Valentina Emilia Balas

Bio: Valentina Emilia Balas is an academic researcher from Aurel Vlaicu University of Arad. The author has contributed to research in topics: Fuzzy logic & Control theory. The author has an hindex of 27, co-authored 319 publications receiving 2911 citations.

Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings

Abstract: Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time-consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real-life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization-based approach to train the NN (NN-PSO). The PSO is employed to find a weight vector with minimum root-mean-square error (RMSE) for the NN. The proposed (NN-PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings’ RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN-PSO model was compared with NN and MLP-FFN (multilayer perceptron feed-forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN-PSO compared to the NN and MLP-FFN classifiers. The NN-PSO achieved 90 % accuracy with 90 % precision, 94.74 % recall and 92.31 % F-Measure.

Multilayer perceptron and neural networks

Abstract: The attempts for solving linear inseparable problems have led to different variations on the number of layers of neurons and activation functions used. The backpropagation algorithm is the most known and used supervised learning algorithm. Also called the generalized delta algorithm because it expands the training way of the adaline network, it is based on minimizing the difference between the desired output and the actual output, through the downward gradient method (the gradient tells us how a function varies in different directions). Training a multilayer perceptron is often quite slow, requiring thousands or tens of thousands of epochs for complex problems. The best known methods to accelerate learning are: the momentum method and applying a variable learning rate. The paper presents the possibility to control the induction driving using neural systems.

Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm

Abstract: Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control, automatic generation control ( AGC ) plays a crucial role. In this paper, multi-area ( Five areas: area 1, area 2, area 3, area 4 and area 5 ) reheat thermal power systems are considered with proportional-integral-derivative ( PID ) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm ( FFA ). The experimental results demonstrated the comparison of the proposed system performance ( FFA-PID ) with optimized PID controller based genetic algorithm ( GA-PID ) and particle swarm optimization ( PSO ) technique ( PSO-PID ) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error ( ITAE ) cost function with one percent step load perturbation ( 1 % SLP ) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot / undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.

Estimating heating load in buildings using multivariate adaptive regression splines, extreme learning machine, a hybrid model of MARS and ELM

Abstract: Heating load and cooling forecasting are essential for estimating energy consumption, and consequently, helping engineers in improving the energy performance right from the design phase of buildings. The capacity of heating ventilation and air-conditioning system of the building contributes to the operation cost. Moreover, building being one of the sectors with heavy energy use, it is required to develop an accurate model for energy forecasting of building and constructing energy-efficient buildings. This paper explores different machine learning techniques for predicting the heating load and cooling load of residential buildings. Among these methods, we focus on advanced techniques like Multivariate Adaptive Regression Splines (MARS), Extreme Learning Machine (ELM) and a hybrid model of MARS and ELM along with a comparison of the results with those of more conventional methods like linear regression, neural network, Gaussian processes and Radial Basis Function Network. The MARS model is a non-parametric regression model that splits the data and fits each interval into a basis function and ELM is similar to a Single Layer Feed-forward Neural Network except that in ELM randomly assigned input weights are not updated. As an improvement, we have tried a hybrid model that uses MARS to evaluate the importance of every parameter in the prediction and these important parameters have been fed to the ELM to build hybrid model and it can be seen that this boosts the ELM performance to match up to the accuracy of MARS with lesser computation time. Finally, a comparative study examines the performances of the different techniques by measuring different performance metrics.

Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity

Abstract: This work presents an application of bio-inspired flower pollination algorithm (FPA) for tuning proportional–integral–derivative (PID) controller in load frequency control (LFC) of multi-area interconnected power system. The investigated power system comprises of three equal thermal power systems with appropriate PID controller. The controller gain [proportional gain (K p), integral gain (K i) and derivative gain (K d)] values are tuned by using the FPA algorithm with one percent step load perturbation in area 1 (1 % SLP). The integral square error (ISE) is considered the objective function for the FPA. The supremacy performance of proposed algorithm for optimized PID controller is proved by comparing the results with genetic algorithm (GA) and particle swarm optimization (PSO)-based PID controller under the same investigated power system. In addition, the controller robustness is studied by considering appropriate generate rate constraint with nonlinearity in all areas. The result cumulative performance comparisons established that FPA-PID controller exhibit better performance compared to performances of GA-PID and PSO-PID controller-based power system with and without nonlinearity effect.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …