Physics-informed neural networks (PINNs) offer a promising route to bridge device-level simulations and compact circuit models. In this work, we present a hybrid modeling framework that integrates TCAD datasets with a baseline compact model and applies a PINN correction to capture stress-condition effects with high fidelity. The proposed approach achieves ≤ 2% route mean square error (RMSE) across more than 2,000 bias points, maintaining stable predictions under temperature (273–373 K) and radiation (0–100 krad) variations. Extracted Berkeley Short-channel IGFET Model (BSIM) parameters enable direct SPICE simulation, ensuring compatibility with standard circuit design workflows. For deployment, the trained PINN is exported as a quantized ONNX model, achieving sub-millisecond inference and ultra-low energy consumption (0.25 pJ/op) on a Cortex-M55 platform. This dual pathway supports both high-accuracy circuit simulation and real-time edge inference, making it suitable for embedded applications under constrained conditions. Comparative analysis with recent ANN-based models confirms that our physics-informed approach offers superior interpretability, SPICE readiness, and deployment efficiency. All datasets, code, and models are released to support reproducibility, benchmarking, and further research in compact modeling and edge-AI integration.
The increasing penetration of distributed generation (DG) significantly complicates Distribution System State Estimation (DSSE) by introducing stochasticity and uncertainty. This paper proposes a novel DSSE framework that unlike conventional methods simultaneously estimates the system state, load demands, and DGs output power through a unified constrained optimization model. The model is efficiently solved using the Whale Optimization Algorithm (WOA), whose unique balance of exploration and exploitation enables robust solution search in complex, active distribution networks. Simulation studies on standard IEEE 37-bus and 69-bus test systems reveal that the proposed WOA-based approach achieves outstanding accuracy. For the 37-bus system, WOA attains a Maximum Individual Relative Error (MIRE) of 1.15% and a Maximum Individual Absolute Error (MIAE) of 2.303 on load estimation. On the larger 69-bus system, the method further reduces these errors yielding a MIRE of 0.886% and a MIAE of 1.12 for load, and 0.73% and 1.058 for DG power estimation, respectively. Across all experiments, WOA consistently outperforms leading metaheuristics including ABC, PSO, and GA highlighting its superior accuracy, scalability, and robustness for real-world DSSE challenges.
Dynamic Network Reconfiguration (DNR) is a vital and effective technique for reducing energy loss. Due to its complexity, nonlinearity, and large-scale optimization challenge, DNR is still a very difficult problem. This paper presents a new strategy for improving the DNR's stability and reliability under Real-Time Operation Mode (RTOM). It addresses a simultaneous optimization technique within various limitations and constraints about network power flow, voltage limits, output generation of Renewable Energy Resources (RER), Distributed generation mode, and network load profile. In real-time operating mode, it optimizes Distributed Generations Sizing and Location (DG_SL) for Renewable Energy and Dynamic Network Reconfiguration (DNR). Reducing the overall daily active and reactive energy losses of the network, raising the Voltage Stability Index (VSI), distributing the load more evenly, and enhancing distribution efficiency in real-time operation mode are the primary goals. A Multi-Objective Decision-Making Approach (MODMA) based on the Analytic-Hierarchy Process (AHP) and Crow Search Algorithm (CSA). To evaluate the practicality of the proposed method, MATLAB simulations were conducted on the IEEE 33- and 69-bus networks. In the IEEE 33-bus case, the proposed AHP–CSA framework achieves up to 91.75% reduction in daily active losses and more than 90.70% reduction in daily reactive losses, with the Voltage Stability Index consistently improved toward unity. In the IEEE 69-bus case, the method delivers up to 81.78% reduction in daily active losses and 59.78% reduction in daily reactive losses, also enhancing the overall voltage stability profile. These outcomes confirm the effectiveness and robustness of the proposed approach for real-time distribution network operation with renewable DG integration.
A mobile robot must be autonomous to avoid obstacles while traveling towards the target. Dynamic obstacle avoidance remains a significant challenge in mobile robotics. Although reactive navigation strategies have been applied to address this problem, relying on the single-stage module often results in limited efficiency and restricted overall performance. This paper proposes combining an adaptive neuro-fuzzy inference system (ANFIS) and a neural network (NN). The data for obstacle severity classification were used to train the Neural Network. The relative velocity and distance between the mobile robot and obstacles determine the zone. Zone 1 is dangerous, and Zone 5 is safe. This paper uses the ANFIS to avoid obstacles during the mobile robot's motion and to avoid collisions. Based on our empirical study, three essential features have been considered in this paper: the relative speed, distance, and angle between the robot and the obstacle as inputs to the obstacle avoidance system ANFIS. The output was a suggested steering angle and speed for the mobile robot. The simulation results for the tested cases show the capability of the proposed controller to avoid static and dynamic obstacles in a fully known environment. Our results show that the ANFIS System enhances the proposed controller's performance, reducing path length, processing time, and the number of iterations compared to state-of-the-art research papers. The proposed work demonstrated better performance in path length reduction (approximately 6%) and time taken reduction to reach the target, which is reduced by about 60%.
In this paper, the decoding order error of successive interference cancellation (SIC) of multicarrier nonorthogonal multiple access (NOMA) due to the random walk of the users and position estimation deviation is considered in resource allocation. This factor extremely degrades the performance of NOMA in terms of sum rate and outage probability. Therefore, two optimal power allocation strategies for users are derived that maximize the sum rate and minimize the outage probability. The simulation results show that by considering the decoding order error in resource allocation, better performance can be achieved compared to the previous power allocation algorithms without considering this fact, which are a well-known water filling algorithm and a power allocation that maximizes the rate with minimum rate constraint.
Blocking interference poses significant challenges to the accuracy and reliability of navigation systems by obstructing the communication path. Single-frequency receivers are generally more susceptible to blocking interference due to their limited ability to compensate for obstructed signals or access alternative signal sources. The integration of Global Navigation Satellite Systems (GNSS) is among the most effective strategies for mitigating blocking interference. By combining signals from multiple sources, the likelihood of accessing stable and reliable signals significantly improves. The four Global GNSS include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou, and Galileo. This paper examines the challenges of system integration in addressing navigation equations and proposes suitable solutions. Two datasets were collected under conditions of blocking disturbances, and receiver performance was simulated across 14 different modes using a software platform. The results were analyzed considering factors such as the number of satellites in view, satellite positions, extracted positions, as well as Root Mean Square (RMS), Geometric Dilution of Precision (GDOP), and Position Dilution of Precision (PDOP) parameters. In these scenarios, the GPS system in single-frequency mode, the combination of GPS and GLONASS in dual mode, and the combination of GPS, GLONASS, and Galileo in triple mode demonstrated the best performance. However, the best performance, irrespective of computational load and hardware complexity, was achieved in the quadruple integration mode.
This article presents the design and optimization of a Minkowski fractal slot-integrated antipodal Vivaldi antenna (MFS-AVA) for brain stroke detection. The antenna is proposed on a 65 × 65 × 1.6 mm³ FR-4 substrate and integrates a tapered slot radiator with a microstrip feed. Key parameters are optimized through parametric analysis. The exponential curve of the radiator arms and edge conductor is fine-tuned for improved bandwidth and impedance matching, while Minkowski fractal slots enhance the reflection coefficient, gain, and directivity. Simulated using CST Studio Suite 2016, the antenna attains an extensive bandwidth spanning from 1.23 GHz to 12 GHz, a maximum gain of 9 dBi, and a radiation efficiency of 87%. The radiation pattern exhibits a directional beam with minimal side lobes, making it suitable for focused microwave imaging. Compared to a conventional design, the MFS-AVA shows improved S11, VSWR, and surface current performance. Its effectiveness is validated using a four-layered tissue-mimicking cylindrical human head model, confirming adequate field penetration and compliance with safety standards. These results demonstrate the proposed antenna’s suitability for UWB microwave imaging in brain stroke detection.
Real-time control applications, crucial in robotics, industrial automation, and medical devices, demand precise and predictable timing for reliable operation. This paper presents an experimental investigation into the latency performance of various Linux kernels, including standard Linux, a low-latency kernel, Xenomai, and a real-time kernel patched with PREEMPT_RT. Our test setup utilizes a data acquisition card to measure the latency between sending and receiving a pulse signal through analog input-output channels, generated by a C++ code. This latency metric serves as an indicator of the responsiveness of the kernel and other control objects on a specific computer system. Our experiments were conducted under a wide range of conditions to comprehensively assess latency performance. This includes different versions of standard and real-time Linux kernels, varying numbers of CPU cores, program priority levels, data saving rates, a range of data acquisition cards, communication protocols, thread assignments to processor cores, and test durations. The results highlight the importance of long-term testing to accurately determine the maximum latency. Furthermore, the findings demonstrate significantly lower latency for the PREEMPT_RT patched kernel across various tests, indicating its suitability for demanding real-time control applications that require tight timing constraints.
Signal forecasting in the medical field has many applications, such as signal correction and anomaly detection. According to this application, robust forecasting is required to obtain a signal identical to the original signal. This study proposes a forecasting technique that obtains a robust signal that can be used in different applications. A long short-term memory neural network (LSTM-NN) was used to predict future samples from present and past samples. An Electroencephalography (EEG) dataset was used to test this technique. Four channels were used as input examples, one of which was the predicted output. All four channel samples were fed into the four networks to predict the future samples. To decrease complexity, only one hidden layer is used for this purpose. The statistical results are promising for applications that require an almost perfectly predicted signal. The number of hidden cells is first very low (five cells only), which gives a Root Mean Square Error of less than 20, whereas when the number of hidden cells is increased to 100, the Root Mean Square Error (RMSE) is approximately 7.5 for all four channels.
CMOS technology, after contributing a lot to electronics world, is now facing difficulties in designing of more efficient circuits in terms of compactness, power efficiency and speed. It is happening due to various side effects being generated on account of further down scaling of feature size. The Quantum Dot Cellular Automata (QCA) technology seems to be alternate and promising technology for designing of more efficient circuits. The cryptographic encoder and decoder are the key component for secure and safe communication. This paper presents an efficient design of 1:2 demultiplexer, 1:4 demultiplexer and 4:1 multiplexer which are further used to design a cryptographic nano communication circuit. The proposed circuits are efficient in terms of energy, area and speed. The architectures are designed through multilayer approach in QCA technology that makes it compact. The efficiency of the proposed circuits has been verified through the tool QCA Designer 2.0.3.
In contemporary power systems, it is crucial to ensure stable voltage levels to mitigate the fluctuations resulting from diverse load conditions. On-load tap changers (OLTCs) play a pivotal role in addressing these fluctuations by dynamically adjusting the number of turns in the transformer winding. This study investigates the integration of OLTCs within transformer designs, focusing on various methodologies related to tap winding order and configurations, which are vital for both electrical and magnetic performance. A comprehensive review of the operational principles governing different types of OLTCs is provided, highlighting their significance in voltage regulation. Furthermore, this paper analyzes the impact of linear OLTC winding order on the short-circuit impedance of a 30 MVA transformer. The findings underscore the importance of OLTC selection and design in optimizing transformer performance.
The present study aims to design, analyze, and simulate the synchronous reluctance motor (SynRM) based on the IEC90L frame and IE4 efficiency class. Initially, the permissible losses are calculated for the SynRM considering the given efficiency class. The SynRM is then designed using the calculated losses to generate the highest possible output power. In order to achieve optimal performance in terms of output power and power factor (PF), a parametric per-unit system is utilized for SynRM analysis, and the dimensions of various parts of the motor are determined based on design inputs (copper losses and magnetic loading). Subsequently, given this parametric model and the changing range of per-unit parameters, the characteristics of the available motors are thoroughly monitored with respect to output parameters, and the motor model is selected. To validate the analytical model, the finite element analysis (FEA) is conducted for the selected model, and the simulation results are compared with those of the analysis method and design inputs. Ultimately, to enhance overall motor performance, an optimization process was conducted, followed by a comprehensive evaluation of the optimized model to assess efficiency and torque improvements.
The rising demand for electricity has led to the installation of renewable-based distributed generators in a power system network to meet the increasing load. The eco-friendly nature of these DGs is another compelling reason to incorporate them in a power system network but their installation process requires careful consideration such as determining the optimal quantity and location because these factors have a significant impact on various constraints and parameters of the power system network. The main objective of this paper is to determine the optimal siting and sizing of Type-1 and Type-2 DGs in a power system network such that network has minimum real and reactive power losses in the transmission lines, also fuel cost of convectional generators is reduced and voltage profile is improved. For this purpose, hybrid GA-PSO approach is developed and implemented on case 33 bus system and results were compared under different loading conditions such as 100%, 150%, 200% to show which type of DG is most effective. Further, the evaluated results have been compared with other algorithms including OCDE, WOA, SFSA, TGA and EJSA in order to ensure the validity of the suggested approach. The numerical results validate the performance of this proposed technique for DG unit placement.
This paper introduces an improved multi-conductor transmission line (MTL) model for transformers' high-frequency transient and frequency response analysis, overcoming limitations in traditional models that fail to capture complex electromagnetic interactions during high-frequency events, such as lightning strikes and switching operations. The model accurately reflects real-world transformer behaviors under transient conditions by integrating particle swarm optimization (PSO) for efficient parameter estimation and incorporating frequency-dependent losses. The combined use of PSCAD and Python minimizes computational overhead, enabling high-fidelity simulations closely aligned with experimental transformer data. Validation against real transformer measurements demonstrates the model’s reliability in capturing high-frequency responses, essential for transformer diagnostics. This novel approach offers a practical tool for studying transformer frequency response analysis, which is an important tool in transformer diagnosis.
This paper introduces a unique rotor pole configuration for an Axial-Field Flux-Switching Permanent Magnet (AFFSPM) machine, focused on minimizing cogging torque (CT), reducing torque ripple (TR), and improving average torque (AT). This innovative design is based on the standard rotor configuration of the AFFSPM machine, with a Reversed Radial Pole (RRP) placement that this new topology will be recognized as RRPAFFSPM. To thoroughly evaluate the proposed design's effectiveness, sensitivity analysis will be conducted to determine the significance of geometric parameters and identify the best topology in comparison studies. Extensive 3D finite element analysis (FEA) confirms the design's effectiveness, demonstrating substantial reductions in CT and TR, along with an increase in AT. These results suggest that the desired rotor pole configuration is a promising solution for high-performance electric machines in demanding different applications.
Grounding systems are critical for ensuring electrical safety, minimizing fault currents, and enhancing infrastructure reliability, particularly in regions with high-resistivity soil. This study presents the design, simulation, and field implementation of a low-resistance earthing system integrating bentonite, charcoal, and sodium chloride to reduce soil resistivity. Using ETAP software, the performance of the Finite Element Method (FEM) and IEEE Std. 80-2013 grounding models are compared under a 30kA fault current scenario. FEM simulations predict a ground resistance of 0.028 Ω and a Ground Potential Rise (GPR) of 627.4 V, while the IEEE method yields 0.269 Ω and 5996.5 V, respectively. Field measurements using a UNI-T Ground Tester validate the FEM results, recording an actual ground resistance of 0.023 Ω, well below the IEEE-recommended 1 Ω threshold, surpassing this conventional benchmark by 98%. A comparative analysis of recent studies highlights the superiority of the composite material approach. The FEM model’s accuracy in capturing soil stratification and material effects is validated, while safety metrics (step/touch voltages) adhere to the IEEE standard. This work bridges theoretical innovation and practical implementation, offering a replicable framework for resilient grounding systems in challenging environments.
In electric propulsion systems for high-power applications, multi-phase Permanent Magnet Synchronous Motors (PMSMs) are highly advantageous due to their fast dynamic response and high reliability. This study investigates a twelve-phase PMSM with double stator windings, where each winding is powered by a single-phase H-bridge inverter. The control of both H-bridge inverters for each phase is managed by a dedicated microcontroller. Given the independence of the control systems (microcontrollers) and the absence of data exchange between them, the modeling is conducted in the 12-phase stationary reference frame. To address non-sinusoidal back-EMF phase voltages and mitigate torque ripple, a harmonic current injection method is independently applied to each phase. A model-free predictive current and speed controller (MFPCSC), based on an ultra-local model, is employed, replacing conventional PI or hysteresis current controllers. Additionally, extended state observers (ESOs) are designed to estimate uncertainties and parameter mismatches. Under fault conditions, a fault-tolerant control strategy is implemented, where the current angle of healthy windings is adjusted to suppress the second harmonic in the remaining healthy windings, thereby reducing torque ripple. The effectiveness of the proposed control methods is validated through simulations, both under normal operating conditions and various fault scenarios.
Electromagnetic Tube Expansion (EMTE) is a high-velocity forming process that utilizes transient magnetic fields to plastically deform tubular workpieces without physical contact. The process requires the generation of large currents via a capacitor bank, producing intense magnetic pressures to achieve deformation. While EMTE offers significant advantages in precision and efficiency, a comprehensive understanding of the interplay between key working conditions and deformation mechanisms remains crucial for optimizing its performance. This paper presents a numerical investigation into the effects of critical working conditions on the electromagnetic tube expansion process. Using a coupled finite element model, the transient magnetic field and resultant tube deformation are analyzed under varying conditions. The results provide insights into the relationship between process parameters and deformation outcomes, highlighting the potential for optimizing EMTE systems for enhanced efficiency and uniformity. This study contributes to advancing the theoretical and practical understanding of EMTE, by offering guidance for the design of more effective forming strategies and equipment.
Synchros are electromagnetic sensors utilized to determine the angular position of a rotating shaft. This paper examines the impact of leakage flux from the Rotary Transformer (RT) on the induced voltages and the position detection accuracy of the Wound-Rotor (WR) synchro. Various methods are proposed to mitigate the negative effects of leakage flux from the RT. The leakage flux paths, which couple with the signal winding, are identified. Based on this analysis, the optimal distance between the sensor and the RT is calculated to minimize the adverse effects of leakage flux on the synchro's accuracy. Additionally, the RT structure is modified to reduce the leakage flux. Another effective approach involves the use of Electromagnetic Interference (EMI) shielding. In this context, a shield frame is designed for the RT, and the impact of different shield materials on reducing leakage flux is investigated. The results show that a copper-based shield significantly reduces the adverse effects of leakage flux and improves the sensor’s accuracy. To evaluate the effectiveness of the proposed methods, they are assessed through 3-D Time-Stepping Finite Element Analysis (3-D TSFEA) and experimental measurements on a prototype sensor. The experimental results show close agreement with the 3-D TSFEA, confirming the accuracy of the findings.
This research explores the demands of compressive sensing (CS) and Machine learning (ML) in biomedical signal processing. The sparse spasmodic sampling (SSS) technique has gained significant attention in compressive sensing. The SSS samples the signal irregularly and spasmodically. Combining machine learning (ML) with Sparse Spasmodic Sampling (SSS) enhances accuracy and improves anomaly detection in biomedical signals. We propose a machine learning-based novel fusion technique that enhances sparse spasmodic sampling (ML-SSS). Mathematical analysis, extensive simulations, and experimental results show notable improvements in reconstruction accuracy and precision. The reconstruction using the proposed model achieves a high signal-to-noise ratio (SNR) of up to 42 dB at a high compression factor of 10%. The achieved accuracy is approximately 95%, and the precision is about 93.3% when detecting abnormalities. This approach paves the way for advanced applications in signal processing and medical imaging, where efficient data acquisition and processing are critical. The proposed framework offers a promising direction for bridging the gap between compressive sensing and intelligent algorithms in anomaly detection.
