A convex model approach for structure non-probabilistic reliability analysis

2017 ◽  
Vol 231 (5) ◽  
pp. 508-515 ◽  
Author(s):  
Zhengmao Yang ◽  
Yanjuan Zhang ◽  
Wenjun Meng ◽  
Jianghui Cai
Keyword(s):  
Stress Response ◽  
Regression Function ◽  
Metal Structure ◽  
Support Vector ◽  
Bridge Crane ◽  
The Finite Element Method ◽  
Convex Model ◽  
Novel Approach ◽  
Vector Machines ◽  
Dangerous Point

In this article, a novel approach, that is, convex model method of set theory, is proposed to investigate the non-probabilistic reliability of bridge crane. Considering the metal structure system of the bridge crane, the finite element method is applied to obtain the stress response of the structure dangerous point. Then, the sample of stress response of the structure danger point and uncertain parameters are obtained. Finally, based on support vector machines, the structure implicit regression function of the system is replaced by explicit expression that calculates the non-probabilistic reliability of the structure. Results show that this approach is useful and efficient to solve the problem of non-probabilistic reliability in the metal structure.

A novel approach to mass detection in digital mammography based on Support Vector Machines(SVM)

2003 ◽  
pp. 399-401 ◽  
Author(s):  
Renato Campanini ◽  
Armando Bazzani ◽  
Alessandro Bevilacqua ◽  
Dante Bollini ◽  
Danilo Dongiovanni ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Digital Mammography ◽  
Support Vector ◽  
Mass Detection ◽  
Novel Approach ◽  
Vector Machines

scholarly journals A novel approach for accurate prediction of spontaneous passage of ureteral stones: Support vector machines

2006 ◽  
Vol 69 (1) ◽  
pp. 157-160 ◽  
Author(s):  
F. Dal Moro ◽  
A. Abate ◽  
G.R.G. Lanckriet ◽  
G. Arandjelovic ◽  
P. Gasparella ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Accurate Prediction ◽  
Ureteral Stones ◽  
Support Vector ◽  
Spontaneous Passage ◽  
Novel Approach ◽  
Vector Machines

Outdoor Navigating Scene Labeling Using Importance Factor Based I-RELIEF and Feature Weighted Support Vector Machines

2014 ◽  
Vol 511-512 ◽  
pp. 467-474
Author(s):  
Jun Tu ◽  
Cheng Liang Liu ◽  
Zhong Hua Miao
Keyword(s):  
Support Vector Machines ◽  
Image Data ◽  
Kernel Functions ◽  
Support Vector ◽  
Terrain Classification ◽  
Relief Algorithm ◽  
Novel Approach ◽  
Scene Labeling ◽  
Vector Machines ◽  
Importance Factor

Feature selection plays an important role in terrain classification for outdoor robot navigation. For terrain classification, the image data usually have a large number of feature dimensions. The better selection of features usually results in higher labeling accuracy. In this work, a novel approach for terrain perception using Importance Factor based I-Relief algorithm and Feature Weighted Support Vector Machines (IFIR-FWSVM) is put forward. Firstly, the weight of each feature for classification is computed by using Importance Factor based I-Relief algorithm (IFIR) and the irrelevant features are eliminated. Then the weighted features are used to compute the kernel functions of SVM and trained the classifier. Finally, the trained SVM is employed to predict the terrain label in the far-field regions. Experimental results based on DARPA datasets show that the proposed method IFIR-FWSVM is superior over traditional SVM.

Handedness tests for preschool children: A novel approach based on graphics tablets and support vector machines

2012 ◽  
Vol 12 (4) ◽  
pp. 1390-1398 ◽  
Author(s):  
Thiemo Gruber ◽  
Britta Meixner ◽  
Johann Prosser ◽  
Bernhard Sick
Keyword(s):  
Preschool Children ◽  
Support Vector Machines ◽  
Support Vector ◽  
Novel Approach ◽  
Vector Machines

scholarly journals Hyperspectral image classification using support vector machines

2020 ◽  
Vol 9 (4) ◽  
pp. 684
Author(s):  
Jonnadula Dr.J.Harikiran Harikiran
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Bidimensional Empirical Mode Decomposition

In this paper, a novel approach for hyperspectral image classification technique is presented using principal component analysis (PCA), bidimensional empirical mode decomposition (BEMD) and support vector machines (SVM). In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. This component is supplied as input to BEMD algorithm, which divides the component into four parts, the first three parts represents intrensic mode functions (IMF) and last part shows the residue. These BIMFs and residue image is further taken as input to the SVM for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analyticalperformance in comparison to some established methods.

Automatic evaluation of online learning interaction content using domain concepts

2020 ◽  
Vol 38 (3) ◽  
pp. 421-445
Author(s):  
Di Wu ◽  
Lei Wu ◽  
Alexis Palmer ◽  
Dr Kinshuk ◽  
Peng Zhou
Keyword(s):  
Online Learning ◽  
Online Course ◽  
Support Vector ◽  
Content Type ◽  
Educational Domain ◽  
Novel Approach ◽  
Vector Machines ◽  
Quality Dimensions ◽  
Supporting Teachers

Purpose Interaction content is created during online learning interaction for the exchanged information to convey experience and share knowledge. Prior studies have mainly focused on the quantity of online learning interaction content (OLIC) from the perspective of types or frequency, resulting in a limited analysis of the quality of OLIC. Domain concepts as the highest form of interaction are shown as entities or things that are particularly relevant to the educational domain of an online course. The purpose of this paper is to explore a new method to evaluate the quality of OLIC using domain concepts. Design/methodology/approach This paper proposes a novel approach to automatically evaluate the quality of OLIC regarding relevance, completeness and usefulness. A sample of OLIC corpus is classified and evaluated based on domain concepts and textual features. Findings Experimental results show that random forest classifiers not only outperform logistic regression and support vector machines but also their performance is improved by considering the quality dimensions of relevance and completeness. In addition, domain concepts contribute to improving the performance of evaluating OLIC. Research limitations/implications This paper adopts a limited sample to train the classification models. It has great benefits in monitoring students’ knowledge performance, supporting teachers’ decision-making and even enhancing the efficiency of school management. Originality/value This study extends the research of domain concepts in quality evaluation, especially in the online learning domain. It also has great potential for other domains.

scholarly journals Reliability Analysis-Based Numerical Calculation of Metal Structure of Bridge Crane

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Wenjun Meng ◽  
Zhengmao Yang ◽  
Xiaolong Qi ◽  
Jianghui Cai
Keyword(s):  
Finite Element ◽  
Orthogonal Design ◽  
Random Perturbation ◽  
Mapping Function ◽  
Random Variable ◽  
Metal Structure ◽  
Extreme Condition ◽  
Bridge Crane ◽  
Analysis And Design ◽  
Dangerous Point

The study introduced a finite element model of DQ75t-28m bridge crane metal structure and made finite element static analysis to obtain the stress response of the dangerous point of metal structure in the most extreme condition. The simulated samples of the random variable and the stress of the dangerous point were successfully obtained through the orthogonal design. Then, we utilized BP neural network nonlinear mapping function trains to get the explicit expression of stress in response to the random variable. Combined with random perturbation theory and first-order second-moment (FOSM) method, the study analyzed the reliability and its sensitivity of metal structure. In conclusion, we established a novel method for accurately quantitative analysis and design of bridge crane metal structure.

scholarly journals Novel Approach for Rooftop Detection Using Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Hayk Baluyan ◽  
Bikash Joshi ◽  
Amer Al Hinai ◽  
Wei Lee Woon
Keyword(s):  
High Rate ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Histogram Method ◽  
Novel Approach ◽  
Learning Techniques ◽  
Vector Machines ◽  
First Pass ◽  
Homogeneous Regions ◽  
Improved Accuracy

A new method for detecting rooftops in satellite images is presented. The proposed method is based on a combination of machine learning techniques, namely, k-means clustering and support vector machines (SVM). Firstly k-means clustering is used to segment the image into a set of rooftop candidates—these are homogeneous regions in the image which are potentially associated with rooftop areas. Next, the candidates are submitted to a classification stage which determines which amongst them correspond to “true” rooftops. To achieve improved accuracy, a novel two-pass classification process is used. In the first pass, a trained SVM is used in the normal way to distinguish between rooftop and nonrooftop regions. However, this can be a challenging task, resulting in a relatively high rate of misclassification. Hence, the second pass, which we call the “histogram method,” was devised with the aim of detecting rooftops which were missed in the first pass. The performance of the model is assessed both in terms of the percentage of correctly classified candidates as well as the accuracy of the estimated rooftop area.

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