Learning solutions to a Cauchy problem for the modified Helmholtz equations using LS-SVM

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ziku Wu ◽  
Xiaoming Han ◽  
GuoFeng Li
Keyword(s):  
Cauchy Problem ◽  
Least Square ◽  
Support Vector ◽  
Helmholtz Equations ◽  
Content Type ◽  
Mesh Free ◽  
Ill Posed ◽  
Approximate Function ◽  
Modified Helmholtz Equations ◽  
First Time

Purpose The purpose of this paper is to develop a mesh-free algorithm based on the least square support vector machines method for numerical simulation of the modified Helmholtz equations. Design/methodology/approach The proposed method deals with a Cauchy problem for the modified Helmholtz equations. The algorithm converts the problem into a quadratic programming. It can be divided into three steps. First, some training points are allocated. Then, an approximate function is constructed. Finally, the shape parameters are estimated. Findings The proposed method's stability is discussed. Numerical experiments are conducted to check the efficiency of the algorithm. The proposed method is found to feasible for the ill-posed problems of the modified Helmholtz equations. Originality/value The originality lies in that the proposed method is applied to solve the modified Helmholtz equations for the first time, and the expected results are obtained.

Insurance regulations, risk and performance in Ghana

2019 ◽  
Vol 28 (1) ◽  
pp. 74-96
Author(s):  
Baah Aye Kusi ◽  
Abdul Latif Alhassan ◽  
Daniel Ofori-Sasu ◽  
Rockson Sai
Keyword(s):  
Random Effect ◽  
Least Square ◽  
Insurance Market ◽  
Content Type ◽  
Ordinary Least Square ◽  
Negative Effect ◽  
And Performance ◽  
First Time

Purpose This study aims to examine the hypothesis that the effect of insurer risks on profitability is conditional on regulation, using two main regulatory directives in the Ghanaian insurance market as a case study. Design/methodology/approach This study used the robust ordinary least square and random effect techniques in a panel data of 30 insurers from 2009 to 2015 to test the research hypothesis. Findings The results suggest that regulations on no credit premium and required capital have insignificant effects on profitability of insurers. On the contrary, this study documents evidence that both policies mitigate the effect of underwriting risk on profitability and suggests that regulations significantly mitigate the negative effect of underwriting risk to improve profitability. Practical implications The finding suggests that policymakers and regulators must continue to initiate, design and model regulations such that they help tame risk to improve the performance of insurers in Ghana. Originality/value This study provides first-time evidence on the role of regulations in controlling risks in a developing insurance market.

Continuous wide spectrum odor sensing for electronic nose system

Sensor Review ◽  
2018 ◽  
Vol 38 (2) ◽  
pp. 223-230
Author(s):  
Wenli Zhang ◽  
Fengchun Tian ◽  
An Song ◽  
Zhenzhen Zhao ◽  
Youwen Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Wide Spectrum ◽  
Least Square ◽  
Support Vector ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Sensing Element ◽  
Content Type ◽  
System Errors

Purpose This paper aims to propose an odor sensing system based on wide spectrum for e-nose, based on comprehensive analysis on the merits and drawbacks of current e-nose. Design/methodology/approach The wide spectral light is used as the sensing medium in the e-nose system based on continuous wide spectrum (CWS) odor sensing, and the sensing response of each sensing element is the change of light intensity distribution. Findings Experimental results not only verify the feasibility and effectiveness of the proposed system but also show the effectiveness of least square support vector machine (LSSVM) in eliminating system errors. Practical implications Theoretical model of the system was constructed, and experimental tests were carried out by using NO2 and SO2. System errors in the test data were eliminated using the LSSVM, and the preprocessed data were classified by euclidean distance to centroids (EDC), k-nearest neighbor (KNN), support vector machine (SVM), LSSVM, respectively. Originality/value The system not only has the advantages of current e-nose but also realizes expansion of sensing array by means of light source and the spectrometer with their wide spectrum, high resolution characteristics which improve the detection accuracy and realize real-time detection.

Fast circular shapes detection in cylindrical ECT sensors by design selection and nonlinear black-box modeling

2017 ◽  
Vol 36 (1) ◽  
pp. 2-17 ◽  
Author(s):  
Yacine Oussar ◽  
Cedric Margo ◽  
Jérôme Lucas ◽  
Stéphane Holé
Keyword(s):  
Cost Effective ◽  
Black Box ◽  
Least Square ◽  
Support Vector ◽  
Content Type ◽  
Circular Shape ◽  
Box Models ◽  
Satisfactory Precision ◽  
Design Selection ◽  
Capacitance Tomography

Purpose Within the framework of image reconstruction in cylindrical electrical capacitance tomography (ECT) sensors, the purpose of this study is to select the structure of a sensor in terms of number and size of the electrodes, to predict the radius and the position of a single circular shape lying in the cross-section defined by the sensor electrodes. Design/methodology/approach Nonlinear black-box models using a set of physically independent capacitances and least-square support vector machines models selected with a sophisticated validation method are implemented. Findings The coordinates of circular shapes are well estimated in fixed and variable permittivity environments even with noisy data. Various numerical experiments are presented and discussed. Sensors formed by three or four electrodes covering 50 per cent of the sensor perimeter provide the best prediction performances. Research limitations/implications The proposed method is limited to the detection of a single circular shape in a cylindrical ECT sensor. Practical implications This method can be advantageously implemented in real-time applications, as it is numerically cost-effective and necessitates a small amount of measurements. Originality/value The contribution is two-fold: a fast computation of a circular shape position and radius with a satisfactory precision compared to the sensor size, and the determination of a cylindrical ECT sensor architecture that allows the most efficient predictions.

Categorization of fabric design using multi-class least-square support vector machine

2014 ◽  
Vol 26 (1) ◽  
pp. 58-66 ◽  
Author(s):  
A. Ghosh ◽  
T. Guha ◽  
R. Bhar
Keyword(s):  
Pattern Recognition ◽  
Design Methodology ◽  
Inequality Constraints ◽  
Recognition System ◽  
Least Square ◽  
Support Vector ◽  
Content Type ◽  
Reasonable Degree ◽  
Vector Machines ◽  
Fabric Design

Purpose – The purpose of this paper is to give an approach for categorization of diverse textile designs using their textural features as extracted from their gray images by means of multi-class least-square support vector machines (LS-SVM). Design/methodology/approach – In this work, the authors endeavor to devise a pattern recognition system based on LS-SVM which performs a multi-class categorization of three basic woven designs namely plain, twill and sateen after analyzing their features. Findings – The result establishes that LS-SVM is able to classify the fabric design with a reasonable degree of accuracy and it outperforms the standard SVM. Originality/value – The algorithmic simplicity of LS-SVM resulting from replacement of inequality constraints by equality ones and ability of handling noisy data by accommodating an error variable in its algorithm make it eminently suitable for textile pattern recognition. This paper offers a maiden application of LS-SVM in textile pattern recognition.

Spectroscopy quantitative analysis cotton content of blend fabrics

2016 ◽  
Vol 28 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Xudong Sun ◽  
Mingxing Zhou ◽  
Yize Sun
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Content Type ◽  
Svm Model ◽  
Calibration Methods ◽  
Learning Machine ◽  
Mean Square Errors

Purpose – The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics. Design/methodology/approach – In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with LS-SVM model. The correlation coefficient of prediction (r p ) and root mean square errors of prediction were 0.98 and 4.50 percent, respectively. Findings – The results suggest that NIR technique combining with LS-SVM method has significant potential to quantitatively analyze cotton content in blend fabrics. Originality/value – It may have commercial and regulatory potential to avoid time consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

Variable snap-off study for 3D additive screen printing

2020 ◽  
Vol 32 (3) ◽  
pp. 430-445
Author(s):  
Yacheng Wang ◽  
Peibo Li ◽  
Yuegang Liu ◽  
Yize Sun ◽  
Liuyuan Su
Keyword(s):  
Theoretical Model ◽  
Regression Model ◽  
Design Methodology ◽  
Screen Printing ◽  
Least Square ◽  
Support Vector ◽  
Content Type ◽  
Printing Quality ◽  
Comparison Results ◽  
The Relationship

Purpose In 3D additive screen printing with constant snap-off, the inhomogeneous screen counterforce will influence the printing force and reduce the printing quality. The purpose of this paper is to study the relationship between scraper position, snap-off and screen counterforce and develop a variable snap-off curve for 3D additive screen printing to improve the printing quality. Design/methodology/approach An experiment was carried out; genetic algorithm (GA) optimization theoretical model, backpropagation neural network regression model and least square support vector machine regression model were established to study the relationship between scraper position, snap-off and screen counterforce. The absolute errors of counterforce of three models with the experiment results were less than 1.5 N, which was tolerated and the three models were considered valid. The comparison results showed that GA optimization theoretical model performed best. Findings The results suggest that GA optimization theoretical model performed best to represent the relationship, and it was used to develop a variable snap-off curve. With the variable snap-off curve in 3D additive screen printing, the inhomogeneous screen counterforce was weakened and the printing quality was improved. Originality/value In printing production, the variable snap-off curve in 3D additive screen printing helps improve the printing quality; this study is of prime importance to the 3D additive screen printing.

Different or similar? Acceptability of aseptic-packaged cooked rice among consumers, researchers and experts

2015 ◽  
Vol 117 (5) ◽  
pp. 1564-1580
Author(s):  
Han Sub Kwak ◽  
Misook Kim ◽  
Yoonhwa Jeong
Keyword(s):  
Descriptive Analysis ◽  
Strong Relationship ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Product Development Process ◽  
Rice Cake ◽  
Content Type ◽  
Cooked Rice ◽  
First Time

Purpose – The purpose of this paper is to compare the acceptance ratings and drivers of liking and disliking attributes of aseptic-packaged cooked rice by consumers, researchers and experts. Design/methodology/approach – Descriptive analysis (DA) was conducted using trained panelists. Acceptability was measured by consumers, researchers and experts. The results of DA and acceptability were analyzed using partial least square regression. Findings – There was no strong relationship among the three groups in their rating patterns for the samples (r=−0.342-0.445). The liking factors for each group were as follows: consumers (rice cake flavor and moisture), researchers (wet wood flavor and whiteness) and experts (wet wood flavor and size of rice). The disliking factors for each group were as follows: consumers (wet wood flavor and brown particle), researchers (moisture) and experts (old rice aroma). The consumers, researchers and experts seemed to have different acceptances and key descriptive attributes for aseptic-packaged cooked rice. Research limitations/implications – The consensus by researchers during the product development process required caution with regard to the fact that the evaluation by the researchers could be different from what consumers or experts prefer. Practical implications – Setting-up in-house panelists group would be minimized the discrepancy between consumers and researches. Originality/value – This study contributes to understanding of the acceptability by food researchers and comparing to consumers and experts for the first time in sensory field.

AN ERROR ANALYSIS OF LAVRENTIEV REGULARIZATION IN LEARNING THEORY

2009 ◽  
Vol 02 (01) ◽  
pp. 129-140
Author(s):  
J. K. Sahoo ◽  
Arindama Singh
Keyword(s):  
Inverse Problem ◽  
Learning Theory ◽  
Regularization Parameter ◽  
Least Square ◽  
Support Vector ◽  
Lavrentiev Regularization ◽  
Vector Machines ◽  
Ill Posed ◽  
Regularization Networks ◽  
Context Of Learning

In this paper we study how Lavrentiev regularization can be used in the context of learning theory, especially in regularization networks that are closely related to support vector machines. We briefly discuss formulations of learning from examples in the context of ill-posed inverse problem and regularization. We then study the interplay between the Lavrentiev regularization of the concerned continuous and discretized ill-posed inverse problems. As the main result of this paper, we give an improved probabilistic bound for the regularization networks or least square algorithms, where we can afford to choose the regularization parameter in a larger interval.

Prediction of construction projects’ costs based on fusion method

2017 ◽  
Vol 34 (7) ◽  
pp. 2396-2408 ◽  
Author(s):  
Fang Shutian ◽  
Zhao Tianyi ◽  
Zhang Ying
Keyword(s):  
Construction Projects ◽  
Design Methodology ◽  
Construction Cost ◽  
Filter Method ◽  
Support Vector ◽  
Content Type ◽  
Filtering Method ◽  
The Cost ◽  
First Time ◽  
Practical Implications

Purpose This study aims to predict the construction cost in China, the authors purposed a fused method. Design/methodology/approach The authors extracted 22 factors which may influence the cost and performed the correlation analysis with cost. They chose the highest 10 factors to predict cost by the fused method. The method fused the Kalman filter with least squares support vector machine and multiple linear regression. Findings Ten factors which affect the cost most were found. The construction cost in China can be predicted by the presented method precisely. The statistical filter method could be used in the field of construction cost prediction. Research limitations/implications The construction cost and construction interior factors are a business secret in China. So, the authors only collected 24 buildings’ data to perform the experiments. Practical implications There is no standard and precise method to predict construction cost in China, so the presented method offers a new way to judge the feasibility of projects and select design schemes of construction. Originality/value The authors purposed a new fused method to predict construction cost. It is the first time that the statistical filtering method was used in this field. The effectiveness was verified by the experiments. Ten factors which have a high relationship with construction cost were found.

Virality over YouTube: an empirical analysis

2014 ◽  
Vol 24 (5) ◽  
pp. 629-647 ◽  
Author(s):  
Gohar Feroz Khan ◽  
Sokha Vong
Keyword(s):  
Social Capital ◽  
Empirical Model ◽  
Application Programming Interface ◽  
Partial Least Square ◽  
Least Square ◽  
Content Type ◽  
Application Programming ◽  
Media Platform ◽  
First Time ◽  
The Relationship

Purpose – The purpose of this paper is to seek reasons for some videos going viral over YouTube (a type of social media platform). Design/methodology/approach – Using YouTube APIs (Application Programming Interface) and Webometrics analyst tool, the authors collected data on about 100 all-time-most-viewed YouTube videos and information about the users associated with the videos. The authors constructed and tested an empirical model to understand the relationship among users’ social and non-social capital (e.g. User Age, Gender, View Count, Subscriber, Join Date, Total Videos Posted), video characteristics (Post Date, Duration, and Video Category), external network capital (in-links and hit counts), and Virality (Likes, Dislikes, Favorite Count, View Count, and Comment Count). Partial least square and Webometric analysis was used to explore the association among the constructs. Findings – Among other findings, the results showed that popularity of the videos was not only the function of YouTube system per se, but that network dynamics (e.g. in-links and hits counts) and offline social capital (e.g. fan base and fame) play crucial roles in the viral phenomenon, particularly view count. Originality/value – The authors for the first time constructed and tested an empirical model to find out the determinants of viral phenomenon over YouTube.

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