Modeling Relation Between Forward Speed and Propeller Speed of the Cruising AUV Using Multiple Regression Analysis

This study addresses predicting the relationship between forward speed and propeller speed of an autonomous underwater vehicle (AUV) with a towed flexible cable using multiple regression analysis. Accurate prediction of the propeller speed corresponding to forward speed is important. The prediction requires considering various factors such as the dynamic behavior of a flexible cable, tidal currents, and AUV motions. The regression analysis based on in-service data of the AUV, therefore, established the relation between the forward speed and propeller speed considering the other factors. On the other hand, minimal independent variables in the regression model are desirable to avoid multicollinearity and overfitting. Variable selection based on the t-Test and sparse modeling was carried out to remove insignificant variables. We confirmed that the regression model presented in this study was in agreement with the observed data sufficiently, and the residuals of the regression model followed a normal distribution. The propeller speed predicted by the regression model considering only the forward speed of the AUV was comparable to the result predicted by the CFD calculation, not including the other factors. The result indicates the regression analysis can validate the results based on experiments and a numerical simulation.

Regresi Terboboti Geografis dengan Fungsi Pembobot Kernel Gaussian pada Kekuatan Sinyal Seluler

Cellular signal strength may be affected by its location, so researches concerning signal strength need information about location and analysis method that observe spatial aspect. Spatial Regression analysis evaluates location in modeling relation between explanatory variables and response variable. One of the spatial regression analyses is Geographically Weighted Regression (GWR). This method utilizes location to create weight matrix using certain weighting function. GWR analysis with Gaussian kernel weighting function creates better model than Ordinary Least Square model. The model created using GWR is local model which parameter estimation differs in each observation point. Clustering of observation point is performed to summarize the result of GWR. The number of optimum clusters in clustering based on coefficient is five clusters while the number of optimum clusters in clustering based on p value of t test is four clusters.

Predicting the Actual Strength of Open-End Spun Yarn Using Mechanical Model

On this article, modeling relation of rotor speed and rotor diameter toward yarn tension on take-offnozzle has been validated by theoretical approach and numeric computation.It shows that value ofyarn tension is F=(1,4.10-13)Tt nR2 dR2 [cN]. Excessively high spinning tension always results in anintolerable increase in yarn breakage. This paper presents a comparative study of theoretical andreference-based experimental result for predicting the actual strength of open-end spinning system.

Geographically Weighted Ridge Regression dalam Kasus Multikolinearitas Pada Indeks Pembangunan Manusia di Kabupaten/Kota Provinsi Jawa Timur

Linear Regression Analysis is a statistical method for modeling relation between two variable, response and explanatory variable. Geograpically Weighted Regression (GWR) is the development of linier regression analysis if the case of spatial divers case. Local multicollinearity is a condition when explanatory variables had correlated with each observation location. Geograpically Weighted Ridge Regression (GWRR) is a method used to model data containing local multicollinearity on spatial data. GWRR model was developed from ridge regression by adding weight as additional information. The study aims to model spatial data containing local multicollinearity to the Human Development Index (HDI) in the districts/municipalities of eastern Java Province in 2015. The result of this study was indicate that the indicator of the average length of school is a dominant indicator that affects HDI.