Scientia Iranica
Volume:29 Issue: 5, Sep-Oct 2022

There are several scientific workflow applications which need vast amount of processing so the cloud offerings give the sense of economic. Workflow scheduling has drastic impact on gaining desired quality of service (QoS). The main objective of workflow scheduling is to minimize the makespan. The workflow scheduling is formulated to a discrete optimization problem which is NP-Hard. This paper presents a novel discrete grey wolf optimizer (D-GWO) for scientific workflow scheduling problems in heterogeneous cloud computing platforms in the aim of minimizing makespan. Although the original GWO had great achievements in continuous optimization problems, it seems clear gap in utilizing GWO for combinatorial discrete optimization problems. It revolves around the fact that the continuous changes in search space during the course of discrete optimization lead inefficient or meaningless solutions. To this end, the proposed algorithm is customized in such a way to optimize discrete workflow scheduling problem by leveraging some new binary operators and Walking Around approaches to balance between exploration and exploitation in discrete search space. Scientific unstructured workflows were investigated in different circumstances to prove effectiveness of proposed novel meta-heuristic algorithm. The simulation results witnessed the superiority of proposed D-GWO against other state-of-the-arts in terms of scheduling metrics.

Medical data mining is considered as a new solution to analyze medical data and discover knowledge . Medical data mining has a high potential for discovering hidden patterns in medical data. In the present era, some studies have been conducted on the relationship between environmental quality and diseases , which have clearly indicated the impact of environmental quality indicators, such as environmental pollutants, on diseases. In this study, environmental conditions in diabetes were investigated based on medical data mining technique. Diabetes is considered as a global threat affecting human health. An ensemble classifier based on genetic algorithm(ECGA) method was designed to study the environmental conditions in diabetes. In the designed ensemble classifier, the decision tree, random forest, k-nearest neighbor, and naive bayes were used. It was found that ECGA was more accurate than the base classifier algorithms. In addition, three datasets were collected from different regions of Iran with different climatic conditions. It was found that environmental conditions can affect diabetes disease.

Separation of desired objects from undesired ones is one of the most important issues in the computational geometry. It is tended to cover the desired objects by one or a couple of geometric shapes in a way that all of the desired objects are included by the covering shapes, while the undesired objects are excluded. We study separation of polylines by minimal triangles with a given fixed angle and present O(N log N)-time algorithm, where N is the number of all the desired and undesired polylines. By a minimal triangle, we mean a triangle in which all of its edges are tangential to the convex hull of the desired polylines. The motivation for studying this separation problem stems from that we need to separate bichromatic objects that are modeled by polylines not points in real life scenarios.

Online handwritten Urdu character recognition is one of the key technologies for intelligent interface on smart phones and touch screens. It is a challenging research topic as Urdu script has many similar character groups. A novel similar character discrimination method for online handwritten Urdu character recognition is proposed in this paper which includes pre-classification, feature extraction and fine classification process. The pre-classifier enables the discrimination of similar characters by putting them in distinct smaller subsets according to stroke number and diacritics. Then structural features and wavelet features are extracted. Finally, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Recurrent Neural Network (RNN) classifiers are compared for fine classification within subsets. Results of RNN classifier without using the proposed pre-classifier and features have also been obtained to check the end-to-end capability of the RNN classifier. Experimental results show that the proposed method is efficient and achieves an overall accuracy of 96% on a large-scale self-collected dataset. It is feasible to extend this method for other Arabic scripts.

The emission of radio waves from Extensive Air Showers (EAS), initiated by ultrahigh-energy cosmic rays, has been attributed to geomagnetic emission and charge excess processes. At frequencies from 10 to 100 MHz this process leads to coherent radiation. Nowadays, the radio detection technique is used in many experiments consisting in studying EAS. One of them is the Auger Engineering Radio Array (AERA), located at the Pierre Auger Observatory. The frequency band observed by the AERA radio stations is 30-80 MHz. This investigatedfrequency range is often highly contaminated by human-made and narrow-band radio frequency interferences (RFI). The suppression of this contamination is crucial to lower the rate of spurious triggers.An adaptive filter based on the Least Mean Squares (LMS) algorithm can be an alternative to the currently used IIR-notch non-adaptive filter. The paper presents 32/64-stage filters based on a non-canonical FIR filter implemented into cost-effective CycloneIV and CycloneV Altera FPGAs with a sufficient safety margin of the registered performance for a global clockabove 200 MHz to satisfy the Nyquist criterion.

Demand response (DR) is proven to be very efficacious for load mitigation especially in peak time period. On the other hand, DR facilitates both consumers, system operator as well as producers to moderate their payments while reducing system operating costs. Offer cost minimization is currently used as the clearing mechanism associated with locational marginal pricing scheme to determine the consumers’ payments. Such clearing and pricing mechanisms are inconsistent as the system costs is being minimized, while the payments are calculated based upon marginal prices. Payment cost minimization auction as a price-based clearing mechanism is envisaged to be an effective alternative to solve such a crucial issue. This paper shows how to include DR in PCM mechanism to further reduce the consumers’ payment. It facilitates utilizing price responsive consumers for load shifting DR in a PCM auction. The optimization problem is modeled as a mixed-integer nonlinear bi-level programming. Duality theorem, KKT conditions and integer algebra are used to convert such a complicated optimization problem to a single level MILP problem. This problem is then solved by CPLEX in GAMS. The impacts are studied by implementing the proposed formulation to solve the clearing problem in the case studies deriving promising numerical results.

Distribution Static Synchronous Compensator (D-STATCOM) is a shunt compensator in the distribution systems that one of the most important tasks is to improve the imbalance, reduce and eliminate the nonlinear loads harmonics. Distributed generation resources such as photovoltaic (PV) array can be used as the DC input of D-STATCOM. In this paper, PV array and DC/DC boost convertor are used to stabilize the DC link voltage of D-STATCOM. The main advantage of the proposed method is that for all time provides continuous compensation. Another power quality issues are neutral current in four-wire systems that are created due to the harmonics and system imbalance. Zig-Zag transformer is one of the ways for compensating neutral current and providing isolation between the convertor and flow of the fundamental zero sequence component which contains harmonic neutral current. Role and performance of the distribution shunt compensator in the improvement of power quality indices depends on the performance of its control system. In this paper, the control scheme of synchronous reference frame theory based on fuzzy controller is used for D-STATCOM based on PV and Zig-Zag transformer. Performance and behaviour of the proposed system examined using Matlab/Simulink software and the results will be presented.

Induction motors have been traditionally used in industrial applications ranging from a fraction of horse-power up to several Megawatts due to their substantial benefits. Induction drives with more than three-phases are superior to the 3-phase induction drives in terms of overall-volume, torque fluctuations, current passing each stator-winding, ohmic-loss, efficiency and reliability in the case of stator-windings open-circuit fault. These benefits are especially more attractive in variable speed drivers due to the reduced capacity of power-electronics switches. This paper aims to develop an optimal electromagnetic-thermal design procedure of a high-power seven-phase induction motor suitable for variable-speed applications. In this multi-objective design approach, the objective function is defined aiming to increase the efficiency, power-factor, power-to-weight ratio, starting-torque as well as decrease the starting-current. Furthermore, the electrical, mechanical, dimensional, magnetic and thermal limitations are included in this optimization study in order to ensure practical realization of the designed machine. The coupled-circuit method is employed for nonlinear electromagnetic modeling while the current displacement phenomenon is considered in rotor parameters calculations. A lumped-parameter-thermal model is established for calculating heat rises of different parts in each iteration of optimization study. Finally, the performance characteristics of the optimally designed 1-MW 4-pole motor are verified using 2D-FE analyses.

Under unbalanced grid condition, in a doubly-fed induction generator (DFIG), voltage, current, and flux of the stator become asymmetric. Therefore, active-reactive power and torque will be oscillating. In DFIG controlling rotor side converter (RSC) aims to eliminate power and torque oscillations. However, simultaneous elimination of the power and torque oscillations is not possible. Also, grid side converter (GSC) aims to regulate DC-Link voltage. In this paper, in order to regulate DC-Link voltage, an extended state observer (ESO) based on a generalized proportional-integral (GPI) controller, is employed. In this controlling method, DC-Link voltage is controlled without measuring the GSC current, and due to using the GPI controller, the improved dynamic response is resistant against voltage changes, and the settling time is reduced. A versatile rotor position computation algorithm (RPCA) is utilized to measure the rotor speed. This algorithm is simple, yet it is accurate and is resistant to changes in the resistance of the rotor and stator. The simulations are implemented by MATLAB software in the synchronous positive and negative sequence reference (d-q).

One of the major issues related to traffic monitoring systems is analyzing the behavior of vehicles and identifying their characteristics. In this paper, an automated algorithm is proposed for calibration of the road cameras. This calibration is used to estimate the speed and dimensions of the passing vehicles. In this method, a motion plane is obtained in the initial frames according to the direction of moving vehicles. After modeling the background, removing shadows, and identifying the exact boundaries of passing vehicles, a number of common vehicles are recognized by the Bag of Words method. Given the actual metric dimensions of these vehicles and the equivalent dimensions on the motion plane, the metric coefficients are calculated and the calibration process is completed. Each passing vehicle is projected on the motion plane, and speed and dimensions are calculated by tracking it along the road. To test the accuracy of the proposed method, we constructed a ground-truth video dataset, by simultaneous capturing the road vehicles using RGB and speed cameras. Furthermore, to identify common vehicles, a dataset of vehicle images was collected. The proposed method evaluated on our dataset and the mean error of 1.15 km/h is achieved. Accordingly, it outperforms previous methods.

This paper proposes a new adaptive sliding mode (ASM) decentralized excitation controller to improve the stability of multi-machine power systems under different perturbations such as system’ parametric and structural uncertainties. The stability of the closed-loop system is proved by Lyapunov stability theory. The proposed controller is evaluated through simulation on the standard IEEE 33-bus-bar power system which contains 6 synchronous machines and a HVDC-link. The simulation results indicate good robustness and satisfactory performance of the proposed controller. Moreover, in this paper, using the space-phasor based sequence networks method, a procedure for the dynamic analysis of modern power systems under the transient asymmetrical faults is presented. The method considers the complete dynamics of the synchronous machines and the HVDC-link and provides the possibility of taking into account the sequence networks dynamics.

In recent years, synchronous reluctance motor (SynRM) has attracted the attention of researchers and well-known companies have been involved in designing and manufacturing electric motors due to its simplicity (no need for a rotor cage and permanent magnet), the similarity of its production line analogous to that of the induction motor, low production cost and also, compared to the induction motor, better performance, if optimally designed. Saliency ratio, defined as the ratio of the d axis inductance to the q axis inductance, is the most important parameter in designing a synchronous reluctance motor which has a major impact on achieving both maximum power factor and maximum torque. The current study aimed to provide a comprehensive approach to design a series of synchronous reluctance motors using both combined methods and finite element analysis to achieve an algorithm which is based on the similarity between flux lines and the shape of flux barriers to achieve both maximum torque and minimum torque ripple. The designed motor is intended to be used in an electric scooter application. Therefore, the geometrical dimensions of the motor, such as the motor stack outer diameter and length, are held constant in all cases.

Microgrids as the local area power systems are changing the power system landscape due to their potential of offering a viable solution for integrating renewable energy resources into the main grid. From the operational point of view, microgrids should have the appropriate power electronic interfaces, control schemes, as well as monitoring and automation infrastructures to provide the required flexibility and meet the related IEEE 1547 standard requirements. This paper describes some of the efforts made in the smart microgrid educational laboratory to provide these facilities and create a real-world conditions needed to conduct researches and teach the related courses. Laboratory works not only increase the practical skills of the students but also can motivate them to pursue theoretical courses with more passion. The introduced facilities are somehow unique for the integration of both electric and communication infrastructures which overcome the shortcomings of not considering data transfer challenges in the studies. Complete hardware design of power plant components, and incorporation of solar photovoltaic (PV) and two types of wind turbine generations are some of the efforts made to bring the real-world conditions in the laboratory. In the load-related side, dynamic behaviors of the various types of motors are modeled.