scholarly journals RESEARCH ON DESIGNING COMPOSITE TECHNIQUES FOR OBTAINING THE 3D HYBRID COMPOSITES WITH CONDUCTIVE AND SEMICONDUCTIVE PROPERTIES FOR SENSORS AND ACTUATORS

2019 ◽  
Vol 2019 ◽  
pp. 196-199
Author(s):  
Raluca Maria AILENI ◽  
Laura CHIRIAC
Keyword(s):  
Hybrid Composites ◽  
Orthogonal Transformation ◽  
Principal Component ◽  
Rf Plasma ◽  
Sensors And Actuators ◽  
Advanced Technologies ◽  
Dependent Variables ◽  
3D Composite ◽  
Orthogonal Set ◽  
Plasma Microwave

In the paper are presented several aspects concerning the experimental preparation for sensors and actuators by using the factorial scheme based on independent and dependent variables and principal component analysis. The leading technologies envisaged are the classical ones (padding, coating, and printing) and advanced technologies such as RF plasma, microwave, and 3D printing. PCA is a statistical procedure well known by researchers and is based on orthogonal transformation of the variables possible correlated into a set of variable linearly uncorrelated (PC). The resulting vectors are a linear combination of the variables and contain x observation and represent an uncorrelated orthogonal set. Besides, in this paper are presented several technological flows for obtaining conductive or semiconductive 3D composite materials by using the standard and advanced technologies above mentioned

scholarly journals COMPARISON OF FUZZY SUBTRACTIVE CLUSTERING AND FUZZYC-MEANS

Kursor ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Annisa Eka Haryati ◽  
Sugiyarto Sugiyarto ◽  
Rizki Desi Arindra Putri
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Clustering ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Subtractive Clustering ◽  
Validity Index ◽  
Fuzzy Clustering Method ◽  
Dependent Variables ◽  
Better Than

Multivariate statistics have related problems with large data dimensions. One method that can be used is principal component analysis (PCA). Principal component analysis (PCA) is a technique used to reduce data dimensions consisting of several dependent variables while maintaining variance in the data. PCA can be used to stabilize measurements in statistical analysis, one of which is cluster analysis. Fuzzy clustering is a method of grouping based on membership values ​​that includes fuzzy sets as a weighting basis for grouping. In this study, the fuzzy clustering method used is Fuzzy Subtractive Clustering (FSC) and Fuzzy C-Means (FCM) with a combination of the Minkowski Chebysev distance. The purpose of this study was to compare the cluster results obtained from the FSC and FCM using the DBI validity index. The results obtained indicate that the results of clustering using FCM are better than the FSC.

scholarly journals Electric Load Forecasting for Internet of Things Smart Home Using Hybrid PCA and ARIMA Algorithm

2021 ◽  
pp. 425-430
Author(s):  
Hamdi W. Rotib ◽  
◽  
Muhammad B. Nappu ◽  
Zulkifli Tahir ◽  
Ardiaty Arief ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Smart Home ◽  
Load Forecasting ◽  
Principal Component ◽  
Touch Screen ◽  
Infrared Light ◽  
Raspberry Pi ◽  
Electric Load ◽  
Sensors And Actuators ◽  
Electric Load Forecasting

Many types of research have been conducted for the development of Internet of Things (IoT) devices and energy consumption forecasting. In this research, the electric load forecasting is designed with the development of microcontrollers, sensors, and actuators, added with cameras, Liquid Crystal Display (LCD) touch screen, and minicomputers, to improve the IoT smart home system. Using the Python program, Principal Component Analysis (PCA) and Autoregressive Integrated Moving Average (ARIMA) algorithms are integrated into the website interface for electric load forecasting. As provisions for forecasting, a monthly dataset is needed which consists of electric current variables, number of individuals living in the house, room light intensity, weather conditions in terms of temperature, humidity, and wind speed. The main hardware parts are ESP32, ACS712, electromechanical relay, Raspberry Pi, RPi Camera, infrared Light Emitting Diode (LED), Light Dependent Resistor (LDR) sensor, and LCD touch screen. While the main software applications are Arduino Interactive Development Environment (IDE), Visual Studio Code, and Raspberry Pi OS, added with many libraries for Python 3 IDE. The experimental results provided the fact that PCA and ARIMA can predict short-term household electric load accurately. Furthermore, by using Amazon Web Services (AWS) cloud computing server, the IoT smart home system has excellent data package performances.

scholarly journals Sensor and Actuator Needs for More Intelligent Gas Turbine Engines

2010 ◽  
Author(s):  
Sanjay Garg ◽  
Klaus Schadow ◽  
Wolfgang Horn ◽  
Hugo Pfoertner ◽  
Ion Stiharu
Keyword(s):  
Gas Turbine ◽  
State Of The Art ◽  
Life Time ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Defense Acquisition ◽  
Sensors And Actuators ◽  
Efficient Operation ◽  
Aerospace Research ◽  
Advanced Technologies

This paper provides an overview of the controls and diagnostics technologies, that are seen as critical for more intelligent gas turbine engines (GTE), with an emphasis on the sensor and actuator technologies that need to be developed for the controls and diagnostics implementation. The objective of the paper is to help the “Customers” of advanced technologies, defense acquisition and aerospace research agencies, understand the state-of-the-art of intelligent GTE technologies, and help the “Researchers” and “Technology Developers” for GTE sensors and actuators identify what technologies need to be developed to enable the “Intelligent GTE” concepts and focus their research efforts on closing the technology gap. To keep the effort manageable, the focus of the paper is on “On-Board Intelligence” to enable safe and efficient operation of the engine over its life time, with an emphasis on gas path performance.

scholarly journals Contribution to the Chromatic Characterization of Unifloral Honeys from Galicia (NW Spain)

Foods ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 233 ◽  
Author(s):  
Olga Escuredo ◽  
María Shantal Rodríguez-Flores ◽  
Sergio Rojo-Martínez ◽  
María Carmen Seijo
Keyword(s):  
Physicochemical Properties ◽  
Principal Component ◽  
Total Content ◽  
Physicochemical Characteristics ◽  
Dependent Variables ◽  
Close Relationship ◽  
Forward Stepwise ◽  
Unifloral Honey

Honey color and other physicochemical characteristics depend mainly on the botanical and geographical origin. The study of these properties could make easier a correct classification of unifloral honey. This work determined the palynological characteristics and some physicochemical properties such as pH, electrical conductivity, and color (Pfund scale and the CIELa*b* coordinates), as well as the total content of the bioactive compounds phenols and flavonoids of ninety-three honey samples. Samples were classified as chestnut, blackberry, heather, eucalyptus, and honeydew honey. The study showed a close relationship between the physicochemical variables and the botanical origin. The five types of honey presented different physicochemical properties among them. A principal component analysis showed that Hue, lightness, b*, and Chroma variables were important for the honey types classification, followed by Erica pollen, pH, Cytisus, and Castanea variables. A forward stepwise regression analysis was performed introducing as dependent variables the color (mm Pfund) and the Chroma and the Hue variables. The regression models obtained explained 86%, 74%, and 86% of the variance of the data, respectively. The combination of the chromatic and physicochemical and pollen variables through the use of multivariable methods was optimal to characterize and group the honey samples studied.

Research on the Relationship Between Turning Temperature Rising and Turning Vibration Based on Particle Swarm Optimization

2020 ◽  
Author(s):  
Daquan Li ◽  
Shuncai Li ◽  
Yuting Hu ◽  
Ziyao Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Temperature Rise ◽  
Particle Swarm ◽  
Principal Component ◽  
Interface Temperature ◽  
Depth Of Cut ◽  
Swarm Optimization ◽  
Independent Variables ◽  
Dependent Variables

In order to study the correlation between turning temperature, turning vibration and turning parameters, a prediction model for turning temperature (workpiece-tool interface temperature) was established. Through the turning test, the turning temperature near the knifepoint was collected by the infrared thermometer, and the time domain signal of turning vibration was collected by the three-way acceleration sensor. Principal component analysis (PCA) and response surface method (RSM) were used to analyze the characteristic values of vibration acceleration and turning temperature under different turning parameters. The analysis shows that the cutting depth (depth of cut) is the key factor that affects the turning vibration and the turning temperature. Model A was established with turning parameters as independent variables and turning temperature rise as dependent variables, and model C was established with turning parameters and turning vibration as independent variables and turning temperature rise as dependent variables. B and D are the models obtained by using adaptive particle swarm optimization (APSO) algorithm based on A and C. According to the test results of the model, the correlation coefficient of the prediction model is D>C=B>A, indicating that the multiple regression model B and D optimized by APSO can better predict the turning temperature rise.

scholarly journals COMPOSED TECHNIQUES FOR OBTAINING OF THE 3D HYBRID COMPOSITES FOR ATTENUATION OF ELECTROMAGNETIC FIELD

2019 ◽  
Vol 2019 ◽  
pp. 208-211
Author(s):  
Raluca Maria AILENI ◽  
Laura CHIRIAC
Keyword(s):  
Electromagnetic Field ◽  
Electromagnetic Radiation ◽  
Hybrid Composites ◽  
Principal Component ◽  
Advanced Materials ◽  
Electromagnetic Properties ◽  
Plan Selection ◽  
Conductive Yarns ◽  
Window Blinds ◽  
The Military

In the paper are presented several aspects concerning the composed methods for obtaining the materials capable of reducing the electromagnetic field by reflection/absorption. This study offers a structured presentation about advanced materials with electromagnetic properties that can be used to develop screens for electromagnetic attenuation. Besides is presented a factorial plan, selection of the eigenvectors, and analysis based on principal component. The ACP (principal component analysis) could solve the problems in selecting the optimal parameters used in experiments by reducing the complexity of the data and analyzing the variance of the variables from influencing factors. The investigation of the methods for electromagnetic radiation screens development is attractive due to the area of application, such as protection of the electronic designed for hospitals and special applications in the military area and in the field of transport, but also for the protection of the houses required due to the use of electronic devices (phones, PC, TV) involving mobile or Wifi/WLAN networks. In general, for the attenuation of electromagnetic radiation in the home can use paints, curtains, window blinds or carpets made of fabric, knitted or non-woven materials, with conductive yarns, fibers or polymeric film with adequate electroconductive or electromagnetic properties.

Automatic Face Emotion Recognition With the Aid of Probability-Based Bird Swarm-Trained Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 1-24
Author(s):  
Bhagyashri Devi ◽  
M. Mary Synthuja Jain Preetha
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Face Detection ◽  
Orthogonal Transformation ◽  
Principal Component ◽  
Discrete Wavelet ◽  
The Face ◽  
Bird Swarm Algorithm ◽  
Trained Neural Network ◽  
Face Emotion Recognition

This paper intends to develop a novel FER model, which consists of four stages: (1) face detection, (2) feature extraction, (3) dimension reduction, and (4) classification. In this context, the face detection is done using Viola Jones method (VJ). It is the first object recognition model to offer better recognition rates in real-time. Further, features extraction techniques like local binary pattern (LBP) and discrete wavelet transform (DWT) are used for extracting the features from face detected images. Moreover, the dimension reduction of features is done using principal component analysis (PCA), which is an arithmetical process that exploits an orthogonal transformation to exchange a group of annotations of probably interrelated constraints. The classification procedure is performed using neural network (NN), with the new training algorithm called bird swarm algorithm, which is modified based on probability and hence termed as probability-based BSA (P-BSA).

Applied-Information Technology in Fusion SVM Partial Least Squares Analysis of Experimental Data in the Body of PET

2014 ◽  
Vol 951 ◽  
pp. 185-188
Author(s):  
Xian Zhou ◽  
Ying Xiong ◽  
Wang Ping Xiong ◽  
Ling Zhu Xiong
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Model Fitting ◽  
Principal Component ◽  
The Body ◽  
Support Vector ◽  
Linear Distribution ◽  
Partial Least Squares Analysis ◽  
Dependent Variables

Experimental data for the human body PET multi-variable, non-linear distribution and other characteristics, the use of fusion partial least squares support vector machine variables effectively extracted from the principal component, reducing the number of variables and the exclusion of noise information to construct a linear regression with the dependent variables model, fitting the model has good accuracy and generalization for PET clinical trials provide effective technical support and research ideas.

Constrained Principal Component Analysis: Various Applications

2002 ◽  
Vol 27 (2) ◽  
pp. 105-145 ◽  
Author(s):  
Michael A. Hunter ◽  
Yoshio Takane
Keyword(s):  
Principal Component Analysis ◽  
Bootstrap Method ◽  
Principal Component ◽  
Component Analysis ◽  
Psychological Research ◽  
Unified Framework ◽  
Dependent Variables ◽  
Criterion Variables ◽  
Order Structures ◽  
The Bootstrap Method

Constrained Principal Component Analysis (CPCA) is a method for structural analysis of multivariate data. It combines regression analysis and principal component analysis into a unified framework. This article provides example applications of CPCA that illustrate the method in a variety of contexts common to psychological research. We begin with a straightforward situation in which the structure of a set of criterion variables is explored using a set of predictor variables as row (subjects) constraints. We then illustrate the use of CPCA using constraints on the columns of a set of dependent variables. Two new analyses, decompositions into finer components and fitting higher order structures, are presented next, followed by an illustration of CPCA on contingency tables, and CPCA of residuals that includes assessing reliability using the bootstrap method.

A LEAST-SQUARES MODEL TO ORTHOGONAL LINEAR DISCRIMINANT ANALYSIS

2010 ◽  
Vol 24 (04) ◽  
pp. 635-650 ◽  
Author(s):  
TAIPING ZHANG ◽  
BIN FANG ◽  
YUAN YAN TANG ◽  
ZHAOWEI SHANG ◽  
GUANGHUI HE
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Least Squares ◽  
Orthogonal Transformation ◽  
Computational Cost ◽  
Data Sets ◽  
Linear Discriminant ◽  
Standard Data ◽  
The Difference ◽  
Orthogonal Set

Orthogonal transformation can delete the correlations among candidate features such that the extracted features do not disturb each other. An orthogonal set of discriminant vectors is more powerful than the classical discriminant vectors. In this paper, we present a new orthogonal linear discriminant analysis (OLDA) model based on least-squares approximation called LS-OLDA for pattern classification, which aims to find an orthogonal transformation W and a diagonal matrix D such that the difference between [Formula: see text] and WDWT is minimized in the least-squares sense, and the trace of D is maximized simultaneously. Theoretical analysis shows that the proposed model coincides with classical OLDA criterion. The experimental results on different standard data sets compared with related methods show that LS-OLDA achieves or approximates closely to the best accuracy, and has lower computational cost.

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