Showing 110 results for Ica
S. Prasad Tiwari,
Volume 19, Issue 3 (9-2023)
Abstract
In spite of the numerous benefits over the traditional power distribution system, protection of the microgrid is a challenging and complex task. The varying fault resistances due to dissimilar grounding conditions can affect the performance of the protection scheme. Under such conditions, the magnitude of the fault current can vary from lower to higher level. In addition to the above, the dissimilar magnitude of fault current during grid connected and islanded mode demands a protection scheme that can easily discriminate the mode of operation. The magnitude of fault current in grid-connected and islanded modes needs a robust protection scheme. In this regard, an ensemble of subspace kNN based robust protection scheme has been proposed to detect the faulty conditions of the microgrid. The tasks of the mode detection, fault detection/classification as well as faulty line identification has been carried out in the proposed work. In the proposed protection scheme, discrete wavelet transform (DWT) has been used for processing of the data. After recording the voltage and current signals at bus-1, the protection scheme has been validated. The validation of the protection scheme in Section 6 reveals that the protection scheme is efficiently working.
Ayotunde Abimbola Ayorinde, Sulaiman Adeniyi Adekola, Ike Mowete,
Volume 19, Issue 4 (12-2023)
Abstract
This paper, using the circuit-geometric features of the Method of Moments (MoM), presents a comprehensive analytical treatment of an exponentially non-uniform helical antenna (ENH), mounted on a ground plane of finite extent. Earlier investigations reported in the literature established that the introduction of an exponential non-uniformity in the turns spacing of an otherwise uniformly wound helical antenna significantly improves its axial ratio and power gain profiles, but failed to address two important questions; one concerning the influence of the degree of non-uniformity on the antenna performance: and the other, the associated return loss profile, which is of particular importance in practical applications. It is shown in this paper, that when a properly designed impedance matching circuitry is introduced, a return loss of the ENH of close to 60 dB is achievable; without compromising axial ratio and gain performances. Indeed, axial ratio bandwidth remained unchanged at 54.55% for both the impedance-matched and unmatched ENHs, whilst maximum gain changed marginally from 14.19dB, for the unmatched ENH to 14.18dB for the impedance-matched antenna.
Maryam Akbari, Sattar Mirzakuchaki, Mahdi Fazeli, Mohammad Reza Tarihi,
Volume 19, Issue 4 (12-2023)
Abstract
In light of the growing prevalence of Internet of Things (IoT) devices, it has become essential to incorporate cryptographic protection techniques for high-security applications. Since IoT devices are resource-constraints in terms of power and area, finding cost-effective ways to enhance their security is necessary. Physical unclonable function (PUF) is considered a trusted hardware security mechanism that generates true and intrinsic randomness by extracting the inherent process variations of circuits. In this paper, a novel pure magnetic memory-based PUF is presented. The fundamental building blocks of the proposed PUF design are magnetic devices, the so-called mCells. These magnetoresistive devices exclusively utilize Magnetic Tunnel Junction (MTJ) components. Using purely MTJ in the main memory and sense amplifier in the proposed PUF leads to high randomness, high reliability, low power, and ultra-compact occupation area. The Monte Carlo HSPICE simulation results demonstrate that the proposed PUF achieves a uniqueness of 49.89%, uniformity of 50.02 %, power consumption of 1.43 µW, and an area occupation of 0.01 µm2 per bit.
Pardis Asghari, Alireza Zakariazadeh,
Volume 19, Issue 4 (12-2023)
Abstract
This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.
Ali Jabbari, Hassan Moradzadeh, Rasul Lotfi,
Volume 19, Issue 4 (12-2023)
Abstract
Along with the development of hybrid electric vehicles, researchers are trying to reduce existing limitations such as noise and environmental concerns and improve the efficiency and reliability of these systems. The use of magnetic gear technology is one of the solutions that have been recently proposed to remove these limitations and achieve higher benefits. In this paper, a mechanically coupled magnetic geared (MCMG) machine has been introduced. An accurate analytical model based on the subdomain method is presented to calculate the magnetic machine performance. To do this, first, a pseudo-Cartesian coordinate system is specified, and then the constitutive equations, i.e. Laplace’s and Poisson’s equations are rewritten for different regions of the machine. The separation of variables method was used to determine the general solution of the equations. Then by applying appropriate interface and boundary conditions, the Fourier coefficients of the equations were determined. To verify the analytical results, the performance of the proposed magnetic machine is numerically simulated using the finite element method in commercial software, and then a prototype is built and tested in three distinct modes. By comparing the analysis results with numerical simulation results and experimental tests, the high accuracy of the proposed analytical model can be confirmed.
Tasqiatul Qulbi Kamila Huda, I Gede Puja Astawa, Yoedy Moegiharto, Mohamad Ridwan, Budi Aswoyo, Anang Budikarso, Ida Anisah, Faridatun Nadziroh,
Volume 20, Issue 1 (3-2024)
Abstract
The progress of 5G networks is propelled by wireless technology, specifically mobile internet and smart devices. This article provides an in-depth analysis of the fundamental elements of 5G technology, encompassing the advancement of cellular networks, simultaneous transmission capabilities, energy efficiency enhancements, and the implementation of cooperative communication. This study examines the application of simultaneous wireless information and power transfer (SWIPT) in cooperative device-to-devices (D2D) communication. Specifically, it investigates relay selection using decode-forward (DF) protocols and considers the issue of self-interference. Radio frequency based energy harvesting (RF-EH) is proposed to address power limitations in device-to-device (D2D) communication. This article describes the development of this technology and suggests a system architecture that employs time-switching relaying (TSR) techniques to enhance the power efficiency of base stations. This research aims to assess data transfer efficiency in two-way cooperative communication systems by incorporating many technologies.
Ramin Safaeian, Mahmoud Tabandeh ,
Volume 20, Issue 2 (6-2024)
Abstract
Directed Acyclic Graphs stand as one of the prevailing approaches for representing causal relationships within a set of variables. With observational or interventional data, certain undirected edges within a causal DAG can be oriented. Performing intervention can be done in two different settings, passive and active. Here, we prove that an optimal intervention set can be obtained based on the minimum vertex cover of a graph. We propose an algorithm that efficiently identifies such an optimal intervention set for chordal graphs within polynomial time. Performing intervention on this optimal set recovers all the undirected edges in graph G, regardless of the underlying ground truth DAG. Furthermore, we present an algorithm for evaluating the performance of passive algorithms. This evaluation provides insights into how many intervention steps of a specific algorithm are required to recover all edges in the causal graph for any possible underlying ground truth in the equivalence class. Experimental findings underscore that the number of nodes in the optimal intervention set increases with growing the number of nodes in a graph, where the edge density is fixed, and also increases with the rising edge density in a graph with a fixed number of nodes.
Aws Alazawi, Huda Jameel, Mohammed Mohsen,
Volume 20, Issue 2 (6-2024)
Abstract
This study explores the use of distortion product otoacoustic emission (DPOAE) as a hearing screening modality for newborns and adults with hearing impairment. The goal is to improve cochlear response by developing digital filter characteristics to make it consistent for specialists to make accurate diagnoses. To accomplish this, the proposed system consists of a DPOAE ER-10C as stimulation and cochlear response probe, a digital signal processor, an oscilloscope, PC, and audio cables. Real-time distortion product frequency components were extracted using a signal processor of TMS320C6713. To validate the system, a senior medical physicist at Baghdad Medical City in Iraq conducted a study with five hearing-normal volunteers ages 38 and 55 at the center for hearing and communication. The results showed an ability to extract distortion product components in real-time implementation, with the superiority of shape parameters greater than 0.5. In addition, the quantization of filter coefficients was compared for both floating-point arithmetic and fixed-point arithmetic. Noisy environment-based noise reduction techniques have to be investigated by considering the implementation of robust digital signal processing techniques. Finally, the proposed system would contribute to advancements in hearing screening and treatment for those with hearing impairment.
Chhaya Belwal, Kunwar Singh, Shireesh Kumar Rai,
Volume 20, Issue 2 (6-2024)
Abstract
This paper introduces a floating flux-controlled meminductor emulator, implemented using two voltage differencing differential difference amplifier (VDDDA) along with a memristor and capacitor. Grounded and floating configurations are simulated with TSMC 0.18 µm level-49 BSIM3 CMOS process parameters in LTspice, showcasing the performance of the proposed circuits. The circuit features electronic tunability, allowing for the adjustment of nonlinear flux through the tuning of bias voltage. Simulation results validate the frequency-dependent current-flux dynamics of the proposed meminductor emulator. The simulation results, which involve frequency-dependent pinched hysteresis loops, transient analysis, non-volatility, and Monte Carlo analysis of the proposed meminductor, affirm the functionality and adequacy of the proposed design. A Chua’s oscillator is realized using proposed VDDDA-based meminductor as non-linear element.
Mohamad Almas Prakasa, Mohamad Idam Fuadi, Muhammad Ruswandi Djalal, Imam Robandi, Dimas Fajar Uman Putra,
Volume 20, Issue 3 (9-2024)
Abstract
The unbalanced load distribution in the electrical distribution network caused crucial power losses. This condition occurs in one of the electrical distribution networks, 20 kV Tarahan Substation, Province of Bandar Lampung, Indonesia. This condition can be maintained using optimal reconfiguration with the integration of Distributed Generation (DG) based on Renewable Energy (RE). This study demonstrates the optimal reconfiguration of the 20 kV Tarahan Substation with the integration of the Photovoltaic (PV) and Battery Energy Storage System (BESS). The reconfiguration process is optimized by using the Firefly Algorithm (FA). This process is conducted in the 24-hour simulation with various load profiles. The optimal reconfiguration is investigated in two scenarios based on without and with DG integration. The optimal configuration with more balanced load distribution conducted by FA reduces the power losses by up to 31.39% and 32.38% in without and with DG integration, respectively. Besides that, the DG integration improves the lowest voltage bus in the electrical distribution network from 0.95 p.u to 0.97 p.u.
Ayoub Hamidi, Ahmad Cheldavi, Asghar Habibnejad Korayem,
Volume 20, Issue 3 (9-2024)
Abstract
This paper proposes a structure for concrete composite materials that effectively attenuates transmitted power through the composite slab across a wide frequency range. The proposed structure is practical for electromagnetic interference shielding applications. To assess its effectiveness, the proposed structure has been compared with two other structures: a traditional wire mesh used in reinforced composites and an array of helices, a cutting-edge technique for manufacturing lightweight concretes with significant improvements in shielding properties. The comparison among full-wave simulation results indicates that the proposed method leverages the benefits of both techniques. It achieves a shielding effectiveness exceeding 30 dB from low frequencies up to 8.5 GHz and beyond 55 dB from low frequencies up to 4 GHz. Furthermore, an experimental measurement was conducted to validate the full-wave simulation results. An experimental sample was fabricated according to the simulated proposed structure, and the measured shielding effectiveness confirmed the composite's capability in wideband electromagnetic shielding. Theoretically, the proposed structure can enhance the concrete's mechanical characteristics while improving its shielding effectiveness, making it suitable for designing ultra-high-performance concretes.
Dalila Yessad,
Volume 20, Issue 4 (11-2024)
Abstract
This paper introduces the CTDRCepstrum, a novel feature extraction technique designed to differentiate various human activities using Doppler radar classification. Real data were collected from a Doppler radar system, capturing nine return echoes while monitoring three distinct human activities: walking, fast walking, and running. These activities were performed by three subjects, either individually or in pairs. We focus on analyzing the Doppler signatures using time-frequency reassignment, emphasizing its advantages such as improved component separability. The proposed CTDRCepstrum explores different window functions, transforming each echo signal into three forms of Short-Time Fourier Transform reassignments (RSTFT): time RSTFT (TSTFT), time derivative RSTFT (TDSTFT), and reassigned STFT (RSTFT). A convolutional neural network (CNN) model was then trained using the feature vector, which is generated by combining the cepstral analysis results of each RSTFT form. Experimental results demonstrate the effectiveness of the proposed method, achieving a remarkable classification accuracy of 99.83% by using the Bartlett-Hanning window to extract key features from real-time Doppler radar data of moving targets.
Arizadayana Zahalan, Samila Mat Zali, Ernie Che Mid, Noor Fazliana Fadzail,
Volume 21, Issue 2 (6-2025)
Abstract
Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and safety remain critical because they are prone to various faults, mainly when operating in harsh environmental conditions. This study addresses these issues by exploring fault detection and classification in PV arrays using neural network (NN) -based techniques. A PV array model, consisting of 3x6 PV modules, was simulated using MATLAB Simulink to replicate real-world conditions and analyse various fault scenarios. An open circuit, a short circuit, and a degrading fault are the three types of faults considered in this study. The NN was trained on a dataset generated from the MATLAB Simulink model, encompassing normal operating and fault conditions. This training enables the network to learn the distinctive patterns associated with each fault type, enhancing its detection accuracy and classification capabilities. Simulation results demonstrate that the NN-based approach effectively identifies and classifies the three types of faults.
Hanim Suraya Mohd Mokhtar, Aimi Salihah Abdul Nasir, Mohammad Faridun Naim Tajuddin, Muhammad Hafeez Abdul Nasir, Kumuthawathe Ananda Rao,
Volume 21, Issue 2 (6-2025)
Abstract
The rapid growth of photovoltaic (PV) systems has highlighted the need for efficient and reliable defect detection to maintain system performance. Electroluminescence (EL) imaging has emerged as a promising technique for identifying defects in PV cells; however, challenges remain in accurately classifying defects due to the variability in image quality and the complex nature of the defects. Existing studies often focus on single image enhancement techniques or fail to comprehensively compare the performance of various image enhancement methods across different deep learning (DL) models. This research addresses these gaps by proposing an in-depth analysis of the impact of multiple image enhancement techniques on defect detection performance, using various deep learning models of low, medium, and high complexity. The results demonstrate that mid-complexity models, especially DarkNet-53, achieve the highest performance with an accuracy of 94.55% after MSR2 enhancement. DarkNet-53 consistently outperformed both lower-complexity models and higher-complexity models in terms of accuracy, precision, and F1-score. The findings highlight that medium-depth models, enhanced with MSR2, offer the most reliable results for photovoltaic defect detection, demonstrating a significant improvement over other models in terms of accuracy and efficiency. This research provides valuable insights for optimizing defect detection systems in photovoltaic applications, emphasizing the importance of both model complexity and image enhancement techniques for robust performance.
Edy Victor Haryanto S, Aimi Salihah Abdul Nasir, Mohd Yusoff Mashor, Bob Subhan Riza, Zeehaida Mohamed,
Volume 21, Issue 2 (6-2025)
Abstract
Malaria is a parasitic disease that causes significant morbidity and mortality worldwide. Early diagnosis and treatment are crucial for preventing complications and improving patient outcomes. Microscopic examination of blood smears remains the gold standard for malaria diagnosis, but it is time-consuming and requires skilled technicians. Deep learning has emerged as a promising tool for automated image analysis, including malaria diagnosis. In this study, we propose a novel approach for identifying malaria parasites in microscopic images using the GoogLeNet. Our method includes enhancement with the AGCS method, color transformation with grayscale, adaptive thresholding for segmentation, extraction, and GoogLeNet-based classification. We evaluated our method on a dataset of malaria blood smear images and achieved an accuracy of 95%, demonstrating the potential of GoogLeNet for automated malaria diagnosis.
Gholamreza Khademevatan, Ali Jalali,
Volume 21, Issue 3 (8-2025)
Abstract
A novel simplified EKV model base analog/RF CMOS design pre-SPICE tool is presented in this paper. Addition to facilitating the sizing process, this CAD tool can also optimize circuit characteristics. By having a web address, users can access it without installing any software. Using a graphical and a numerical view, the designer can select degrees of freedom and observe the MOS circuit performance. Through the use of charts versus IC, the graphical view can show tradeoffs in circuit performance in real-time. Charts can be displayed simultaneously in both linear and logarithmic scales. IC CRIT , is also available and can be displayed on the charts. This tool is not limited to one process and it is possible to select different processes. It is efficient for pre-SPICE designs, enhancing intuitive understanding and the designer's experience for future projects while eliminating the need for trial-and-error simulations. Furthermore, the predicted results align well with simulation outcomes, demonstrating the effectiveness of the design and optimization method presented. Two methodologies for selecting optimum ICs are presented by this tool. These are illustrated by the study of linearity indices, AIP3 and IIP3, in one-stage and two-stage differential amplifiers and the design of a single-ended OTA.
Somayeh Talebzadeh, Reza Radfar, Abbas Toloei Ashlaghi,
Volume 21, Issue 3 (8-2025)
Abstract
The accurate prediction of electricity demand is crucial for efficient energy management and grid operation. However, the complexities of demand patterns, weather variability, and socioeconomic factors make it challenging to forecast demand with high accuracy. To address this challenge, this research proposes a novel hybrid machine-learning approach for predicting electricity demand. In this research, first, different regression methods were investigated to solve the problem, the results showed that the multi-layer perceptron (MLP) regression model has the best performance in predicting electricity demand. Furthermore, the proposed system, BIMLP (Bagging-Improved MLP), is designed to iteratively improve its parameters using a binary search algorithm and reduce the learning error using bagging, a technique for ensemble learning. The proposed system was applied to the Electric Power Consumption data set and achieved a value of 0.9734 in the r2 criterion. The results of implementing and evaluating the proposed system demonstrate its satisfactory performance compared to existing techniques.
M.e. S.m Mehzabeen , Phd R Gayathri, Pattunnarajam Paramasaivam , Phd Ramya A,
Volume 21, Issue 4 (11-2025)
Abstract
Hepatitis C virus (HCV) detection is a critical aspect of early intervention and effective management of the disease. This paper presents a comprehensive study focused on enhancing the detection accuracy of HCV through the integration of advanced techniques - SMOTE, Optuna, and SHAP - alongside extensive exploratory data analysis (EDA). The study addresses class imbalance using Synthetic Minority Over-sampling Technique (SMOTE), optimizes model performance with Optuna for hyperparameter tuning, and provides model interpretability using SHAP (SHapley Additive exPlanations). EDA is leveraged to gain valuable insights into the dataset's characteristics, ensuring robust data preprocessing and feature engineering. The results show 97% improved HCV detection performance, highlighting the efficacy of the proposed methodology in medical diagnostics and aiding healthcare professionals in making informed clinical decisions.
Tara Sistani, Seyed Javad Kazemitabar,
Volume 21, Issue 4 (11-2025)
Abstract
Forests play several vital roles in our lives and provide various resources. However, in recent years, the increasing frequency of wildfires has led to the widespread burning and destruction of many forests and wildlands. Therefore, detecting forest fires and finding suitable solutions to address this issue has become one of the critical challenges for researchers. Today, with the advancement of artificial intelligence, forest fire detection using deep learning is an important method with the aim of increasing the efficiency of forest fire detection and monitoring systems. In this article, a method based on a type of convolutional neural network called Xception is proposed for classifying forest fire images. In this method, transfer learning technique is used on the proposed neural network and a new classifier is designed for the problem. Also, various hyperparameters have been used to optimize the performance of the proposed model. The proposed method is performed on the DeepFire dataset, which contains 1900 images equally divided between fire and no-fire classes. The results obtained from the implementation of the proposed method show that this method with an accuracy of 99.47% has achieved a favorable performance in classifying forest fire images.
Mohammad Reza Eesazadeh, Zahra Nasiri-Gheidari,
Volume 21, Issue 4 (11-2025)
Abstract
This research focuses on electromagnetic position sensors, particularly synchros, which play a crucial role in the closed-loop control systems of permanent magnet synchronous machines (PMSMs). Compared to two-phase resolvers, three-phase synchros provide enhanced reliability by ensuring continued operation even in the event of an open-circuit fault. One of the key challenges in designing such sensors lies in selecting optimal windings and configurations while also developing efficient modeling techniques to minimize computational complexity. To address this issue, the study introduces a matrix-based method for designing wound rotor (WR) synchros. This approach allows for flexible configurations depending on the number of pole pairs and stator tooth counts. The proposed design methodology ensures adaptability and precision, making it a valuable tool for engineers working on electromagnetic sensor development. To validate the effectiveness of the proposed method, the Field Reconstruction Method (FRM) is employed, providing a fast and accurate modeling technique that can be implemented using MATLAB. Additionally, a comparative analysis is conducted with finite element analysis (FEA) to confirm the accuracy and reliability of the approach. Results demonstrate that the matrix-based method is an efficient and effective solution for optimizing WR synchro designs, significantly improving performance and computational efficiency.
