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An intrusion detection system (IDS) is a vital security component of modern computer networks. With the increasing amount of sensitive services that use computer network-based infrastructures, IDSs need to be more intelligent and autonomous. Aside from autonomy, another important feature for an IDS is its ability to detect zero-day attacks. To address these issues, in this paper, we propose an IDS which reduces the amount of manual interaction and needed expert knowledge and is able to yield acceptable performance under zero-day attacks. Our approach is to use three learning techniques in parallel: gated recurrent unit (GRU), convolutional neural network as deep techniques and random forest as an ensemble technique. These systems are trained in parallel and the results are combined under two logics: majority vote and "OR" logic. We use the NSL-KDD dataset to verify the proficiency of our proposed system. Simulation results show that the system has the potential to operate with a very low technician interaction under the zero-day attacks. We achieved 87:28% accuracy on the NSL-KDD's "KDDTest+" dataset and 76:61% accuracy on the challenging "KDDTest-21" with lower training time and lower needed computational resources., Comment: 5 pages

As a new emerging approach, Virtual Synchronous Generator (VSG) control for interfacing inverters in renewable energy-based microgrids has attracted significant research interest. However, VSG control cannot ensure proportional reactive power sharing among distributed generation (DG) units when unequal transmission line impedances are involved, although active power sharing remains proportional among DGs. In this paper, an adaptive virtual impedance approach is proposed, which can be incorporated in a VSG control to alleviate impedance difference at the inverter output and improve reactive power sharing among DGs. It is applicable to any VSG control, either conventional or modified types. The proposed approach is implemented on a modified VSG control scheme, known as Fuzzy secondary controller (FSC)-based VSG control, in this paper. Its performance is demonstrated through several case and sensitivity studies using MATLAB/Simulink simulation; and is further validated by comparing with an existing method.

the smart controllers, including The fuzzy logic controller, is a supreme achievement, particularly in the experiential perimeter. Compared to standard controllers, there is a remarkable need to test their output in real time. This test can facilitate the management of the intelligent process and provide the actual system with more complicated application. Fuzzy logic and other intelligent methods of processes control work successfully, especially regarding the experimental environment. This makes it necessary to check up on their real-time performance versus conventional methods. These will contribute to the control of the smart process and present more intricate uses in the real world. In this paper, fuzzy logic is used for the self-tuning PID controllers to control load-frequency of multi-source power systems, Compared to the standard proportional – integral – derivative controller one. The proposed Controller is set against the conventional type of PID controller. It is simulated in MATLAB/SIMULINK environment.

With the increasing use of inverters in the industry, the need to reduce the output harmonics is felt more than ever. Interstellar multi-level structures have grown exponentially due to benefits such as harmonic reduction and loss reduction. In multilevel inverters, nonlinear and complex equations become more complex when the number of surfaces increases. One of the ways to find optimal keying angles is to reduce the harmonics of the whole Newton-Raphson method. One of the main disadvantages of the Newton-Raphson method is the strong dependence of the answers to the initial guesses. By choosing different initial guesses, different answers may be obtained or results will not converge. And their solution is not possible by conventional methods of numerical solution of nonlinear equations such as the Newton-Ruffson method. In this thesis, one of a variety of Intelligent Algorithms is used to solve the Newton-Raphson method problem. The particle swarm algorithm has been selected based on the proper background in solving problems of high complexity to solve the problem of optimal keying angles in this dissertation. In this thesis, the particle swarm algorithm has been improved. Simulation results for various multilevel converters have been presented and represent the high efficiency of the improved type of particle swarm algorithm in determining the appropriate angle of fire for reducing the high harmonics and generating a waveform with very low harmonic distortion and near sinusoidal.

A visual sensor network (VSN) is a part of wireless sensor networks (WSNs) that consists of different kinds of sensors to ensure the coverage of a certain area. We study the “Maximum Coverage with Minimum Sensors” (MCMS) problem for VSNs consisting of randomly deployed targets and directional sensors. The existing solutions cause high computational complexity and unnecessary coverage redundancy. To overcome such problems, we devise a modified version of an existing centralized greedy algorithm that comes with a new target oriented approach minimizing the number of active sensors and reducing the run time. The approach is based on a novel idea of conflict edge between two or more sensors. As far as we know, no previous work has used conflict edge concept to solve the MCMS problem. Our strategy ensures that no target in the network will remain uncovered. Finally, our simulation and few comparison analyses show that the method provides the most robust results.

With increasing renewable distributed generation (DG) units connected to utility power grids, deterioration of power quality at the point of common coupling (PCC) becomes a major concern. Power electronic devices in DG units generate harmonics, these harmonics along with harmonics from other nonlinear load in the system might cause excessive harmonic distortion at the PCC. In this paper, an extensive literature review is conducted focusing on harmonic mitigation techniques through advanced control methods for grid-interfacing inverter. Harmonics generated by solar photovoltaic (PV) system are firstly reviewed, potential resonance due to LCL filter is discussed. Then various control methods for harmonics mitigation are reviewed and summarized. This systematic review can facilitate better understanding of harmonics and develop more advanced control schemes to mitigate harmonics in renewable energy system.

In a microgrid with distributed generation (DG) units, proper control of the grid interfacing inverter is critical to facilitate safe and stable operation of the system. The droop control mechanism is a widely accepted control method in a microgrid operation. However, a droop controller is unable to provide inertia support in such systems, and thus, cannot contribute in primary frequency control during disturbances. The virtual synchronous generator (VSG) method was introduced in 2007 for the grid interfacing inverter control, this approach can provide virtual inertia support along with emulation of steady-state droop characteristics of synchronous generators. In this paper, a fuzzy secondary controller (FSC) based VSG control scheme is proposed for voltage and frequency regulation in microgrids. The proposed control scheme shows significantly improved dynamic performance when compared with conventional droop and conventional VSG control through several case studies. Its effectiveness and robustness is further evaluated through sensitivity studies.

The increasing volume of information about goods and services has been growing confusion for online buyers in cyberspace and this problem still continues. One of the most important ways to deal with the information overload is using a system called recommender system. The task of a recommender system is to offer the most appropriate and the nearest product to the user's demands and needs. In this system, one of the main problems is the cold start challenge. This problem occurs when a new user logs on and because there is no sufficient information available in the system from the user, the system won't be able to provide appropriate recommendation and the system error will rise. In this paper, we propose to use a new measurement called opinion leaders to alleviate this problem. Opinion leader is a person that his opinion has an impact on the target user. As a result, in the case of a new user logging in and the user — item's matrix sparseness, we can use the opinion of opinion leaders to offer the appropriate recommendation for new users and thereby increase the accuracy of the recommender system. The results of several conducted tests showed that opinion leaders combined with recommender systems will effectively reduce the recommendation errors.

Many rural communities are not able to access the main power grid supply due to high cost associated with grid extension. The stand-alone renewable energy based hybrid power system is a significant adaptation to cope with increasing power demand along with environmental consideration in rural communities. In such a system, determining the correct size and capacity of renewable generating units to supply continuous and reliable electricity is of great importance. For this purpose, the reliability of a standalone renewable energy based hybrid generation system is evaluated in this paper to meet the load demand of a residential building located in St. John's, Newfoundland and Labrador (NL), Canada. A probabilistic reliability evaluation approach, Monte Carlo simulation technique is adopted to compute the reliability index, Loss of Load Probability (LOLP) utilizing the generation models, renewable resources data and load demand data for a whole year. The main advantage of this technique over the deterministic approaches is its ability to provide quantitative reliability assessment taking the actual system behavior into consideration. The system reliability is evaluated based on LOLP values, which are computed considering different combinations of renewable energy mixtures in the generation system. In this paper, three dominant renewable energy resources, solar, wind and hydro, are taken into consideration, and the most reliable energy mixture is determined. Furthermore, system reliability is assessed considering variation in total generation capacity. This type of analyses will be useful for the system designers to determine total capacity and optimum sizing of the system before installation.

The virtual synchronous machine (VSM) method is a power electronics based approach which was first introduced by Beck and Hesse in 2007 for renewable energy integration modeling. This method controls grid-connected renewable energy sources as electromechanical synchronous machines, which makes renewable energy sources behave as conventional power generation stations if viewed from the power grid. During the last decade, some developments have been made to operate power electronic converters as VSMs, which offers a promising way to design advanced control systems for renewable energy integration. Most publications on this topic actually occurred within the past two years. As an emerging new method with huge potential, the VSM method has been used to reduce voltage and frequency fluctuation of renewable energy sources, and improve power system stability. In this paper, a comprehensive literature review is conducted for the VSM method. The principle, and current state of the art control techniques to implement this method are reviewed and summarized in the paper.

Electronic business (E-business) and increasing advances and growth of its risk have resulted in the fact that marketers pay so much attention to the trust, satisfaction and loyalty of customers in order to enhance the income and reduce the competition risk. Many studies have been conducted on the effective factors on trust. But yet no definite method has been suggested to determine the effective factors to provide questionnaire or start the research. In this article, by using the researches of scientists about E-Trust in an online environment, OMMTrust hypothesis to identify indicators/indexes and optimal criteria which are influential on E-Trust, are proposed to initiate the research and one sample of it will be implemented by SPSS software and finally, suggestions will be presented for future researches.

The Internet influence is increasing in daily life as a developing technology. On the other hand, as the capacity of images, sounds and digital videos develops, the undesirable destructive and illegal copyright techniques increase. Watermarking embeds copyright information in a given media, so that it is not recognizable for human visual system. In this paper, a digital image watermarking algorithm in YCbCr color space and DCT domain is proposed that uses coefficients exchange for embedding watermark bits and Genetic algorithm to select the target Y component coefficients of the host image. One of the differences between this method and other methods of coefficients exchange is using Arnold transform to scramble the Y component of the host image in order to spread the distortions of watermarking and increase robustness against cutting attacks. Another difference is using Genetic algorithm for selecting two suitable coefficients to embed watermark bits. The third difference is in defining confidence interval variable and increasing the two coefficients distance equal to the size of this variable, hence watermarking transparency and robustness would be balanced. The evaluation results suggest that the coefficients exchange approach is improved.

The impact of High voltage direct current (HVDC) transmission on power system dynamic stability is mainly discussed in this paper and the system dynamic response is analyzed under small disturbance. The establishment of linearized model of a single machine infinite bus (SMIB) system equipped with Voltage source converter (VSC) based HVDC is also presented in this paper. Generally, power system dynamic stability is maintained by PSS which may adversely affect the voltage profile. HVDC based damping controller can be an effective solution to this problem which is designed using phase compensation technique to enhance damping of low frequency oscillations. The performance and effectiveness of the proposed controller is demonstrated through eigen value analysis and non linear time domain simulations. Moreover the performance of the controller with different control input signals is evaluated and most effective control input is determined. Simulation results carried by MATLAB show the effectiveness of proposed control mechanism to strengthen the system dynamic stability.

The increasing complexities in softwares, have reduced the efficiency of the common methods of software testing and urged the necessity to use new and optimal methods to produce test case (TC) to cover high percentage of target plan to find existing errors. Thus, today, the production of TC is considered to be an important aim in software testing methods. Particle Swarm Optimization (PSO) is an intelligent technique based on the collective movement of the particles inspired by social behavior of the flocks of birds and schools of fish. After full introduction of this algorithm and the reasons behind usage PSO, the present study will propose a method for automatic production of the TC, such that the highest code covering is done by the minimum TC. For better analysis of the results, a plan was investigated as a case study with the proposed method. As the evolutionary structures such as genetic algorithm (GA) with high percent of code covering are being used for a long time, thus to prove the optimization of the proposed method, the results are compared with the GA.

In this paper, a nonlinear autoregressive (NAR) recurrent neural network is used for the prediction of the next 18 data samples of each time series in a set of 11 unknown dynamics in NN3 Database. The models are built on the reconstructed state spaces of data and no other domain knowledge is available to be used. Here, we clarify that the employed method is in part similar to a superior subclass of recurrent neural network, namely the nonlinear autoregressive model with exogenous inputs (NARX). Using the extensive available research about NARX networks, we briefly explain that our model is preferred to the both non-recursive and even other recurrent predictors, because of its intrinsic ability for learning long term dependencies in time series. As the desired values of the predicted time series are not available yet, no analysis have been performed on the presented results.

Multi-Chip Power Modules (MCPMs) allow for integration of high power semiconductor devices and control circuitry into one compact package which yields improved reliability and reduced size, cost, and complexity. The layout design process of an MCPM is time consuming and very multidisciplinary, spanning thermal, electrical, and mechanical issues. A software tool is introduced in this paper which allows for a user to draw a `stick figure' of a desired MCPM layout which is transformed into a multi-objective optimization problem by the tool. After optimization, a user can browse a set of results which form a trade-off curve of approximately Pareto optimal thermal and electrical parasitic layout performance.

Optimal design of the decoupled sliding mode control (SMC) is a great and worthwhile challenge by regarding appropriate design parameters and objective function. In the present study, Genetic Algorithm (GA) is employed as an effectual smart evolutionary algorithm to design optimally the control coefficients of an Inverted Pendulum (IP) and to eliminate tedious and repetitive trial-and-error process. When decoupled SMC is used to address the problem, there has to be a trade-off between the error of the position and the error of the angle. Hence, a Multi-objective Genetic algorithm is used to design optimally the Pareto front of the problem by regarding the errors as objective functions. Moreover, simulation has been done by considering the case with external disturbance and without external disturbance. Then, the results implemented in the MATLAB software environment are contrasted with the experimental results. Numerical results and comparison analysis elucidates the superiority of the optimal robust proposed controller over the traditional decoupled sliding mode controller in terms of attenuating the chattering and actuator variations.

This paper focuses on the designed implementation and experimental analysis of hybrid PV-diesel power system to meet the residential loads in Dhaka city. The optimal design of the system is 11kW peak in residential building. National Renewable Energy Laboratories (NREL) HOMER software was used to perform the techno-economic evaluation of Hybrid PV-Diesel-Battery power system. We used a 1kW PV module to get 4% penetration, two 4kW generators and battery. Here sensitivity evaluation is also done to see the impact of solar insolation, PV investment cost, diesel fuel price on the optimum result. This system gives lowest COE US$ 0.355/kWh and total net present cost (NPC) of US$ 164,106 with a renewable fraction of 25%. This proposed hybrid power system can reduce CO2 emission by about 922 tons per year compared to diesel generator only.

In this paper, a novel method of generating cancelable cryptographic key using local features from fingerprint minutiae points has been proposed. Local features from input fingerprint image are determined and minutiae points are extracted using Gabor filter. Then a template matrix is generated from extracted minutiae points. Two directed acyclic graphs (DAG) are generated from the template matrix taking minutiae coordinates as edges. Binary values are assigned to each edge based on local features of the minutiae points-orientation, frequency and phase. Then for each DAG, the proposed algorithm selects each node as source and determines the longest reachable path, traverses the path and generates a binary string. Thus, two binary strings are generated from the two DAGs. Finally the proposed algorithm performs a simple rotation and combination operation on the two strings and generates four cryptographic keys. We have performed experiments on existing benchmark fingerprint databases and based on security analysis of our method we conclude that our proposed method is a simple and efficient method that generates four cancelable cryptographic keys and the proposed process has very strong irreversible property.

In this paper we have proposed a novel method to detect the defects in woven fabric based on the abrupt changes in the intensity of fabric image due to the defects and have constructed a classification model to properly identify the defects. We have also improved an existing method based on histogram processing for the classifier. In classification model we have implemented Artificial Neural Network (ANN). Both of our newly proposed method and improved technique have outperformed the existing methods. We have implemented K-validation to estimate the performance of our classification model. Additionally we have analyzed the performance of our classification model for different experimental parameters. Finally we have presented a comparative analysis of these techniques.

In this paper, in order to Pareto design of cyclone separators, pressure drop and collection efficiency Δp and η in a set of cyclones are numerically investigated using Computational Fluid Dynamic (CFD) techniques. Then, the neural network is used to obtain polynomial models for the effects of geometrical parameters of the cyclones on both the pressure drop and the collection efficiency. The obtained simple polynomial models are used to find the best possible combinations of the pressure drop and collection efficiency, known as the Pareto front based on a novel multi-objective algorithm.

This paper presents an evaluation of the ability of a non-ionic surfactant solution (Triton X100) for removal of Flourene, Phenanthrene from soil artificially contaminated with crude oil supplied from Tehran refinery. PAHs fractions were gained in accord to MOOPAM standard and analyzed by GC-FID. The effects of mixing time, surfactant solution concentration and volume were evaluated at 4 levels. Results show that in volume 100 cc the least removal efficiencies were achieved for both components (19.2 and 17.7 for Flourene and Phenanthrene, respectively). And the highest efficiencies (60.5 and 52.7 for Flourene and Phenanthrene, respectively) were gained at 4 g/l surfactant solution concentration. The experimental results were obtained for the optimum condition when the surfactant solution volume and concentration were 400 ml and 2 g/l, respectively with one hour mixing time.

In this paper we propose an all-optical photonic crystal controllable logic gate based on nonlinear ring resonator. Simulation and analysis have been done by finite difference time domain and plane wave expansion method.

In this paper we propose a novel ultra compact photonic crystal resonator and investigate add/drop filter based on it. Simulation and analysis have been done by finite difference time domain and plane wave expansion method.