scholarly journals Variable Frequency Signal Carrying Nonlinear Transmission Line - Modeling using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 3981-3986
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Transmission Line ◽  
Linear Models ◽  
Interpolation Space ◽  
Behavior Modeling ◽  
Time Interval ◽  
Data Set ◽  
Nonlinear Transmission Line ◽  
Nonlinear Transmission

in modeling of complex systems, manual creation and maintenance of the appropriate behavior is found to be the key problem. Behavior modeling using machine learning has found successful in modeling and simulation. This paper presents artificial neural network (ANN) modeling of transmission line carrying frequency varying signal using machine learning. This work uses proper orthogonal decomposition (POD) based reduced order modeling. In this proposed work, snapshot sets of complex mathematical model of nonlinear transmission line and also linear model are obtained at different time interval. These snapshot sets are arranged in matrix form separately for nonlinear and linear models. POD method is applied on both the matrices separately. This reduces the order of the matrix which is used as input and output data set for neural network training through machine learning technique. Trained neural network model has been verified using different untrained data set. The proposed algorithm determines the dimension of the interpolation space prompting a considerable decrease in the computational expense. The proposed algorithm doesn't force any imperatives on the topology of the appropriate circuit or kind of the nonlinear segments and hence relevant to general nonlinear systems.

scholarly journals A nonlinear transmission line based on periodically placed silicon high voltage switches

AIP Advances ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 125020
Author(s):  
Ilya V. Romanchenko ◽  
Vladimir Yu. Konev ◽  
Valery V. Barmin ◽  
Pavel V. Priputnev ◽  
Sergey N. Maltsev
Keyword(s):  
Transmission Line ◽  
High Voltage ◽  
Nonlinear Transmission Line ◽  
Nonlinear Transmission

Generation of kilovolt-subnanosecond pulses using a nonlinear transmission line

1993 ◽  
Vol 4 (8) ◽  
pp. 893-895 ◽  
Author(s):  
R J Baker ◽  
D J Hodder ◽  
B P Johnson ◽  
P C Subedi ◽  
D C Williams
Keyword(s):  
Transmission Line ◽  
Nonlinear Transmission Line ◽  
Nonlinear Transmission

Exact and soliton solutions to nonlinear transmission line model

2016 ◽  
Vol 87 (2) ◽  
pp. 767-773 ◽  
Author(s):  
M. M. El-Borai ◽  
H. M. El-Owaidy ◽  
H. M. Ahmed ◽  
A. H. Arnous
Keyword(s):  
Transmission Line ◽  
Soliton Solutions ◽  
Transmission Line Model ◽  
Nonlinear Transmission Line ◽  
Line Model ◽  
Nonlinear Transmission

scholarly journals Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study (Preprint)

2019 ◽  
Author(s):  
Longxiang Su ◽  
Chun Liu ◽  
Dongkai Li ◽  
Jie He ◽  
Fanglan Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Data Set ◽  
Machine Learning Methods ◽  
Heparin Treatment ◽  
Neural Network Algorithm ◽  
Mimic Iii ◽  
Machine Learning Models

BACKGROUND Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. OBJECTIVE The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. METHODS Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. RESULTS Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. CONCLUSIONS The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

scholarly journals Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7709
Author(s):  
Serena Cerfoglio ◽  
Manuela Galli ◽  
Marco Tarabini ◽  
Filippo Bertozzi ◽  
Chiarella Sforza ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Knee Joint ◽  
Field Analysis ◽  
Ground Reaction Forces ◽  
Drop Jump ◽  
Joint Moments ◽  
Data Set ◽  
Joint Kinetics ◽  
Reaction Forces

Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.

scholarly journals Study of Pulsed RF Signal Extraction and Irradiation from a Capacitive Nonlinear Transmission Line

2018 ◽  
Vol 5 (10) ◽  
pp. 121-133 ◽  
Author(s):  
Leandro C. Silva ◽  
José O. Rossi ◽  
Elizete G. L. Rangel ◽  
Lucas R. Raimundi ◽  
Edl Schamiloglu
Keyword(s):  
Transmission Line ◽  
Signal Extraction ◽  
Nonlinear Transmission Line ◽  
Nonlinear Transmission ◽  
Pulsed Rf ◽  
Rf Signal
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