scholarly journals Predicting Tunnel Squeezing Using the SVM-BP Combination Model

2021 ◽  
Author(s):  
Zhen HUANG ◽  
Minxing Liao ◽  
Haoliang Zhang ◽  
Jiabing Zhang ◽  
Shaokun Ma ◽  
...  
Keyword(s):  
Large Scale ◽  
Back Propagation ◽  
Classification Performance ◽  
Propagation Model ◽  
Support Vector ◽  
Characteristic Parameters ◽  
Svm Model ◽  
Tunnel Diameter ◽  
Squeezing Prediction ◽  
Bp Model

Abstract Rock squeezing has a large influence on tunnel construction safety; thus, when designing and constructing tunnels it is highly important to use a reliable method for predicting tunnel squeezing from incomplete data. In this study, a combination SVM-BP (support vector machine-back-propagation) model is proposed to classify the deformation caused by surrounding rock squeezing. We designed different characteristic parameters and three types of classifiers (an SVM model, a BP model, and the proposed SVM-BP model) for the tunnel-squeezing prediction experiments and analysed the accuracy of predictions by different models and the influences of characteristic parameters on the prediction results. In contrast to other prediction methods, the proposed SVM-BP model is verified to be reliable. The results show that four characteristics: tunnel diameter (D), tunnel buried depth (H), rock quality index (Q) and support stiffness (K) reflect the effect of rock squeezing sufficiently for classification. The SVM-BP model combines the advantages of both an SVM and a BP neural network. It possesses flexible nonlinear modelling ability and the ability to perform parallel processing of large-scale information. Therefore, the SVM-BP model achieves better classification performance than do the SVM or BP models separately. Moreover, coupling D, H, and K has a significant impact on the predicted results of tunnel squeezing.

Abutment scour in clear-water and live-bed conditions by GMDH network

2013 ◽  
Vol 67 (5) ◽  
pp. 1121-1128 ◽  
Author(s):  
Mohammad Najafzadeh ◽  
Gholam-Abbas Barani ◽  
Masoud Reza Hessami Kermani
Keyword(s):  
Back Propagation ◽  
Support Vector ◽  
Group Method ◽  
Scour Depth ◽  
Back Propagation Algorithm ◽  
Clear Water ◽  
Sediment Size ◽  
Svm Model ◽  
Vector Machines ◽  
Abutment Scour

In the present study, the Group Method of Data Handling (GMDH) network has been utilized to predict abutments scour depth for both clear-water and live-bed conditions. The GMDH network was developed using a Back Propagation algorithm (BP). Input parameters that were considered as effective variables on abutment scour depth included properties of sediment size, geometry of bridge abutments, and properties of approaching flow. Training and testing performances of the GMDH network were carried out using dimensionless parameters that were collected from the literature. The testing results were compared with those obtained using the Support Vector Machines (SVM) model and the traditional equations. The GMDH network predicted the abutment scour depth with lower error (RMSE (root mean square error) = 0.29 and MAPE (mean absolute percentage of error) = 0.99) and higher (R = 0.98) accuracy than those performed using the SVM model and the traditional equations.

scholarly journals Learning Neural Bag-of-Matrix-Summarization with Riemannian Network

2019 ◽  
Vol 33 ◽  
pp. 8746-8753
Author(s):  
Hong Liu ◽  
Jie Li ◽  
Yongjian Wu ◽  
Rongrong Ji
Keyword(s):  
Vector Space ◽  
Large Scale ◽  
Metric Learning ◽  
Back Propagation ◽  
Classification Performance ◽  
Feature Representation ◽  
Superior Performance ◽  
Constant Length ◽  
Key Innovation ◽  
Geometric Space

Symmetric positive defined (SPD) matrix has attracted increasing research focus in image/video analysis, which merits in capturing the Riemannian geometry in its structured 2D feature representation. However, computation in the vector space on SPD matrices cannot capture the geometric properties, which corrupts the classification performance. To this end, Riemannian based deep network has become a promising solution for SPD matrix classification, because of its excellence in performing non-linear learning over SPD matrix. Besides, Riemannian metric learning typically adopts a kNN classifier that cannot be extended to large-scale datasets, which limits its application in many time-efficient scenarios. In this paper, we propose a Bag-of-Matrix-Summarization (BoMS) method to be combined with Riemannian network, which handles the above issues towards highly efficient and scalable SPD feature representation. Our key innovation lies in the idea of summarizing data in a Riemannian geometric space instead of the vector space. First, the whole training set is compressed with a small number of matrix features to ensure high scalability. Second, given such a compressed set, a constant-length vector representation is extracted by efficiently measuring the distribution variations between the summarized data and the latent feature of the Riemannian network. Finally, the proposed BoMS descriptor is integrated into the Riemannian network, upon which the whole framework is end-to-end trained via matrix back-propagation. Experiments on four different classification tasks demonstrate the superior performance of the proposed method over the state-of-the-art methods.

Mining ICDDR, B Hospital Surveillance Data and Exhibiting Strategies for Balancing Large Unbalanced Datasets

2015 ◽  
Vol 10 (1) ◽  
pp. 39-66 ◽  
Author(s):  
Adnan Firoze ◽  
Rashedur M. Rahman
Keyword(s):  
Large Scale ◽  
Multinomial Logistic Regression ◽  
Short Interval ◽  
Classification Performance ◽  
Computer Applications ◽  
Surveillance Data ◽  
Critical Conditions ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Fitting Problem

This research uses a number of classifier models on Hospital Surveillance data to classify admitted patients according to their critical conditions. Three class labels were used to distinguish the criticality of the admitted patients. Furthermore, set forth are two distinct approaches to address the over-fitting problem in the unbalanced dataset since the frequency of instances of the class ‘low' is significantly higher than the other two classes. Apart from trimming the dataset to balance the classes, this work has dealt with the over-fitting problem by introducing the ‘Synthetic Minority Over-sampling Technique' (SMOTE) algorithm coupled with Locally Linear Embedding (LLE). It has constructed three models that applied the neural, and multinomial logistic regression classifications and finally compared the performance of the work's models with the models developed by Rahman and Hasan (2011) where they used several decision tree models to classify the same dataset using tenfold cross validation. Additionally, for a comprehensive comparative analysis, this work has compared the classification performance of the authors' novel third model using support vector machine (SVM). After comparison, the work shows that one of the authors' models surpasses all prior models in terms of classification performance, taking into account the performance time trade-off, giving them an efficient model that handles large scale unbalanced datasets efficiently with standard classification performance. The models developed in this research can become imperative tools to doctors when large numbers of patients arrive in a short interval especially during epidemics. Since, intervention of machines become a necessity when doctors are scarce, computer applications powered by these models are helpful to diagnose and measure the criticality of the newly arrived patients with the help of the historical data kept in the surveillance database.

A Forecasting Method of Kostiakov Infiltration Model Parameters Based on Back-Propagation Model

2012 ◽  
Vol 610-613 ◽  
pp. 2899-2903
Author(s):  
Chong Wen Cao ◽  
Gui Sheng Fan
Keyword(s):  
Back Propagation ◽  
Influence Factors ◽  
Propagation Model ◽  
Water Infiltration ◽  
Physical Parameters ◽  
Model Parameters ◽  
Soil Infiltration ◽  
Infiltration Process ◽  
Infiltration Model ◽  
Bp Model

Based on test data of field soil water infiltration, Back-propagation (BP) model of predicting Kostiakov infiltration model parameters was established after analyzing the primary influence factors of water infiltration model parameters. The results indicate that BP model can reflect the non-linear relationship between the model parameters and the physical parameters of the soil; BP model is high accuracy for prediction soil infiltration model parameters. It can be referred as a new method to predict soil infiltration process using soil physical parameters.

Fault Diagnosis of Pump Based on a Hybrid HMM/SVM Model

2011 ◽  
Vol 188 ◽  
pp. 629-635
Author(s):  
Xia Yue ◽  
Chun Liang Zhang ◽  
Jian Li ◽  
H.Y. Zhu
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Model ◽  
Large Scale ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Double Layers ◽  
Support Vector ◽  
Practical Applications ◽  
Average Recognition Rate ◽  
Svm Model

A hybrid support vector machine (SVM) and hidden Markov model (HMM) model was introduced into the fault diagnosis of pump. This model had double layers: the first layer used HMM to classify preliminarily in order to get the coverage of possible faults; the second layer utilized this information to activate the corresponding SVMs for improving the recognition accuracy. The structure of this hybrid model was clear and feasible. Especially the model had the potential of large-scale multiclass application in fault diagnosis because of its good scalability. The recognition experiments of 26 statuses on the ZLH600-2 pump showed that the recognition capability of this model was sound in multiclass problems. The recognition rate of one bearing eccentricity increased from SVM’s 84.42% to 89.61% while the average recognition rate of hybrid model reached 95.05%. Although some goals while model constructed did not be fully realized, this model was still very good in practical applications.

scholarly journals Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaosheng Yang ◽  
Duo Mei ◽  
Qingfang Yang ◽  
Huxing Zhou ◽  
Xiaowen Li
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Traffic Flow ◽  
Large Scale ◽  
Support Vector ◽  
Traffic Flow Prediction ◽  
Model Based ◽  
Flow Prediction ◽  
Svm Model ◽  
Parallel Ga

To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM) model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface). The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

scholarly journals Piecewise-Smooth Support Vector Machine for Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Qing Wu ◽  
Wenqing Wang
Keyword(s):  
Support Vector Machine ◽  
Large Scale ◽  
Unconstrained Minimization ◽  
Classification Performance ◽  
Classification Systems ◽  
Piecewise Smooth ◽  
Support Vector ◽  
Smooth Functions ◽  
Novel Method ◽  
Smooth Support Vector Machine

Support vector machine (SVM) has been applied very successfully in a variety of classification systems. We attempt to solve the primal programming problems of SVM by converting them into smooth unconstrained minimization problems. In this paper, a new twice continuously differentiable piecewise-smooth function is proposed to approximate the plus function, and it issues a piecewise-smooth support vector machine (PWSSVM). The novel method can efficiently handle large-scale and high dimensional problems. The theoretical analysis demonstrates its advantages in efficiency and precision over other smooth functions. PWSSVM is solved using the fast Newton-Armijo algorithm. Experimental results are given to show the training speed and classification performance of our approach.

scholarly journals Selection of Support Vector Candidates Using Relative Support Distance for Sustainability in Large-Scale Support Vector Machines

2020 ◽  
Vol 10 (19) ◽  
pp. 6979
Author(s):  
Minho Ryu ◽  
Kichun Lee
Keyword(s):  
Support Vector Machines ◽  
Quadratic Programming ◽  
Decision Trees ◽  
Programming Problem ◽  
Large Scale ◽  
Classification Performance ◽  
Quadratic Programming Problem ◽  
Support Vector ◽  
Training Time ◽  
Vector Machines

Support vector machines (SVMs) are a well-known classifier due to their superior classification performance. They are defined by a hyperplane, which separates two classes with the largest margin. In the computation of the hyperplane, however, it is necessary to solve a quadratic programming problem. The storage cost of a quadratic programming problem grows with the square of the number of training sample points, and the time complexity is proportional to the cube of the number in general. Thus, it is worth studying how to reduce the training time of SVMs without compromising the performance to prepare for sustainability in large-scale SVM problems. In this paper, we proposed a novel data reduction method for reducing the training time by combining decision trees and relative support distance. We applied a new concept, relative support distance, to select good support vector candidates in each partition generated by the decision trees. The selected support vector candidates improved the training speed for large-scale SVM problems. In experiments, we demonstrated that our approach significantly reduced the training time while maintaining good classification performance in comparison with existing approaches.

scholarly journals EXTRACTION OF BUILT-UP AREAS USING CONVOLUTIONAL NEURAL NETWORKS AND TRANSFER LEARNING FROM SENTINEL-2 SATELLITE IMAGES

2018 ◽  
Vol XLII-3 ◽  
pp. 79-85 ◽  
Author(s):  
V. S. Bramhe ◽  
S. K. Ghosh ◽  
P. K. Garg
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Satellite Images ◽  
Spatial Information ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Sentinel 2

With rapid globalization, the extent of built-up areas is continuously increasing. Extraction of features for classifying built-up areas that are more robust and abstract is a leading research topic from past many years. Although, various studies have been carried out where spatial information along with spectral features has been utilized to enhance the accuracy of classification. Still, these feature extraction techniques require a large number of user-specific parameters and generally application specific. On the other hand, recently introduced Deep Learning (DL) techniques requires less number of parameters to represent more abstract aspects of the data without any manual effort. Since, it is difficult to acquire high-resolution datasets for applications that require large scale monitoring of areas. Therefore, in this study Sentinel-2 image has been used for built-up areas extraction. In this work, pre-trained Convolutional Neural Networks (ConvNets) i.e. Inception v3 and VGGNet are employed for transfer learning. Since these networks are trained on generic images of ImageNet dataset which are having very different characteristics from satellite images. Therefore, weights of networks are fine-tuned using data derived from Sentinel-2 images. To compare the accuracies with existing shallow networks, two state of art classifiers i.e. Gaussian Support Vector Machine (SVM) and Back-Propagation Neural Network (BP-NN) are also implemented. Both SVM and BP-NN gives 84.31 % and 82.86 % overall accuracies respectively. Inception-v3 and VGGNet gives 89.43 % of overall accuracy using fine-tuned VGGNet and 92.10 % when using Inception-v3. The results indicate high accuracy of proposed fine-tuned ConvNets on a 4-channel Sentinel-2 dataset for built-up area extraction.

scholarly journals Application of GA-SVM method with parameter optimization for landslide development prediction

2013 ◽  
Vol 1 (5) ◽  
pp. 5295-5322 ◽  
Author(s):  
X. Z. Li ◽  
J. M. Kong
Keyword(s):  
Parameter Optimization ◽  
Large Scale ◽  
Factor Models ◽  
Support Vector ◽  
Single Factor ◽  
Svm Model ◽  
High Prediction ◽  
Basic Characteristics ◽  
Landslide Development ◽  
The Right

Abstract. Prediction of landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. Support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of SVM model. In this study, we presented an application of GA-SVM method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in some hydro - electrical engineering area of Southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multi-factor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that, the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest RSME of 0.0009 and the biggest RI of 0.9992.

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