Prediction of Electrical Resistance with Conductive Sewing Patterns by Combining Artificial Neural Networks and Multiple Linear Regressions

Prediction of Electrical Resistance with Conductive Sewing Patterns by Combining Artificial Neural Networks and Multiple Linear Regressions

This study aims to estimate the impact of sewing thread patterns on changes in the resistance of conductive yarns coated with silver paste. Firstly, the structure of the conductive yarns was examined, and various variations in the length and angle of individual sewing stitches were observed and anal...

Full description

Bibliographic Details
Main Authors:	JunHyeok Jang, JooYong Kim
Format:	Article
Language:	English
Published:	MDPI AG 2023-10-01
Series:	Polymers
Subjects:	conductive yarn wearable technology artificial neural network multiple linear regression analysis optimization design
Online Access:	https://www.mdpi.com/2073-4360/15/20/4138

Similar Items

Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece
by: Christos Mattas, et al.
Published: (2021-08-01)

Concept of a Wearable Temperature Sensor for Intelligent Textile
by: Robert Hudec, et al.
Published: (2020-01-01)

The Development of an IMU Integrated Clothes for Postural Monitoring Using Conductive Yarn and Interconnecting Technology
by: Sung-Won Kang, et al.
Published: (2017-11-01)

Linear Regression Model of the Ash Mass Fraction and Electrical Conductivity for Slovenian Honey
by: Mojca Jamnik, et al.
Published: (2008-01-01)

Highly Stretchable and Flexible Melt Spun Thermoplastic Conductive Yarns for Smart Textiles
by: G. M. Nazmul Islam, et al.
Published: (2020-11-01)

Sensor Selection for Tidal Volume Determination via Linear Regression—Impact of Lasso versus Ridge Regression
by: Bernhard Laufer, et al.
Published: (2023-08-01)

Artificial Neural Networks and Multiple Linear Regression for Filling in Missing Daily Rainfall Data
by: Ioannis Papailiou, et al.
Published: (2022-09-01)

Research progress in preparation of conductive yarn and its application in smart wearable devices
by: MA Pei-pei, et al.
Published: (2021-10-01)

Durability Study of Embroidery Electrode Made of Stainless Steel Blended Yarn
by: Tianyu Wang, et al.
Published: (2022-10-01)

Predicting microbial response to anthropogenic environmental disturbances using artificial neural network and multiple linear regression
by: Saloni Sharma, et al.
Published: (2021-06-01)

Multiple Linear Regression Analysis of Canada’s Freight Transportation Framework
by: Jamileh Yousefi, et al.
Published: (2023-05-01)

Single-Layer Pressure Textile Sensors with Woven Conductive Yarn Circuit
by: Gaeul Kim, et al.
Published: (2020-04-01)

Predicting the Chemical Attributes of Fresh Citrus Fruits Using Artificial Neural Network and Linear Regression Models
by: Adel M. Al-Saif, et al.
Published: (2022-11-01)

Multiple Linear Regression versus Automatic Linear Modelling
by: S. Genç, et al.
Published: (2024-02-01)

Reasonably conduct the multiple linear regression analysis combined with the propensity score analysis
by: Hu Chunyan, et al.
Published: (2022-12-01)

Conductive Electrospun Polyaniline/Polyvinylpyrrolidone Nanofibers: Electrical and Morphological Characterization of New Yarns for Electronic Textiles
by: Patrícia Perdigão, et al.
Published: (2020-04-01)

Development of Stainless Steel Yarn with Embedded Surface Mounted Light Emitting Diodes
by: Abdella Ahmmed Simegnaw, et al.
Published: (2022-04-01)

Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost
by: Tea Sokač Cvetnić, et al.
Published: (2024-03-01)

Dynamics of lorentz force and cross-diffusion effects on ethylene glycol based hybrid nanofluid flow amidst two parallel plates with variable electrical conductivity: A multiple linear regression analysis
by: Gadamsetty Revathi, et al.
Published: (2023-01-01)

Linear and Non-Linear Regression Analysis on the Prediction of Compressive Strength of Sodium Hydroxide Pre-Treated Crumb Rubber Concrete
by: Hamza Aamir, et al.
Published: (2023-08-01)

Accurate Prediction of Concentration Changes in Ozone as an Air Pollutant by Multiple Linear Regression and Artificial Neural Networks
by: Svajone Bekesiene, et al.
Published: (2021-02-01)

Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece
by: Stavroula Dimitriadou, et al.
Published: (2022-07-01)

Movement Recognition through Inductive Wireless Links: Investigation of Different Fabrication Techniques
by: Giuseppina Monti, et al.
Published: (2023-09-01)

Biosafety management strategy based on multiple linear regression model
by: Zhao Chao, et al.
Published: (2024-01-01)

Dry Weight Prediction of <i>Wedelia trilobata</i> and <i>Wedelia chinensis</i> by Using Artificial Neural Network and MultipleLinear Regression Models
by: Ahmad Azeem, et al.
Published: (2023-05-01)

Modeling and Optimization of Process Parameters for Nutritional Enhancement in Enzymatic Milled Rice by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)
by: Anjineyulu Kothakota, et al.
Published: (2021-12-01)

Electromechanical Characterization of Commercial Conductive Yarns for E-Textiles
by: Yu Chen, et al.
Published: (2023-08-01)

PERBANDINGAN METODE MULTIPLE LINEAR REGRESSION (MLR) DAN REGRESSION KRIGING (RK) DALAM PEMETAAN KETEBALAN TANAH DIGITAL
by: Muhammad Fauzan Ramadhan, et al.
Published: (2023-01-01)

The Application of Multiple Linear Regression and Artificial Neural Network Models for Yield Prediction of Very Early Potato Cultivars before Harvest
by: Magdalena Piekutowska, et al.
Published: (2021-04-01)

PREDICTION OF COMPUTER GAME ADDICTION IN CHILDREN USING DEVELOPED ARTIFICIAL NEURAL NETWORKS (ANN) AND MULTIPLE LINEAR REGRESSION (MLR) MODELS
by: Esma UZUNHİSARLIKCI, et al.
Published: (2021-12-01)

A Novel Concept to Predict Cotton Yarns’ Coefficient of Variation and Hairiness Index by Online Collected Data During Winding Process
by: Xu Duo, et al.
Published: (2022-12-01)

Internal Factor Analysis of Non-Performing Loans Using Multiple Linear Regression Method
by: Muhammad Irfandi, et al.
Published: (2023-06-01)

Implementation of Multiple Linear Regression Methods as Prediction of Village Spending on Village Financial Management System
by: Nisa Hanum Harani, et al.
Published: (2019-12-01)

Textile-Based Weft Knitted Strain Sensors: Effect of Fabric Parameters on Sensor Properties
by: William Richard Kennon, et al.
Published: (2013-08-01)

Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors
by: Avval Zhila Mohajeri, et al.
Published: (2015-01-01)

A Comparison of Multiple Linear Regression and Random Forest Regression to Evaluate the Price of Residential Units (Case Study: North Valiasr, Tabriz)
by: Mohammad Ali Kooshesh Vatan, et al.
Published: (2021-09-01)

The Use of Multiple Linear Regression Model for Predicting The Influence of Weather to The Prevalence of Newcastle Disease in Chicken in North Aceh
by: Mahdi Abrar
Published: (2008-01-01)

Methods for the determination of the linear density of yarns from packages
by: 8096 British Standards Institution

Evaluation of Artificial Neural Network and Multiple Linear Regression Models to Estimate the Daily Missing data Flow (Runoff) in (Case Study: Santeh Gauging Station- Kordestan Province)
by: M. Isazadeh, et al.
Published: (2018-02-01)

Quantitative Structure-Activity Relationship Study on Thiosemicarbazone Derivatives as Antitubercular agents Using Artificial Neural Network and Multiple Linear Regression
by: Mehdi Mousavi, et al.
Published: (2020-05-01)