Majlesi Journal of Electrical Engineering
Volume:5 Issue: 4, Dec 2011

Intrusion Detection System (IDS) deals with very large amount of data that includes redundant and irrelevant features. Therefore feature selection is a necessary data pre-processing step to design IDSs that are lightweight. In this paper, a novel feature selection method based on data mining techniques is proposed which uses fuzzy association rules to obtain the optimum feature subset. In this research, the fuzzy ARTMAP neural network is used as the classifier to evaluate the goodness of the obtained feature subset. The effectiveness of proposed method is evaluated by experiments on KDD Cup99 dataset. According to the performance comparisons with some other machine learning methods that have used the same dataset, the proposed method is the most efficient on detection rate, false alarm rate and cost per example.

An important technique in image and video processing is global motion estimation (GME). The common GME methods can be classified in direct and indirect categories. Whereas the direct global motion estimation techniques boast reasonable precision they tend to suffer from high complexity. As with indirect methods, though presenting lower complexity they mostly exhibit lower accuracy than their direct counterparts. In this paper, the authors introduce a robust algorithm for GME with near identical accuracy and almost 50-times faster than MPEG-4 verification model (VM). This approach entails two stages in which, first, motion vector of sampled block is employed to obtain initial GME then Levenberg-Marquardt algorithm is applied to the subsampled pixels to optimize the initial GME values. As will be shown, the proposed solution exhibits remarkable accuracy and speed features with experimental results distinctively bearing them out.

In this article, a combined method is used to improving transient stability. In power systems, the maximum use of existing capacities along with the increased powers transferred through the transition lines make transient stability studies even more important. When the fault occurs, the kinetic energy of system is increased, and if the system kinetic energy exceeds a certain amount, system instability will occur. Generator tripping is one of the most effective methods for improving stability in case of serious faults. In this method, we trip a number of units of a certain power plant unit for stabilizing the system. In fact, by removing the generator, we decrease the kinetic energy of the system so that stability can be achieved. In generator tripping, for the above-mentioned, we should reach stability by tripping the least generator possible. Due to its thermal limitations, fixed place of resistor bank and possibility of back swing, the braking resistor is less efficient than generator tripping. In combined method, system stability against severe turbulence is reached through minimization tripping of generator units. In this method, we first decrease intensity of fault by applying braking resistor, and then, for the purpose of improving transient stability, we try to reduce kinetic energy by removing the least possible amount of producing the desired units at the right time. Simulations on 9-bus or 3-generator system were conducted, and satisfactory results were obtained.

In this paper the transient stability of an electric power system is improved by fuzzy logic controlled superconducting magnetic energy storage (SMES). The effectiveness of the proposed fuzzy controlled SMES is compared with a conventional proportional integral (PI) controlled SMES. In addition to it a comparison between the fuzzy controlled SMES and fuzzy controlled braking resistor (BR) is also carried out. The simulation results show that under 3 phase fault the fuzzy controlled SMES performance is better than PI controlled SMES. Furthermore, the performance of SMES is better than that of BR. The proposed method provides a very simple and effective means of improvement of transient stability.

Surface electromyography (SEMG) from respiratory muscles is a non-invasive and effective method of studying neuromuscular diseases, muscle fatigue, enhancement of muscular function and also human-computer interface. This signal is contaminated by different noises. These include environmental noises like power line noise and also internal noises such as electrocardiographic noise. The clean EMG signal can be extremely useful for pathological purposes. In this study, diaphragmatic EMG signals were recorded with Power Lab system from seven subjects. The signals showed contamination due to power line interference (PLI) and also cardiac activity. Adaptive filters were used to reduce cardiac noise as well as 50 Hz (the fundamental) power line noise and its harmonics. Recursive least squares algorithm was used for the structure of the adaptive filter. Different values of the filter parameters; filter order and forgetting factor were examined for the noise removal purpose. Performance of the adaptive filter was quantified by signal-to-noise ratio and coherence measures for simulated data. The results show that we can successfully eliminate PLI and ECG noise from SEMG signals with adaptive filters. The figures and tables obtained help to decide which parameters of the filter are the best for our study.

Electricity market plays an important role in improving the economics of electrical power system. Transmission network is vital entity in an open access restructured electricity market. Whenever transmission network congestion occurs in an electricity market, it divides the market in different zones and the trading price of electricity will no longer remains the same for the whole system. Bidding strategies in an electricity market, where by changing the bid, market player changes the revenue of every participant of the market. In this paper, the bidding strategy problem with congestion management is modeled as an optimization problem and solved using Particle Swarm Optimization (PSO). Search procedure of PSO is based on the concept of combined effect of cognitive and social learning of the members in a group. The effectiveness of the proposed method is tested with a numerical example and the results are compared with Genetic Algorithm (GA) approach. The results shows that PSO takes less computational time and maximizing the social welfare compared to GA approach.

Autonomous vehicle present a several problems in the modern commercialized electric vehicle (EV). One of the weakest points of electric vehicles is the battery system, vehicle autonomous depend on the battery state of charge (SOC). In this paper novel strategy of EV power electronics studies based on direct torque space vector modulation technique in the several speed variations. The basic idea of this work that the state SOC equal 70% and the developed model take into consideration the present state under several speed variations. The performances of the proposed strategy controller give a good torque control instead of the speed stability improvement. Moreover, the future industrial''s vehicle must take into considerations the obtained results into design steps.

With the growing demands of home networking, the existing networking technologies couldn’t satisfy user expectations any more. Today networking technologies is interested that don’t need new cable and have high bandwidth. HomePNA (Home Phoneline Networking Alliance) is a solution.HomePNA3.0 and Ethernet was similar in MAC sublayer; hence, HomePNA3.0/Ethernet reconfigurable implementation was appeared cost-effective. But the difference between HomePNA3.1 and Ethernet increased. In this paper, we propose new reconfigurable hybrid architecture. We implement it in behavioural level using VHDL language. Our implementation was able to have HomePNA3.1 and Ethernet MACs together. Our synthesis show that our implementation decrease number of logical element by 32%.