Neslihan Serap Sengor

The application of ANN technique to automatic generation control for multi-area power system

This paper presents an application of layered artificial neural network controller (ANN) to study automatic generation control (AGC) problem in a four-area interconnected power system that three areas include steam turbines and the other area includes a hydro turbine. Each area of steam turbine in the system contains the reheat effect non-linearity of the steam turbine and the area of hydro turbine contains upper and lower constraints for generation rate. Only one ANN controller, which controls the inputs of each area in the power system together, is considered. In the study, back propagation-through-time algorithm is used as ANN learning rule. By comparing the results for both cases, the performance of ANN controller is better than conventional controllers.

The application of ANN technique to load-frequency control for three-area power system

This paper includes an application of layered artificial neural network controller to study the load-frequency control problem in a power system. The control scheme guarantees that steady state error of frequencies and inadvertent interchange of tie-lines are maintained in a given tolerance limitation. The proposed control has been designed for a three-area interconnected power system such that two areas include steam turbines and the other area includes a hydro turbine. Only one artificial neural network (ANN) controller, which controls the inputs of each area in the power system together, is considered. In the study, a back propagation-through-time algorithm is used as a neural network learning rule. The performance of the power system is simulated by using a conventional integral controller and ANN controller, separately. By comparing the results for both cases, the performance of an ANN controller is better than conventional controllers.

Automatic Generation Control by Using ANN Technique

This paper investigates an application of layered artificial neural network for automatic generation control of the power system. Computer simulations on the interconnected power system with two areas that include reheater effect and also the governor deadband effect show that the artificial neural network control scheme proposed is effective in damping out oscillations resulted by load perturbations. Only one artificial neural network controller, which controls the inputs of each area in the power system together, is considered. By comparing the obtained results with conventional controllers, it is shown that the performance of artificial neural network controller is better than conventional controllers. In this paper, back propagation-through-time algorithm is used as neural network learning rule.This paper investigates an application of layered artificial neural network for automatic generation control of the power system. Computer simulations on the interconnected power system with two areas that include reheater effect and also the governor deadband effect show that the artificial neural network control scheme proposed is effective in damping out oscillations resulted by load perturbations. Only one artificial neural network controller, which controls the inputs of each area in the power system together, is considered. By comparing the obtained results with conventional controllers, it is shown that the performance of artificial neural network controller is better than conventional controllers. In this paper, back propagation-through-time algorithm is used as neural network learning rule.

The recent developments in microwave design

Artificial neural networks have been used as an important technique in microwave modelling and optimisation. This paper gives an overview and recent developments on the knowledge-based neural modelling techniques in microwave modelling and design. The knowledge-based artificial neural networks are constructed by incorporating the existing knowledge such as empirical formulas, equivalent circuit models and semi-analytical equations in neural network structures. When one of the knowledge-based methods can not provide sufficient accuracy, two of them can be used in the same modelling process. This combination of methods is named hybrid technique. Using knowledge-based techniques requires less training data and has better extrapolation performance than classical neural networks. The advantages of using knowledge-based neural network modelling are demonstrated with microwave device modelling applications.

A dwell time approach to the stability of switched linear systems based on the distance between eigenvector sets

In this article, a sufficient condition on the minimum dwell time that guarantees the stability of switched linear systems is given. The proposed method interprets the stability of switched linear systems through the distance between the eigenvector sets of subsystem matrices. Thus, an explicit relation in view of stability is obtained between the family of the involved subsytems and the set of admissible switching signals.

On the realization of NPN-only log-domain chaotic oscillators

Two new chaotic oscillators with double-scroll dynamics based on the log-domain circuit-design technique are presented. As the circuits operate in log domain, they are suitable for low-voltage, high-frequency operation. The important key parameters of the oscillators are electronically tunable, thus, they are well suited for integrated circuit implementation. Furthermore, the circuits do not employ any PNP transistors, hence they can be implemented in cheap bipolar processes, which do not offer high-quality vertical PNPs. Experimental results verifying the feasibility of the circuits are given.

Content-based copy detection by a subspace learning based video fingerprinting scheme

We propose a video copy detection scheme that employs a transform domain global video fingerprinting method. Video fingerprinting has been performed by the subspace learning based on nonnegative matrix factorization (NMF). It is shown that the binary video fingerprints extracted from the basis and gain matrices of the NMF representation enable us to efficiently represent the spatial and temporal content of a video segment respectively. An extensive performance evaluation has been carried out on the query and reference dataset of CBCD task of TRECVID 2011. Our results are compared with the average and the best performance reported for the task. Also NDCR and F1 rates are reported in comparison to the performance achieved via the global methods designed by the TRECVID 2011 participants. Results demonstrate that the proposed method achieves higher correct detection rates with good localization capability for the transformation of text/logo insertion, strong re-encoding, frame dropping, noise addition, gamma change or their mixtures; however there is still potential for improvement to detect copies with picture-in-picture transformations. It is also concluded that the introduced binary fingerprinting scheme is superior to the existing transform based methods in terms of the compactness.

The Efficiency of Difference Mapping in Space Mapping-Based Optimization

In space mapping, a time-consuming but accurate fine model is used along with a less accurate but fast coarse model to reduce the overall computational effort. In this work, techniques using the difference mapping concept will be introduced. These techniques are efficient in reducing the computational effort while improving convergence. Difference mapping is constructed similarly to the mechanism used in space mapping, but, unlike space mapping, it facilitates the use of terminating conditions based on the simultaneous use of input and output values. Rigorous mathematical expressions related to difference mapping techniques will be given, and the improvement provided by these techniques will be discussed. Furthermore, to expose the efficiency of using the difference in input and output, simulation results obtained for high-dimensional applications will be given.

A biophysical network model displaying the role of basal ganglia pathways in action selection

Basal ganglia circuits are known to have role in a wide range of behaviour spanning from movement initiation to high order cognitive processes as reward related learning. Here, the intention is to have a biophysically realistic model of basal ganglia circuit for voluntary motor action selection. The ultimate aim is to provide a framework for models which could help comprehension of complex processes. To fulfill this aim a model capable of simulating direct, indirect and hyperdirect pathways with modified Hodgkin-Huxley neuron model is proposed. This model considers more neural structures than the works similar in the literature and can simulate activity of neurons in the neural structures taking part in action selection. The model proposed is shown to be versatile as the simulation results obtained are similar to the neuron activity recordings of the considered neural structures published previously.

The recent developments in knowledge based neural modeling

Abstract Artificial neural networks have been recognized as an important technique in microwave modeling and optimization. This paper gives an overview and recent developments on the knowledge based neural modeling techniques in microwave modeling and design. The knowledge based artificial neural networks are constructed by incorporating the existing knowledge such as empirical formulas, equivalent circuit models and semi-analytical equations in neural network structures. The existing knowledge reduces the complexity of neural network model. This combination requires less training data and has better extrapolation performance than classical neural networks. The advantages of using knowledge based neural network modeling are demonstrated with two microwave modeling applications such as characteristic impedance modeling of thin-film microstrip line and parametric modeling of the differential via holes.