A Numerical Investigation of Mixing Processes in a Novel Combustor Application

A mixing process in a staggered toothed-indented shaped channel was investigated. It was studied in two steps: (1) numerical simulations for different sizes of the boundary-contour were performed by using a CFD code; (2) these results were used for simulation-data modeling for prediction of mixing performances across the whole field of changing geometric and the aerodynamic stream parameters. Support vector machine (SVM) technique, known as a new type of self learning machine, was selected to carry out this stage. The suitability of this application method was demonstrated in comparison with a neural network (NN) method. The established modeling system was then applied to some further studies of the prototype mixer, including observations of the mixing performance in three special cases and performing optimizations of the mixing processes for two conflicting objectives and hereby obtaining the Pareto optimum sets.

Download Full-text

Optimization ELM Based on Rough Set for Predicting the Label of Military Simulation Data

Mathematical Problems in Engineering ◽

10.1155/2014/706178 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8

Author(s):

Xiao-jian Ding ◽

Ming Lei

Keyword(s):

Extreme Learning Machine ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Support Vector ◽

Model Parameters ◽

Simulation Data ◽

Simple Rules ◽

Multivariate Discretization ◽

Learning Machine

By combining rough set theory with optimization extreme learning machine (OELM), a new hybrid machine learning technique is introduced for military simulation data classification in this study. First, multivariate discretization method is implemented to convert continuous military simulation data into discrete data. Then, rough set theory is employed to generate the simple rules and to remove irrelevant and redundant variables. Finally, OELM is compared with classical extreme learning machine (ELM) and support vector machine (SVM) to evaluate the performance of both original and reduced military simulation datasets. Experimental results demonstrate that, with the help of RS strategy, OELM can significantly improve the testing rate of military simulation data. Additionally, OELM is less sensitive to model parameters and can be modeled easily.

Download Full-text

Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

Mathematical Problems in Engineering ◽

10.1155/2017/5321815 ◽

2017 ◽

Vol 2017 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Jingzong Yang ◽

Xiaodong Wang ◽

Zao Feng ◽

Guoyong Huang

Keyword(s):

Pattern Recognition ◽

Impulse Response ◽

Information Entropy ◽

Operating Conditions ◽

Support Vector ◽

Entropy Theory ◽

Pattern Recognition Method ◽

Response Signal ◽

Recognition Method ◽

Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

Download Full-text

Prediction of Band Gap Energy of Doped Graphitic Carbon Nitride Using Genetic Algorithm-Based Support Vector Regression and Extreme Learning Machine

Symmetry ◽

10.3390/sym13030411 ◽

2021 ◽

Vol 13 (3) ◽

pp. 411

Author(s):

Taoreed O. Owolabi ◽

Mohd Amiruddin Abd Rahman

Keyword(s):

Genetic Algorithm ◽

Visible Light ◽

Band Gap ◽

Carbon Nitride ◽

Activation Function ◽

Graphitic Carbon ◽

Graphitic Carbon Nitride ◽

Support Vector ◽

Learning Machine ◽

Photocatalytic Applications

Graphitic carbon nitride is a stable and distinct two dimensional carbon-based polymeric semiconductor with remarkable potentials in organic pollutants degradation, chemical sensors, the reduction of CO2, water splitting and other photocatalytic applications. Efficient utilization of this material is hampered by the nature of its band gap and the rapid recombination of electron-hole pairs. Heteroatom incorporation due to doping alters the symmetry of the semiconductor and has been among the adopted strategies to tailor the band gap for enhancing the visible-light harvesting capacity of the material. Electron modulation and enhancement of reaction active sites due to doping as evident from the change in specific surface area of doped graphitic carbon nitride is employed in this work for modeling the associated band gap using hybrid genetic algorithm-based support vector regression (GSVR) and extreme learning machine (ELM). The developed GSVR performs better than ELM-SINE (with sine activation function), ELM-TRANBAS (with triangular basis activation function) and ELM-SIG (with sigmoid activation function) model with performance enhancement of 69.92%, 73.59% and 73.67%, respectively, on the basis of root mean square error as a measure of performance. The four developed models are also compared using correlation coefficient and mean absolute error while the developed GSVR demonstrates a high degree of precision and robustness. The excellent generalization and predictive strength of the developed models would ultimately facilitate quick determination of the band gap of doped graphitic carbon nitride and enhance its visible-light harvesting capacity for various photocatalytic applications.

Download Full-text

An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features

BioData Mining ◽

10.1186/s13040-021-00242-1 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Ji-Yong An ◽

Fan-Rong Meng ◽

Zi-Ji Yan

Keyword(s):

Ion Channel ◽

Extreme Learning Machine ◽

Nuclear Receptor ◽

Drug Target ◽

Computational Method ◽

Evolutionary Information ◽

Support Vector ◽

Weighted Extreme Learning Machine ◽

Speed Up ◽

Learning Machine

Abstract Background Prediction of novel Drug–Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target. Results In the paper, we proposed a novel computational method called WELM-SURF based on drug fingerprints and protein evolutionary information for identifying DTIs. More specifically, for exploiting protein sequence feature, Position Specific Scoring Matrix (PSSM) is applied to capturing protein evolutionary information and Speed up robot features (SURF) is employed to extract sequence key feature from PSSM. For drug fingerprints, the chemical structure of molecular substructure fingerprints was used to represent drug as feature vector. Take account of the advantage that the Weighted Extreme Learning Machine (WELM) has short training time, good generalization ability, and most importantly ability to efficiently execute classification by optimizing the loss function of weight matrix. Therefore, the WELM classifier is used to carry out classification based on extracted features for predicting DTIs. The performance of the WELM-SURF model was evaluated by experimental validations on enzyme, ion channel, GPCRs and nuclear receptor datasets by using fivefold cross-validation test. The WELM-SURF obtained average accuracies of 93.54, 90.58, 85.43 and 77.45% on enzyme, ion channels, GPCRs and nuclear receptor dataset respectively. We also compared our performance with the Extreme Learning Machine (ELM), the state-of-the-art Support Vector Machine (SVM) on enzyme and ion channels dataset and other exiting methods on four datasets. By comparing with experimental results, the performance of WELM-SURF is significantly better than that of ELM, SVM and other previous methods in the domain. Conclusion The results demonstrated that the proposed WELM-SURF model is competent for predicting DTIs with high accuracy and robustness. It is anticipated that the WELM-SURF method is a useful computational tool to facilitate widely bioinformatics studies related to DTIs prediction.

Download Full-text

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Sensors ◽

10.3390/s20030660 ◽

2020 ◽

Vol 20 (3) ◽

pp. 660 ◽

Cited By ~ 1

Author(s):

Fang Liu ◽

Liubin Li ◽

Yongbin Liu ◽

Zheng Cao ◽

Hui Yang ◽

...

Keyword(s):

Kernel Method ◽

Back Propagation ◽

Vibration Signal ◽

Back Propagation Neural Network ◽

Industrial Applications ◽

Performance Degradation ◽

Support Vector ◽

Krill Herd Algorithm ◽

Krill Herd ◽

Learning Machine

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

Download Full-text

Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

ISRN Soil Science ◽

10.5402/2012/346439 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10

Author(s):

Pijush Samui

Keyword(s):

Support Vector Machine ◽

Three Dimensional ◽

Site Characterization ◽

Standard Penetration Test ◽

Support Vector ◽

Characterization Model ◽

Svm Model ◽

Input Variables ◽

Learning Machine ◽

A Site

The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values () spread over 220 sq km area of Bangalore. The model is applied for corrected () values. The three input variables (, , and , where , , and are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, , and ε) has been also presented.

Download Full-text

Fabrication of Tunable Silk Materials Through Microfluidic Mixers

Volume 10: Micro- and Nano-Systems Engineering and Packaging ◽

10.1115/imece2016-65623 ◽

2016 ◽

Author(s):

Joseph R. Nalbach ◽

Dave Jao ◽

Douglas G. Petro ◽

Kyle M. Raudenbush ◽

Shibbir Ahmad ◽

...

Keyword(s):

Soft Lithography ◽

Mixing Processes ◽

Microfluidic Mixing ◽

Microfluidic Mixer ◽

Continuous Mixing ◽

Mixing Performance ◽

3D Printed ◽

Distribution Uniformity ◽

Mixing Patterns ◽

Microfluidic Mixers

A common method to precisely control the material properties is to evenly distribute functional nanomaterials within the substrate. For example, it is possible to mix a silk solution and nanomaterials together to form one tuned silk sample. However, the nanomaterials are likely to aggregate in the traditional manual mixing processes. Here we report a pilot study of utilizing specific microfluidic mixing designs to achieve a uniform nanomaterial distribution with minimal aggregation. Mixing patterns are created based on classic designs and then validated by experimental results. The devices are fabricated on polydimethylsiloxane (PDMS) using 3D printed molds and soft lithography for rapid replication. The initial mixing performance is validated through the mixing of two solutions with colored dyes. The microfluidic mixer designs are further analyzed by creating silk-based film samples. The cured film is inspected with scanning electron microscopy (SEM) to reveal the distribution uniformity of the dye particles within the silk material matrix. Our preliminary results show that the microfluidic mixing produces uniform distribution of dye particles. Because the microfluidic device can be used as a continuous mixing tool, we believe it will provide a powerful platform for better preparation of silk materials. By using different types of nanomaterials such as graphite (demonstrated in this study), graphene, carbon nanotubes, and magnetic nanoparticles, the resulting silk samples can be fine-tuned with desired electrical, mechanical, and magnetic properties.

Download Full-text

System Identification of TEG Themostatic System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.551.384 ◽

2014 ◽

Vol 551 ◽

pp. 384-388 ◽

Cited By ~ 1

Author(s):

Dong Jing Yang ◽

Jin Wu Gao ◽

Le Lun Jiang ◽

Tan Xiao

Keyword(s):

System Identification ◽

Control Strategy ◽

Measuring Instrument ◽

Analysis Method ◽

Simulation Data ◽

Arx Model ◽

Data Matching ◽

New Type ◽

Matching Degree ◽

Lower Temperature

Thrombelastograph device (TEG) is a measuring instrument of blood viscoelastic properties during coagulation. The measuring temperature of TEG is fixed at 37oC while in some surgery cases, lower temperature surroundings may be adopted. Therefore a new type of TEG with a controllable themostatic system has been designed to mimic various temperature surroundings in surgery. In this paper, a small-sized high accuracy thermostatic system for TEG was designed and its system identification was built to facilitate the development of control strategy. ARX model was supposed to analyze the system identification of the thermostatic system by Matlab System Identification Toolbox. Residual analysis method was adopted to verify the identified model. The results showed that the simulation data of ARX model was consistence with the measured data (matching degree was about 93%). Transfer function of the system can be applied to develop its control strategy.

Download Full-text

Analysis and Management of COVID-19 Using Computational Intelligence Technologies

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3696 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1642-1648

Author(s):

Xiangmin Meng ◽

Jie Zhang

Keyword(s):

Computational Intelligence ◽

Fuzzy System ◽

World Economy ◽

Support Vector ◽

Medical Institutions ◽

The World ◽

Treatment Measures ◽

Epidemic Prediction ◽

Learning Machine ◽

Intelligent Model

After the outbreak of COVID-19, the world economy and people’s health have been greatly challenged. What is the law of the spread of COVID-19, when will it reach its peak, and when will it be effectively controlled? These have all become major issues of common concern throughout China and the world. Based on this background, this article introduces a variety of classic computational intelligence technologies to predict the spread of COVID-19. Computational intelligence technology mainly includes support vector machine regression (SVR), Takagi-Sugeuo-Kang fuzzy system (TSK-FS), and extreme learning machine (ELM). Compare the predictions of the infection rate, mortality rate, and recovery rate of the COVID-19 epidemic in China by each intelligent model in 5 and 10 days, the effectiveness of the computational intelligence algorithm used in epidemic prediction is verified. Based on the prediction results, the patients are classified and managed. According to the time of illness, physical fitness and other factors, patients are divided into three categories: Severe, moderate, and mild. In the case of serious shortage of medical equipment and medical staff, auxiliary medical institutions take corresponding treatment measures for different patients.

Download Full-text