Earticle

Distributed generation is a form of electric power privatization. Consumers install their own power station to supply the local loads and/or share in the utility loads. Many protective problems have been developed due to the existence of these distributed generations. However, a protective strategy should be developed to protect the system and the generator itself from different hazards. One problem with such generators is an unwanted islanding phenomenon. This paper introduces a hybrid passive method for islanding detection to minimize the non-detective zone. This method based on composed of rate of change of frequency over power under each event and rate of change of DG reactive power under each event (ROCORP). Simulation results which are carried out on software PSCAD/EMTDC shows good performance of this method.

Clustering has an extensive and long history in a variety of scientific fields. Several recent studies of complex networks have suggested that the clustering analysis on networks has been an emerging research issue in data mining due to its variety of applications. Many graph clustering algorithms have been proposed in recent past, however, this clustering approach remains a challenging problem to solve real-world situation. In this work, we propose an aspiration criteria based graph clustering algorithm using stochastic local search for generating lower cost clustering results in terms of robustness and optimality for real-world complex network problems. In our proposed algorithm, all moves are meaningful and effective during the whole clustering process which indicates that moves are only accepted if the target node has neighbouring nodes in the destination cluster (moves to an empty cluster are the only exception to this instruction). An adaptive approach in our method is in incorporating the aspiration criteria for the best move (lower-cost changes) selection when the best non-tabu move involvements much higher cost compared to a tabued move then the tabued move is permitted otherwise the best non-tabu move is acceptable. Extensive experimentation with synthetic and real power-law distribution benchmark datasets show that our algorithm outperforms state-of-the-art graph clustering techniques on the basis of cost of clustering, cluster size, normalized mutual information (NMI) and modularity index of clustering results.

This study seeks to focus on Bayesian and non-Bayesian estimation for the shape parameter of the Kumaraswamy distribution under type-II censored samples. Maximum likelihood estimation and Bayes estimation have been obtained using asymmetric loss functions. Posterior predictive distributions along with posterior predictive intervals have been derived under simple and mixture priors. Elicitation of hyper-parameter through prior predictive approach has also been discussed. As analytical comparison is difficult, so comparisons among these estimators have been made using Monte Carlo simulation study and some interesting comparisons have been presented. The findings of the study indicate that the Bayes estimation is superior to classical estimation under the suitable prior.

Redundancy and inconsistency are universal features of the turbine vibration fault diagnosis. If we can provide a solution to the problem, it should be very meaningful that the fault diagnosis data included redundant and inconsistent information could be used to decision-making rules of fault diagnosis. A novel data mining approach for fault diagnosis of turbine generator unit is proposed based on a decision tree in this paper. In terms of history samples library of turbine generator faults, the method applies entropy-based information gain as heuristic information to select test attributes, and uses ID3 algorithm to generate the decision tree and distilling classification rules are handled. The research shows the method not only possesses rapid induction learning ability and classification speed, but also can effectively compress data and save memory, and is an effect turbine generator fault diagnosis method. In the end, a practical application indicates the validities of the method.

After constructing graph representations for a set of web documents, there are several techniques to determine the similarity between same-type objects. This is achieved by graph matching. The measure of similarity may be based on the size of the maximum common subgraph. In this paper, we are interested in the problem of maximum common subgraph(MCS) and median graph computation for the purpose of graph clustering using backtracking search. Median of a graph helps in the extension of prevalent term frequency based clustering algorithms to graph based clustering.

This project related to develop and implementation of autonomous vision based mobile robot following human. Human tracking algorithm will be developed to allow a mobile robot to follow a human. A wireless camera will be used for image capturing, and Matlab will be use to process the image captured, followed by controlling the mobile robot to follow the human. This system will allow the robot to differentiate a human in a picture. The foreground and background will be separated and the foreground is used to determine the object whether it’s human or not. Then classification algorithm is applied to find the centroid of the human. This centroid is then compared with the center of the image to get the location of the human with respect to the camera, either at the left or right of the camera. If the human is not in the center of the camera view, then corrective measures is taken so that the human will be in the center of the camera view. Data for the centroid of human is shown through the Graphical User Interface (GUI).

This research contributes to the on-going research effort by exploring alternate methods for soft computing optimization the highly nonlinear and uncertain systems. This research addresses two basic issues related to the control of an uncertain system; (1) design of a robust feedback controller, and (2) the design of a parallel artificial intelligence based optimization to increase the result qualification. The robust backstepping controller proposed in this research is used to further demonstrate the appealing features exhibited by the continuum robot. Robust feedback controller is used to position control of continuum robot in presence of uncertainties. Using Lyapunov type stability arguments, a robust backstepping controller is designed to achieve this objective. The controller developed in this research is designed in two steps. Firstly, a robust stabilizing torque is designed for the nominal continuum robot dynamics derived using the constrained Lagrangian formulation. Next, the fuzzy logic methodology applied to it to solution uncertainty problem by parallel optimization. The fuzzy model free optimization is formulated to minimize the problem of nonlinear formulation of uncertain systems.

Soft Computing tools are becoming very popular in solving hydrological problems. These tools have immense strength to deal with such complex problems. Water Table elevation estimation is an important aspect to understand the mechanism of ground water resources. The present study aims at the application of Artificial Neural Networks (ANN) & Fuzzy logic for simulation of water table elevation. This paper also investigates the best model to forecast water table elevation. Ten ANN models are developed in this study. These developed models are trained, tested and validated on the available data of Budaun District. Comparing observed data and the estimated data through developed ANN models and Fuzzy models, it has been observed that the developed Fuzzy models predict better results for four models and for model-5 ANN bore better results.

A proxy signature allows a designated person, called a proxy signer, to sign the message on behalf of the original signer. Proxy signatures are very useful tools when one needs to delegate his/her signing capability to other party. A number of proxy signature schemes have been proposed and succeeded for proxy delegations, but the schemes are in defective in proxy revocations. In this paper, we propose proxy signature scheme based on RSA cryptosystem. Our scheme does not consider proxy revocation mechanism, but it is efficient than the existing RSA-based schemes.

Image inpainting is a technique of filling unknown image region with known information from the surrounding of the unknown region in such a way that the result is logically accepted. Texture and structure completion in inpainting method is the main challenge in image inpainting and it is still an open problem to be tackled in the near future. In this paper, we propose a new inpainting algorithm based on morphological erosion and dilation. The erosion operation are used to shrink the unknown area and the dilation operation are used to take the information and texture of the surrounding area. The proposed method shows some promising results in synthetic, texture and natural image.

Variable speed wind turbine with doubly-fed induction generator (DFIG) is gradually replacing fixed speed one with squirrel cage induction generator (SCIG) in wind power applications. It is worthwhile to compare these two generators with respect to operating principle, controlling ability, etc. This paper concentrates on analyzing active/reactive power relationship in steady-state between DFIG and SCIG by simulation on MATLAB. Another key task investigated in this paper is comparison of electromechanical torque-slip characteristics between DFIG and SCIG with several different conditions such as interconnecting network strength and type, generator terminal voltage, and rotor resistance. Comparison results verify that DFIG has distinct features over SCIG.