scholarly journals Crack Forms Sensitivity-Based Prediction on Subsurface Cracks Depth in Ultrasonic-Vibration-Assisted Grinding of Optical Glasses

2021 ◽  
Vol 11 (16) ◽  
pp. 7553
Author(s):  
Peiyi Zhao ◽  
Lei Zhang ◽  
Xianli Liu
Keyword(s):  
Ultrasonic Vibration ◽  
Least Square ◽  
Effective Control ◽  
Support Vector ◽  
Prediction Errors ◽  
Optical Glasses ◽  
Subsurface Cracks ◽  
Proposed Model ◽  
Grey Relational ◽  
Ultrasonic Vibration Assisted Grinding

Subsurface cracks in ultrasonic-vibration-assisted grinding (UVAG) of optical glasses often exhibit diverse forms and proportions. Due to the variety of loads involved in crack formation and propagation, the crack forms and propagation depths have different sensitivities to each process parameter. Predicting the maximum subsurface cracks depth (MSSCD) by considering the varying effects of process parameters plays a key role in implementing effective control of the UVAG process. In this work, the subsurface crack forms and their proportions are investigated by conducting 40 sets of UVAG experiments. The varying effects of the grinding and ultrasonic parameters on the crack form proportions are unveiled by using grey relational analysis. The weighted least square support vector machine (WLS-SVM) prediction model for the MSSCD was developed. Twelve sets of UVAG experiments were carried out to validate the proposed model. The results show that arc-shaped cracks and bifurcated cracks account for 72.5% of all cracks, while ultrasonic vibration amplitude influences most of the proportions of arc-shaped and bifurcated cracks. Compared to other widely used prediction methods, the maximum and average relative prediction errors of the proposed model are 10.54% and 5.59%, respectively, which proves the high prediction accuracy of the model.

A NOVEL FIVE-CATEGORY LOAN-RISK EVALUATION MODEL USING MULTICLASS LS-SVM BY PSO

2012 ◽  
Vol 11 (04) ◽  
pp. 857-874 ◽  
Author(s):  
JIE CAO ◽  
HONGKE LU ◽  
WEIWEI WANG ◽  
JIAN WANG
Keyword(s):  
Financial Regulation ◽  
Evaluation Model ◽  
Least Square ◽  
Support Vector ◽  
Accuracy Rate ◽  
Rural Credit ◽  
Proposed Model ◽  
Loan Risk ◽  
Regulation Approach ◽  
Risk Evaluation Model

Five-category loan classification (FCLC) is an international financial regulation approach. Recently, the application and implementation of FCLC in the Chinese microfinance bank has mostly relied on subjective judgment, and it is difficult to control and lower loan risk. In view of this, this paper is dedicated to researching and solving this problem by constructing the FCLC model based on improved particle-swarm optimization (PSO) and the multiclass, least-square, support-vector machine (LS-SVM). First, LS-SVM is the extension of SVM, which is proposed to achieve multiclass classification. Then, improved PSO is employed to determine the parameters of multiclass LS-SVM for improving classification accuracy. Finally, some experiments are carried out based on rural credit cooperative data to demonstrate the performance of our proposed model. The results show that the proposed model makes a distinct improvement in the accuracy rate compared with one-vs.-one (1-v-1) LS-SVM, one-vs.-rest (1-v-r) LS-SVM, 1-v-1 SVM, and 1-v-r SVM. In addition, it is an effective tool in solving the problem of loan-risk rating.

A COMPARITIVE ANALYSIS AND TIME SERIES FORECASTING OF MONTHLY STREAM FLOW DATA USING HYBRID MODEL

2015 ◽  
Vol 76 (13) ◽  
Author(s):  
Siraj Muhammed Pandhiani ◽  
Ani Shabri
Keyword(s):  
Hybrid Model ◽  
Stream Flow ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Flow Data ◽  
Indus River ◽  
Proposed Model

In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting for two major rivers in Pakistan. The monthly stream flow forecasting results are obtained by applying this model individually to forecast the rivers flow data of the Indus River and Neelum Rivers. The root mean square error (RMSE), mean absolute error (MAE) and the correlation (R) statistics are used for evaluating the accuracy of the WLSSVM, the proposed model. The results are compared with the results obtained through LSSVM. The outcome of such comparison shows that WLSSVM model is more accurate and efficient than LSSVM.

Model-Based EEG Analysis: Proposal and Verification of Mathematical Model for Application to Neurotechnology

2019 ◽  
Author(s):  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Mathematical Model ◽  
Binary Classification ◽  
Least Square ◽  
Mental Condition ◽  
Support Vector ◽  
Model Parameters ◽  
Eeg Analysis ◽  
Analysis Method ◽  
Model Based ◽  
Proposed Model

Abstract We have modeled dynamics of EEG with one degree of freedom nonlinear oscillator and examined the relationship between mental state of humans and model parameters simulating behavior of EEG. At the IMECE conference last year, Our analysis method identified model parameters sequentially so as to match the waveform of experimental EEG data of the alpha band using one second running window. Results of temporal variation of model parameters suggested that the mental condition such as degree of concentration could be directly observed from the dynamics of EEG signal. The method of identifying the model parameters in accordance with the EEG waveform is effective in examining the dynamics of EEG strictly, but it is not suitable for practical use because the analysis (parameter identification) takes a long time. Therefore, the purpose of this study is to test the proposed model-based analysis method for general application as a neurotechnology. The mathematical model used in neuroscience was improved for practical use, and the test was conducted with the cooperation of four subjects. model parameters were experimentally identified approximately every one second by using least square method. We solved a binary classification problem of model parameters using Support Vector Machine. Results show that our proposed model-based EEG analysis is able to discriminate concentration states in various tasks with an accuracy of over 80%.

scholarly journals On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yong Yang ◽  
Shuaishuai Zheng ◽  
Zhilu Ai ◽  
Mohammad Mahdi Molla Jafari
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Biogas Production ◽  
Input Parameter ◽  
Machine Learning Algorithms ◽  
Least Square ◽  
Support Vector ◽  
Robust Algorithms ◽  
Inference System ◽  
Proposed Model

This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model.

An Artificial Intelligent Centered Object Inspection System Using Crucial Images

2018 ◽  
Vol 5 (1) ◽  
pp. 44-57 ◽  
Author(s):  
Santosh Kumar Sahoo ◽  
B. B. Choudhury
Keyword(s):  
Principal Component ◽  
Cuckoo Search ◽  
Basic Function ◽  
Least Square ◽  
Support Vector ◽  
Inspection System ◽  
Bench Mark ◽  
Test Functions ◽  
Proposed Model ◽  
Object Inspection

This article proposes a unique optimization algorithm like Adaptive Cuckoo Search (AdCS) algorithm followed by an Intrinsic Discriminant Analysis (IDA) to design an intelligent object classifier for inspection of defective object like bottle in a manufacturing unit. By using this methodology the response time is very faster than the other techniques. The projected scheme is authenticated using different bench mark test functions along with an effective inspection procedure for identification of bottle by using AdCS, Principal-Component-Analysis (PCA) and IDA. Due to this the projected procedures terms as PCA+IDA for dimension reduction in addition to this AdCS-IDA for classification or identification of defective bottles. The analyzed response obtained from by an application of AdCS algorithm followed by IDA and compared to other algorithm like Least-Square-Support-Vector-Machine (LSSVM), Linear Kernel Radial-Basic-Function (RBF) to the proposed model, the earlier applied scheme reveals the remarkable performance.

scholarly journals Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1231 ◽  
Author(s):  
Abobakr Saeed Abobakr Yahya ◽  
Ali Najah Ahmed ◽  
Faridah Binti Othman ◽  
Rusul Khaleel Ibrahim ◽  
Haitham Abdulmohsin Afan ◽  
...  
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Oxygen Demand ◽  
Quality Analysis ◽  
Support Vector ◽  
Prediction Errors ◽  
Ungauged Catchments ◽  
Machine Model ◽  
River Catchment ◽  
Proposed Model

Water quality analysis is a crucial step in water resources management and needs to be addressed urgently to control any pollution that may adversely affect the ecosystem and to ensure the environmental standards are being met. Thus, this work is an attempt to develop an efficient model using support vector machine (SVM) to predict the water quality of Langat River Basin through the analysis of the data of six parameters of dual reservoirs that are located in the catchment. The proposed model could be considered as an effective tool for identifying the water quality status for the river catchment area. In addition, the major advantage of the proposed model is that it could be useful for ungauged catchments or those lacking enough numbers of monitoring stations for water quality parameters. These parameters, namely pH, Suspended Solids (SS), Dissolved Oxygen (DO), Ammonia Nitrogen (AN), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) were provided by the Malaysian Department of Environment (DOE). The differences between dual scenarios 1 and 2 depend on the information from prior stations to forecast DO levels for succeeding sites (Scenario 2). This scheme has the capacity to simulate water-quality accurately, with small prediction errors. The resulting correlation coefficient has maximum values of 0.998 and 0.979 after the application of Scenario 1. The approach with Type 1 SVM regression along with 10-fold cross-validation methods worked to generate precise results. The MSE value was found to be between 0.004 and 0.681, with Scenario 1 showing a better outcome.

A Prediction Model for Surface Roughness in Milling Based on Projection Pursuit Regression

2013 ◽  
Vol 312 ◽  
pp. 143-147
Author(s):  
Xue Qin Pan ◽  
Na Zhu ◽  
Yu Cai Dong ◽  
Cui Xiang Liu ◽  
Min Lin ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Projection Pursuit ◽  
Least Square ◽  
Support Vector ◽  
Prediction Errors ◽  
Machined Surface ◽  
Projection Pursuit Regression ◽  
Milling Parameters

A novel prediction model for surface roughness based on Projection Pursuit Regression was proposed in this paper. Based on the new model, the effects of milling parameters on surface roughness in milling can be predicted, and the predicted value of surface roughness in the whole working range can be reached with the limited test data, thus the variation law of quality of machined surface following milling parameters can be obtained. Compared with the least square support vector machine, it can be revealed that on the base of the same samples, the construction speed of this Projection Pursuit Regression is 1~2 higher in order of magnitude than that of the least square support vector machine, while the prediction errors are 40 % of the latter. Thus, the prediction model based on Projection Pursuit Regression can be established fast and be forecasted in high-precision, it is suitable for prediction of surface roughness.

Wind Power Ramp Forecasting Based on Least-Square Support Vector Machine

2014 ◽  
Vol 535 ◽  
pp. 162-166 ◽  
Author(s):  
Di Gan ◽  
De Ping Ke
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Least Square ◽  
Support Vector ◽  
Proposed Model ◽  
Ramp Events ◽  
Definition Of ◽  
Short Time

Wind power ramp forecasting is very significant for grid integration of large wind energy. A ramp event is defined as the sharp increase or decrease of wind power on a large scale in short time. A methodology for wind power ramp forecasting is described. The method is based on Least Square Support Vector Machine (LSSVM) and the definition of ramp events by filtering the original signal. The performance of the proposed model is evaluated on a wind farm in China, which shows that LSSVM model is competent in forecasting wind power ramp events.

MODELING OF VIOLENT CRIME RATES WITH ECONOMIC INDICATORS USING HYBRIDIZATION OF GREY RELATIONAL ANALYSIS AND SUPPORT VECTOR REGRESSION

2015 ◽  
Vol 76 (1) ◽  
Author(s):  
Razana Alwee ◽  
Siti Mariyam Shamsuddin ◽  
Roselina Sallehuddin
Keyword(s):  
Support Vector Regression ◽  
Hybrid Model ◽  
Violent Crime ◽  
Historical Data ◽  
Crime Rates ◽  
Economic Indicators ◽  
Support Vector ◽  
Relational Analysis ◽  
Proposed Model ◽  
Grey Relational

Regression and econometric models are commonly applied in modeling of violent crime rates. However, these models are mainly linear and only capable in modeling linear relationships. Moreover, the econometric models are quite complex to develop. Although time series model is a promising alternative tool, limited historical data of crime rates makes the standard time series models less suitable for modeling the violent crime rates. Thus, in this study, a hybrid model that can handle limited historical data is proposed for modeling the violent crime rates. The proposed hybrid model combines grey relational analysis and support vector regression. Since inaccurate parameters setting leads to inaccuracy of support vector regression model, particle swarm optimization is used to increase the accuracy of the model. The proposed hybrid model is used to model the violent crime rates of United State based on economic indicators. The proposed model also has additional features such as able to choose the data series for economic indicators and significant economic indicators for the violent crime rates. The experimental results showed that the proposed model produces more accurate forecast as compared to multiple linear regression in forecasting the violent crime rates.

scholarly journals Bed material load estimation in channels using machine learning and meta-heuristic methods

2017 ◽  
Vol 20 (1) ◽  
pp. 100-116 ◽  
Author(s):  
Shahram Sahraei ◽  
Mohammad Reza Alizadeh ◽  
Nasser Talebbeydokhti ◽  
Maryam Dehghani
Keyword(s):  
Machine Learning ◽  
Pso Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Proposed Model ◽  
Load Transport ◽  
Efficient Alternative ◽  
Material Load ◽  
Decision Tree Method ◽  
Bed Material

Abstract This study is trying to develop an alternative approach to the issues of sediment transport simulation. A machine learning method, named least square support vector regression (LSSVR) and a meta-heuristic approach, called particle swarm optimization (PSO) algorithm are used to estimate bed material load transport rate. PSO algorithm is utilized to calibrate the parameters involved in the model to facilitate a desirable simulation by LSSVR. Implementing on a set of laboratory and field data, the model is capable of performing more satisfactorily in comparison to candidate traditional methods. Similarly, the proposed method has a better performance than a specific version of decision tree method. To enhance the model, the variables are scaled in logarithmic form, leading to an improvement in the results. Thus, the proposed model can be an efficient alternative to conventional approaches for the simulation of bed material load transport rates providing comparable accuracy.

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