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weighted projection regression method</title> <meta name="description" content="Search results for: locally weighted projection regression method"> <meta name="keywords" content="locally weighted projection regression method"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" 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class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="locally weighted projection regression method"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 22340</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: locally weighted projection regression method</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22340</span> Influence of Parameters of Modeling and Data Distribution for Optimal Condition on Locally Weighted Projection Regression Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farhad%20Asadi">Farhad Asadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Javad%20Mollakazemi"> Mohammad Javad Mollakazemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Aref%20Ghafouri"> Aref Ghafouri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=local%20nonlinear%20estimation" title="local nonlinear estimation">local nonlinear estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=LWPR%20algorithm" title=" LWPR algorithm"> LWPR algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20training%20method" title=" online training method"> online training method</a>, <a href="https://publications.waset.org/abstracts/search?q=locally%20weighted%20projection%20regression%20method" title=" locally weighted projection regression method"> locally weighted projection regression method</a> </p> <a href="https://publications.waset.org/abstracts/14554/influence-of-parameters-of-modeling-and-data-distribution-for-optimal-condition-on-locally-weighted-projection-regression-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14554.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">502</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22339</span> Orthogonal Regression for Nonparametric Estimation of Errors-In-Variables Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anastasiia%20Yu.%20Timofeeva">Anastasiia Yu. Timofeeva</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=grade%20point%20average" title="grade point average">grade point average</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20regression" title=" orthogonal regression"> orthogonal regression</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20regression%20spline" title=" penalized regression spline"> penalized regression spline</a>, <a href="https://publications.waset.org/abstracts/search?q=locally%20weighted%20regression" title=" locally weighted regression"> locally weighted regression</a> </p> <a href="https://publications.waset.org/abstracts/11927/orthogonal-regression-for-nonparametric-estimation-of-errors-in-variables-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11927.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">416</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22338</span> Weighted Rank Regression with Adaptive Penalty Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kang-Mo%20Jung">Kang-Mo Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of regularization for statistical methods has become popular. The least absolute shrinkage and selection operator (LASSO) framework has become the standard tool for sparse regression. However, it is well known that the LASSO is sensitive to outliers or leverage points. We consider a new robust estimation which is composed of the weighted loss function of the pairwise difference of residuals and the adaptive penalty function regulating the tuning parameter for each variable. Rank regression is resistant to regression outliers, but not to leverage points. By adopting a weighted loss function, the proposed method is robust to leverage points of the predictor variable. Furthermore, the adaptive penalty function gives us good statistical properties in variable selection such as oracle property and consistency. We develop an efficient algorithm to compute the proposed estimator using basic functions in program R. We used an optimal tuning parameter based on the Bayesian information criterion (BIC). Numerical simulation shows that the proposed estimator is effective for analyzing real data set and contaminated data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20penalty%20function" title="adaptive penalty function">adaptive penalty function</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20penalized%20regression" title=" robust penalized regression"> robust penalized regression</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20selection" title=" variable selection"> variable selection</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20rank%20regression" title=" weighted rank regression"> weighted rank regression</a> </p> <a href="https://publications.waset.org/abstracts/79449/weighted-rank-regression-with-adaptive-penalty-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79449.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">475</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22337</span> Solving Single Machine Total Weighted Tardiness Problem Using Gaussian Process Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanatchapong%20Kongkaew">Wanatchapong Kongkaew</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title="Gaussian process regression">Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=iterated%20local%20search" title=" iterated local search"> iterated local search</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20machine%20total%20weighted%20tardiness" title=" single machine total weighted tardiness"> single machine total weighted tardiness</a> </p> <a href="https://publications.waset.org/abstracts/6433/solving-single-machine-total-weighted-tardiness-problem-using-gaussian-process-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6433.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22336</span> Modelling and Maping Malnutrition Toddlers in Bojonegoro Regency with Mixed Geographically Weighted Regression Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elvira%20Mustikawati%20P.H.">Elvira Mustikawati P.H.</a>, <a href="https://publications.waset.org/abstracts/search?q=Iis%20Dewi%20Ratih"> Iis Dewi Ratih</a>, <a href="https://publications.waset.org/abstracts/search?q=Dita%20Amelia"> Dita Amelia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bojonegoro has proclaimed a policy of zero malnutrition. Therefore, as an effort to solve the cases of malnutrition children in Bojonegoro, this study used the approach geographically Mixed Weighted Regression (MGWR) to determine the factors that influence the percentage of malnourished children under five in which factors can be divided into locally influential factor in each district and global factors that influence throughout the district. Based on the test of goodness of fit models, R2 and AIC values in GWR models are better than MGWR models. R2 and AIC values in MGWR models are 84.37% and 14.28, while the GWR models respectively are 91.04% and -62.04. Based on the analysis with GWR models, District Sekar, Bubulan, Gondang, and Dander is a district with three predictor variables (percentage of vitamin A, the percentage of births assisted health personnel, and the percentage of clean water) that significantly influence the percentage of malnourished children under five. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GWR" title="GWR">GWR</a>, <a href="https://publications.waset.org/abstracts/search?q=MGWR" title=" MGWR"> MGWR</a>, <a href="https://publications.waset.org/abstracts/search?q=R2" title=" R2"> R2</a>, <a href="https://publications.waset.org/abstracts/search?q=AIC" title=" AIC"> AIC</a> </p> <a href="https://publications.waset.org/abstracts/2014/modelling-and-maping-malnutrition-toddlers-in-bojonegoro-regency-with-mixed-geographically-weighted-regression-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2014.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">296</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22335</span> Application of Nonparametric Geographically Weighted Regression to Evaluate the Unemployment Rate in East Java</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sifriyani%20Sifriyani">Sifriyani Sifriyani</a>, <a href="https://publications.waset.org/abstracts/search?q=I%20Nyoman%20Budiantara"> I Nyoman Budiantara</a>, <a href="https://publications.waset.org/abstracts/search?q=Sri%20%20Haryatmi"> Sri Haryatmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Gunardi%20Gunardi"> Gunardi Gunardi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> East Java Province has a first rank as a province that has the most counties and cities in Indonesia and has the largest population. In 2015, the population reached 38.847.561 million, this figure showed a very high population growth. High population growth is feared to lead to increase the levels of unemployment. In this study, the researchers mapped and modeled the unemployment rate with 6 variables that were supposed to influence. Modeling was done by nonparametric geographically weighted regression methods with truncated spline approach. This method was chosen because spline method is a flexible method, these models tend to look for its own estimation. In this modeling, there were point knots, the point that showed the changes of data. The selection of the optimum point knots was done by selecting the most minimun value of Generalized Cross Validation (GCV). Based on the research, 6 variables were declared to affect the level of unemployment in eastern Java. They were the percentage of population that is educated above high school, the rate of economic growth, the population density, the investment ratio of total labor force, the regional minimum wage and the ratio of the number of big industry and medium scale industry from the work force. The nonparametric geographically weighted regression models with truncated spline approach had a coefficient of determination 98.95% and the value of MSE equal to 0.0047. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=East%20Java" title="East Java">East Java</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20geographically%20weighted%20regression" title=" nonparametric geographically weighted regression"> nonparametric geographically weighted regression</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial" title=" spatial"> spatial</a>, <a href="https://publications.waset.org/abstracts/search?q=spline%20approach" title=" spline approach"> spline approach</a>, <a href="https://publications.waset.org/abstracts/search?q=unemployed%20rate" title=" unemployed rate"> unemployed rate</a> </p> <a href="https://publications.waset.org/abstracts/66912/application-of-nonparametric-geographically-weighted-regression-to-evaluate-the-unemployment-rate-in-east-java" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66912.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">321</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22334</span> Analysis of Factors Affecting the Number of Infant and Maternal Mortality in East Java with Geographically Weighted Bivariate Generalized Poisson Regression Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Luh%20Eka%20Suryani">Luh Eka Suryani</a>, <a href="https://publications.waset.org/abstracts/search?q=Purhadi"> Purhadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Poisson regression is a non-linear regression model with response variable in the form of count data that follows Poisson distribution. Modeling for a pair of count data that show high correlation can be analyzed by Poisson Bivariate Regression. Data, the number of infant mortality and maternal mortality, are count data that can be analyzed by Poisson Bivariate Regression. The Poisson regression assumption is an equidispersion where the mean and variance values are equal. However, the actual count data has a variance value which can be greater or less than the mean value (overdispersion and underdispersion). Violations of this assumption can be overcome by applying Generalized Poisson Regression. Characteristics of each regency can affect the number of cases occurred. This issue can be overcome by spatial analysis called geographically weighted regression. This study analyzes the number of infant mortality and maternal mortality based on conditions in East Java in 2016 using Geographically Weighted Bivariate Generalized Poisson Regression (GWBGPR) method. Modeling is done with adaptive bisquare Kernel weighting which produces 3 regency groups based on infant mortality rate and 5 regency groups based on maternal mortality rate. Variables that significantly influence the number of infant and maternal mortality are the percentages of pregnant women visit health workers at least 4 times during pregnancy, pregnant women get Fe3 tablets, obstetric complication handled, clean household and healthy behavior, and married women with the first marriage age under 18 years. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20bisquare%20kernel" title="adaptive bisquare kernel">adaptive bisquare kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=GWBGPR" title=" GWBGPR"> GWBGPR</a>, <a href="https://publications.waset.org/abstracts/search?q=infant%20mortality" title=" infant mortality"> infant mortality</a>, <a href="https://publications.waset.org/abstracts/search?q=maternal%20mortality" title=" maternal mortality"> maternal mortality</a>, <a href="https://publications.waset.org/abstracts/search?q=overdispersion" title=" overdispersion"> overdispersion</a> </p> <a href="https://publications.waset.org/abstracts/98212/analysis-of-factors-affecting-the-number-of-infant-and-maternal-mortality-in-east-java-with-geographically-weighted-bivariate-generalized-poisson-regression-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98212.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">159</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22333</span> The Use of Geographically Weighted Regression for Deforestation Analysis: Case Study in Brazilian Cerrado</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ana%20Paula%20Camelo">Ana Paula Camelo</a>, <a href="https://publications.waset.org/abstracts/search?q=Keila%20Sanches"> Keila Sanches</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Geographically Weighted Regression (GWR) was proposed in geography literature to allow relationship in a regression model to vary over space. In Brazil, the agricultural exploitation of the Cerrado Biome is the main cause of deforestation. In this study, we propose a methodology using geostatistical methods to characterize the spatial dependence of deforestation in the Cerrado based on agricultural production indicators. Therefore, it was used the set of exploratory spatial data analysis tools (ESDA) and confirmatory analysis using GWR. It was made the calibration a non-spatial model, evaluation the nature of the regression curve, election of the variables by stepwise process and multicollinearity analysis. After the evaluation of the non-spatial model was processed the spatial-regression model, statistic evaluation of the intercept and verification of its effect on calibration. In an analysis of Spearman’s correlation the results between deforestation and livestock was +0.783 and with soybeans +0.405. The model presented R²=0.936 and showed a strong spatial dependence of agricultural activity of soybeans associated to maize and cotton crops. The GWR is a very effective tool presenting results closer to the reality of deforestation in the Cerrado when compared with other analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deforestation" title="deforestation">deforestation</a>, <a href="https://publications.waset.org/abstracts/search?q=geographically%20weighted%20regression" title=" geographically weighted regression"> geographically weighted regression</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use" title=" land use"> land use</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analysis" title=" spatial analysis"> spatial analysis</a> </p> <a href="https://publications.waset.org/abstracts/85043/the-use-of-geographically-weighted-regression-for-deforestation-analysis-case-study-in-brazilian-cerrado" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85043.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">363</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22332</span> Some Results for F-Minimal Hypersurfaces in Manifolds with Density</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Abdelmalek">M. Abdelmalek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we study the hypersurfaces of constant weighted mean curvature embedded in weighted manifolds. We give a condition about these hypersurfaces to be minimal. This condition is given by the ellipticity of the weighted Newton transformations. We especially prove that two compact hypersurfaces of constant weighted mean curvature embedded in space forms and with the intersection in at least a point of the boundary must be transverse. The method is based on the calculus of the matrix of the second fundamental form in a boundary point and then the matrix associated with the Newton transformations. By equality, we find the weighted elementary symmetric function on the boundary of the hypersurface. We give in the end some examples and applications. Especially in Euclidean space, we use the above result to prove the Alexandrov spherical caps conjecture for the weighted case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=weighted%20mean%20curvature" title="weighted mean curvature">weighted mean curvature</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20manifolds" title=" weighted manifolds"> weighted manifolds</a>, <a href="https://publications.waset.org/abstracts/search?q=ellipticity" title=" ellipticity"> ellipticity</a>, <a href="https://publications.waset.org/abstracts/search?q=Newton%20transformations" title=" Newton transformations"> Newton transformations</a> </p> <a href="https://publications.waset.org/abstracts/160174/some-results-for-f-minimal-hypersurfaces-in-manifolds-with-density" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160174.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">93</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22331</span> A Comparison of Smoothing Spline Method and Penalized Spline Regression Method Based on Nonparametric Regression Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Autcha%20Araveeporn">Autcha Araveeporn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a study about a nonparametric regression model consisting of a smoothing spline method and a penalized spline regression method. We also compare the techniques used for estimation and prediction of nonparametric regression model. We tried both methods with crude oil prices in dollars per barrel and the Stock Exchange of Thailand (SET) index. According to the results, it is concluded that smoothing spline method performs better than that of penalized spline regression method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20regression%20model" title="nonparametric regression model">nonparametric regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20spline%20regression%20method" title=" penalized spline regression method"> penalized spline regression method</a>, <a href="https://publications.waset.org/abstracts/search?q=smoothing%20spline%20method" title=" smoothing spline method"> smoothing spline method</a>, <a href="https://publications.waset.org/abstracts/search?q=Stock%20Exchange%20of%20Thailand%20%28SET%29" title=" Stock Exchange of Thailand (SET)"> Stock Exchange of Thailand (SET)</a> </p> <a href="https://publications.waset.org/abstracts/2974/a-comparison-of-smoothing-spline-method-and-penalized-spline-regression-method-based-on-nonparametric-regression-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2974.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22330</span> A Fuzzy Nonlinear Regression Model for Interval Type-2 Fuzzy Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Poleshchuk">O. Poleshchuk</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Komarov"> E. Komarov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interval%20type-2%20fuzzy%20sets" title="interval type-2 fuzzy sets">interval type-2 fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20regression" title=" fuzzy regression"> fuzzy regression</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20interval" title=" weighted interval"> weighted interval</a> </p> <a href="https://publications.waset.org/abstracts/6138/a-fuzzy-nonlinear-regression-model-for-interval-type-2-fuzzy-sets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6138.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">373</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22329</span> Resistivity Tomography Optimization Based on Parallel Electrode Linear Back Projection Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yiwei%20Huang">Yiwei Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chunyu%20Zhao"> Chunyu Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Jingjing%20Ding"> Jingjing Ding</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrical Resistivity Tomography has been widely used in the medicine and the geology, such as the imaging of the lung impedance and the analysis of the soil impedance, etc. Linear Back Projection is the core algorithm of Electrical Resistivity Tomography, but the traditional Linear Back Projection can not make full use of the information of the electric field. In this paper, an imaging method of Parallel Electrode Linear Back Projection for Electrical Resistivity Tomography is proposed, which generates the electric field distribution that is not linearly related to the traditional Linear Back Projection, captures the new information and improves the imaging accuracy without increasing the number of electrodes by changing the connection mode of the electrodes. The simulation results show that the accuracy of the image obtained by the inverse operation obtained by the Parallel Electrode Linear Back Projection can be improved by about 20%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrical%20resistivity%20tomography" title="electrical resistivity tomography">electrical resistivity tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20simulation" title=" finite element simulation"> finite element simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20optimization" title=" image optimization"> image optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20electrode%20linear%20back%20projection" title=" parallel electrode linear back projection"> parallel electrode linear back projection</a> </p> <a href="https://publications.waset.org/abstracts/112189/resistivity-tomography-optimization-based-on-parallel-electrode-linear-back-projection-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112189.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22328</span> Analysis of Spatial Heterogeneity of Residential Prices in Guangzhou: An Actual Study Based on Point of Interest Geographically Weighted Regression Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zichun%20Guo">Zichun Guo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Guangzhou's house price has long been lower than the other three major cities; with the gradual increase in Guangzhou's house price, the influencing factors of house price have gradually been paid attention to; this paper tries to use house price data and POI (Point of Interest) data, and explores the distribution of house price and influencing factors by applying the Kriging spatial interpolation method and geographically weighted regression model in ArcGIS. The results show that the interpolation result of house price has a significant relationship with the economic development and development potential of the region and that different POI types have different impacts on the growth of house prices in different regions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=POI" title="POI">POI</a>, <a href="https://publications.waset.org/abstracts/search?q=house%20price" title=" house price"> house price</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20heterogeneity" title=" spatial heterogeneity"> spatial heterogeneity</a>, <a href="https://publications.waset.org/abstracts/search?q=Guangzhou" title=" Guangzhou"> Guangzhou</a> </p> <a href="https://publications.waset.org/abstracts/185907/analysis-of-spatial-heterogeneity-of-residential-prices-in-guangzhou-an-actual-study-based-on-point-of-interest-geographically-weighted-regression-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185907.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">55</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22327</span> The Complete Modal Derivatives</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sebastian%20Andersen">Sebastian Andersen</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20N.%20Poulsen"> Peter N. Poulsen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of basis projection in the structural dynamic analysis is frequently applied. The purpose of the method is to improve the computational efficiency, while maintaining a high solution accuracy, by projection the governing equations onto a small set of carefully selected basis vectors. The present work considers basis projection in kinematic nonlinear systems with a focus on two widely used basis vectors; the system mode shapes and their modal derivatives. Particularly the latter basis vectors are given special attention since only approximate modal derivatives have been used until now. In the present work the complete modal derivatives, derived from perturbation methods, are presented and compared to the previously applied approximate modal derivatives. The correctness of the complete modal derivatives is illustrated by use of an example of a harmonically loaded kinematic nonlinear structure modeled by beam elements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=basis%20projection" title="basis projection">basis projection</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</a>, <a href="https://publications.waset.org/abstracts/search?q=kinematic%20nonlinearities" title=" kinematic nonlinearities"> kinematic nonlinearities</a>, <a href="https://publications.waset.org/abstracts/search?q=modal%20derivatives" title=" modal derivatives"> modal derivatives</a> </p> <a href="https://publications.waset.org/abstracts/92260/the-complete-modal-derivatives" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92260.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">237</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22326</span> A Straightforward Approach for Determining the Weights of Decision Makers Based on Angle Cosine and Projection Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiang%20Yang">Qiang Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ping-An%20Du"> Ping-An Du</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Group decision making with multiple attribute has attracted intensive concern in the decision analysis area. This paper assumes that the contributions of all the decision makers (DMs) are not equal to the decision process based on different knowledge and experience in group setting. The aim of this paper is to develop a novel approach to determine weights of DMs in the group decision making problems. In this paper, the weights of DMs are determined in the group decision environment via angle cosine and projection method. First of all, the average decision of all individual decisions is defined as the ideal decision. After that, we define the weight of each decision maker (DM) by aggregating the angle cosine and projection between individual decision and ideal decision with associated direction indicator μ. By using the weights of DMs, all individual decisions are aggregated into a collective decision. Further, the preference order of alternatives is ranked in accordance with the overall row value of collective decision. Finally, an example in a chemical company is provided to illustrate the developed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=angel%20cosine" title="angel cosine">angel cosine</a>, <a href="https://publications.waset.org/abstracts/search?q=ideal%20decision" title=" ideal decision"> ideal decision</a>, <a href="https://publications.waset.org/abstracts/search?q=projection%20method" title=" projection method"> projection method</a>, <a href="https://publications.waset.org/abstracts/search?q=weights%20of%20decision%20makers" title=" weights of decision makers"> weights of decision makers</a> </p> <a href="https://publications.waset.org/abstracts/35292/a-straightforward-approach-for-determining-the-weights-of-decision-makers-based-on-angle-cosine-and-projection-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35292.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22325</span> Notes on Frames in Weighted Hardy Spaces and Generalized Weighted Composition Operators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shams%20Alyusof">Shams Alyusof</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is to enrich the studies of the frames due to their prominent role in pure mathematics as well as in applied mathematics and many applications in computer science and engineering. Recently, there are remarkable studies of operators that preserve frames on some spaces, and this research could be considered as an extension of such studies. Indeed, this paper is to we characterize weighted composition operators that preserve frames in weighted Hardy spaces on the open unit disk. Moreover, it shows that this characterization does not apply to generalized weighted composition operators on such spaces. Nevertheless, this study could be extended to provide more specific characterizations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frames" title="frames">frames</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20weighted%20composition%20operators" title=" generalized weighted composition operators"> generalized weighted composition operators</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20Hardy%20spaces" title=" weighted Hardy spaces"> weighted Hardy spaces</a>, <a href="https://publications.waset.org/abstracts/search?q=analytic%20functions" title=" analytic functions"> analytic functions</a> </p> <a href="https://publications.waset.org/abstracts/156372/notes-on-frames-in-weighted-hardy-spaces-and-generalized-weighted-composition-operators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156372.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">121</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22324</span> A Simulation Tool for Projection Mapping Based on Mapbox and Unity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noriko%20Hanakawa">Noriko Hanakawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Masaki%20Obana"> Masaki Obana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A simulation tool has been proposed for big-scale projection mapping events. The tool has four main functions based on Mapbox and Unity utilities. The first function is building a 3D model of real cities by MapBox. The second function is a movie projection to some buildings in real cities by Unity. The third function is a movie sending function from a PC to a virtual projector. The fourth function is mapping movies with fitting buildings. The simulation tool was adapted to a real projection mapping event that was held in 2019. The event has been finished. The event had a serious problem in the movie projection to the target building. The extra tents were set in front of the target building. The tents became the obstacles to the movie projection. The simulation tool can be reappeared the problems of the event. Therefore, if the simulation tool was developed before the 2019 projection mapping event, the problem of the tents’ obstacles could be avoided with the simulation tool. In addition, we confirmed that the simulation tool is useful to make a plan of future projection mapping events in order to avoid obstacles of various extra equipment such as utility poles, planting trees, monument towers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=projection%20mapping" title="projection mapping">projection mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=projector%20position" title=" projector position"> projector position</a>, <a href="https://publications.waset.org/abstracts/search?q=real%203D%20map" title=" real 3D map"> real 3D map</a>, <a href="https://publications.waset.org/abstracts/search?q=avoiding%20obstacles" title=" avoiding obstacles"> avoiding obstacles</a> </p> <a href="https://publications.waset.org/abstracts/140107/a-simulation-tool-for-projection-mapping-based-on-mapbox-and-unity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140107.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">203</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22323</span> Support Vector Regression with Weighted Least Absolute Deviations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kang-Mo%20Jung">Kang-Mo Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Least squares support vector machine (LS-SVM) is a penalized regression which considers both fitting and generalization ability of a model. However, the squared loss function is very sensitive to even single outlier. We proposed a weighted absolute deviation loss function for the robustness of the estimates in least absolute deviation support vector machine. The proposed estimates can be obtained by a quadratic programming algorithm. Numerical experiments on simulated datasets show that the proposed algorithm is competitive in view of robustness to outliers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=least%20absolute%20deviation" title="least absolute deviation">least absolute deviation</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20programming" title=" quadratic programming"> quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=weight" title=" weight"> weight</a> </p> <a href="https://publications.waset.org/abstracts/23674/support-vector-regression-with-weighted-least-absolute-deviations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23674.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">527</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22322</span> Influence of Locally Made Effective Microorganisms on the Compressive Strength of Concrete</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Nura%20Isa">Muhammad Nura Isa</a>, <a href="https://publications.waset.org/abstracts/search?q=Magaji%20Muhammad%20Garba"> Magaji Muhammad Garba</a>, <a href="https://publications.waset.org/abstracts/search?q=Dauda%20Dahiru%20Danwata">Dauda Dahiru Danwata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A lot of research was carried out to improve the technology of concrete, some of which include the introduction of new admixture in concrete production such as effective microorganisms. Researches carried out in Japan and Malaysia indicated that the Effective Microorganisms improve the strength and durability of concrete. Therefore, the main objective of this research is to assess the effect of the locally made effective microorganisms on the compressive strength of concrete in Nigeria. The effective microorganisms were produced locally. The locally made effective microorganism was added in 3%, 5%, 10% and 15% to replace the mixing water required. The results of the tests indicated that the concrete specimens with 3% content of locally made EM-A possessed the highest compressive strength, this proved the 3% to be the optimum dosage of locally made EM-A in the concrete. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=locally%20made%20effective%20microorganisms" title="locally made effective microorganisms">locally made effective microorganisms</a>, <a href="https://publications.waset.org/abstracts/search?q=compressive%20strength" title=" compressive strength"> compressive strength</a>, <a href="https://publications.waset.org/abstracts/search?q=admixture" title=" admixture"> admixture</a>, <a href="https://publications.waset.org/abstracts/search?q=fruits%20and%20vegetable%20wastes" title=" fruits and vegetable wastes"> fruits and vegetable wastes</a> </p> <a href="https://publications.waset.org/abstracts/37475/influence-of-locally-made-effective-microorganisms-on-the-compressive-strength-of-concrete" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37475.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">344</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22321</span> Influence of Replacement used Reference Coordinate System for Georeferencing of the Old Map of Europe</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jakub%20Havlicek">Jakub Havlicek</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiri%20Cajthaml"> Jiri Cajthaml</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article describes the effect of the replacement of the used reference coordinate system in the georeferencing of an old map of Europe. In particular, it was the map entitled “Europe, the Map of Rivers and Mountains on a 1 : 12 000 000 Scale”, elaborated by professor D. Cipera and Dr. J. Metelka for Otto’s Geographic Atlas of 1924. The work was most likely produced using the equal-area conic (Albers) projection. The map was georeferenced into three types of projection – the equal-area conic, cylindrical Plate Carrée and cylindrical Mercator map projection. The map was georeferenced by means of the affine and the second-order polynomial transformation. The resulting georeferenced raster datasets from the Plate Carrée and Mercator projection were projected into the equal-area conic projection by means of projection equations. The output is the comparison of drawn graphics, the magnitude of standard deviations for individual projections and types of transformation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=georeferencing" title="georeferencing">georeferencing</a>, <a href="https://publications.waset.org/abstracts/search?q=reference%20coordinate%20system" title=" reference coordinate system"> reference coordinate system</a>, <a href="https://publications.waset.org/abstracts/search?q=transformation" title=" transformation"> transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=standard%20deviation" title=" standard deviation"> standard deviation</a> </p> <a href="https://publications.waset.org/abstracts/27471/influence-of-replacement-used-reference-coordinate-system-for-georeferencing-of-the-old-map-of-europe" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27471.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22320</span> Studying Projection Distance and Flow Properties by Shape Variations of Foam Monitor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyun-Kyu%20Cho">Hyun-Kyu Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun-Su%20Kim"> Jun-Su Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Choon-Geun%20Huh"> Choon-Geun Huh</a>, <a href="https://publications.waset.org/abstracts/search?q=Geon%20Lee%20Young-Chul%20Park"> Geon Lee Young-Chul Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the relationship between flow properties and fluid projection distance look into connection for shape variations of foam monitor. A numerical analysis technique for fluid analysis of a foam monitor was developed for the prediction. Shape of foam monitor the flow path of fluid flow according to the shape, The fluid losses were calculated from flow analysis result.. The modified model used the length increase model of the flow path, and straight line of the model. Inlet pressure was 7 [bar] and external was atmosphere codition. am. The results showed that the length increase model of the flow path and straight line of the model was improved in the nozzle projection distance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=injection%20performance" title="injection performance">injection performance</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</a>, <a href="https://publications.waset.org/abstracts/search?q=foam%20monitor" title=" foam monitor"> foam monitor</a>, <a href="https://publications.waset.org/abstracts/search?q=Projection%20distance" title=" Projection distance"> Projection distance</a> </p> <a href="https://publications.waset.org/abstracts/58090/studying-projection-distance-and-flow-properties-by-shape-variations-of-foam-monitor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58090.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">347</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22319</span> A Generalized Weighted Loss for Support Vextor Classification and Multilayer Perceptron</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Filippo%20Portera">Filippo Portera</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=loss" title="loss">loss</a>, <a href="https://publications.waset.org/abstracts/search?q=binary-classification" title=" binary-classification"> binary-classification</a>, <a href="https://publications.waset.org/abstracts/search?q=MLP" title=" MLP"> MLP</a>, <a href="https://publications.waset.org/abstracts/search?q=weights" title=" weights"> weights</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/163661/a-generalized-weighted-loss-for-support-vextor-classification-and-multilayer-perceptron" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163661.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">95</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22318</span> Use of Locally Available Organic Resources for Soil Fertility Improvement on Farmers Yield in the Eastern and Greater Accra Regions of Ghana</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebenezer%20Amoquandoh">Ebenezer Amoquandoh</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Bruce%20Sarpong"> Daniel Bruce Sarpong</a>, <a href="https://publications.waset.org/abstracts/search?q=Godfred%20K.%20Ofosu-Budu"> Godfred K. Ofosu-Budu</a>, <a href="https://publications.waset.org/abstracts/search?q=Andreas%20Fliessbach"> Andreas Fliessbach</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Soil quality is at stake globally, but under tropical conditions, the loss of soil fertility may be existential. The current rates of soil nutrient depletion, erosion and environmental degradation in most of Africa’s farmland urgently require methods for soil fertility restoration through affordable agricultural management techniques. The study assessed the effects of locally available organic resources to improve soil fertility, crop yield and profitability compared to business as usual on farms in the Eastern and Greater Accra regions of Ghana. Apart from this, we analyzed the change of farmers’ perceptions and knowledge upon the experience with the new techniques; the effect of using locally available organic resource on farmers’ yield and determined the factors influencing the profitability of farming. Using the Difference in Mean Score and Proportion to estimate the extent to which farmers’ perceptions, knowledge and practices have changed, the study showed that farmers’ perception, knowledge and practice on the use of locally available organic resources have changed significantly. This paves way for the sustainable use of locally available organic resource for soil fertility improvement. The Propensity Score Matching technique and Endogenous Switching Regression model used showed that using locally available organic resources have the potential to increase crop yield. It was also observed that using the Profit Margin, Net Farm Income and Return on Investment analysis, it is more profitable to use locally available organic resources than other soil fertility amendments techniques studied. The results further showed that socioeconomic, farm characteristics and institutional factors are significant in influencing farmers’ decision to use locally available organic resources and profitability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=soil%20fertility" title="soil fertility">soil fertility</a>, <a href="https://publications.waset.org/abstracts/search?q=locally%20available%20organic%20resources" title=" locally available organic resources"> locally available organic resources</a>, <a href="https://publications.waset.org/abstracts/search?q=perception" title=" perception"> perception</a>, <a href="https://publications.waset.org/abstracts/search?q=profitability" title=" profitability"> profitability</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability" title=" sustainability "> sustainability </a> </p> <a href="https://publications.waset.org/abstracts/119335/use-of-locally-available-organic-resources-for-soil-fertility-improvement-on-farmers-yield-in-the-eastern-and-greater-accra-regions-of-ghana" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119335.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22317</span> Parameter Estimation via Metamodeling </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergio%20Haram%20Sarmiento">Sergio Haram Sarmiento</a>, <a href="https://publications.waset.org/abstracts/search?q=Arcady%20Ponosov"> Arcady Ponosov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on appropriate multivariate statistical methodology, we suggest a generic framework for efficient parameter estimation for ordinary differential equations and the corresponding nonlinear models. In this framework classical linear regression strategies is refined into a nonlinear regression by a locally linear modelling technique (known as metamodelling). The approach identifies those latent variables of the given model that accumulate most information about it among all approximations of the same dimension. The method is applied to several benchmark problems, in particular, to the so-called ”power-law systems”, being non-linear differential equations typically used in Biochemical System Theory. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title="principal component analysis">principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20law%20of%20mass%20action" title=" generalized law of mass action"> generalized law of mass action</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20estimation" title=" parameter estimation"> parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=metamodels" title=" metamodels"> metamodels</a> </p> <a href="https://publications.waset.org/abstracts/23814/parameter-estimation-via-metamodeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23814.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">517</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22316</span> Urban Energy Demand Modelling: Spatial Analysis Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hung-Chu%20Chen">Hung-Chu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Han%20Qi"> Han Qi</a>, <a href="https://publications.waset.org/abstracts/search?q=Bauke%20de%20Vries"> Bauke de Vries</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy consumption in the urban environment has attracted numerous researches in recent decades. However, it is comparatively rare to find literary works which investigated 3D spatial analysis of urban energy demand modelling. In order to analyze the spatial correlation between urban morphology and energy demand comprehensively, this paper investigates their relation by using the spatial regression tool. In addition, the spatial regression tool which is applied in this paper is ordinary least squares regression (OLS) and geographically weighted regression (GWR) model. Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and building volume are explainers of urban morphology, which act as independent variables of Energy-land use (E-L) model. NDBI and NDVI are used as the index to describe five types of land use: urban area (U), open space (O), artificial green area (G), natural green area (V), and water body (W). Accordingly, annual electricity, gas demand and energy demand are dependent variables of the E-L model. Based on the analytical result of E-L model relation, it revealed that energy demand and urban morphology are closely connected and the possible causes and practical use are discussed. Besides, the spatial analysis methods of OLS and GWR are compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20demand%20model" title="energy demand model">energy demand model</a>, <a href="https://publications.waset.org/abstracts/search?q=geographically%20weighted%20regression" title=" geographically weighted regression"> geographically weighted regression</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20difference%20built-up%20index" title=" normalized difference built-up index"> normalized difference built-up index</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20difference%20vegetation%20index" title=" normalized difference vegetation index"> normalized difference vegetation index</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20statistics" title=" spatial statistics"> spatial statistics</a> </p> <a href="https://publications.waset.org/abstracts/101697/urban-energy-demand-modelling-spatial-analysis-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101697.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22315</span> Enhanced Thai Character Recognition with Histogram Projection Feature Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benjawan%20Rangsikamol">Benjawan Rangsikamol</a>, <a href="https://publications.waset.org/abstracts/search?q=Chutimet%20Srinilta"> Chutimet Srinilta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research paper deals with extraction of Thai character features using the proposed histogram projection so as to improve the recognition performance. The process starts with transformation of image files into binary files before thinning. After character thinning, the skeletons are entered into the proposed extraction using histogram projection (horizontal and vertical) to extract unique features which are inputs of the subsequent recognition step. The recognition rate with the proposed extraction technique is as high as 97 percent since the technique works very well with the idiosyncrasies of Thai characters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title="character recognition">character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20projection" title=" histogram projection"> histogram projection</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron" title=" multilayer perceptron"> multilayer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=Thai%20character%20features%20extraction" title=" Thai character features extraction "> Thai character features extraction </a> </p> <a href="https://publications.waset.org/abstracts/11674/enhanced-thai-character-recognition-with-histogram-projection-feature-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11674.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22314</span> Mutagenicity Evaluation of Locally Produced Biphasic Calcium Phosphate Using Ames Test</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nur%20Fathin%20Alia%20Che%20Wahab">Nur Fathin Alia Che Wahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Thirumulu%20Ponnuraj%20Kannan"> Thirumulu Ponnuraj Kannan</a>, <a href="https://publications.waset.org/abstracts/search?q=Zuliani%20Mahmood"> Zuliani Mahmood</a>, <a href="https://publications.waset.org/abstracts/search?q=Ismail%20Ab.%20Rahman"> Ismail Ab. Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanafi%20Ismail"> Hanafi Ismail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Locally produced Biphasic Calcium Phosphate (BCP) consists of hydroxyapatite (HA) and β-tricalcium phosphate (β-TCP) which is a promising material for dentin and bone regeneration as well as in tissue engineering applications. The study was carried out to investigate the mutagenic effect of locally produced BCP using Ames test. Mutagenicity was evaluated with and without the addition of metabolic activation system (S9). This study was performed on Salmonella typhimurium TA98, TA102, TA1537, and TA1538 strains using preincubation assay method. The doses tested were 5000, 2500, 1250, 625, 313 µg/plate. Negative and positive controls were also included. The bacteria were incubated for 48 hours at 37 ± 0.5 °C. Then, the revertant colonies were counted. Data obtained were evaluated using non-statistical method. The mean number of revertant colonies in strains with and without S9 mix treated with locally produced BCP was less than double when compared to negative control for all the tested concentrations. The results from this study indicate that the locally produced BCP is non-mutagenic under the present test conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ames%20test" title="ames test">ames test</a>, <a href="https://publications.waset.org/abstracts/search?q=biphasic%20calcium%20phosphate" title=" biphasic calcium phosphate"> biphasic calcium phosphate</a>, <a href="https://publications.waset.org/abstracts/search?q=dentin%20regeneration" title=" dentin regeneration"> dentin regeneration</a>, <a href="https://publications.waset.org/abstracts/search?q=mutagenicity" title=" mutagenicity"> mutagenicity</a> </p> <a href="https://publications.waset.org/abstracts/51753/mutagenicity-evaluation-of-locally-produced-biphasic-calcium-phosphate-using-ames-test" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51753.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">323</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22313</span> Chaotic Sequence Noise Reduction and Chaotic Recognition Rate Improvement Based on Improved Local Geometric Projection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rubin%20Dan">Rubin Dan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingcai%20Wang"> Xingcai Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ziyang%20Chen"> Ziyang Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A chaotic time series noise reduction method based on the fusion of the local projection method, wavelet transform, and particle swarm algorithm (referred to as the LW-PSO method) is proposed to address the problem of false recognition due to noise in the recognition process of chaotic time series containing noise. The method first uses phase space reconstruction to recover the original dynamical system characteristics and removes the noise subspace by selecting the neighborhood radius; then it uses wavelet transform to remove D1-D3 high-frequency components to maximize the retention of signal information while least-squares optimization is performed by the particle swarm algorithm. The Lorenz system containing 30% Gaussian white noise is simulated and verified, and the phase space, SNR value, RMSE value, and K value of the 0-1 test method before and after noise reduction of the Schreiber method, local projection method, wavelet transform method, and LW-PSO method are compared and analyzed, which proves that the LW-PSO method has a better noise reduction effect compared with the other three common methods. The method is also applied to the classical system to evaluate the noise reduction effect of the four methods and the original system identification effect, which further verifies the superiority of the LW-PSO method. Finally, it is applied to the Chengdu rainfall chaotic sequence for research, and the results prove that the LW-PSO method can effectively reduce the noise and improve the chaos recognition rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Schreiber%20noise%20reduction" title="Schreiber noise reduction">Schreiber noise reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=0-1%20test%20method" title=" 0-1 test method"> 0-1 test method</a>, <a href="https://publications.waset.org/abstracts/search?q=chaotic%20sequence%20denoising" title=" chaotic sequence denoising"> chaotic sequence denoising</a> </p> <a href="https://publications.waset.org/abstracts/150600/chaotic-sequence-noise-reduction-and-chaotic-recognition-rate-improvement-based-on-improved-local-geometric-projection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150600.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">199</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22312</span> A Nonlocal Means Algorithm for Poisson Denoising Based on Information Geometry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dongxu%20Chen">Dongxu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Yipeng%20Li"> Yipeng Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an information geometry NonlocalMeans(NLM) algorithm for Poisson denoising. NLM estimates a noise-free pixel as a weighted average of image pixels, where each pixel is weighted according to the similarity between image patches in Euclidean space. In this work, every pixel is a Poisson distribution locally estimated by Maximum Likelihood (ML), all distributions consist of a statistical manifold. A NLM denoising algorithm is conducted on the statistical manifold where Fisher information matrix can be used for computing distribution geodesics referenced as the similarity between patches. This approach was demonstrated to be competitive with related state-of-the-art methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title="image denoising">image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20noise" title=" Poisson noise"> Poisson noise</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20geometry" title=" information geometry"> information geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlocal-means" title=" nonlocal-means"> nonlocal-means</a> </p> <a href="https://publications.waset.org/abstracts/51221/a-nonlocal-means-algorithm-for-poisson-denoising-based-on-information-geometry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51221.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">285</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22311</span> Defect Localization and Interaction on Surfaces with Projection Mapping and Gesture Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiang%20Wang">Qiang Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyang%20Yu"> Hongyang Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=MingRong%20Lai"> MingRong Lai</a>, <a href="https://publications.waset.org/abstracts/search?q=Miao%20Luo"> Miao Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a method for accurately localizing and interacting with known surface defects by overlaying patterns onto real-world surfaces using a projection system. Given the world coordinates of the defects, we project corresponding patterns onto the surfaces, providing an intuitive visualization of the specific defect locations. To enable users to interact with and retrieve more information about individual defects, we implement a gesture recognition system based on a pruned and optimized version of YOLOv6. This lightweight model achieves an accuracy of 82.8% and is suitable for deployment on low-performance devices. Our approach demonstrates the potential for enhancing defect identification, inspection processes, and user interaction in various applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=defect%20localization" title="defect localization">defect localization</a>, <a href="https://publications.waset.org/abstracts/search?q=projection%20mapping" title=" projection mapping"> projection mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=gesture%20recognition" title=" gesture recognition"> gesture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOv6" title=" YOLOv6"> YOLOv6</a> </p> <a href="https://publications.waset.org/abstracts/165856/defect-localization-and-interaction-on-surfaces-with-projection-mapping-and-gesture-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165856.pdf" target="_blank" class="btn btn-primary 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