Dynamic Model of Land Area Changes in the East Coast of Surabaya

2017 ◽  
Vol 862 ◽  
pp. 138-143
Author(s):  
Viv Djanat Prasita ◽  
Nuhman ◽  
Nurul Rosana
Keyword(s):  
Linear Regression ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
East Coast ◽  
Coastal Areas ◽  
City Government ◽  
Linear Regression Method ◽  
Land Area ◽  
Conservation Area ◽  
Coastal Land

Management of coastal areas on the east coast of Surabaya (Pamurbaya) is very important because this region is largely a conservation area which serves to maintain the balance of the ecosystem of Surabaya. This study aimed to analyze changes in the coastal land and create a dynamic model of coastal land area changes in Pamurbaya. The method used is the field survey, the linear regression method and dynamic modeling. Dynamic modeling using the software Stella 4.0. The results showed that the changing of used land can be explained by the dynamic model described by a linear regression model. The result is useful to predict the pamurbaya condition for next few years and useful as inputs to city government in managing coastal areas of Pamurbaya.

Rainfall Prediction in South-Eastern Part of Bangladesh by Linear Regression Method

2018 ◽  
Vol 6 (6) ◽  
pp. 119
Author(s):  
Mohammad Shohidul Islam ◽  
Sultana Easmin Siddika ◽  
S M Injamamul Haque Masum
Keyword(s):  
Linear Regression ◽  
Correlation Coefficients ◽  
Rain Gauge ◽  
Regression Method ◽  
Linear Regression Method ◽  
Climate Data ◽  
Rainfall Forecasting ◽  
Statistical Parameters ◽  
Time Duration ◽  
Rainfall Prediction

Rainfall forecasting is very challenging task for the meteorologists. Over the last few decades, several models have been utilized, attempting the successful analysing and forecasting of rainfall. Recorded climate data can play an important role in this regard. Long-time duration of recorded data can be able to provide better advancement of rainfall forecasting. This paper presents the utilization of statistical techniques, particularly linear regression method for modelling the rainfall prediction over Bangladesh. The rainfall data for a period of 11 years was obtained from Bangladesh Meteorological department (BMD), Dhaka i.e. that was surface-based rain gauge rainfall which was acquired from 08 weather stations over Bangladesh for the years of 2001-2011. The monthly and yearly rainfall was determined. In order to assess the accuracy of it some statistical parameters such as average, meridian, correlation coefficients and standard deviation were determined for all stations. The model prediction of rainfall was compared with true rainfall which was collected from rain gauge of different stations and it was found that the model rainfall prediction has given good results.

An alternative model of the vibronic coupling in octahedral complexes

1988 ◽  
Vol 53 (6) ◽  
pp. 1134-1140
Author(s):  
Martin Breza ◽  
Peter Pelikán
Keyword(s):  
Linear Regression ◽  
Alternative Model ◽  
Potential Surface ◽  
Analytical Formula ◽  
Coupling Model ◽  
Regression Method ◽  
Vibronic Coupling ◽  
Linear Regression Method ◽  
Active Systems ◽  
Jahn Teller

It is suggested that for some transition metal hexahalo complexes, the Eg-(a1g + eg) vibronic coupling model is better suited than the classical T2g-(a1g + eg) model. For the former, alternative model, the potential constants in the analytical formula are evaluated from the numerical map of the adiabatic potential surface by using the linear regression method. The numerical values for 29 hexahalo complexes of the 1st row transition metals are obtained by the CNDO/2 method. Some interesting trends of parameters of such Jahn-Teller-active systems are disclosed.

The Accuracy Assessment of Weapon System Using Grey Linear Regression Method

2012 ◽  
Vol 268-270 ◽  
pp. 1809-1813
Author(s):  
Dai Yu Zhang ◽  
Bao Wei Song ◽  
Zhou Quan Zhu
Keyword(s):  
Linear Regression ◽  
Fuzzy Theory ◽  
Accuracy Assessment ◽  
Regression Method ◽  
Small Sample ◽  
Easy Access ◽  
Linear Regression Method ◽  
Grey Theory ◽  
Weapon System ◽  
Complex Engineering

The accuracy assessment of weapon system is always a complex engineering. How to make the most of the information given in only a few tests and obtain reasonable estimate is always a problem. Based on the fuzzy theory and grey theory, a grey linear regression method is presented. From the numerical example, we can see that this method provides an easy access to deal with data in small sample case and may have potential use in the analysis of weapon performance.

scholarly journals Demineralization Depth Using QLF and a Novel Image Processing Software

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Jun Wu ◽  
Zachary R. Donly ◽  
Kevin J. Donly ◽  
Steven Hackmyer
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Polarized Light ◽  
Region Of Interest ◽  
Linear Regression Method ◽  
Percent Loss ◽  
Significant Linear Correlation ◽  
Processing Software ◽  
Image Processing Software ◽  
Imagej Software

Quantitative Light-Induced fluorescence (QLF) has been widely used to detect tooth demineralization indicated by fluorescence loss with respect to surrounding sound enamel. The correlation between fluorescence loss and demineralization depth is not fully understood. The purpose of this project was to study this correlation to estimate demineralization depth. Extracted teeth were collected. Artificial caries-like lesions were created and imaged with QLF. Novel image processing software was developed to measure the largest percent of fluorescence loss in the region of interest. All teeth were then sectioned and imaged by polarized light microscopy. The largest depth of demineralization was measured by NIH ImageJ software. The statistical linear regression method was applied to analyze these data. The linear regression model wasY=0.32X+0.17, whereXwas the percent loss of fluorescence andYwas the depth of demineralization. The correlation coefficient was 0.9696. The two-tailed t-test for coefficient was 7.93, indicating theP-value=.0014. TheFtest for the entire model was 62.86, which shows theP-value=.0013. The results indicated statistically significant linear correlation between the percent loss of fluorescence and depth of the enamel demineralization.

scholarly journals Planetary Gearbox Dynamic Modeling Considering Bearing Clearance and Sun Gear Tooth Crack

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2638
Author(s):  
Xianhua Chen ◽  
Xingkai Yang ◽  
Ming J. Zuo ◽  
Zhigang Tian
Keyword(s):  
Dynamic Model ◽  
Dynamic Modeling ◽  
Failure Modes ◽  
Gear Tooth ◽  
Working Environment ◽  
The Sun ◽  
Planetary Gearbox ◽  
Bearing Clearance ◽  
Vibration Responses ◽  
Planet Gear

Planetary gearbox systems are critical mechanical components in heavy machinery such as wind turbines. They may suffer from various failure modes, due to the harsh working environment. Dynamic modeling is a useful method to support early fault detection for enhancing reliability and reducing maintenance costs. However, reported studies have not considered the sun gear tooth crack and bearing clearance simultaneously to analyze their combined effect on vibration characteristics of planetary gearboxes. In this paper, a dynamic model is developed for planetary gearboxes considering the clearance of planet gear, sun gear, and carrier bearings, as well as sun gear tooth crack levels. Bearing forces are calculated considering bearing clearance, and the dynamic model equations are updated accordingly. The results reveal that the combination of bearing clearances can affect the vibration response with sun gear tooth crack by increasing the kurtosis. It is found that the effect of planet gear bearing clearance is very small, while the sun gear and carrier bearing clearance has clear impact on the vibration responses. These findings suggest that the incorporation of bearing clearance is important for planetary gearbox dynamic modeling.

Perancangan Aplikasi Prediksi Jumlah Pendaftar Siswa Baru Dengan Metode Regresi Linier (Studi Kasus: SMA RK Deli Murni Bandar Baru)

2020 ◽  
Vol 3 (3) ◽  
pp. 330-334
Author(s):  
Novita Ria Lase ◽  
Fristi Riandari
Keyword(s):  
Linear Regression ◽  
Regression Method ◽  
Point Of View ◽  
Computational Techniques ◽  
Linear Regression Method ◽  
Simple Linear Regression ◽  
Tuition Fees ◽  
Use Of Data ◽  
New Students ◽  
New Student

The problem of the SMA RK Deli Murni Bandar Baru school is to predict how many facilities that need to be provided for new students such as chairs, tables and others. This study discusses the prediction of the number of new student registrants at SMA RK Deli Murni Bandar Baru based on the amount of tuition fees using a simple linear regression method. From a commercial point of view, the use of data mining can be used to handle the explosion of data volumes, using computational techniques can be used to produce information needed which is an asset that can increase the competitiveness of an institution. Prediction is almost the same as classification and estimation, except that in the prediction the value of the results will be in the future. This system can be used to predict the number of applicants in the following year to help the school. The advantage is that this simple linear regression method is very simple so that it is easy to calculate and use. Saves the time needed to solve problems, especially those that are very complex.

An application of QSRR approach and multiple linear regression method for lipophilicity assessment of flavonoids

2019 ◽  
Vol 164 ◽  
pp. 681-689 ◽  
Author(s):  
Mariusz Zapadka ◽  
Mateusz Kaczmarek ◽  
Bogumiła Kupcewicz ◽  
Przemysław Dekowski ◽  
Agata Walkowiak ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Method ◽  
Linear Regression Method ◽  
Multiple Linear Regression Method

Sensor Dynamic Modeling Based on LS-SVM and NGA

2008 ◽  
Vol 381-382 ◽  
pp. 439-442
Author(s):  
Qi Wang ◽  
Zhi Gang Feng ◽  
K. Shida
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Least Squares ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Support Vector ◽  
Local Minima ◽  
Highly Nonlinear ◽  
Sharing Function

Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.

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