scholarly journals A soft computing approach to tunnel face stability in a probabilistic framework

2021 ◽  
Author(s):  
Enrico Soranzo ◽  
Carlotta Guardiani ◽  
Wei Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soft Computing ◽  
Support Pressure ◽  
Tunnel Face ◽  
Soft Computing Techniques ◽  
The Face ◽  
Tunnel Face Stability ◽  
Artificial Neural ◽  
Face Support

AbstractTunnel face is important for shallow tunnels to avoid collapses. In this study, tunnel face stability is studied with soft computing techniques. A database is created based on the literature which is used to train some broadly adopted soft computing techniques, ranging from linear regression to the artificial neural network. The soil dry density, cohesion, friction angle, cover depth and the tunnel diameter are used as the input parameters. The soft computing techniques state whether the face support is stable and predict the face support pressure. It is found that the artificial neural network outperforms the other techniques. The face support pressure is predicted with the artificial neural network for statistically distributed samples, and the failure probability is obtained with Monte Carlo simulations. In this way, the stability of the tunnel face can be reliably assessed and the support pressure can be estimated fairly accurately.

scholarly journals Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators

2021 ◽  
Vol 13 (17) ◽  
pp. 9509
Author(s):  
Mosa Machesa ◽  
Lagouge Tartibu ◽  
Modestus Okwu
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Soft Computing ◽  
Particle Swarm Optimisation ◽  
Soft Computing Techniques ◽  
Artificial Neural ◽  
Thermoacoustic Refrigerators

Thermoacoustic refrigerators are emerging devices that make use of meaningful high-pressure sound waves to induce cooling. Despite the accelerated progress in the field of thermoacoustics, knowledge of the heat transfer process in the heat exchange of the devices is still developing. This work applies different soft computing techniques, namely, an artificial neural network trained by particle swarm optimisation (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANNs) to predict the oscillatory heat transfer coefficient in the heat exchangers of a thermoacoustic device. This study provides the details of the parametric analysis of an artificial neural network model trained by particle swarm optimisation. The solution model considers the number of neurons, the swarm population, and the acceleration factors to develop and analyse the architecture of several models. The regression model (R2) and mean squared error (MSE) were used to evaluate the accuracy of the models. The result showed that the proposed soft computing techniques can potentially be used for the modelling and the analysis of the oscillatory heat transfer coefficient with a higher level of accuracy. The result reported in this study implies that the prediction of the OHTC can be considered for the enhancement of thermoacoustic refrigerators performances.

scholarly journals Developed artificial neural network based human face recognition

2019 ◽  
Vol 16 (3) ◽  
pp. 1279
Author(s):  
Maryam Mahmood Hussein ◽  
Ammar Hussein Mutlag ◽  
Hussain Shareef
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Soft Computing ◽  
Back Propagation ◽  
Human Interaction ◽  
Video Observation ◽  
Human Face Recognition ◽  
The Face ◽  
Artificial Neural

<p>Face recognition has become one of the most important challenging problems in personal computer-human interaction, video observation, and biometric. Many algorithms have been developed in the recent years. Theses algorithms are not sufficiently robust to address the complex images. Therefore, this paper proposes soft computing algorithm based face recognition. One of the most promising soft computing algorithms which is back-propagation artificial neural network (BP-ANN) has been proposed. The proposed BP-ANN has been developed to improve the performance of the face recognition. The implementation of the developed BP-ANN has been achieved using MATLAB environment. The developed BP-ANN requires supervised training to learn how to anticipate results from the desired data. The BP-ANN has been developed to recognition 10 persons. Ten images have been used for each person. Therefore, 100 images have been utilized to train the developed BP-ANN. In this research 50 images have been used for testing purpose. The results show that the developed BP-ANN has produced a success ratio of 82%.</p>

scholarly journals Applications of Soft Computing Techniques for Pulmonary Tuberculosis Diagnosis

2019 ◽  
Vol 8 (4) ◽  
pp. 119-127
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pulmonary Tuberculosis ◽  
Soft Computing ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Diagnostic Model ◽  
Soft Computing Techniques ◽  
Tuberculosis Diagnosis ◽  
Artificial Neural

Recently, several interesting research studies have been reported on soft computing approaches. Soft computing approaches are solving several kinds of problems and provide alternative solutions. Different Soft computing techniques or approaches have been applied in medical care data for effective diagnosis prediction. Those approaches implemented on diseases diagnosing of pulmonary tuberculosis and obtaining better results in comparison to traditional approaches. This approach is an aggregation of methodologies that were combined various model and provide solutions to those problems that are difficult to handle in real-world situations. Researchers keep developing of an accurate and reliable intelligent decision-making method for the construction of pulmonary tuberculosis diagnosis system. The existing diagnostic testing system procedures are not only tedious, they also take a long time to analyze. Therefore, the diagnosis of tuberculosis still requires further improvements to new rapid and accurate diagnostic model and techniques that enable higher sensitivity and specificity to be achieved, thus promoting disease control and Prevention. State of the art makes approaches to soft computing more powerful, more reliable and more efficient. The importance of this review paper is to distinguish the different soft computing approaches used to support pulmonary tuberculosis disease diagnosis, identification, prediction and intelligent classification. In the field, researchers and medical practitioners look forward to using approaches to soft computing. Some of these are an artificial neural network, genetic algorithm, and support vector machine, fuzzy logic etc. latest methods in the diagnostic field uses artificial neural network. Some of the other benefits of Artificial neural network is an easy - to - optimize, resources and adoptable non - linear modeling of expansive data sets and predictive inference accuracy demonstrating that artificial neural network could serve as a valuable decision support tool in various fields, including medicine

scholarly journals Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

2020 ◽  
Author(s):  
Jibril Abdulsalam ◽  
Abiodun Ismail Lawal ◽  
Ramadimetja Lizah Setsepu ◽  
Moshood Onifade ◽  
Samson Bada
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Gene Expression Programming ◽  
Mean Bias Error ◽  
Energy Yield ◽  
Multilinear Regression ◽  
Soft Computing Techniques ◽  
Artificial Neural

Abstract Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass by using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE), and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10-10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

Soft Computing Tools to Predict Progression of Percent Embedment of Aggregates in Chip Seals

2018 ◽  
Vol 2672 (12) ◽  
pp. 32-39 ◽  
Author(s):  
Aksel Seitllari ◽  
M. Emin Kutay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soft Computing ◽  
Embedment Depth ◽  
Test Results ◽  
Multilinear Regression ◽  
Neuro Fuzzy ◽  
Chip Seal ◽  
Regression Techniques ◽  
Artificial Neural

In this study, soft computing and multilinear regression techniques were employed to develop models for prediction of progression of chip seal percent embedment depth ( Pe). The model uses inputs such as cumulative equivalent traffic volume, Vialit test results, dust content of aggregates, and initial embedment depth. Multilinear regression, adaptive neuro-fuzzy system, and artificial neural network techniques were used to estimate the Pe. The contribution of the variables affecting Pe was evaluated through a sensitivity analysis. The results indicate that while most of the proposed models were able to predict the Pe reasonably, the artificial neural network model performed the best.

scholarly journals Wind energy location prediction between meteorological stations using ANN

2015 ◽  
Vol 16 (6) ◽  
pp. 1135-1144
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Wind Energy ◽  
Tamil Nadu ◽  
Energy Potential ◽  
Soft Computing Techniques ◽  
Wind Energy Potential ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<div> <p>Wind Energy is one of the important sources of renewable energy. There is a need to prepare the availability of wind energy in the area where there is no measured wind speed data. For this type of situation, it seems to be necessary to predict the wind energy potential using such as wind speed using artificial neural network (ANN) method. Soft computing techniques are widely used now days in the study of wind energy potential estimation. In this study the wind energy potential between neighborhood meteorological tower stations is predicted using Artificial Neural Network technique. One of the most suitable areas of Tamil Nadu for wind power generation is some locations in the districts of Tirunelveli, Thoothukudi, Kanyakumari, Theni, Coimbatore, and Dindigul. Along the southeast coastline of Tamil Nadu there are no valleys and mountains besides the mountains are situated away from the sea coast in many regions. Therefore, these regions are exposed to northerly winds that are not as strong as the southerly winds.</p> </div> <p>&nbsp;</p>

scholarly journals New Method for Locker Door with Face Security System Using Backpropagation Algorithm

2018 ◽  
Vol 7 (2.13) ◽  
pp. 402
Author(s):  
Y Yusmartato ◽  
Zulkarnain Lubis ◽  
Solly Arza ◽  
Zulfadli Pelawi ◽  
A Armansah ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Back Propagation ◽  
The Face ◽  
Artificial Neural ◽  
Hidden Layer

Lockers are one of the facilities that people use to store stuff. Artificial neural networks are computational systems where architecture and operations are inspired by the knowledge of biological neurons in the brain, which is one of the artificial representations of the human brain that always tries to stimulate the learning process of the human brain. One of the utilization of artificial neural network is for pattern recognition. The face of a person must be different but sometimes has a shape similar to the face of others, because the facial pattern is a good pattern to try to be recognized by using artificial neural networks. Pattern recognition on artificial neural network can be done by back propagation method. Back propagation method consists of input layer, hidden layer and output layer.  

scholarly journals Forecasting of Wind Induced Pressure on Setback Building Using Artificial Neural Network

2020 ◽  
Author(s):  
Amlan Kumar Bairagi ◽  
Sujit Kumar Dalui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cfd Simulation ◽  
Incidence Angle ◽  
Pressure Coefficient ◽  
Tall Building ◽  
The Face ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Target Data

The wind load on an irregular plan shape tall building is quite different compared to a conventional plan shape tall building. Especially the aerodynamic parameters have extreme change due to the variety of setbacks at one or more the disparity of level. This paper highlights the prediction of pressure coefficient on square, single (20 %) setback and double (10 %) setback buildings for any wind incidence angle by CFD simulation and validated with Artificial Neural Network (ANN) and fast Fourier transform. The ANN is a widely used and efficient tool for different types of analyses. The 0° to 180° wind incidence angles (WIAs) considered as input data and respective face wise pressure coefficient (Cp) used as target data. The Levenberg-Marquardt training function and Mean Square Error (MSE) performance function used to train the target data. The face wise graphs of CFD, ANN and FFT are plotted in a single graph and the Cp of the surface checked by any random WIAs. Amazingly, the Cp of random WIA by ANN is almost similar to CFD. Furthermore, the error of ANN is 0.6 % to 2.5 %, which is negligible. According to this predicted graph, the design Cp of any WIA can be easily calculated and implement directly in the design.

The Prediction of Needle Penetration Force in Woven Denim Fabrics Using Soft Computing Models

2014 ◽  
Vol 9 (4) ◽  
pp. 155892501400900 ◽  
Author(s):  
Ezzatollah Haghighat ◽  
Saeed Shaikhzadeh Najar ◽  
Seyed Mohammad Etrati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Logic Model ◽  
Needle Penetration ◽  
Fuzzy Logic Model ◽  
Penetration Force ◽  
Artificial Neural ◽  
Computing Models

The aim of this paper was to predict the needle penetration force in denim fabrics based on sewing parameters by using the fuzzy logic (FL) model. Moreover, the performance of fuzzy logic model is compared with that of the artificial neural network (ANN) model. The needle penetration force was measured on the Instron tensile tester. In order to plan the fuzzy logic model, the sewing needle size, number of fabric layers and fabric weight were taken into account as input parameters. The output parameter is needle penetration force. In addition, the same parameters and data are used in artificial neural network model. The results indicate that the needle penetration force can be predicted in terms of sewing parameters by using the fuzzy logic model. The difference between performance of fuzzy logic and neural network models is not meaningful ( RFL=0.971 and RANN=0.982). It is concluded that soft computing models such as fuzzy logic and artificial neural network can be utilized to forecast the needle penetration force in denim fabrics. Using the fuzzy logic model for predicting the needle penetration force in denim fabrics can help the garment manufacturer to acquire better knowledge about the sewing process. As a result, the sewing process may be improved, and also the quality of denim apparel increased.

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