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Quality of Service (QoS) in networking is a way of managing the network resources effectively. QoS has been widely used in traditional network and can also be implemented in Software Defined Network (SDN). Software Defined Network is a new network paradigm that decouples the control plane from the data plane in the network and thus create a network that is scalable, dynamic and easily manageable....
In recent years, several new methods for missing data estimation have been developed. Real world datasets possess the properties of big data being volume, velocity and variety. With an increase in volume which includes sample size and dimensionality, existing imputation methods have become less effective and accurate. Much attention has been given to narrow Artificial Intelligence frameworks courtesy...
This paper presents an optimal placement of multi-type FACTS devices within the grid for maximizing the system loadability. The model selects the best device, location and setting considering a limited budget. The model is solved using genetic algorithms. Results shows that there is nonlinear relationship between the budget and the system loadability. Furthermore, there is a budget threshold above...
This paper suggests an adaptive 2-D Optical CDMA coding system based on one-coincidence frequency hopping (OCFH) code combined with an optical orthogonal code (OOC) in the format OCFH/OOC, suitable for the fast frequency hopping optical code division multiple access (FFH-OCDMA) channel, encoded by the Bragg gratings encoder with an aim to optimize the access network in terms of number of users and...
This paper presents a method for evaluating, both qualitatively and quantitatively, the effects of specific rotor design parameters on the performance of a Synchronous Reluctance Machine (SynRM). The method uses multi-factor experimental design, with Analysis of Variance (ANOVA), and Finite Element Analysis (FEA) to determine the optimal rotor design parameter according to a specific objective. Using...
Transmission lines are very important component of the electric power system. Therefore it is necessary to predict and detect transmission lines fault types and locations to enhance the power system protection scheme and increase its reliability. This paper investigates the use of four powerful machine learning classifiers to detect and predict fault types and locations over a 750KV, 600km long power...
This study explores the application of artificial intelligence on the causal relationship between mining production index and electricity load. The data used is the total mining production index and total electricity consumption in the mining sector sampled on a monthly basis from January 1985 to December 2011 in South Africa. Optimally-pruned and basic extreme learning machines were used to develop...
This paper presents an optimal placement of TCSC which is a FACTS (Flexible Alternative Current transmission Systems) controller in order to increase the loadbility of the system. The optimization problem is solved using the genetic algorithm. In this study the availablity of the budget is taken in consideration. The result show that the increase in loadability can be restricted by the availability...
In this paper it presents a methodology that aims to deliver a near optimal Distributed Generation (DG) in the process of allocation DG units by using a hybrid genetic algorithm, Hence, the main aim is to minimize power losses in DG. The proposed algorithm in this paper involves two main parts of algorithms an artificial neural network (ANN) that found to evaluate the fitness function in the generation...
This paper presents a comprehensive study of leakage reduction techniques applicable to CMOS based devices. In the process, mathematical equations that model the power-performance trade-offs in CMOS logic circuits are presented. From those equations, suitable techniques for leakage reduction as pertaining to CMOS devices are deduced. Throughout this research it became evident that designing CMOS devices...
Low power IC solutions are in great demand with the rapid advancement of handheld devices, wearables, smart cards and radio frequency identification bringing a massive amount of new products to market that all have the same primary need: Powering the device as long as possible between the need to recharge the batteries while at the same time dramatically decreasing the device leakage currents. The...
Although cryptography constitutes a considerable part of the overall security architecture for several use cases in embedded systems, cryptographic devices are still vulnerable to the diversity types of side channel attacks. Improvement in performance of Strained Silicon MOSFETs utilizing conventional device scaling has become more complex, because of the amount of physical limitations associated...
In this paper six single classifiers (support vector machine, artificial neural network, naïve Bayesian classifier, decision trees, radial basis function and k nearest neighbors) were utilized to predict water dam levels in a deep gold mine underground pump station. Also, Bagging and Boosting ensemble techniques were used to increase the prediction accuracy of the single classifiers. In order to enhance...
Automobile industry is an integral component of the South African economy. The industry contributes 6 to 7% to the country's economy and is occupied by major international automobile companies. In this highly competitive industry, insights into overall after sales customer satisfaction is of great value to the companies to better serve customers and retain their competitive advantage. Following established...
Four optimization algorithms (genetic algorithm, simulated annealing, particle swarm optimization and random forest) were applied with an MLP based auto associative neural network on two classification datasets and one prediction dataset. This work was undertaken to investigate the effectiveness of using auto associative neural networks and optimization algorithms in missing data prediction and classification...
MotivationRoad traffic accidents are among the top leading causes of deaths and injuries of various levels in South Africa. With the wealth and huge amount of data generated from road traffic accidents, the issue of traffic accident prediction has become a central challenge in the field of transportation data analysis. Such accident prediction is designed to detect patterns involved in dangerous crashes...
In this paper a comparison between an ensembles (multi-classifier) constructed of several machine learning methods (support vector machine, artificial neural network, naive Bayesian classifier, decision trees, radial basis function and k nearest neighbors) versus each single classifiers of these methods in term of gold mine underground dam levels prediction is presented. The ensembles as well as the...
This paper presents an experiment that consists of constructing auto-regressive moving average (ARMA), neural networks and neuro-fuzzy models with historical electricity consumption time series data to create models that can be used to forecast consumption in the future. The data was sampled on a monthly basis from January 1985 to December 2011. An ARMA, multilayer perceptron neural network with back...
Three computational intelligence algorithms (k-nearest neighbors, a naïve Bayes' classifier, and decision trees) were applied on a double pump station mine to monitor and predict the dam levels and energy consumption. This work was carried out to inspect the feasibility of using computational intelligence in certain aspects of the mining industry. If successful, computational intelligence systems...
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