scholarly journals Study on process of chemical fiber filament automatic doffing system based on simulation platform and machine learning

2021 ◽  
Vol 16 ◽  
pp. 155892502110548
Author(s):  
Hongxin Zhu ◽  
Kun Zou ◽  
Wenlan Bao
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Production Line ◽  
Simulation Platform ◽  
Chemical Fiber ◽  
Production Lines ◽  
The Neural Network ◽  
Spss Software ◽  
The Relationship

In recent years, a large number of automatic equipment has been introduced into the chemical fiber filament doffing production line, but the related research on the fully automatic production line technology is not yet mature. At present, it is difficult to collect data due to test costs and confidentiality. This paper proposes to develop a simulation platform for a chemical fiber filament doffing production line, which enables us to effectively obtain data and quantitatively study the relationship between the number of manual interventions and other process parameters of the production line. Considering that the parameter research is a multi-factor problem, an orthogonal test was designed by using SPSS software and was carried out by using a simulation platform. The multiple linear regression (MLR) and the neural network optimized by genetic algorithm were adopted to fit the relationship between the number of manual interventions and other parameters of the production line. The SPSS software was applied to obtain the standardized coefficients of the multiple linear regression fitting and the neural network mean impact value (MIV) algorithm was applied to obtain the magnitude and direction of the impact of different parameters on the number of manual interventions. The above results provide important reference for the design of similar new production lines and for the improvement of old production lines.

scholarly journals Predict the amount of Cu using the four Ca, Al, P, S Elements by Multiple Linear Regression Method

2021 ◽  
Vol 9 (9) ◽  
pp. 1088-1092
Author(s):  
Hamed Nazerian
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Method ◽  
Multivariate Linear Regression ◽  
Linear Regression Method ◽  
Multiple Linear Regression Method ◽  
Spss Software ◽  
Exploration Area ◽  
The Relationship ◽  
South Khorasan

Abstract: The study area is located in Sarbisheh city in South Khorasan province, Iran. Copper estimation was performed by multivariate linear regression method to facilitate the use of previous analyses to predict this element in other areas, reduce costs and also reduce the number of samples. For this purpose, by obtaining a basic formula from estimating the amount of Cu with one of the promising points samples, the amount of copper in other parts of the exploration area was investigated. Several analyses were taken from the exploratory area after calculations to validate the regression. The regression results of new and old data were compared and estimation acceptable. These calculations were performed by SPSS software, according to the four elements Ca, Al, P, S, the results obtained and the relationship presented has acceptable validity. Keywords: Multivariate linear regression, Cu estimation, SPSS, Iran.

scholarly journals Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

2021 ◽  
Vol 13 (2) ◽  
pp. 777
Author(s):  
Irena Ištoka Otković ◽  
Aleksandra Deluka-Tibljaš ◽  
Sanja Šurdonja ◽  
Tiziana Campisi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Multiple Linear Regression Model ◽  
Urban Environments ◽  
The Neural Network ◽  
The City ◽  
Traffic Behavior

Modeling the behavior of pedestrians is an important tool in the analysis of their behavior and consequently ensuring the safety of pedestrian traffic. Children pedestrians show specific traffic behavior which is related to cognitive development, and the parameters that affect their traffic behavior are very different. The aim of this paper is to develop a model of the children-pedestrian’s speed at a signalized pedestrian crosswalk. For the same set of data collected in the city of Osijek—Croatia, two models were developed based on neural network and multiple linear regression. In both cases the models are based on 300 data of measured children speed at signalized pedestrian crosswalks on primary city roads located near a primary school. As parameters, both models include the selected traffic infrastructure features and children’s characteristics and their movements. The models are validated on data collected on the same type of pedestrian crosswalks, using the same methodology in two other urban environments—the city of Rijeka, Croatia and Enna in Italy. It was shown that the neural network model, developed for Osijek, can be applied with sufficient reliability to the other two cities, while the multiple linear regression model is applicable with relatively satisfactory reliability only in Rijeka. A comparative analysis of the statistical indicators of reliability of these two models showed that better results are achieved by the neural network model.

scholarly journals HUBUNGAN ANTARA UMUR DENGAN CALVING INTERVAL, DAYS OPEN, DAN SERVICE PER CONSEPTION SAPIFRIESIAN HOLSTEIN (FH)

2020 ◽  
Vol 8 (2) ◽  
pp. 94
Author(s):  
Hanifah Muslimah Ananda ◽  
Wurlina Wurlina ◽  
Nove Hidajati ◽  
Mas’ud Hariadi ◽  
Abdul Samik ◽  
...  
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Data Collection ◽  
Dairy Cattle ◽  
Multiple Linear Regression ◽  
Calving Interval ◽  
Spss Software ◽  
Days Open ◽  
The Relationship

The purpose of this research was to know the relationship between age with calving inteval (CI), days open (DO), and service per conception (S/C) in Friesian Holstein dairy cattle (FH). The research was started on Desember 2017 to January 2018 in PT. Greenfields Indonesia partnerships, KecamatanWagir Kabupaten Malang. The materials of the search used were 100 heads of dairy cattle which had at least two times of parturition. The method used in this research was a survey and data collection. The data were obtained from the records of reproduction. Data analysis was multiple linear regression using SPSS software 21.0 version. The results showed that the values of CI (434,9±58,9 days, 449,4±66,2 days, and 431,8±59,2 days), DO (218,9±58,7 days, 218,9±58,7 days, dan 217,6±54,1 days), dan S/C (3,2±1,8 times, 4,3±1,9 times, 2,6±1,1 times) for the ages of 4, 5, 6. The relationship between age with CI and DO were not significant (P>0,05), but the relationship between age with S/C was significant (P<0,05). The value of S/C increased on dairy cattle at age of 4 to 5 years and started to decreased at the age of 6 years.

scholarly journals Estudio comparativo de los algoritmos backpropagation (bp) y multiple linear regression (mlr) a través del análisis estadístico de datos aplicado a redes neuronales artificiales

2020 ◽  
Vol 9 (3) ◽  
pp. 144-152
Author(s):  
Iván Mesias Hidalgo-Cajo ◽  
Saul Yasaca-Pucuna ◽  
Byron Geovanny Hidalgo-Cajo ◽  
Diego Patricio Hidalgo-Cajo ◽  
Nelly Baltazara Latorre-Benalcázar
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sea Level ◽  
Total Error ◽  
Back Propagation ◽  
The Neural Network ◽  
Power Turbine ◽  
La Red ◽  
Network Type

El objetivo de la investigación es comparar el algoritmo Backpropagation desarrollado por el usuario bajo software libre Java y el algoritmo Multiple Linear Regression, dicha comparación demanda del análisis estadístico decriptivo basado en redes neuronales artificiales. Se utilizó específicamente dos modelos de algoritmos de predicción aplicados a 451 patrones o registros a procesar que están repartidos en las primeras 401 filas para entrenamiento de la red neuronal y los otros 50 registros para validación y prueba, conformado por  4 variables de entrada (Height above sea level, Fall, Net fall, Flux) y 1 variable a predecir (Power turbine), para las diferentes pruebas los parámetros de entrenamiento y selección con los mejores resultados son: Architecture of the neural network, Type of scaling of data, Initial range of weight and thresholds, Learning rate and Momentum, Batched / online, Number of training epochs. Entre los resultados de comparación de los algoritmos analizados se determinó que el error en mayor iteracciones es menor que son respuestas de los 50 patrones de prueba. En el algoritmo Multiple Linear Regression  la variable real es el valor de la variable a predecir, esta variable es la suministrada a predecir por el usuario y es el valor que se predijo de la red neuronal, la variable prediction es la diferencia que se hace de la resta de los anteriores errores y se lo realiza para calcular el error y el total error es el valor mínimo a obtener que representa el error calculado de todos los datos, es decir el porcentaje de error de la red neuronal de back-propagation. Entre más bajo es este porcentaje mejor será la red, porque menor será su porcentaje de error.

scholarly journals A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Vladimir M. Krasnopolsky ◽  
Ying Lin
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Model Ensemble ◽  
Multimodel Ensemble ◽  
Learning From Data ◽  
Nonlinear Approach ◽  
The Neural Network ◽  
Model Ensembles ◽  
High Precipitation

A novel multimodel ensemble approach based on learning from data using the neural network (NN) technique is formulated and applied for improving 24-hour precipitation forecasts over the continental US. The developed nonlinear approach allowed us to account for nonlinear correlation between ensemble members and to produce “optimal” forecast represented by a nonlinear NN ensemble mean. The NN approach is compared with the conservative multi-model ensemble, with multiple linear regression ensemble approaches, and with results obtained by human forecasters. The NN multi-model ensemble improves upon conservative multi-model ensemble and multiple linear regression ensemble, it (1) significantly reduces high bias at low precipitation level, (2) significantly reduces low bias at high precipitation level, and (3) sharpens features making them closer to the observed ones. The NN multi-model ensemble performs at least as well as human forecasters supplied with the same information. The developed approach is a generic approach that can be applied to other multi-model ensemble fields as well as to single model ensembles.

PENGARUH HARGA DAN BRAND TRUST TERHADAP KEPUTUSAN PEMBELIAN OPPO SMARTPHONE (Studi kasus pada OPPO Store, Sinergy Celular Medan)

2019 ◽  
Vol 14 (1) ◽  
pp. 17
Author(s):  
Andi S Tarigan ◽  
Zulkarnaian Siregar
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coefficient Of Determination ◽  
Purchase Decision ◽  
Brand Trust ◽  
Significance Level ◽  
Research Hypothesis ◽  
Analysis Technique ◽  
The Relationship

AbstrakPenelitian ini bertujuan untuk mengetahui Pengaruh Harga dan Brand Trust Terhadap Keputusan Pembelian pada Sinergy Celular Medan.Sampel dalam penelitian ini adalah seluruh pengunjung Sinergy Celular Medan sebanyak 77 orang.Teknik pengumpulan data yang digunakan adalah melalui kuesioner (angket) yaitu dengan cara menyebarkan kuesioner kepada sampel (responden) dan mengumpulkannya kembali. Teknik analisis data yang digunakan adalah Regresi Linear Berganda.Sebelum data diregresikan maka terlebih dahulu di uji keterkaitannya antara variabel, datanya diuji menggunakan uji normalitas data, multikolinearitas, dan heterokedastisitas.Serta untuk mengetahui kontribusi faktor Harga dan Brand TrustTerhadap Keputusan Pembelian digunakan rumus Koefisien Determinasi (R2). Hipotesis penelitian diterima apabila t hitung >  t tabel dengan tingkat signifikansi 0,1. Nilai t tabel dalam penelitian ini 1,993. Nilai t hitung variabel X1 sebesar 2,107 t hitung lebih besar dari t tabel maka hipotesis di terima, nilai t hitung variabel X2   sebesar 3,405 t hitung lebih besar dari t tabel maka hipotesis di terima. Kata kunci: Harga, Brand Trust, Keputusan Pembelian AbstractThis study aims to determine the Influence of Price and Brand Trust on Purchasing Decision at Sinergy Celular Medan. The sample in this study is all visitors Sinergy Celular Medan as many as 77 people.Data collection technique used is through questionnaire (questionnaire) that is by distributing questionnaires to the sample (respondent) and collect it back. Data analysis technique used is Multiple Linear Regression. Before the data is diregresikan then first in the test the relationship between variables, the data tested using the test of data normality, multicollinearity, and heterokedastisitas. And to know the contribution of price factors and Brand Trust Against Purchase Decision is used the formula Coefficient of Determination (R2). Research hypothesis accepted if t arithmetic> t table with significance level 0,1. The value of t table in this study is 1,993. Value t arithmetic variable X1 of 2.107 t arithmetic greater than t table then the hypothesis received, the value of t arithmetic variable X2 of 3.405 t arithmetic greater than t table then the hypothesis received. Keywords: Price, Brand Trust, Purchase Decision

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

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