in excessive smoothing of the recovered high-frequency spectrum, too dull subjective hearing, and insufficient expression ability. In order to solve this problem, a neural network model PCA-NN (principal components analysis-neural network) based on principal component image analysis is proposed. Based on the nonlinear characteristics of the audio image signal, the model reduces the dimension of high-dimensional data and realizes the effective recovery of the high-frequency detailed spectrum of audio signal in phase space. The results show that the PCA-NN, i.e., neural network based on principal component analysis, is superior to other audio expansion algorithms in subjective and objective evaluation; in log spectrum distortion evaluation, PCA-NN algorithm obtains smaller LSD. Compared with EHBE, Le, and La, the average LSD decreased by 2.286 dB, 0.51 dB, and 0.15 dB, respectively. The above results show that in the image frequency band expansion of college English listening, the neural network algorithm based on principal component analysis (PCA-NN) can obtain better high-frequency reconstruction accuracy and effectively improve the audio quality.

scholarly journals Low-order Spherical Harmonic HRTF Restoration using a Neural Network Approach

2020 ◽  
Author(s):  
Benjamin Tsui ◽  
William A. P. Smith ◽  
Gavin Kearney
Keyword(s):  
Neural Network ◽  
Spherical Harmonic ◽  
High Frequency ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Network Approach ◽  
Spectral Difference ◽  
Neural Network Approach ◽  
Frequency Representation ◽  
Head Related Transfer Function

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse Head Related Transfer Function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions in high frequency representation of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of Convolutional Auto-Encoder (CAE) and Denoising Auto-Encoder (DAE) models is proposed to restore the high frequency distortion in SH interpolated HRTFs. Results are evaluated using both Perceptual Spectral Difference (PSD) and localisation prediction models, both of which demonstrate significant improvement after the restoration process.

Analytic Design Method Based on Homothetic Shape of Magnetic Cores for High-Frequency Transformers

2007 ◽  
Vol 22 (5) ◽  
pp. 2070-2080 ◽  
Author(s):  
FranÇois Forest ◽  
Eric Laboure ◽  
Thierry Meynard ◽  
Mohand Arab
Keyword(s):  
High Frequency ◽  
Design Method ◽  
Magnetic Cores ◽  
Analytic Design ◽  
Analytic Design Method
Sign in / Sign up

Export Citation Format

Share Document