Scientia Iranica
Volume:27 Issue: 3, May-Jun 2020

This paper address automatic keyword extraction in Persian and English text documents. Generally, for keyword extraction in a text, a weight is assigned to each token and words having higher weights are selected as the keywords. We have proposed four methods for weighting the words and have compared these methods with five previous weighting techniques. The previous methods used in this paper are term frequency (TF), term frequency inverse document frequency (TF-IDF), variance, discriminative feature selection (DFS), and document length normalization based on unit words (LNU). The proposed weighting methods are based on using variance features and include variance to TF-IDF ratio, variance to TF ratio, the intersection of TF and variance, and the intersection of variance and IDF. For evaluation, the documents are clustered using the extracted keywords as feature vectors, and K-means, expectation maximization (EM), and Ward hierarchical clustering methods. The entropy of the clusters and pre-defined classes of the documents are used as the evaluation metric. For the evaluations, we have collected and labelled Persian documents. Results show that our proposed weighting method, variance to TF ratio, has the best performance for Persian. Also, the best entropy is resulted by variance to TD-IDF ratio for English.

Discovering association rules is a useful and common technique for data mining in which relations and co-dependencies between datasets are shown. One of the most important challenges of data mining is to discover the rules of continuous numerical datasets. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the support and confidence criteria. In this paper a multi-objective algorithm for mining quantitative association rules is proposed. The procedure is based on the Genetic Algorithm, and there is no need there is no need to determine the extent of the threshold for the support and confidence criteria. By proposing a multi-criteria method, the useful and attractive rules and the most suitable numerical intervals are discovered, without the need to discrete numerical values and the determination of the minimum support threshold and minimum confidence threshold. Different criteria are used to determine appropriate rules. In this algorithm, the selected rules are extracted based on confidence, interestingness, and cosine2. The results obtained from real-world datasets demonstrate the effectiveness of the proposed approach. The algorithm is used to examine three datasets and the results show the performance superiority of the proposed algorithm compared to similar algorithms.

Using machine learning techniques for constructing automated test oracles have been successful in recent years. However, existing machine learning based oracles have deficiencies when applied to software systems with low observability, such as embedded software, cyber-physical systems, multimedia software programs, and computer games. This paper proposes a new black box approach to construct automated oracles which can be applied to software systems with low observability. The proposed approach employs an Artificial Neural Network (ANN) algorithm which uses input values as well as corresponding pass/fail outcomes of the program under test, as the training set. To evaluate the performance of the proposed approach, we have conducted extensive experiments on several benchmarks. The results manifest the applicability of the proposed approach to software systems with low observability as well as its higher accuracy in comparison to a well-known machine learning based method. We have also assessed the effect of different parameters on the accuracy of the proposed approach.

Sensitivity analysis (SA) is a vital task for decision making in economic management. In this paper, a novel fuzzy sensitivity analyzer (FSA) is proposed to analyze the sensitivity of economic variables. The proposed FSA algorithm consists of an adaptive neuro-fuzzy inference system (ANFIS) that is adjusted for forecasting economic time series. Based on the output of ANFIS, FSA can determine the importance degree of parameters. In the numerical studies, the proposed method is applied for the sensitivity analysis of oil and gold time series. According to the results, FSA indicates that oil price is highly dependent upon the inflation rate, dollar index and market index while OPEC production level and gold price have less impact. Furthermore, in the gold price modeling, the highest sensitivity is obtained from silver price while demand for gold is more a function of market index and inflation rate. The proposed method can be used in many SA applications.

In recent years, integrated use of demand- and supply-side resources has been performed by electric utilities, because of its potential attractiveness, both at operation and economic levels. Demand Response Resources (DRRs) can be used as demand side options which are the consequence of implementing Demand Response Programs (DRPs). DRPs comprise the actions taken by end-use customers to reduce their electricity consumption in response to electricity market’s high prices; and/or reliability problems on the electricity network. In this paper, a dynamic economic model of DRPs is derived based upon the concept of flexible elasticity of demand and the customer benefit function. Precise modeling of these virtual negawatt resources helps system operators to investigate the impact of responsive loads on power system studies. This paper also aims to prioritize multifarious DRPs by means of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and entropy methods. Performance of the proposed model is investigated through numerical studies using a standard IEEE test system.

The risk imposed by the stochastic nature of wind energy sources has always been a major barrier despite their proliferation in power systems. To further penetrate these sources, this paper draws upon dynamic prices, which realize demand response potentials along with decimating the risk involved. To do so, a model is first established to study the impacts of activating demand response, on the risk index in a system with a high penetration of wind resources. Then, the model is used to estimate the extra wind capacity that can be hosted by the system such that the risk remains within the acceptable range. The well-being indices are calculated via sequential Monte Carlo simulation approach and Fuzzy theory. The demand response with dynamic prices is modeled by self and cross elasticity coefficients of different load sectors. The performance and applicability of the proposed model are verified through simulations on the IEEE Reliability Test System. (IEEE-RTS).

In this paper, solution of a system of linear differential equations of distributed order in the Riemann-Liouville sense is analytically obtained. The distributed order relaxation equation is a special case of the system investigated in this paper. The solution of the mentioned system is introduced on the basis of a function which can be considered as the distributed order generalization of the matrix Mittag-Leffler functions. It is shown that this generalized function in two special cases of the weight function can be expressed in terms of the multivariate Mittag-Leffler functions and the Wright functions.

In this paper, the wavelet transform domain least mean squares (WTDLMS) adaptive algorithm with variablestep-size (VSS) is established. The step-size changes according to the largest decrease in mean square deviation. To keep the computational complexity low, the Haar wavelet transform (HWT) is utilized as a transform. In addition, the mean square performance analysis of the VSS-WTDLMS is studied in the stationary and nonstationary environments and the theoretical relations for transient and steady-state performances are established. The simulation results show that the proposed VSS-WTDLMS has faster convergence rate and lower misadjustment than conventional WTDLMS. The theoretical relations are also verified by presenting various experimental results.

Utilization of electric vehicles’ battery to provide frequency regulation service in electricity markets is a technically feasible and economically attractive idea. The role of aggregators as a middleman between electric vehicle owners and the frequency regulation market has been discussed in literature. However, the economic interaction between the aggregator and the vehicle owners on division of interests is still a missing point. In this paper, a new pricing model for aggregators of electric vehicles is proposed in a way, that not only maximizes its profit, but also the vehicle owners have sufficient incentives to take part in the offered Vehicle-to-Grid program. In our proposed model, the aggregator takes into account the depreciation cost of electric vehicle batteries and the cost of net energy transaction between the electric vehicles and the grid, and considers these items in settling accounts with vehicle owners. The proposed model has been implemented on PJM frequency regulation market and the results are discussed in the paper.

Human action recognition has been one of the most active fields of research in computer vision over the last years. Two dimensional action recognition methods are facing serious challenges such as occlusion and missing the third dimension of data. Development of depth sensors has made it feasible to track positions of human body joints over time. This paper proposes a novel method for action recognition which usestemporal 3D skeletal Kinect data. This method introduces the definition of body states and then every action is modeled as a sequence of these states. The learning stage uses Fisher Linear Discriminant Analysis (LDA) to construct discriminant feature space for discriminating the body states. Moreover, this paper suggests the use of the Mahalonobis distance as an appropriate distance metric for the classification of the states of involuntary actions. Hidden Markov Model (HMM) is then used to model the temporal transition between the body states in each action. According to the results, this method significantly outperforms other popular methods, with recognition (recall) rate of 88.64% for eight different actions and up to 96.18% for classifying the class of all fall actions versus normal actions.

n this paper, a new holistic distributed day-ahead energy management approach with desired equilibrium selection capability in a smart distribution grid is proposed. The interaction between customers and the distribution company is modeled as a single-leader multiple-follower Stackelberg game. The interaction among customers is modeled as a non-cooperative generalized Nash game because they meet a common constraint. Customers hold the average of the aggregate load in the appropriate domain to reshape it and improve the Load Factor. The strategy of the distribution company is day-ahead energy pricing obtained through maximizing its profit which is formulated as a stochastic conditional value at risk optimization to consider the uncertainty of the price of electricity in the wholesale market. Customers’ strategies are based on hourly consumption of deferrable loads and scheduled charge/discharge rates of energy storage devices in response to price. It is proved that the generalized Nash game has multiple equilibria; hence, the distributed proximal Tikhonov regularization algorithm is proposed here to achieve the desired equilibrium. The simulation results validate the performance of the proposed algorithm with 31.46% increase in the Load Factor besides 45.89 % and 14.23 % reduction in the maximum aggregate demand and aggregate billing cost, respectively.

The original version of Grey Wolf Optimization (GWO) algorithm has small number of disadvantages of low solving accuracy, bad local searching ability and slow convergence rate. In order to overcome these disadvantages of Grey Wolf Optimizer, a new version of Grey Wolf Optimizer algorithm has been proposed by modifying the encircling behavior and position update equations of Grey Wolf Optimization Algorithm. The accuracy and convergence performance of modified variant is tested on several well known classical further more like sine dataset and cantilever beam design functions. For verification, the results are compared with some of the most powerful well known algorithms i.e. Particle Swarm Optimization, Grey Wolf Optimizer and Mean Grey Wolf Optimization. The experimental solutions demonstrate that the modified variant is able to provide very competitive solutions in terms of improved minimum objective function value, maximum objective function value, mean, standard deviation and convergence rate.

Economic Load Dispatch (ELD) is an important part of cost minimization procedure in power system operation. Different derivative and probabilistic methods are used to solve ELD problems. This paper proposes a powerful Sine Cosine Algorithm (SCA) to explain the ELD issue including equality and inequality restrictions. The main aim of ELD is to satisfy the entire electric load at minimum cost. The SCA is a population based probabilistic method which guides its search agents that are randomly placed in the search space, towards an optimal point using their fitness function and also keeps a track of the best solution achieved by each search agent. SCA is being used to solve the ELD problem with their high exploration and local optima escaping technique. This algorithm confirms that the promising areas of the search space are exploited to have a smooth transition from exploration to exploitation using sine and cosine functions. Simulation results prove that the proposed algorithm surpasses other existing optimization techniques in terms quality of solution obtained and computational efficiency. The final results also prove the robustness of the SCA.

In order to reduce energy losses and improve voltage stability index in distribution system, two different approaches have been proposed and employed including voltage and reactive power control (volt/var control) and distribution network reconfiguration. In the present paper, volt/var control and network reconfiguration in distribution system considering voltage security constraints is modelled as a multi-objective optimization problem. Total electrical energy losses, voltages deviations and voltage stability have been considered as objectives. Also, a new method for distribution network reconfiguration has been utilized for implementation of these two problems simultaneously. In this way, the two problems can be solved in less time. In addition, different nature of loads in each bus is considered in network load modelling. Non-dominated sorting genetic algorithm-II is used to solve this problem. Finally, the effectiveness of the proposed method is evaluated by implementation on the IEEE 33-bus system and a real 77-bus distribution system.

In this paper, a new topology of quasi-Y-source impedance network is presented. This converter utilizes the change of winding factor and shoot through state in order to improve the gain of network. The proposed impedance network employs less turn ratio compared to quasi-Y-source and Y-source network to achieve high voltage gain. The continuous input current of the proposed converter is an advantage particularly for the applications related to the renewable energy sources such as Fuel Cell (FC) and photovoltaic (PV) systems. Furthermore, there is a dc-current-blocking capacitors in the proposed network, which helps to avoid the saturation of the coupled inductor. Operation principles of the converter are discussed and the steady state relations as well as voltage gain and voltage stress across the dc-blocking capacitors are derived. Proposed converter is compared to the conventional quasi-Y-source network, to show the advantages of the converter. Several simulations are done and the results are shown to indicate the performance of the proposed network. In this paper, an experimental prototype of a converter is presented. To prove the validity and consistency of the proposed network, several tests are carried out.This plan, can have a negative gain, similar to the quasi-Y-source network.

Recently, the pulsed power and pulsed electric field systems used in various industries and these systems have found wide applications. For this reason, using the pulsed power generators that in addition to responding to the needs of the user, are providing the advantages of compactness, high flexibility, high repetition rate and cost efficiency is inevitable. In this paper a hybrid solid state pulsed power generator is introduced that is modular and very flexible. This converter which is a combination of Marx and capacitor diode voltage multiplier, is capable of producing high voltage pulses with varying amplitudes at different frequencies. This proposed converter due to having high reliability, low cost, low weight, and structure’s simplicity can cover a wide area of applications. In this paper after introducing the proposed topology, its analytical design is described and its verification is proved by the simulation results in MATLABSIMULINK and by presenting the measurement results taken from the experimental prototype in low voltages.

We considered a new view on transition process from periodic to aperiodic signals, in time and spectral domains, pointing out how the concept of infinity is involved. It contributes to better understanding of the nature of both spectral descriptions, and conditions of their practical use, particularly in unusual cases. There we highlight the invariance in spectrum convergence by introducing some numerical parameters which exactly describe such process. Their behaviour is numerically examined to detail. Also, we considered the opposite transition, from aperiodic to periodic, to clarify the meaning of spectral line. To suggest applicability of our analysis we used an actual seismic signal. By extracting the most prominent waveform part, regarding influence on structures, we formed a periodic signal which line spectrum can clearly show possible resonance with vibrating tones of structures.