A study in the application of multilayer perceptrons to the analysis of chemical sensors systems data

2003 ◽  
Author(s):  
M. Pardo ◽  
G. Sberveglieri
Keyword(s):  
Chemical Sensors ◽  
Multilayer Perceptrons

Comparison of two devices for measuring exercise transcutaneous oxygen pressures in patients with claudication

VASA ◽  
2015 ◽  
Vol 44 (5) ◽  
pp. 355-362 ◽  
Author(s):  
Marie Urban ◽  
Alban Fouasson-Chailloux ◽  
Isabelle Signolet ◽  
Christophe Colas Ribas ◽  
Mathieu Feuilloy ◽  
...  
Keyword(s):  
Oxygen Pressure ◽  
Optical Sensors ◽  
Chemical Sensors ◽  
Transcutaneous Oxygen ◽  
Starting Values ◽  
Two Systems ◽  
Transcutaneous Oxygen Pressure ◽  
New Sensors ◽  
Changes Over Time ◽  
Over Time

Abstract. Summary: Background: We aimed at estimating the agreement between the Medicap® (photo-optical) and Radiometer® (electro-chemical) sensors during exercise transcutaneous oxygen pressure (tcpO2) tests. Our hypothesis was that although absolute starting values (tcpO2rest: mean over 2 minutes) might be different, tcpO2-changes over time and the minimal value of the decrease from rest of oxygen pressure (DROPmin) results at exercise shall be concordant between the two systems. Patients and methods: Forty seven patients with arterial claudication (65 + / - 7 years) performed a treadmill test with 5 probes each of the electro-chemical and photo-optical devices simultaneously, one of each system on the chest, on each buttock and on each calf. Results: Seventeen Medicap® probes disconnected during the tests. tcpO2rest and DROPmin values were higher with Medicap® than with Radiometer®, by 13.7 + / - 17.1 mm Hg and 3.4 + / - 11.7 mm Hg, respectively. Despite the differences in absolute starting values, changes over time were similar between the two systems. The concordance between the two systems was approximately 70 % for classification of test results from DROPmin. Conclusions: Photo-optical sensors are promising alternatives to electro-chemical sensors for exercise oximetry, provided that miniaturisation and weight reduction of the new sensors are possible.

Intelligent Sensor System Based on the Cross-reactive Arrays of Chemical Sensors

2010 ◽  
Vol 6 (5) ◽  
pp. 65-71
Author(s):  
B.A. Snopok ◽  
Keyword(s):  
Chemical Sensors ◽  
Sensor System ◽  
Intelligent Sensor ◽  
The Cross

Real-Time Low-Cost Drift Compensation for Chemical Sensors Using a Deep Neural Network with Hadamard Transform and Additive Layers

2021 ◽  
pp. 1-1
Author(s):  
Diaa Badawi ◽  
Agamyrat Agambayev ◽  
Sule Ozev ◽  
A. Enis Cetin
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Chemical Sensors ◽  
Deep Neural Network ◽  
Low Cost ◽  
Hadamard Transform ◽  
Drift Compensation

scholarly journals The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Multilayer Perceptrons ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Holstein Friesian ◽  
Artificial Neural ◽  
Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

scholarly journals Minimization of the Line Resistance Impact on Memdiode-Based Simulations of Multilayer Perceptron Arrays Applied to Pattern Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fernando Leonel Aguirre ◽  
Nicolás M. Gomez ◽  
Sebastián Matías Pazos ◽  
Félix Palumbo ◽  
Jordi Suñé ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Ex Situ ◽  
Multilayer Perceptrons ◽  
Spice Simulation ◽  
Large Dataset ◽  
High Line ◽  
Calibration Algorithm ◽  
The Impact ◽  
Line Resistance

In this paper, we extend the application of the Quasi-Static Memdiode model to the realistic SPICE simulation of memristor-based single (SLPs) and multilayer perceptrons (MLPs) intended for large dataset pattern recognition. By considering ex-situ training and the classification of the hand-written characters of the MNIST database, we evaluate the degradation of the inference accuracy due to the interconnection resistances for MLPs involving up to three hidden neural layers. Two approaches to reduce the impact of the line resistance are considered and implemented in our simulations, they are the inclusion of an iterative calibration algorithm and the partitioning of the synaptic layers into smaller blocks. The obtained results indicate that MLPs are more sensitive to the line resistance effect than SLPs and that partitioning is the most effective way to minimize the impact of high line resistance values.

scholarly journals Estimation of electrical resistivity using artificial neural networks: a case study from Lublin Basin, SE Poland

2021 ◽  
Author(s):  
Jakub Ważny ◽  
Michał Stefaniuk ◽  
Adam Cygal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Data ◽  
Multilayer Perceptrons ◽  
Borehole Data ◽  
Geological Interpretation ◽  
Se Poland ◽  
Geological Conditions ◽  
Artificial Neural ◽  
Reliable Solution

AbstractArtificial neural networks method (ANNs) is a common estimation tool used for geophysical applications. Considering borehole data, when the need arises to supplement a missing well log interval or whole logging—ANNs provide a reliable solution. Supervised training of the network on a reliable set of borehole data values with further application of this network on unknown wells allows creation of synthetic values of missing geophysical parameters, e.g., resistivity. The main assumptions for boreholes are: representation of similar geological conditions and the use of similar techniques of well data collection. In the analyzed case, a set of Multilayer Perceptrons were trained on five separate chronostratigraphic intervals of borehole, considered as training data. The task was to predict missing deep laterolog (LLD) logging in a borehole representing the same sequence of layers within the Lublin Basin area. Correlation between well logs data exceeded 0.8. Subsequently, magnetotelluric parametric soundings were modeled and inverted on both boreholes. Analysis showed that congenial Occam 1D models had better fitting of TM mode of MT data in each case. Ipso facto, synthetic LLD log could be considered as a basis for geophysical and geological interpretation. ANNs provided solution for supplementing datasets based on this analytical approach.

scholarly journals Facet controlled growth mechanism of SnO2 (101) nanosheet assembled film via cold crystallization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoshitake Masuda
Keyword(s):  
Crystal Growth ◽  
Tin Oxide ◽  
Chemical Sensors ◽  
Seed Layer ◽  
Flat Surface ◽  
Crystallographic Analysis ◽  
Cold Crystallization ◽  
Type I ◽  
Type Ii ◽  
Oxide Substrate

AbstractCold crystallization of SnO2 was realized in aqueous solutions, where crystal growth was controlled to form SnO2 (101) nanosheet assembled films for devices such as chemical sensors. The nanosheets grew directly on a fluorine-doped tin oxide substrate without a seed layer or a buffer layer. The nanosheets had a thickness of 5–10 nm and an in-plane size of 100–1600 nm. Moreover, the large flat surface of the (101) facet was metastable. The thickness of the SnO2 (101) nanosheet assembled film was approximately 800 nm, and the film had a gradient structure that contained many connected nanosheets. TEM results revealed that the predominate branch angles between any two connected nanosheets were 90° and 46.48°, corresponding to type I and type II connections, respectively. These connections were consistent with the calculations based on crystallography. Crystallographic analysis clarified the characteristic crystal growth of the SnO2 (101) nanosheet assembled film in the aqueous solution. Furthermore, we demonstrate that the metastable (101) facet can be exploited to control the rate of crystal growth by adjusting the etching condition.

scholarly journals Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3139
Author(s):  
Julian Varghese ◽  
Catharina Marie van Alen ◽  
Michael Fujarski ◽  
Georg Stefan Schlake ◽  
Julitta Sucker ◽  
...  
Keyword(s):  
Low Noise ◽  
Disease Classification ◽  
Multilayer Perceptrons ◽  
Sensor Validation ◽  
Diagnostic Potential ◽  
A Prospective Study ◽  
Learning Architectures ◽  
Diagnosis Classification ◽  
Healthy Participants ◽  
Digital Biomarkers

Smartwatches provide technology-based assessments in Parkinson’s Disease (PD). It is necessary to evaluate their reliability and accuracy in order to include those devices in an assessment. We present unique results for sensor validation and disease classification via machine learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches throughout a 15-min examination. Symptoms and medical history were captured on the paired smartphone. The amplitude error of both smartwatches reaches up to 0.005 g, and for the measured frequencies, up to 0.01 Hz. A broad range of different ML classifiers were cross-validated. The most advanced task of distinguishing PD vs. DD was evaluated with 74.1% balanced accuracy, 86.5% precision and 90.5% recall by Multilayer Perceptrons. Deep-learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle tremor signs with low noise. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers, but it remains challenging for distinguishing similar disorders.

scholarly journals Advances in Self-Powered Chemical Sensing via a Triboelectric Nanogenerator

Nanoscale ◽  
2020 ◽  
Author(s):  
Congxi Huang ◽  
Guorui Chen ◽  
Ardo Nashalian ◽  
Jun Chen
Keyword(s):  
Environmental Protection ◽  
Human Body ◽  
Chemical Sensors ◽  
Chemical Sensing ◽  
Triboelectric Nanogenerator ◽  
Self Powered ◽  
Human Healthcare

Chemical sensors allow for continuous detection and analysis of underexplored molecules on the human body and the surroundings, which hold bright applications on human healthcare and environmental protection. With the...

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