scholarly journals Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

2017 ◽  
Vol 24 (1) ◽  
pp. 27-44 ◽  
Author(s):  
Stanisław Osowski ◽  
Krzysztof Siwek
Keyword(s):  
Support Vector Machine ◽  
Electronic Nose ◽  
Data Clustering ◽  
Wavelet Transformation ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Noisy Environment ◽  
Special Stress ◽  
Sensor Signals

Abstract The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.

CARDIAC ADVANCED ARRYTHMIA PREDICTION SYSTEMS- CAAPS

2021 ◽  
pp. 54-55
Author(s):  
Pradeep Kumar Radhakrishnan ◽  
Gayathri Ananyajyothi Ambat ◽  
Saihrudya Samhita ◽  
Murugan U S ◽  
Tarig Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Ventricular Arrhythmias ◽  
Deep Neural Network ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Term Memory

There is a constant search for novel methods of classication and predicting cardiac rhythm disorders or arrhythmias. We prefer to classify them as wide complex tachyarrhythmia's or ventricular arrhythmias inclusive of malignant ventricular arrhythmias which with hemodynamic compromise is usually life threatening. Long term and fatality predictions warranting AICD implantation are already available. We have a novel method and robust algorithm with preprocessing and optimal feature selection from ECG signal analysis for such rhythm disorders. Variability of ECG recording makes predictability analysis challenging especially when execution time is of prime importance in tackling resuscitative attempts for MVA. Noisy data needs ltering and preprocessing for effective analysis. Portable devices need more of this ltering prior to data input. Deterministic probabilistic nite state automata (DPFA) which generates a probability strings from the broad morphologic patterns of an ECG can generate a classier data for the algorithm without preprocessing for atrial high rate episodes (AHRE). DPFA can be effectively used for atrial tachyarrhythmias for predictive analysis. The method we suggest is use of optimal classier set for prediction of malignant ventricular arrhythmias and use of DFPA for atrial arrhythmias. Here traditional practices of heart rate variability based support vector machine (SVM), discrete wavelet transform (DWT), principal component analysis (PCA), deep neural network (DNN), convoutional neural network (CNN) or CNN with long term memory (LSTM) can be outperformed. AICD - automatic implantable cardiac debrillator, MVA - Malignant Ventricular Arrhythmias, VT - ventricular tachycardia, VF - ventricular brillation,DFPA deterministic probabilistic nite state automata, SVM -Support Vector Machine, DWT discrete wavelet transform, PCA principal component analysis, DNN deep neural network, CNN convoutional neural network, Convoutional LSTM Long short term memory,RNN recurrent neural network

scholarly journals ELECTRONIC NOSE FOR ANCHOVY FRESHNESS MONITORING BASED ON SENSOR ARRAY AND PATTERN RECOGNITION METHODS: PRINCIPAL COMPONENTS ANALYSIS, LINEAR DISCRIMINANT ANALYSIS AND SUPPORT VECTOR MACHINE

2014 ◽  
pp. 61-67
Author(s):  
A. Amari ◽  
N. El Bari ◽  
B. Bouchikhi
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Tin Dioxide ◽  
Principal Component ◽  
Support Vector ◽  
Current Pattern ◽  
Linear Discriminant ◽  
Pattern Recognition Methods

An electronic nose based system, which employs an array of six inexpensive commercial gas sensors based on tin dioxide (Figaro Engineering Inc., Japan), has been used to analyse the freshness states of anchovies. Fresh anchovies were stored in a refrigerator at 4 ± 1°C over a period of 15 days. Electronic nose measurements need no sample preparation and the results indicated that the spoilage process of anchovies could be followed by using this technique. Conductance responses of volatile compounds produced during storage of anchovy were monitored and the result were analysed by multivariate analysis methods. In this paper principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate whether the electronic nose was able to distinguishing among different freshness states (fresh, moderated and non-fresh samples). The loadings analysis was used to identify the sensors responsible for discrimination in the current pattern file. Therefore, the support vector machines (SVM) method was applied to the new subset, with only the selected sensors, to confirm that a subset of a few sensors can be chosen to explain all the variance. The results obtained prove that the electronic nose can discriminate successfully different freshness state using LDA analysis. Some sensors have the highest influence in the current pattern file for electronic nose. Support vector machine (SVM) model, applied to the new subset of sensors show the good performance.

TIME-FREQUENCY PROCESSING METHOD OF EPILEPTIC EEG SIGNALS

2015 ◽  
Vol 27 (02) ◽  
pp. 1550015 ◽  
Author(s):  
Assya Bousbia-Salah ◽  
Malika Talha-Kedir
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Statistical Features ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
Time Frequency ◽  
Eeg Recordings

Wavelet transform decomposition of electroencephalogram (EEG) signals has been widely used for the analysis and detection of epileptic seizure of patients. However, the classification of EEG signals is still challenging because of high nonstationarity and high dimensionality. The aim of this work is an automatic classification of the EEG recordings by using statistical features extraction and support vector machine. From a real database, two sets of EEG signals are used: EEG recorded from a healthy person and from an epileptic person during epileptic seizures. Three important statistical features are computed at different sub-bands discrete wavelet and wavelet packet decomposition of EEG recordings. In this study, to select the best wavelet for our application, five wavelet basis functions are considered for processing EEG signals. After reducing the dimension of the obtained data by linear discriminant analysis and principal component analysis (PCA), feature vectors are used to model and to train the efficient support vector machine classifier. In order to show the efficiency of this approach, the statistical classification performances are evaluated, and a rate of 100% for the best classification accuracy is obtained and is compared with those obtained in other studies for the same dataset. However, this method is not meant to replace the clinician but can assist him for his diagnosis and reinforce his decision.

Exploration of noisy data in differential electronic nose

2016 ◽  
Vol 35 (4) ◽  
pp. 1382-1392
Author(s):  
Stanislaw Osowski ◽  
Krzysztof Siwek ◽  
Tomasz Grzywacz
Keyword(s):  
Electronic Nose ◽  
Support Vector ◽  
Noisy Environment ◽  
Classifier Systems ◽  
Content Type ◽  
On Line ◽  
Sensor Signals ◽  
High Level ◽  
Baseline Estimation

Purpose – The paper is concerned with exploration of sensor signals in differential electronic nose. It is a special type of nose, which applies double sensor matrices and exploits only their differential signals, which are used in recognition of patterns associated with them. The purpose of this paper is to study the application of differential nose in dynamic measurement of aroma of 11 brands of cigarettes. Design/methodology/approach – The most important task in pattern recognition using electronic nose is its resistance to the noise corrupting the measurement. The authors will analyze and compare the performance of the nose in the noisy environment by applying two classifier systems: the support vector machine (SVM) and random forest (RF) of decision trees. Findings – On the basis of numerical experiments the authors have found that application of SVM as the classifier in the electronic nose is more advantageous than RF, especially at high level of noise and small number of measuring sensors. Its application allowed to recognize 11 brands of cigarettes with the accuracy close to 100 percent. Practical implications – Thanks to application of two identical sensors working in a differential mode the authors avoid the baseline estimation and thus the solution is well suited for on-line dynamic measurements of the process. Originality/value – The paper has studied the advantages and limitations of the differential electronic nose following from the existence of the noise, corrupting the measurements. It has pointed an important role of the applied classifier system in getting the electronic nose of the highest quality.

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