scholarly journals Application of Feature Extraction Methods for Chemical Risk Classification in the Pharmaceutical Industry

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5753
Author(s):  
Mariusz Topolski
Keyword(s):  
Risk Assessment ◽  
Feature Extraction ◽  
Computer Network ◽  
Extraction Methods ◽  
Sensor Data ◽  
Scree Plot ◽  
Kaiser Criterion ◽  
Chemical Threats ◽  
Real Time Simulations ◽  
Discriminant Power

The features that are used in the classification process are acquired from sensor data on the production site (associated with toxic, physicochemical properties) and also a dataset associated with cybersecurity that may affect the above-mentioned risk. These are large datasets, so it is important to reduce them. The author’s motivation was to develop a method of assessing the dimensionality of features based on correlation measures and the discriminant power of features allowing for a more accurate reduction of their dimensions compared to the classical Kaiser criterion and assessment of scree plot. The method proved to be promising. The results obtained in the experiments demonstrate that the quality of classification after extraction is better than using classical criteria for estimating the number of components and features. Experiments were carried out for various extraction methods, demonstrating that the rotation of factors according to centroids of a class in this classification task gives the best risk assessment of chemical threats. The classification quality increased by about 7% compared to a model where feature extraction was not used and resulted in an improvement of 4% compared to the classical PCA method with the Kaiser criterion, with an evaluation of the scree plot. Furthermore, it has been shown that there is a certain subspace of cybersecurity features, which complemented with the features of the concentration of volatile substances, affects the risk assessment of chemical hazards. The identified cybersecurity factors are the number of packets lost, incorrect Logins, incorrect sensor responses, increased email spam, and excessive traffic in the computer network. To visualize the speed of classification in real-time, simulations were carried out for various systems used in Industry 4.0.

Gas Turbine Fault Detection and Diagnosis Using Nonlinear Feature Extraction Methods

2005 ◽  
Author(s):  
Joydeb Mukherjee ◽  
Venkataramana B. Kini ◽  
Sunil Menon ◽  
Lalitha Eswara
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Gas Turbine ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Extraction Methods ◽  
Fault Detection And Diagnosis ◽  
Sensor Data ◽  
Detection And Diagnosis

Accurate gas turbine fault detection and diagnosis (FDD) is essential to improving airline safety as well as in reducing airline costs associated with delays and cancellations. In this paper, we present FDD methods based on feature extraction methods using nonlinear principal component analysis (NLPCA) and curvilinear component analysis (CCA). The underlying principle of both methods is to find the most representative feature space corresponding to gas turbine normal and faulty operations. During operation, new sensor data is located in this feature space and then it is determined whether a particular fault is indicated. NLPCA is an extension of linear PCA methods to the nonlinear domain; therefore, it is intrinsically better suited to nonlinear domains such as the gas turbine engine. The CCA method is another approach to clustering having superior properties for determining cluster manifolds automatically compared to the popular selforganizing map (SOM) method of clustering. The developed methods are tested with snapshot data collected at takeoff, both normal and faulty, from a turbofan gas turbine propulsion engine and the results are presented.

A Review on Feature Extraction Methods for Image Analysis

2020 ◽  
Author(s):  
Vricha Chavan ◽  
​Jimit Shah ◽  
Mrugank Vora ◽  
Mrudula Vora ◽  
Shubhashini Verma
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Extraction Methods

scholarly journals Feature Extraction for Finger-Vein-Based Identity Recognition

2021 ◽  
Vol 7 (5) ◽  
pp. 89
Author(s):  
George K. Sidiropoulos ◽  
Polixeni Kiratsa ◽  
Petros Chatzipetrou ◽  
George A. Papakostas
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Methods ◽  
Identity Recognition ◽  
Learning Framework ◽  
Finger Vein ◽  
The Past ◽  
Biometric Systems ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

scholarly journals Bipartite Network of Interest (BNOI): Extending Co-Word Network with Interest of Researchers Using Sensor Data and Corresponding Applications as an Example

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1668
Author(s):  
Zongming Dai ◽  
Kai Hu ◽  
Jie Xie ◽  
Shengyu Shen ◽  
Jie Zheng ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Extraction Methods ◽  
Knowledge Network ◽  
Sensor Data ◽  
Support Vector ◽  
Bipartite Network ◽  
Classification Models ◽  
Text Data ◽  
Domain Experts ◽  
Problems And Solutions

Traditional co-word networks do not discriminate keywords of researcher interest from general keywords. Co-word networks are therefore often too general to provide knowledge if interest to domain experts. Inspired by the recent work that uses an automatic method to identify the questions of interest to researchers like “problems” and “solutions”, we try to answer a similar question “what sensors can be used for what kind of applications”, which is great interest in sensor- related fields. By generalizing the specific questions as “questions of interest”, we built a knowledge network considering researcher interest, called bipartite network of interest (BNOI). Different from a co-word approaches using accurate keywords from a list, BNOI uses classification models to find possible entities of interest. A total of nine feature extraction methods including N-grams, Word2Vec, BERT, etc. were used to extract features to train the classification models, including naïve Bayes (NB), support vector machines (SVM) and logistic regression (LR). In addition, a multi-feature fusion strategy and a voting principle (VP) method are applied to assemble the capability of the features and the classification models. Using the abstract text data of 350 remote sensing articles, features are extracted and the models trained. The experiment results show that after removing the biased words and using the ten-fold cross-validation method, the F-measure of “sensors” and “applications” are 93.2% and 85.5%, respectively. It is thus demonstrated that researcher questions of interest can be better answered by the constructed BNOI based on classification results, comparedwith the traditional co-word network approach.

scholarly journals Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System

2021 ◽  
Vol 11 (15) ◽  
pp. 6748
Author(s):  
Hsun-Ping Hsieh ◽  
Fandel Lin ◽  
Jiawei Jiang ◽  
Tzu-Ying Kuo ◽  
Yu-En Chang
Keyword(s):  
New York ◽  
Feature Extraction ◽  
Real World ◽  
Extraction Methods ◽  
Real World Data ◽  
Urban Dynamics ◽  
Bike Sharing ◽  
The Government ◽  
Short Time

Research on flourishing public bike-sharing systems has been widely discussed in recent years. In these studies, many existing works focus on accurately predicting individual stations in a short time. This work, therefore, aims to predict long-term bike rental/drop-off demands at given bike station locations in the expansion areas. The real-world bike stations are mainly built-in batches for expansion areas. To address the problem, we propose LDA (Long-Term Demand Advisor), a framework to estimate the long-term characteristics of newly established stations. In LDA, several engineering strategies are proposed to extract discriminative and representative features for long-term demands. Moreover, for original and newly established stations, we propose several feature extraction methods and an algorithm to model the correlations between urban dynamics and long-term demands. Our work is the first to address the long-term demand of new stations, providing the government with a tool to pre-evaluate the bike flow of new stations before deployment; this can avoid wasting resources such as personnel expense or budget. We evaluate real-world data from New York City’s bike-sharing system, and show that our LDA framework outperforms baseline approaches.

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

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