Electrical & Computer Engineering

With increased loading and exploitation of power transmission systems, voltage stability has become a growing concern in electric power utilities. Static analysis methods, such as power flow based methods, have difficulty in evaluating voltage stability due to using simple models for the system components. On the other hand, dynamic modeling and evaluation of voltage stability are complex, expensive and time consuming. In this paper, a neural network (NN) approach is presented for this purpose. This NN can establish a mapping between the operating conditions and the dynamic voltage stability margin (VSM) of each bus. This analysis gives an insight about the robustness of various buses and dynamic voltage security status of the power system. The method is examined on Iran's North-West Transmission Network, and a mean absolute estimation error of 2.3% and a response time less than 0.1 s are obtained. By comparing the results obtained through different methods, it is concluded that the NN approach provides a compromise between accuracy and speed of calculations. Hence, for operational purposes, for which having fast response is vital, using the NN approach is recommended. On the other hand, for the design and development of power systems, using dynamic simulation seems a better approach due to its higher accuracy.

Voltage collapse phenomena are highly affected by the limits of Automatic Voltage Regulator (AVR) voltage that indirectly controls the amount of reactive power generation. Saturation of the limits of the AVR voltage of a unit may result in deterioration of voltage stability. In addition, in some cases when the power network is operating close to the voltage collapse point, the AVR voltage saturation of a unit can change the system voltages immediately from a stable to an unstable state. Thus, a dynamic voltage collapse leading to blackout is expected. This paper presents a steady state analysis of voltage instability associated to the AVR voltage saturation phenomena. For this purpose, a new methodology based on the predictor-corrector method is introduced to trace the total system equilibrium of the power system model. This methodology considers the AVR voltage limits of all generation units and computes both Saddle Node Bifurcation (SNB) and Saddle Limit Induced Bifurcation (SLIB) points. The New England 39-bus system is adopted to illustrate the effectiveness of the proposed method.

This paper discusses a general approach to qualitative modelling of power system stabilizer (PSS) based on fuzzy logic. It proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the optimal structure of a fuzzy model for PSS, thus a qualitative fuzzy PSS is obtained. The qualitative fuzzy PSS (QFPSS) is designed for a synchronous generator, and simulation studies are based on a one machine infinite bus model. Simulation results illustrate the effect of the QFPSS and comparative study of the QFPSS with a conventional (linear) power system stabilizer (CPSS), a fuzzy-logic PSS (FPSS) shows superiority of the proposed stabilizer.

Fourth industrial revolution (IR 4.0) refers to where manufacturing industries absorbed by intelligent equipment such as products and machines which able to generate intelligent systems and networks. This will help to communicate with each other autonomously. Intelligent sensing, wireless sensor networks and CPSs are fundamental technologies which Germany focusing on. IR 4.0 will include effective independent physical systems associated with each other and smart operations will be carried out by the environment. The IR 4.0 as a revolution empowered by application of advanced progresses at era level to bring unused values and organizations for clients and organization itself. Researcher believes that the outcomes of this study may contribute to the manufacturing to understand the challenges of the fourth industrial revolution that may occur and to finds out the solution in order to ensure the success of this emerging industry.

In this paper, we consider the tracking control problem for robot manipulators which are affected by constant bounded disturbances. Three control schemes are applied for the problem, which are composed of integral action and tracking controllers. The goal is improving the accuracy of tracking procedure for a robot manipulator to track a specified reference signal in the presence of constant bounded disturbances. Inverse dynamics controller, improved Lyapunovbased controller with integral action and discontinuous Lyapunov-based controller are three schemes that are evaluated in this paper. The third one is a novel controller that achieves trajectory following without requiring exact knowledge of the nonlinear dynamics. Based on the disturbance rejection scheme, tracking controllers are constructed which are asymptotically stabilizing in the sense of Lyapunov. Furthermore the closed loop system has strong disturbance rejection property. It is shown that how under proper assumptions, the proposed schemes succeed in achieving disturbance rejection at the input of a nonlinear system. Computer simulation results given for a two degree of freedom manipulator with a large payload and fast maneuver, demonstrate that accurate trajectory tracking can be achieved by using the proposed controllers.

Brain image segmentation is one of the most important parts of clinical diagnostic tools. Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore, accurate segmentation of brain images is a very difficult task. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. We presented a review of the methods used in brain segmentation. The review covers imaging modalities, magnetic resonance imaging and methods for noise reduction, inhomogeneity correction and segmentation. We conclude with a discussion on the trend of future research in brain segmentation.

Intensity inhomogeneity is a smooth intensity change inside originally homogeneous regions. Filter-based inhomogeneity correction methods have been commonly used in literatures. However, there are few literatures which compare effectiveness of these methods for inhomogeneity correction. In this paper, a new filter-based inhomogeneity correction method is proposed and the effectiveness of the proposed method and other filter-based inhomogeneity correction methods are compared. The methods with different kernel sizes are applied on MRI brain images and the quality of inhomogeneity correction of different methods are compared quantitatively. Experimental results show the proposed method in a kernel size of 20 * 20 performs almost better than or equal the performance of other methods in all kernel sizes.

Image segmentation is preliminary stage in diagnosis tools and the accurate segmentation of brain images is crucial for a correct diagnosis by these tools. Due to inhomogeneity, low contrast, noise and inequality of content with semantic; brain MRI image segmentation is a challenging job. A post processing algorithm for improvement of clustering accuracy is proposed. The proposed algorithm re-evaluates boundary data to reduce clustering error. Proposed algorithm quantitatively evaluated by applying the mentioned algorithm on two recently reported clustering algorithms. The proposed algorithm improved clustering results and gives comparable results when user interaction is applied to the clustering algorithms.

One of the hottest research areas in recent years is detecting network intrusion patterns in computer networks. Because of dynamic nature of intrusion patterns in networks, intelligently inspecting the behavior of networks and detecting anomalies are mostly desirable. KDD-Cup99 pattern database are used as a standard source of network packets in our research. K-mean, Bayesian method and Support Network Machine (SVM) are used as anomaly detectors. Results show the superiority of SVM over other two methods regarding the accuracy of classifying patterns into normal packets and suspicious ones. It can be concluded that using high dimensional pattern recognition methods have reasonable competence in detecting attack patterns in computer networks.

our lately defined edge-preserving neighborhood is used to improve an already exist extension for Fuzzy C-Mean (FCM). In the defined neighborhood, a window is centered on the pixel. Then, each sample, in the window, is considered the neighbor of the pixel if there is not any edge between the sample and the pixel. Moreover two extensions for Expectation Maximizing (EM) are introduced to have better clustering results in presence of noise. Promising experimental results of our proposed extensions in compare to two state-of-art extensions for FCM demonstrate the potential of proposed modifications.

Brain image segmentation is one of the most important parts of clinical diagnostic tools. Fuzzy C-mean (FCM) is one of the most popular clustering based segmentation methods. In this paper, a review of the FCM based segmentation algorithms for brain MRI images is presented. The review covers algorithms for FCM based segmentation algorithms, their comparative evaluations based on reported results and the result of experiments for neighborhood based extensions for FCM.

Image segmentation is at a preliminary stage of inclusion in diagnosis tools and the accurate segmentation of brain MRI images is crucial for a correct diagnosis by these tools. Due to in-homogeneity, low contrast, noise and inequality of content with semantic; brain MRI image segmentation is a challenging job. A review of the Gaussian Mixture Model based segmentation algorithms for brain MRI images is presented. The review covers algorithms for segmentation algorithms and their comparative evaluations based on reported results.

Nowadays, fast scan techniques are used to reduce scanning times. These techniques raise scanning noise level in MRI systems. Instead of progress made in image de-noising, still, it is challenging. A novel edge-preserving neighbourhood filter for image enhancement is proposed. The main focus of this paper is to propose an adaptive filtering function to account for the image content while try to preserve edge of image. Proposed algorithm uses the edges of image to do edge-preserving neighbourhood filtering. Contribution of a sample, in neighbourhood of a pixel, in filtering, depends on the space between the pixel and the sample. In fact, the sample which there is edge between it and the pixel don't contribute in the grey level estimation. Promising experimental results on simulated and real brain images and comparison with state-of-art de-noising algorithm demonstrate the potential of proposed algorithm.

Abstract. Segmentation of Magnetic resonance images (MRI) based on simply image intensity due to some properties of MR images like inhomogeneity and partial volume effect is prone to error. Hence incorporating expert knowledge in designing segmentation ...

Background: Expectation maximizing (EM) is one of the common approaches for image segmentation. Methods: an improvement of the EM algorithm is proposed and its effectiveness for MRI brain image segmentation is investigated. In order to improve EM performance, the proposed algorithms incorporates neighbourhood information into the clustering process. At first, average image is obtained as neighbourhood information and then it is incorporated in clustering process. Also, as an option, user-interaction is used to improve segmentation results. Simulated and real MR volumes are used to compare the efficiency of the proposed improvement with the existing neighbourhood based extension for EM and FCM. Results: the findings show that the proposed algorithm produces higher similarity index. Conclusions: experiments demonstrate the effectiveness of the proposed algorithm in compare to other existing algorithms on various noise levels.

To improve the quality of expectation maximizing (EM) for brain image segmentation, and to evaluate the accuracy of segmentation results. This brain segmentation study was conducted in Universiti Putra Malaysia in Serdong, Malaysia between February and November 2010 on simulated and real images using novel improvement for EM. The EM-1 (proposed algorithm) was compared with neighborhood based extensions for fuzzy C-mean (FCM). The EM-1 was also applied to all 20 normal real MRI volumes and compared with reported results from the Internet Brain Segmentation Repository. In simulated images, the EM-1 outperforms neighborhood based extensions for FCM. The average similarity index value of the proposed algorithm for all 20 normal images is 0.802. The EM-1 produces the average Jaccard indices ρ higher than other algorithms and near to manual results. The average similarity indices ρ for EM-1 and FCM extensions (FCM with spatial information [FCM-S], Fast Generalized FCM [FGFCM]) for all 20 ...

Numerous multimedia applications and communications are rapidly growing through the Internet. Because most of these multimedia communications are confidential and cannot be known by unauthorized users, secret image sharing has become a key technology for digital images in secured storage and confidential transmission. The ultimate objectives of secret sharing include transparency, authority (resistance to various image tampering and forgery methods), and high capacity of the hidden data. A variety of secret image sharing algorithms and techniques have been proposed in order to satisfy these objectives. These goals were used in this paper to compare current methods of image secret sharing and present their properties and limitations. This paper provided a state-of-the-art review and analysis of different existing secret image sharing methods along with some common standards and guidelines drawn from the literature.

In this paper, a new robust digital image watermarking algorithm which was based on singular value decomposition (SVD) and discrete wavelet transform (DWT) was proposed and simulated for protecting real property rights. A gray scale logo image, rather than a randomly generated Gaussian noise type watermark, was used as a watermark. Its embedding algorithm hid a watermark LL sub-band blocks in the low-low (LL) and high-high (HH) sub-bands of a target non-overlapping block of the host image by modifying singular values (SVs) on SVD version of these blocks. A semi-blind watermark extraction algorithm was designed to estimate the original coefficients. Experimental results showed that the proposed scheme made significant improvements in terms of both transparency and robustness and was superior to the existing methods which were considered in this paper.

Image segmentation is crucial and preliminary stage of almost all medical imaging diagnosis tools. Gaussian Mixture Model (GMM) is one of common methods for image segmentation and usually, Expectation Maximizing (EM) is used to estimate the parameters of this model. In order to improve EM performance in presence of noise, an extension for EM is proposed which incorporates mean-filtered image as neighborhood information in clustering. In addition, the histogram of image is used as input for clustering to speed up the process. Proposed algorithm quantitatively evaluated in compare to current extensions for EM.

Image segmentation is preliminary stage in diagnosis tools and the accurate segmentation of brain images is crucial for a correct diagnosis by these tools. Due to inhomogeneity, low contrast, noise and inequality of content with semantic; brain MRI image segmentation is a challenging job. A post processing algorithm for improvement of clustering accuracy is proposed. The proposed algorithm re-evaluates boundary data to reduce clustering error. Proposed algorithm quantitatively evaluated by applying the mentioned algorithm on two recently reported clustering algorithms. The proposed algorithm improved clustering results and gives comparable results when user interaction is applied to the clustering algorithms.