Adversarial vulnerability of deep neural network-based gait event detection: A comparative study using accelerometer-based data

2022 ◽  
Vol 73 ◽  
pp. 103429
Author(s):  
Jing Tian
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Event Detection ◽  
Deep Neural Network

Using multi-stream hierarchical deep neural network to extract deep audio feature for acoustic event detection

2017 ◽  
Vol 77 (1) ◽  
pp. 897-916 ◽  
Author(s):  
Yanxiong Li ◽  
Xue Zhang ◽  
Hai Jin ◽  
Xianku Li ◽  
Qin Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Event Detection ◽  
Deep Neural Network ◽  
Acoustic Event Detection ◽  
Acoustic Event ◽  
Audio Feature

scholarly journals Tensorflow vs R: A Comparative Study of Usability

2017 ◽  
Author(s):  
Luís Dias ◽  
Rosalvo Neto
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Comparative Study ◽  
Scientific Community ◽  
Deep Neural Network ◽  
Network Algorithms ◽  
Learning Framework ◽  
Usability Tests ◽  
Usability Analysis ◽  
Better Than

Google released on November of 2015 Tensorflow, an open source machine learning framework that can be used to implement Deep Neural Network algorithms, a class of algorithms that shows great potential in solving complex problems. Considering the importance of usability in software success, this research aims to perform a usability analysis on Tensorflow and to compare it with another widely used framework, R. The evaluation was performed through usability tests with university students. The study led do indications that Tensorflow usability is equal or better than the usability of traditional frameworks used by the scientific community.

scholarly journals Direct Short-Term Forecast of Photovoltaic Power through a Comparative Study between COMS and Himawari-8 Meteorological Satellite Images in a Deep Neural Network

2020 ◽  
Vol 12 (15) ◽  
pp. 2357
Author(s):  
Minho Kim ◽  
Hunsoo Song ◽  
Yongil Kim
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Deep Neural Network ◽  
Satellite Images ◽  
Solar Irradiation ◽  
Next Generation ◽  
Short Term ◽  
Input Dataset ◽  
One Year ◽  
Meteorological Satellite

Meteorological satellite images provide crucial information on solar irradiation and weather conditions at spatial and temporal resolutions which are ideal for short-term photovoltaic (PV) power forecasts. Following the introduction of next-generation meteorological satellites, investigating their application on PV forecasts has become imminent. In this study, Communications, Oceans, and Meteorological Satellite (COMS) and Himawari-8 (H8) satellite images were inputted in a deep neural network (DNN) model for 2 hour (h)- and 1 h-ahead PV forecasts. A one-year PV power dataset acquired from two solar power test sites in Korea was used to directly forecast PV power. H8 was used as a proxy for GEO-KOMPSAT-2A (GK2A), the next-generation satellite after COMS, considering their similar resolutions, overlapping geographic coverage, and data availability. In addition, two different data sampling setups were designed to implement the input dataset. The first setup sampled chronologically ordered data using a relatively more inclusive time frame (6 a.m. to 8 p.m. in local time) to create a two-month test dataset, whereas the second setup randomly sampled 25% of data from each month from the one-year input dataset. Regardless of the setup, the DNN model generated superior forecast performance, as indicated by the lowest normalized mean absolute error (NMAE) and normalized root mean squared error (NRMSE) results in comparison to that of the support vector machine (SVM) and artificial neural network (ANN) models. The first setup results revealed that the visible (VIS) band yielded lower NMAE and NRMSE values, while COMS was found to be more influential for 1 h-ahead forecasts. For the second setup, however, the difference in NMAE results between COMS and H8 was not significant enough to distinguish a clear edge in performance. Nevertheless, this marginal difference and similarity of the results suggest that both satellite datasets can be used effectively for direct short-term PV forecasts. Ultimately, the comparative study between satellite datasets as well as spectral bands, time frames, forecast horizons, and forecast models confirms the superiority of the DNN and offers insights on the potential of transitioning to applying GK2A for future PV forecasts.

Real-Time Multi-Event Anomaly Detection using Elliptic Envelope and A Deep Neural Network for Enhanced MPD Robustness

2021 ◽  
Author(s):  
Qifan Gu ◽  
Amirhossein Fallah ◽  
Pradeepkumar Ashok ◽  
Dongmei Chen ◽  
Eric Van Oort
Keyword(s):  
Neural Network ◽  
Event Detection ◽  
Deep Neural Network ◽  
Change Point Detection ◽  
False Alarms ◽  
Two Phase ◽  
Signal Noise ◽  
Lost Circulation ◽  
Gas Kick ◽  
Few Data

Abstract In managed pressure drilling (MPD), robust and fast event detection is critical for timely event identification and diagnosis, as well as executing well control actions as quickly as possible. In current event detection systems (EDSs), signal noise and uncertainties often cause missed and false alarms, and automated diagnosis of the event type is usually restricted to certain event types. A new EDS method is proposed in this paper to overcome these shortcomings. The new approach uses a multivariate online change point detection (OCPD) method based on elliptic envelope for event detection. The method is robust against signal noise and uncertainties, and is able to detect abnormal features within a minute or less, using only a few data points. A deep neural network (DNN) is utilized for estimating the occurrence probability of various drilling events, currently encompassing (but not limited to) six event types: liquid kick, gas kick, lost circulation, plugged choke, plugged bit, and drillstring washout. The OCPD and the DNN are integrated together and demonstrate better performance with respect to robustness and accuracy. The training and testing of the OCPD and the DNN were conducted on a large dataset representing various drilling events, which was generated using a field-validated two-phase hydraulics software. Compared to current EDS methods, the new system shows the following advantages: (1) lower missed alarm rate; (2) lower false alarm rate; (3) earlier alarming; and (4) significantly improved classification capability that also allows for further extension to even more drilling events.

Event Detection and Control of Blood Glucose Levels Using Deep Neural Network

2021 ◽  
pp. 235-245
Author(s):  
Divya Govindaraju ◽  
R. R. Rashmika Shree ◽  
S. Priyanka ◽  
S. Porkodi ◽  
Sutha Subbian
Keyword(s):  
Neural Network ◽  
Blood Glucose ◽  
Event Detection ◽  
Deep Neural Network ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
And Control
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