Iranian Journal of Science and Technology-Transactions of Electrical Engineering最新文献

Extracting and concatenating distilled content from a corpus into a summary is a technique known as extractive summarization. In recent days, extractive summarization of web text has become popular due to the wide usage of social media. Hence various researches have been conducted on extractive summarization of web text, but the processing of huge amounts of web text and understanding the context is difficult due to the requirement of a lot of storage and time. To solve this issue, the continuous bag of words text vectorization model has been used that reduce the processing time by producing a distributed combination of words in vector arrangement. Moreover, the polysemous words are unable to be captured, which makes extraction difficult. Hence a novel Hierarchical Attention pointer Stacked Denoising Variational Autoencoder Model has been proposed in which the SDVAE model forms latent distribution for contextualized words and bidirectional attention mechanism extracts keywords and features from sentences thereby capturing polysemic words. Furthermore, the summary is obtained with dangling anaphora whereas antecedent morphological expression and verb referents are not considered in the summary. Hence a novel Multilayered Competitive Probable Modular Perception Model has been proposed in which the competitive layer scores the sentence and the scored sentences are ranked using string kernel and class conditional probability thereby considering the antecedent morphological expression and then, Graph based Quadruplicate Lexicon Summarization is used that forms quadruplicate lexicon chain in graphical format to eliminate dangling anaphoric expressions. The experimental results obtained show that the proposed model has achieved a comparatively high accuracy of 98.3% and recall, precision, and F-measure of 98%.

This paper presents a novel algorithm for power transformer differential protection based on wavelet transform (WT) and introduces new indices to distinguish internal faults from normal operating conditions and the occurrence of the inrush current. The proposed setting-less algorithm has no limits on the structure, dimension, capacity, and core type of the transformer. For this purpose, six indices based on fault detection functions extracted from WT transform are presented. Then, weighting factors for the indices by using the least squares method are calculated. In order to validate the proposed method, the approach has been evaluated on four transformers with 2 kVA, 10 kVA, 400 kVA, and 125 MVA. The success rate of fault detection in 10 kVA, and 125 MVA transformers was 100% and in 2 kVA and 400 kVA transformers was 93.33% and 94.44%, respectively. Also, the proposed algorithm has a remarkable capability in fast fault detection to protect the power transformer.

In this article, reduced switch-based fault resilience capable multi-level inverter (MLI) with phase disposition pulse width modulation (PD-PWM) strategy is implemented. For reliable power conditioning and monitoring of PV based systems multi-level inverter (MLI) received a lot of attention. The switching device quantity mainly influences of volume and reliability in a MLIs. It is a crucial challenge in on and off-grid applications. Because of the high failure rate of power devices, the reliability of MLI utilized in PV and grid-connected systems is very poor or susceptible. To reduce the switching losses and enhance the MLIs reliability with fault resiliency, the reduced switch component topology is proposed in this work. Instead of a single switch, the suggested configuration is employed with multiple switch fault resiliency, then the reliability of the inverter is enhanced. Further, the PD-PWM switching strategy is employed for the MLI operation. The proposed scheme offers an excellent performance with significant result of THD, switching losses, and efficacy. The implemented inverter topology with PD-PWM strategy is simulated in MATLAB/Simulink along with fault tolerance operation under normal and faulty operation. Also, the real-time operation of proposed topology with experimental setup is validated using field programmable gate array (FPGA) controller.

This study presents a compact co-planar waveguide (CPW) ultra-wideband (UWB) antenna integrated with a novel frequency selective surface (FSS) for gain improvement. The novel FSS is created by etching some slots on a square patch and adding different patches during five steps. The presented FSS provides transmission coefficient lower than − 10 dB and reflection coefficient close to 0 dB across the frequency bands of 3–15.7 GHz, and 3.6–15.1 GHz for transverse electric (TE) and transverse magnetic (TM) modes with adequate angular stability, respectively. Angular stability is maintained up to θ = 85° and 40° in the TE and TM planes, respectively. Furthermore, the reflection phase decreases linearly piecewise, making it appropriate for radiation enhancement of the antenna. A 4 × 4 array of the proposed FSS unit cells is positioned below the circular CPW antenna at an optimized distance of 20 mm. Then, to improve the reflection coefficient, a rectangular notch is etching on the radiating patch. The modified structure operates over an impedance bandwidth (S₁₁ < − 10 dB) of 3.4–13.8 GHz for UWB applications with a peak gain of 7.40 dBi at 12.5 GHz. The overall physical and electrical volumes of the proposed antenna are 32 × 32 × 23.2 mm³ and 0.36λ_L × 0.36λ_L × 0.26λ_L (λ_L is the wavelength at the lower frequency), respectively that is considerably compact. The proposed antenna is fabricated, and the comparison of the simulated and measured results shows a good agreement.

Internet of Things (IoT) applications are popularly involved in day-to-day life. The increase in utilization leads to an increase in network traffic. The incoming users have different intentions in the network and hence security is essential. The data user accesses the data in the cloud that is collected from IoT devices. A large-scale IoT environment has challenges in the provisioning of security as well as the management of access control mechanisms. The problem is a generation of policies and authenticating devices with minimum credentials. In this paper, Blockchain-based decentralized authentication and access control systems are designed. The process of authentication is conducted for the data owner and data user by considering identity, device type, IP address and signature, PUF, and biometric respectively. PUF stands for Physical Unclonable Function, which is a hardware-based security feature that generates a unique identifier for a device based on its physical properties, SALSA20 and PRESENT are encryption algorithms used in the proposed system to encrypt data chunks. SALSA20 is a stream cipher that generates a keystream to encrypt data, while PRESENT is a block cipher that encrypts data in fixed-size blocks These authentication credentials are managed in the blockchain. The credentials are stored in encrypted form using the Key schedule PRESENT algorithm. In the authentication of data users, the number of credentials is selected using fuzzy logic that improves security. To assure data storage security, the data is split into two chunks, and it is encrypted using SALSA20 and PRESENT algorithm. The proposed model is developed in an ifogsim simulator, and the performance metrics are evaluated in terms of authentication time, storage efficiency, running time, throughput, latency, and blocksize.

This paper puts forward a novel Hybrid Coyote Optimization-based Big Bang Big Crunch (HCOB³C) algorithm to design an optimal multi-objective fuzzy control framework applied to Active Suspension Systems (ASS). The suspension system in vehicles is an inherent component that is responsible for yielding passenger comfort and ensuring vehicle stability. Since ASS is a multi-objective, constrained non-linear system, the linear controllers will yield suboptimal results because of the so-called bode sensitivity integral problem. Hence, to handle the non-linearity and constraints in the ASS, we present a constrained multi-objective fuzzy controller optimized using the HCOB³C algorithm. The motivation for the proposed hybrid optimization algorithm is that the conventional Big-Bang Big Crunch Optimization (B³CO) and Coyote Optimization (CO) suffer from two major limitations namely 1. Imbalance between exploration and exploitation and 2. Slow convergence respectively. Hence, we utilize the CO to tune the parameters of B³CO to realize optimal actuator force that can offer precise suspension travel and minimize the chassis vibration even in the case of uneven road profile. The performance of the proposed scheme is experimentally validated on a quarter car ASS system for several realistic road profiles. The experimental results substantiate that the proposed scheme can minimize the vehicle vibration by around 41.6% compared to the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. In general, the HCOB³C-optimized FLC shows a 97% drop in vehicle vibration when compared to a passive system. Moreover, in line with the ISO 2631–1 standards, the analysis of key performance metrics of suspension systems including Frequency-Weighted Root Mean Square (FWRMS) and Vibration Dose Value (VDV) highlights the superiority of the proposed scheme in comparison with the state-of-the-art techniques.

Graphical Abstract

The present manuscript proposes a design for a low-profile linear-polarization wearable antenna embedded on glasses with IoT/ISM applications. To study body proximity antennas in terms of their effects and apt performance, this work uses a homogenous phantom. To meet the design objective, a meander structure with linear polarization is provided. The miniature antenna also provides a gain ranging between 0.8 to 2.9 dBi. The antenna is equipped with specific dimensions and geometry that allow its placement on the temple arms of a set of glasses. The used dipole antenna, with a compact design (11 mm × 44 mm × 0.8 mm) and a microstrip FR4 as the antenna substrate, is located on the frame to the right of the temple. After matching and optimization, the antenna’s reflection coefficient measured less than − 6 dB. At an operating frequency of 2.45 GHz, the antenna demonstrated a gain of 0.8 dBi and an 85% efficiency rate. The antenna's gain increased to 2.9 dBi and maintained a high 67% efficiency rate when tested at 5.8 GHz. Additionally, simulation results showed that the specific absorption rate values ranged between 0.171 and 1.18 W/kg.

An analytical self-inductance calculation method for air-cored planar spiral coils is investigated. To this end, (1) the spiral coil is approximated by a number of concentric circular loops, (2) Poisson equation is solved for the azimuthal magnetic vector potential in the axisymmetric plane via Bessel-Fourier expansion and (3) the inductance is obtained using magnetostatic energy analysis. Since potential distribution is accurately predicted inside the conductors as well as outside, the challenges of many previous works, e.g. Neumann’s formula are overcome. Although the solution is in the series form rather than an integral or closed form; it is fully analytical, requiring no numerical assistance and has a good accuracy. The only drawback is that the accuracy drops as the gap between loops increases. Some case-studies are investigated to prove the efficacy of the proposed approach. Three dimensional finite element analysis results confirm both validity and computational efficiency of the proposed analytical method.

The seamless communication between people and objects made possible by the Internet of Things (IoT) greatly improves our quality of life. It is especially important in the remote healthcare industry, where cutting-edge machine learning and artificial intelligence approaches are having a big impact. These analytics have the power to turn a proactive healthcare campaign from one that is reactive. For remote healthcare applications, this research study suggests an innovative framework called E-DigitTool to precisely identify and diagnose cardiovascular disorders. The digital health records collected by IoT sensors are preprocessed by the system using a Kalman filtering technique. The preprocessed medical data is analyzed using a modern optimization technique called Sine Cosine Optimized Feature Selection (SCO-FS) to identify the most significant features. Based on the chosen attributes, a state-of-the-art classification technology called Weighted Mean Vector Neural Network (WMVNN) is employed to accurately determine the type of sickness. Moreover, an Adaptive Wind Driven Optimization (AWDO) is used to compute the loss function optimum during illness classification, improving the performance and accuracy of the classifier. The main conclusions of the study show that E-DigitTool can analyze massive volumes of medical data with a performance accuracy of up to 99.5% for all datasets, resulting in an error rate of 0.5% and average precision, recall, and F1-score of 99%.

A passivity based sliding mode controller structure is proposed in this paper for controlling the voltage and frequency of a microgrid for both islanded and grid connected mode. Effect of communication channel delay and actuator saturation is also considered. A sliding mode control law is derived in such a way that the overall system become passive. By selecting the appropriate Lyapunov function, the necessary criterion for the system to become passive is developed in terms of inequality. The usefulness of the designed controller is verified with the help of a three phase Subnetwork 1 of CIGRE benchmark medium voltage distribution network. The efficacy of the designed sliding mode based controller is verified for both grid connected and islanded mode. The simulation is performed in MATLAB/SIMULINK platform and the real-time simulation is performed using OPAL-RT.