A modeling study of micro-cracking processes of polyurethane coated cotton fabrics

2017 ◽  
Vol 88 (24) ◽  
pp. 2766-2781 ◽  
Author(s):  
Sinem Güneşoğlu ◽  
Mehmet Yüceer
Keyword(s):  
Industrial Applications ◽  
Least Square ◽  
Vector Model ◽  
Support Vector ◽  
Coated Fabric ◽  
Artificial Neural Network Ann ◽  
Coated Fabrics ◽  
Cracking Process ◽  
Visual Properties ◽  
Modeling Study

Polyurethane (PU) coating became popular in recent decades to achieve water resistance in clothing fabrics with enhanced visual properties. But reduced breathability of coated fabric is a setback for the clothing industry; therefore, there have been various attempts to achieve breathable water-resistant coatings. A new and facile method of enhancing breathability of PU-coated fabrics, which has been called micro-cracking, has been recently studied and highly encouraging outcomes have been obtained for the use of the process in industry. But when any process is considered to have industrial applications, it is essential to conduct not only the optimization but also modeling studies to find out whether the outputs are predictable; the process is controllable and allows us to see how the results are affected by process parameters. This work conducts a modeling study of micro-cracking processes of PU-coated samples to complete this evaluation. For this purpose, an artificial neural network (ANN) and a least square support vector model (LS-SVM) are developed for the prediction of various properties of PU-coated fabrics after micro-cracking. The results showed that the effects of micro-cracking process on various properties of coated fabric could be predicted through ANN or LS-SVM modeling; specifically, the ANN exhibited better performance in the test set of the data. Thus, it is concluded that the results and the measurements were found to be compatible for defining the process as an industrial alternative.

scholarly journals Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jingwei Song ◽  
Jiaying He ◽  
Menghua Zhu ◽  
Debao Tan ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Forecast Model ◽  
The United States ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Prediction Ability ◽  
Chaotic Model ◽  
Accurate Forecast ◽  
Artificial Neural Network Ann

A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%.

Prediction of Properties of Cu-15Ni-8Sn Alloys Based on Least Square Support Vector Machines

2013 ◽  
Vol 813 ◽  
pp. 479-483
Author(s):  
Shan Feng Fang ◽  
Ming Pu Wang
Keyword(s):  
Support Vector Machines ◽  
Predictive Accuracy ◽  
Copper Alloys ◽  
Least Square ◽  
Support Vector ◽  
Promising Alternative ◽  
Vector Machines ◽  
Artificial Neural Network Ann ◽  
Experimental Values ◽  
Leave One Out

A new model based on least square support vector machines (LSSVM) and capable of forecasting mechanical and electrical properties of Cu-15Ni-8Sn alloys has been proposed. Data mining and artificial intelligence techniques of copper alloys are used to examine the forecasting capability of the model. In order to improve predictive accuracy and generalization ability of LSSVM model, leave-one-out-cross-validation (LOOCV) technique is adopted to determine the optimal hyper-parameters of LSSVM automatically. The forecasting performance of the LSSVM model and the artificial neural network (ANN) has been compared with the experimental values. The result shows that the LSSVM model provides slightly better capability of generalized prediction compared to ANN. The present calculated results are consistent with the experimental values, which suggest that the proposed LSSVM model is feasible and efficient and is therefore considerd to be a promising alternative method to forecast the variation of the hardness and electrical conductivity with aging temperature and aging time.

scholarly journals Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5592
Author(s):  
Waqar Muhammad Ashraf ◽  
Ghulam Moeen Uddin ◽  
Syed Muhammad Arafat ◽  
Sher Afghan ◽  
Ahmad Hassan Kamal ◽  
...  
Keyword(s):  
Power Plant ◽  
Thermal Efficiency ◽  
Industry 4.0 ◽  
Process Model ◽  
Least Square ◽  
Process Models ◽  
Plant Performance ◽  
Support Vector ◽  
Unit Load ◽  
Artificial Neural Network Ann

This paper presents a comprehensive step-wise methodology for implementing industry 4.0 in a functional coal power plant. The overall efficiency of a 660 MWe supercritical coal-fired plant using real operational data is considered in the study. Conventional and advanced AI-based techniques are used to present comprehensive data visualization. Monte-Carlo experimentation on artificial neural network (ANN) and least square support vector machine (LSSVM) process models and interval adjoint significance analysis (IASA) are performed to eliminate insignificant control variables. Effective and validated ANN and LSSVM process models are developed and comprehensively compared. The ANN process model proved to be significantly more effective; especially, in terms of the capacity to be deployed as a robust and reliable AI model for industrial data analysis and decision making. A detailed investigation of efficient power generation is presented under 50%, 75%, and 100% power plant unit load. Up to 7.20%, 6.85%, and 8.60% savings in heat input values are identified at 50%, 75%, and 100% unit load, respectively, without compromising the power plant’s overall thermal efficiency.

scholarly journals Preference Comparison of AI Power Tracing Techniques for Deregulated Power Markets

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Hussain Shareef ◽  
Saifunizam Abd. Khalid ◽  
Mohd Wazir Mustafa ◽  
Azhar Khairuddin
Keyword(s):  
Test System ◽  
Load Flow ◽  
Least Square ◽  
Power Markets ◽  
Support Vector ◽  
Real Power ◽  
Flow Information ◽  
Artificial Neural Network Ann ◽  
Tracing Techniques ◽  
Power Tracing

This paper compares the two preference artificial intelligent (AI) techniques, namely, artificial neural network (ANN) and genetic algorithm optimized least square support vector machine (GA-LSSVM) approach, to allocate the real power output of individual generators to system loads. Based on solved load flow results, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the AI techniques compared to those of the MNE method. The AI methods provide the results in a faster and convenient manner with very good accuracy.

Lateral Displacement of Liquefaction Induced Ground Using Least Square Support Vector Machine

2011 ◽  
Vol 2 (2) ◽  
pp. 29-39 ◽  
Author(s):  
Sarat Kumar Das ◽  
Pijush Samui ◽  
Dookie Kim ◽  
N. Sivakugan ◽  
Rajanikanta Biswal
Keyword(s):  
Support Vector Machine ◽  
Model Equation ◽  
Lateral Displacement ◽  
Least Square ◽  
Disaster Mitigation ◽  
Support Vector ◽  
Model Parameters ◽  
Ann Models ◽  
Artificial Neural Network Ann

The determination of lateral displacement of liquefaction induced ground during an earthquake is an imperative task in disaster mitigation. This study investigates the possibility of using least square support vector machine (LSSVM) for the prediction of lateral displacement of liquefaction induced ground during an earthquake. The results have been compared with those obtained using artificial neural network (ANN) models and observed that LSSVM outperformed the ANN models. Model equation has been presented based on the model parameters, which can be used by the professionals. Sensitivity analysis has also been performed to determine the importance of each of the input parameters.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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