Radial Bias Function Research Articles

A sophisticated modeling approach for photovoltaic systems in load frequency control

This paper proposes a new model to mimics accurately the real dynamic and steady-state power generation profiles of a photovoltaic (PV) power plant connected to the power system. The model is targeted at load frequency control (LFC) studies, which takes into account the effect of the dynamic behavior of the system frequency on the PV output power. The artificial neural network using the radial bias function (ANN-RBF) is adopted to model the nonlinear behavior of the PV power plant only considering the frequency deviation as one of the inputs at different operating conditions. Also, the ANN prevents going into complex mathematical analysis to get the dynamic behavior of the output power from the PV plant. The input layer of the ANN-RBF receives the temperature, humidity, irradiation level, and frequency deviation, while the output layer represents the output power from the PV power plant. The main idea to include the frequency deviation in the PV modeling is to represent the dynamic behavior of the PV power that is injected by the PV inverter that is synchronized with the power system using the utility grid angle, i.e. system frequency. The ANN-RBF has been trained using actual data collected from a PV power plant in Aswan, Egypt. The ANN dataset contains significant variations of operating parameters to generalize the output modeling of the PV array. Moreover, the shortages in the previous models for the PV system used in the LFC studies, which are the first-order model, second-order model, and noise-based model, are discussed in detail. The simulation results show that the proposed model of the PV system based on the ANN-RBF is more suitable for the LFC studies than the well-known models based on the first-order representation as it takes into account the dynamic behavior of the system frequency during the load disturbance.

Intelligent Drone-Assisted Fault Diagnosis for B5G-Enabled Space-Air-Ground-Space Networks

The ubiquitous network services provided by the Beyond 5G enabled space-air-ground-sea networks (B5G-SAGS) depends on the reliability of each intelligent device within. However, the QoS of B5G-SAGS could be compromised if there exists faults on individual network. That suggests the significance of fault-diagnosis in the B5G-SAGS design. Previous works on fault diagnosis were designed without extra information to improve diagnosis accuracy. In this paper, we propose an Intelligent Drone-assisted Fault-diagnosis Algorithm (IDFA) utilizing B5G-enabled Multiple-access Edge Computing/Cloud (B5G-MEC) services to detect faulty buoys. Specifically, IFDA first employs a Cubature Kalman Filter based Radial Bias Function Neural Network (CKF-RBFNN) for each fault-diagnosis center to perform preliminary fault detection based on the data provided by both buoys and drones. The data collection path is planned utilizing the deep reinforcement learning algorithm, Deep Deterministic Policy Gradient (DDPG), on B5G-MEC servers for energy efficiency. Eventually, the collective decision made by all fault-diagnosis centers determines the faulty status of each buoy. The theoretical analysis and validation experiments show that: (i) the IDFA has a better diagnosis accuracy in both single fault detection and multi-fault classification while compared with contemporary algorithms; (ii) the IDFA obtains a high aggregation ratio and a low energy cost.

Big Data Sentimental Analysis Using Document to Vector and Optimized Support Vector Machine

With the rapid evolution of the internet, using social media networks such as Twitter, Facebook, and Tumblr, is becoming so common that they have made a great impact on every aspect of human life. Twitter is one of the most popular micro-blogging social media that allow people to share their emotions in short text about variety of topics such as company’s products, people, politics, and services. Analyzing sentiment could be possible as emotions and reviews on different topics are shared every second, which makes social media to become a useful source of information in different fields such as business, politics, applications, and services. Twitter Application Programming Interface (Twitter-API), which is an interface between developers and Twitter, allows them to search for tweets based on the desired keyword using some secret keys and tokens. In this work, Twitter-API used to download the most recent tweets about four keywords, namely, (Trump, Bitcoin, IoT, and Toyota) with a different number of tweets. “Vader” that is a lexicon rule-based method used to categorize downloaded tweets into “Positive” and “Negative” based on their polarity, then the tweets were protected in Mongo database for the next processes. After pre-processing, the hold-out technique was used to split each dataset to 80% as “training-set” and rest 20% “testing-set.” After that, a deep learning-based Document to Vector model was used for feature extraction. To perform the classification task, Radial Bias Function kernel-based support vector machine (SVM) has been used. The accuracy of (RBF-SVM) mainly depends on the value of hyperplane “Soft Margin” penalty “C” and γ “gamma” parameters. The main goal of this work is to select best values for those parameters in order to improve the accuracy of RBF-SVM classifier. The objective of this study is to show the impacts of using four meta-heuristic optimizer algorithms, namely, particle swarm optimizer (PSO), modified PSO (MPSO), grey wolf optimizer (GWO), and hybrid of PSO-GWO in improving SVM classification accuracy by selecting the best values for those parameters. To the best of our knowledge, hybrid PSO-GWO has never been used in SVM optimization. The results show that these optimizers have a significant impact on increasing SVM accuracy. The best accuracy of the model with traditional SVM was 87.885%. After optimization, the highest accuracy obtained with GWO is 91.053% while PSO, hybrid PSO-GWO, and MPSO best accuracies are 90.736%, 90.657%, and 90.557%, respectively.

Different ANN Models for Short Term Electricity Price Forecasting

In a deregulated electiricity market, price forecasting is gaining demand with application of Artificial Neural Network (ANN). The paper deals with price forecasting with different ANN models.like Back Propagation Neural Network( BPNN), Radial Bias Function Neural Network (RBFNN) and Genectic Algorithm based Neural Network (GANN). A contextual investigation is made with the downloaded data of the day-ahead pool market prices of the California Pool Market using the above four different ANN models and the results are compared.

An intelligent lung tumor diagnosis system using whale optimization algorithm and support vector machine

Medical image processing technique are widely used for detection of tumor to increase the survival rate of patients. The development of computer-aided diagnosis system shows improvement in observing the medical image and determining the treatment stages. The earlier detection of tumor reduces the mortality of lung cancer by increasing the probability of successful treatment. In this paper, the intelligent lung tumor diagnosis system is developed using various image processing technique. The simulated steps involve image enhancement, image segmentation, post-processing, feature extraction, feature selection and classification using support vector machine (SVM) kernel. Gray level co-occurrence matrix method is used for extracting the 19 texture and statistical features of lung computed tomography (CT) image. Whale optimization algorithm (WOA) is considered for selection of best prominent feature subset. The contribution provided in this paper is the development of WOA_SVM to automate the aided diagnosis system for determining whether the lung CT image is normal or abnormal. An improved technique is developed using whale optimization algorithm for optimal feature selection to obtain accurate results and constructing the robust model. The performance of proposed methodology is evaluated using accuracy, sensitivity and specificity and obtained as 95%, 100% and 92% using radial bias function support vector kernel.

Cervical cancer prognosis using genetic algorithm and adaptive boosting approach

Cervical cancer is fourth main causes of death in women. Cervix is the main origin of cervical cancer. The idea of this research is to explore and propose an efficient and improved prediction method of cervical cancer. Earlier detection and prediction methods/test were very complex, tedious and requires medical and pathological expertise. In this paper, Machine learning approach is used for prediction and detection of cervical cancer. Integrated approach of Genetic Algorithm and Adaptive Boosting is used for performance evaluation for prediction of disease. Genetic algorithm is used as attribute selector to decrease the number of attributes. This not only declines the computational cost but also reduces the number of parameters for diagnosis. Adaptive Boosting is used to improve the performance of classifiers. C 4.5 Decision Tree and Support Vector Machine (SVM) are proposed for prediction of disease. Initially 32 attributes are used for prediction of cervical cancer. The numbers of attributes are reduced with genetic algorithm and further performance enhancement is proposed with adaptive boosting technique. With proposed integrated approach of genetic algorithm and adaptive boosting the improved accuracy lies between 94.17%-94.69%, sensitivity 97.36%-98.90%, specificity 93.37%-94.72% and precision 93%-95.17% for Support Vector Machine Radial Bias Function (SVM RBF), SVM Linear and Decision Tree.

Applicability of connectionist methods to predict thermal resistance of pulsating heat pipes with ethanol by using neural networks

Pulsating heat pipes (PHPs) are compact and efficient devices in heat transfer which are applicable for several purposes. The thermal resistance of PHPs depends on several parameters. In the present study, four models including multilayer perceptron (MLP), radial bias function (RBF), conjugated hybrid of particle swarm optimization and adaptive neuro-fuzzy inference system (CHPSO ANFIS) are applied to predict the thermal resistance of pulsating heat pipes filled with ethanol. The obtained results indicated that the radial bias function (RBF) model had the highest accuracy among the applied models and can predict the thermal resistance of the PHPs precisely. The R-squared and root mean squared error (RMSE) values for this model were 0.9892 and 0.0650, respectively.

RAPID BLEEDING REGION DETECTION IN WIRELESS CAPSULE ENDOSCOPY VIDEOS

Wireless Capsule Endoscopy (WCC) is a medical imaging technique used to examine parts of the gastrointestinal tract. Computer aided detection is used to increase the speed of detection, better performance and reduce the time. Before finding the bleeding regions the edge regions are first detected and removed. Both the edge and the bleeding regions will share the same Hue value and the luminance should be same for the bleeding and the non -bleeding regions .We use a canny edge detector operator for detecting the edge regions in L channel. Canny edge detector is used to detect more edge pixels and preserve more bleeding pixels based up on canny edge algorithm. This method in edge removal algorithm includes edge detection, edge dilation and edge masking. After the removal of edges, those regions are made in to segment through super-pixel segmentation and regions are classified using Artificial Neural Network by Radial Bias Function (RBF).Â

Evaluating Blind Image Quality Using RBF Neural Network

Blind Image Quality Assessment (BIQA) methods are the most part feeling mindful. The BIQA method learns regression models from preparing images with human subjective scores to predict the perceptual nature of test images. The general quality of image and the nature of every image patches are measured by normal pooling. By coordinating the components of normal picture measurements got from different signs, we take a multivariate Gaussian model of picture patches from an accumulation of unblemished regular pictures. The proposed radial bias function neural network method is used to evaluate the quality of images and this method represents the structure of picture distortions with flexibility.

Evaluating Blind Image Quality Using RBF Neural Network


 
 
 Blind Image Quality Assessment (BIQA) methods are the most part feeling mindful. The BIQA method learns regression models from preparing images with human subjective scores to predict the perceptual nature of test images. The general quality of image and the nature of every image patches are measured by normal pooling. By coordinating the components of normal picture measurements got from different signs, we take a multivariate Gaussian model of picture patches from an accumulation of unblemished regular pictures. The proposed radial bias function neural network method is used to evaluate the quality of images and this method represents the structure of picture distortions with flexibility.
 
 

Integration of Global and Local Salient Features for Scene Modeling in Mobile Robot Applications

Many approaches have recently used global image descriptors and/or local key-point descriptors for scene understanding. In fact these approaches have suffered from lack of spatial information by using local key-point descriptors, and lack of viewpoint and local information by using global image descriptors. To overcome these problems, this paper addresses a novel image descriptor based on salient line segments (SLS), in which the global and local image features are integrated into low dimensional feature vectors. In this descriptor, low level feature maps are first computed in four scales by applying a center-surround competition technique to enhance the dominant edges and suppress small line segments. These maps are then used to extract the SLS of the image patches by creating histogram of gradients in the receptive cells. Afterwards, the global features are formed into a single vector from the coarser scales of the SLSs, and the local feature vectors are formed from the frequency of the appearance of SLSs in the finer scale. Finally, a classification step recognizes the scene of an input image by applying multi-class SVM with a Radial Bias Function (RBF) kernel. The system is performed on image sequences taken from natural scenes by a mobile agent under controlled and unexpected changes in environmental conditions. Experiments on image datasets show that the proposed method is able to classify the scenes more accurately than former methods in mobile agent environments.

Diagnosis of osteoporosis by extraction of trabecular features from hip radiographs using support vector machine: An investigation panorama with DXA

BackgroundLifespan and its quality can be improved by early diagnosis of osteoporosis. Analysis of trabecular boundness on digital hip radiographs could be useful for identifying subjects with low bone mineral density (BMD) or osteoporosis. The main aim of our study was to evaluate the ability of a kernel-based support vector machine (SVM) with respect to diagnosis and add to knowledge about the trabecular features of digital hip radiographs for identifying subjects with low BMD. MethodIn this paper we present an SVM kernel classifier-based computer-aided diagnosis (CAD) system for osteoporotic risk detection using digital hip radiographs. Initially, the original radiograph was intensified, then trabecular features such as boundness, orientation, solidity of spur and delta were evaluated and radial bias function (RBF) based discrimination was manifested. The next step was the evaluation of the diagnostic capability of the proposed method in order to spot subjects with low BMD at the femoral neck in 50 (50.7±14.3 years) South Indian women with no previous history of osteoporotic fracture. Out of 50 subjects, 28 were used to train the classifier and the other 22 were used for testing. ResultsThe proposed system has achieved the highest classification accuracy documented so far by means of a fivefold cross-validation analysis with mean accuracy of 90% (95% confidence interval (CI): 82 to 98%); sensitivity and positive predictive value (PPV) were 90% (95% CI: 82 to 98%) and 89% (95% CI: 81 to 97%), respectively. Pearson's correlation was observed at the level of p<0.001, between extracted image trabecular features with age and BMDs measured by dual energy x-ray absorptiometry (DXA). Extracted image features also demonstrated significant differences between high and low BMD groups at the level of p<0.001. ConclusionOur findings suggest that the proposed CAD system with SVM would be useful for spotting women vulnerable to osteoporotic risk.

Images Enhancement with Brightness Preserving using MRHRBFN

In the image processing, Images Enhancement with Brightness Preserving has many methods likes HE(Histogram Equalization), MHE(Multi-Histogram Equalization), IDBPHE(Image Dependent Brightness Preserving Histogram Equalization). We are proposed a novel methodology for the enhancement of images MRHRBFN (Multi-Resolution Histogram with Radial Bias Function Network). Enhancement process using pixel independent multi histogram method and Radial Bias Function Network. In process of our methodology image are decompose in terms of subbands. The sub band division perform by Curvelet transform. The Curvelet Transform divides two types of bands as higher band and lower band. The separation band of frequency generates a multiple matrix for input of radial bias Function Network. We have radial Bias function network work in low band data, because higher band data preserve brightness of image. The lower frequency matrix calculates bias and proceed weight factor when The lower value of frequency matrix regret reaches the mean value of given image. Finally we get better enhance image in comparison of multi histogram equalization.

Adaptive active queue management controller for TCP communication networks using PSO-RBF models

Addressing performance degradations in end-to-end congestion control has been one of the most active research areas in the last decade. Active queue management (AQM) aims to improve the overall network throughput, while providing lower delay and reduce packet loss and improving network. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. Radial bias function (RBF)-based AQM controller is proposed in this paper. RBF controller is suitable as an AQM scheme to control congestion in TCP communication networks since it is nonlinear. Particle swarm optimization (PSO) algorithm is also employed to derive RBF parameters such that the integrated-absolute error (IAE) is minimized. Furthermore, in order to improve the robustness of RBF controller, an error-integral term is added to RBF equation. The results of the comparison with Drop Tail, adaptive random early detection (ARED), random exponential marking (REM), and proportional-integral (PI) controllers are presented. Integral-RBF has better performance not only in comparison with RBF but also with ARED, REM and PI controllers in the case of link utilization while packet loss rate is small.

Behavioral Modeling of Power Amplifiers With Dynamic Fuzzy Neural Networks

In this letter, dynamic fuzzy neural networks (D-FNN) are applied to model power amplifiers (PAs) with memory effects. The D-FNN model implements Takagi-Sugeno-Kang (TSK) fuzzy systems based on extended radial bias function (RBF) neural networks. The parameters of the model are trained by the online self-organized learning algorithm, in which the neurons can be recruited or deleted dynamically according to their significance to system performance, and the over fitting or over training problems can be avoided. The D-FNN model is validated in our test bench in which a Doherty PA is excited with 10 MHz and 20 MHz worldwide interoperability for microwave access (WiMAX) signals. Experimental results show that the D-FNN model can give an accurate approximation to characterize the wideband PAs with memory effects.

Experiments on direction finder using RBF neural network with post-processing

Evaluation tests are carried out on the direction finder, which has an aperture comprising four monopoles on the circumference of 0.4 λ. Radial bias function (RBF) neural network and a new post-processing procedure are used in the algorithm of DOA estimation. The estimation error was reduced to one-tenth that of MUSIC.