scholarly journals Multisensor systems for gas analysis: optimization of arrays for classification of pharmaceutical products

2004 ◽  
Vol 7 (2) ◽  
pp. 207-216 ◽  
Author(s):  
I.V. Kruglenko ◽  
Keyword(s):  
Pharmaceutical Products ◽  
Gas Analysis ◽  
Multisensor Systems

scholarly journals Multisensory Gas Analysis System Based on Reconstruction Attractors

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 964
Author(s):  
Olga Cheremisina ◽  
Vladimir Kulagin ◽  
Suad El-Saleem ◽  
Evgeny Nikulchev
Keyword(s):  
Electronic Nose ◽  
Gas Sensors ◽  
Fractal Dimensions ◽  
Image Formation ◽  
Gas Analysis ◽  
Multisensor Systems ◽  
Multisensor System ◽  
Analysis System

The paper describes the substance image formation based on the measurements by multisensor systems and the possibility of the development of a gas analysis device like an electronic nose. Classification of gas sensors and the need for their application for the recognition of difficult images of multicomponent air environments are considered. The image is formed based on stochastic transformations, calculations of correlation, and fractal dimensions of reconstruction attractors. The paper shows images created for substances with various structures that were received with the help of a multisensor system under fixed measurement conditions.

Artificial Intelligence Based Technique for the Classification of Incipient Faults in Power Transformer

2015 ◽  
Vol 785 ◽  
pp. 29-33
Author(s):  
Fathiah Zakaria ◽  
Dalina Johari ◽  
Ismail Musirin
Keyword(s):  
Power System ◽  
Power Transformer ◽  
Optimization Technique ◽  
Gas Analysis ◽  
Optimization Techniques ◽  
Computational Technique ◽  
Ann Model ◽  
Great Ability ◽  
Incipient Faults

Power transformer has been identified as crucial and vital equipment in power system. Any disturbance such as faults will result in immense impact to the whole power system. This paper presents the development of an Evolutionary Programming (EP) – Taguchi Method (TM) – Artificial Neural Network (ANN) based technique for the classification of incipient faults in power transformer using Dissolved Gas Analysis (DGA) method based on historical industrial data. It involved the development of ANN model and embedding TM and EP as the optimization techniques in order to enhance the system accuracy and efficiency. ANN is a powerful computational technique that mimics how human brain process information. It has great ability to learn from experiences and examples, hence greatly suitable for classification, pattern recognition and forecasting purposes. In designing the ANN model, there are parameters which need to be chosen wisely. However, there is no systematic ways and guidelines to select the optimal ANN parameters. It is greatly dependent on the design knowledge and experiences of the experts. The process of finding suitable parameters is become difficult, tedious and time consuming, thus optimization technique is needed to overcome the shortcoming. In this study, TM and EP were employed as the optimization techniques to improve the ANN-based model. The findings obtained from the proposed technique have proved the effectiveness of both TM and EP in optimizing the ANN model. As a result, a reliable EP-TM-ANN based system has been successfully developed that can classify incipient faults in power transformer.

scholarly journals Fire-Fly Algorithm Based ANN Classification of Power Transformer Faults

2020 ◽  
Vol 9 (4) ◽  
pp. 1880-1884
Keyword(s):  
Wavelet Transforms ◽  
Performance Metrics ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Power Transformer ◽  
Gas Analysis ◽  
Phase Detection ◽  
Double Line ◽  
Waveform Data

Monitoring and estimating the states of the transformer during faulted phase condition is essential to continuity of supply. Varied techniques are proposed for faulted phase detection to improve condition assessment. In this paper, we propose a novel method to detect and classify power transformer faults using wavelet transform Multi Resolution Analysis (MRA) as feature extracted parameter vector and Fire-Fly Algorithm (FFA) based Artificial Neural network training as classification method. The observed Dissolved Gas Analysis (DGA) waveform data is analyzed with wavelet transforms (WT) to identify abnormalities which is supported by MRA. In MRA, the current, voltage and temperature of winding and oil are decomposed into high and low frequency components. The magnitude of components, signifies the feature vector, gives a detection criteria. After detecting feature vector, dominant coefficients of WT can be used to train the ANN with FFA based learning algorithm. Different types of faults are created on transformer such as Single Line-Ground (SLG), Line-Line (LL), Double Line-Ground LLG, Three phase fault (LLLG) for the analysis using WT and ANN. The detection and classification of the fault signal are executed and examined in different winding location and different fault conditions. Finally, the presented precise model recognizes the faults based on performance metrics with high classification accuracy for various classes.

scholarly journals Presence of a Novel Subtype of Bovine Hepacivirus in China and Expanded Classification of Bovine Hepacivirus Strains Worldwide into 7 Subtypes

Viruses ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 843 ◽  
Author(s):  
Gang Lu ◽  
Jiajun Ou ◽  
Jiawei Zhao ◽  
Shoujun Li
Keyword(s):  
Geographical Distribution ◽  
Bovine Serum ◽  
Genetic Relationships ◽  
Pharmaceutical Products ◽  
Serum Samples ◽  
Viral Contamination ◽  
Unique Genotype ◽  
Individual Donor

The newest member of the Hepacivirus genus, bovine hepacivirus (BovHepV), was first identified in cattle in 2015 and is a novel hepacivirus C virus (HCV)-like virus. This virus has been detected in five countries so far and is classified into four subtypes. Bovine serum is commonly used for cell cultures and is considered the major source of viral contamination of pharmaceutical products. In this study, bovine serum samples were collected from seven countries located in Asia, America, Oceania, and Europe and were tested for BovHepV RNA using nested PCR, in order to: (i) obtain more knowledge on the geographical distribution and subtypes of BovHepV; and (ii) detect the potential contamination of BovHepV in commercial bovine serum samples used for cell culture propagation. The results demonstrated that bovine serum samples from individual donor cattle in China contained BovHepV RNA. After PCR, sequencing, and assembly, the genomes of the Chinese BovHepV strains were obtained. Genetic analysis of the polyprotein gene revealed a protein identity of <77% and a nucleotide identity of <85% between the Chinese BovHepV strains and all other previously reported BovHepV strains. Using cut-off values for determination of HCV genotypes and subtypes, BovHepV strains worldwide were classified into one unique genotype and seven subtypes. The BovHepV strains identified in the present study were classified into a novel subtype, which was provisionally designated subtype G. The genetic relationships among the different BovHepV subtypes were further confirmed through phylogenetic analysis. The present study provides critical insights into BovHepV’s geographical distribution and genetic variability.

Large scale classification of spectral signatures

2015 ◽  
Vol 82 (12) ◽  
Author(s):  
Matthias Richter ◽  
Thomas Längle ◽  
Jürgen Beyerer
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Partial Least Squares ◽  
Large Scale ◽  
Expert Knowledge ◽  
Pharmaceutical Products ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Vector Machines

AbstractHyperspectral sensors are becoming cheaper, faster and more readily available. Apart from industry applications, manufacturers push to bring compact devices into the end-consumer market. This development gives rise to many interesting applications such as the identification of counterfeit pharmaceutical products or the classification of food stuffs. These applications require precise models of the underlying classes. However, building these models from expert knowledge is not feasible. In this paper, we propose to use machine learning techniques to infer a model of many classes from an annotated dataset instead. We investigate the use of three popular methods: support vector machines, random forest classifiers and partial least squares. In contrast to similar approaches using support vector machines, we restrict ourselves to the linear formulation and train the classifiers by solving the primal, instead of dual optimization problem. Our experiments on a large dataset show that the support vector machine approach is superior to random forests and partial least squares in classification accuracy as well as training time.

scholarly journals Application of Intelligent Sensors in Biomarker Detection Using Accurate Data Measurement and Calculation

2021 ◽  
Vol 2083 (3) ◽  
pp. 032035
Author(s):  
Hao Chen
Keyword(s):  
Cancer Biomarkers ◽  
Pharmaceutical Products ◽  
Diagnosis And Treatment ◽  
Basic Knowledge ◽  
Biomarker Detection ◽  
Health Issues ◽  
Mortality And Morbidity ◽  
Intelligent Sensors ◽  
Common Cancer

Abstract Cancer has been one of the most serious health issues of the 21st century. Although improvements in the treatment of cancer with new pharmaceutical products and technology remain a significant challenge for cancer biologists and oncologists. Early and accurate screening and analysis technology to diagnose the disease are essential for improving the survival rate and reducing mortality and morbidity. Scientists have discovered the clinical application of cancer biomarkers for cancer diagnosis and treatment. Biosensors technology appears to be the only hope for timely diagnosis and treatment of cancer, since they exhibit remarkable analytical performance. In this review, we will discuss about basic knowledge and classification of biosensors, common cancer biomarkers and some applications of biosensors in cancer biomarker detection.

scholarly journals Multilevel SVM and AI based Transformer Fault Diagnosis using the DGA Data

2021 ◽  
Vol 2 (3) ◽  
pp. 1-16
Author(s):  
GVSSN Srirama Sarma ◽  
Keyword(s):  
Graphical Representation ◽  
Gaussian Function ◽  
Gas Analysis ◽  
Kernel Functions ◽  
Fault Classification ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Training Samples ◽  
Fault State

The Dissolved Gas Analysis (DGA) is utilized as a test for the detection of incipient problems in transformers, and condition monitoring of transformers using software-based diagnosis tools has become crucial. This research uses dissolved gas analysis as an intelligent fault classification of a transformer. The Multilayer SVM technique is used to determine the classification of faults and the name of the gas. The learned classifier in the multilayer SVM is trained with the training samples and can classify the state as normal or fault state, which contains six fault categories. In this paper, polynomial and Gaussian functions are utilized to assess the effectiveness of SVM diagnosis. The results demonstrate that the combination ratios and graphical representation technique is more suitable as a gas signature and that the SVM with the Gaussian function outperforms the other kernel functions in diagnosis accuracy.

Classification of Fault and Failure Types Determined by Dissolved Gas Analysis for Transformers

2019 ◽  
Vol 14 (4) ◽  
pp. 1665-1674
Author(s):  
Yonghyun Kim ◽  
Dongjin Kweon ◽  
Taesik Park ◽  
Seonghwan Kim ◽  
Jang-Seob Lim
Keyword(s):  
Gas Analysis ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Failure Types

CLASSIFICATION OF COMPLEX MIXTURES WITH AN ELECTRONIC NOSE: THE CASE OF PHARMACEUTICAL PRODUCTS

2004 ◽  
Author(s):  
Giorgio Pennazza ◽  
Antonella Macagnano ◽  
Eugenio Martinelli ◽  
Roberto Paolesse ◽  
Corrado Di Natale ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Pharmaceutical Products ◽  
Complex Mixtures
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