Scientia Iranica
Volume:30 Issue: 1, Jan-Feb 2023

In this paper, the functionality of mobile wireless sensor networks (MWSNs) is analytically modeled and evaluated using stochastic reward nets (SRNs). In MWSNs, mobile nodes can move around to collect data from the environment and send it to the sink. These nodes use a limited battery as the power source that can be charged according to environmental conditions. If the battery does not have enough power, the mobile node is disabled, and therefore it cannot move around and collect/send data. The data collected by the nodes will expire if they are not received by the sink in a timely manner. In order to avoid data expiration, mobile nodes can send their data to other nodes around themselves, but this also increases the power consumption because of further communication. Therefore, environmental and movement conditions, and communication between nodes can have a major impact on the functionality of the MWSNs. These challenges are considered in the paper and the proposed models analyze the impact of different environmental, communication, and movement conditions on the functionality of MWSNs. The results obtained from the proposed models demonstrate that the environmental and movement conditions have a greater impact on system functionality than the communication conditions.

Over recent decades, there has been a growing interest in semi-supervised clustering. Compared to the supervised or unsupervised clustering methods for solving different real-life problems, reviewed articles show that semi-supervised clustering methods are more powerful, and even a small amount of supervised information can significantly improve the results of unsupervised methods. One popular method of incorporating partial supervised information is through labeled data. In this study, we propose a semi-supervised clustering algorithm called ConvexClust. The proposed method improves data clustering using a geometric view borrowed from the Lune concept in the connectivity index and 10% of labeled data. Clustering starts with the use of labeled data and the formation of a convex hull. It continues over the labeling of non-labeled data and the updating of the convex hull in an iterative process. Evaluations of three UCI datasets and sixteen artificial datasets show that the proposed method outperforms the other semi-supervised and traditional clustering techniques.

Speech recognition and in other word communication between computers and human as a sub field of computational linguistics or Natural Language Processing (NLP) has a long history. ASR (Automatic Speech Recognition), TTS (Text to Speech), STT (Speech to Text), CSR (continuous speech recognition), IVR (Interactive Voice Response) systems are different approaches to solve problems in this area. Hybrid deep neural network (DNN) - hidden Markov model (HMM) has been shown to significantly improve speech recognition performance over the conventional GMM-HMM. The performance improvement is partially attributed to the ability of the DNN to model complex correlations in speech features. In this paper, we show that extracting prosodic features for Persian language (Farsi) can be obtained by using CNNs for segmentation and labeling speech for short texts. By using 128 and 200 filters for CNN and special architecture we reach 19.46 error in detection rate and also better time consumption in comparison with RNNs. One other advantages of using CNN is simplification of learning procedure. Experimental results show that CNN networks can be a good feature extractor for speech recognition in Farsi or other languages.

There is a variety of items which should be taken into consideration by a regional market manager (RMM). Participants in the market, technical constraint, price variation/reaction, electricity-price uncertainty and types of the applied demand response program are some instances in this regard. One of the demand response programs is Emergency Demand Response Program (EDRP) which is considered in this paper. In the present study, the objective function of the RMM is formulated in a market environment in order to determine the optimal demand, incentive and power purchased with considering some of technical constraints such as incentive limits, demand limits, power purchased and power balance. Co-evolutionary Improved Teaching Learning-Based Optimization (C-ITLBO) is applied to maximize the RMM’s profit. Furthermore, determination of the demand level in the EDRP is performed on the basis of a logarithmic model which includes the price elasticity matrix (PEM) is included. The reserve supplied due to Aggregators (AGGs) is also prioritized using the reserve-margin factor (RMF). In addition, information-gap decision theory (IGDT) is applied to model uncertainty in the initial electricity price. The above mentioned items are modeled in a multi-level formulation.

This paper investigates fixed-time nonsingular terminal sliding mode control of second-order nonlinear systems in the presence of matched and mismatched disturbances. Using estimation of the mismatched disturbance estimated by a fixed-time disturbance observer, a novel nonlinear dynamic sliding surface is designed. This estimation is utilized in designing a completely novel nonlinear dynamics sliding surface whereby the fixed-time convergence of the sliding motion is guaranteed in spite of mismatched disturbance. The convergence time of the closed-loop system including disturbance observer and control system is guaranteed to be uniform with respect to initial conditions. Moreover, the proposed controller avoids chattering phenomenon by producing a continuous control signal.

This paper proposes a comprehensive model to determine the retailer strategy for purchasing electrical power from the wholesale and/or local market in an active distribution network. The uncertainties associated with the load and distributed generation resources in the active distribution network, the wholesale market price and the behavior of the local market players, are all considered in the presented model. A retailer in the demand response program is employed as retailers’ ability to govern the risks. A risk-based decision-making scheme is provided in this paper which takes into account every instrument that is accessible for retailers along with their associated uncertainties. The major target of this paper is to maximize the retailer benefit concerning a tolerable risk. In order to model risks, the scenario theories are exploited and for solving the optimization problem, particle swarm optimization (PSO) has been utilized. The proposed scheme has been simulated on an actual network and the obtained results confirm the effectiveness and computability of this method.

This paper constructs a new five–dimensional hyper–chaotic system with complex dynamic behaviors. It also analyzes the chaotic attractor, bifurcation diagram, equilibrium points, Poincare map, Kaplan–Yorke dimension and Lyapunov exponent behaviors. We prove that the introduced new hyper-chaotic system has complex and nonlinear behaviors. Next, the work describes fast terminal sliding mode control scheme for the control and synchronization of the new hyper–chaotic system. Stability analysis is performed using the Lyapunov stability theory. For the synchronization, both master and slave systems are perturbed by different parameter and model uncertainties. Both steps of the sliding mode controller have finite–time convergence properties. Subsequently, it has been shown that the state paths of both master–slave systems can reach each other in a finite time. One of the main features of the proposed controller is the finite time stability of the terminal sliding surface designed with high–order power function of error and derivative of error. Finally, using the MATLAB simulation, the results are confirmed for the new hyper–chaotic system.

Electric vehicle penetration in the transport section is increasing and replacing the conventional fossil fuel based vehicles. Still, EV has not received success due to some limitations such as cost of the vehicle, battery capacity and availability of charging station. The availability of charging station depends on its geographical location. At the same time, location in the electrical network affects the energy loss and voltage deviation. Therefore, a road network of urban area overlapped with a 33-bus distribution network has been taken as test system for this work. Allocation of EV charging stations and photovoltaic energy resources as renewable distributed generation have been attempted simultaneously using 2-layer optimization. Differential Evolution and Harris Hawks Optimization techniques have been employed to solve the problem and the final results have been validated using eight other established optimization techniques. 2m point estimation method has been used to take care of uncertainties related to electric vehicles and PV. Monte-carlo simulation is also applied to cross verify the performance. The land cost, customer accessibility to charging station have been taken into account to allocate it at proper places. The whole work has been performed based on the 24-hrs dynamically varying electric vehicles flow and PV outputs.

Future wireless communication needs antenna with multifunctional operation. This paper focuses on Terahertz Antenna that could be easily integrated with micro and nano devices. In this paper, an octagonal shaped Microstrip Fractal Antenna loaded with the EBG structure is designed for tri-band terahertz application. Triple band characteristic achieved by fractal radiating patch is loaded with Electronic Band Gap (EBG). The antenna is working on triple band characteristics at 948 GHz, 984GHz and, 1040 GHz with overall dimensions of 700x900 µm2. The result and performance show that the recommended antenna will be compatible with compact wireless devices and Monolithic Microwave Integrated Circuit (MMIC). All simulation work has been done using electromagnetic software Ansoft High Frequency Structure Simulator (HFSS) and CST studio suite. The electromagnetic features like S11 parameters, VSWR, gain, efficiency and the radiation characteristics of such antenna are also explored. The simulation results show that this antenna has 9 dB realized gain at 0.948 THz resonating frequency.

The communication presents a novel microstrip line-fed compact square slot circular polarized antenna for Ku-band applications. The proposed square slot antenna with circular trimmed corners is fabricated on the FR-4 substrate with the defected ground on the opposite side of the patch. The axial ratio bandwidth (ARBW) is considerably enhanced by adding a shorting pin and the impedance bandwidth (IBW) is improved by etching pentagon- shaped slot in the defected ground. The antenna offers wide CP impedance bandwidth of 4.96GHz (13.6GHz-18.56GHz)) and simulated ARBW 4.85GHz (14.51-19.30GHz) and positive gain in the Ku-band. The presented antenna is appropriate for Ku-band applications.

An analytic method is proposed to reduce the number of elements of a Uniformly Spaced Antenna Arrays (USAAs). To this end, both excitations and positions of a Nonuniformly Spaced Antenna Array (NSAA) are obtained by equating the Fourier's coefficients of array factors of NSAA to those of predesigned USAA, in two steps. At first step, the array is considered USAA and excitations of elements are determined. At the second step, the position of elements are determined for the excitations obtained in the first step. These two steps are repeated several times to increase the accuracy. In fact, this method is the extension of Fourier's Coefficients Equating (FCE) method previously introduced to design NSAAs. The effectiveness of the presented method for both pencil beam and shaped beam patterns is verified by some comprehensive examples.