Non-invasive quantitative diagnosis of liver fibrosis with an artificial neural network

2021 ◽  
Author(s):  
Jiaguang Song ◽  
Yuezhong Zhang ◽  
Jinling Cheng ◽  
Shi Wang ◽  
Zhi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Liver Fibrosis ◽  
Quantitative Diagnosis ◽  
Non Invasive ◽  
Artificial Neural

Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

2022 ◽  
Author(s):  
Massine GANA ◽  
Hakim ACHOUR ◽  
Kamel BELAID ◽  
Zakia CHELLI ◽  
Mourad LAGHROUCHE ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Vibration Analysis ◽  
Low Cost ◽  
Piezoelectric Sensor ◽  
Integrated System ◽  
Easy Access ◽  
Non Invasive ◽  
Artificial Neural

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

2017 ◽  
Vol 14 (9) ◽  
pp. 095601 ◽  
Author(s):  
Huimin Sun ◽  
Yaoyong Meng ◽  
Pingli Zhang ◽  
Yajing Li ◽  
Nan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Non Invasive ◽  
Artificial Neural

Non-Invasive Optical Blood Glucose Measurement based on Discrete Fourier Transform and Fast Artificial Neural Network: Fasting Normal Glucose Participants Case Study

2019 ◽  
Author(s):  
Renan Prasta Jenie ◽  
Evy Damayanthi ◽  
Irzaman Irzaman ◽  
Rimbawan Rimbawan ◽  
Dadang Sukandar ◽  
...  
Keyword(s):  
Neural Network ◽  
Clinical Trial ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Blood Glucose ◽  
Discrete Fourier Transform ◽  
Glucose Measurement ◽  
Non Invasive ◽  
Artificial Neural

A prototype non-invasive blood glucose level measurement optical device (NI-BGL-MOD) has been developed. The NI-BGL-MOD uses a discrete Fourier transform (DFT) method and a fast artificial neural network algorithm to optimize device performance. The appropriate light-emitting diode for the sensory module was selected based on near-infrared spectrophotometry of a blood glucose model and human blood. DFT is implemented in an analog-to-digital converter module. An in vitro trial using the blood glucose model along with a clinical trial involving 110 participants were conducted to evaluate the performance of the prototype. The root-mean-square error of the prototype was 10.8 mg/dl in the in vitro trial and 3.64 mg/dl in the clinical trial, which is lower than the ISO-15197:2016 mandated value of 10 mg/dl. In each trial, consensus error grid analysis indicated that the measurement error was within the safe range. The sensitivity and specificity of the prototype were 0.83 (0.36, 1.00) and 0.90 (0.55, 1.00) in the in vitro trial and 0.81 (0.75, 0.85) and 0.83 (0.78, 0.87) in the clinical trial, respectively. In general, the proposed NI-BGL-MOD demonstrated good performance than gold-standard measurement. Key words: Non-invasive blood glucose measurement, optical device, discrete Fourier transform, multi-formulatric regression, fast artificial neural network

scholarly journals Method of Non-Invasive determination of wheel rim technical condition using vibration measurement and artificial neural network

Measurement ◽  
2021 ◽  
Vol 185 ◽  
pp. 110050
Author(s):  
M. Borecki ◽  
A. Rychlik ◽  
O. Vrublevskyi ◽  
A. Olejnik ◽  
M.L. Korwin-Pawlowski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Technical Condition ◽  
Vibration Measurement ◽  
Non Invasive ◽  
Wheel Rim ◽  
Artificial Neural
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