Search
328 articles for “errors”
-
DC Motor Control using Deep Reinforcement Learning for Enhanced Robustness and Precision
Abstract: DC motors remain the workhorse of industrial automation and mobile robotics, but achieving simultaneous high-speed transient response and negligible steady-state error under variable load conditions continues to challenge classical Proportional-Integral-Derivative (PID) controllers. These model-dependent systems often require extensive tuning and struggle to maintain optimal performance when confronted with parametric uncertainties, non-linear friction, or sudden voltage fluctuations. This study presents a novel, model-free control paradigm utilizing Deep Reinforcement Learning (DRL)—specifically, a …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 3, Issue 2, 2025 · pp. 22–29 Read article
-
Log Identification and Monitoring System Using Generative AI
Abstract: In contemporary software ecosystems, application and infrastructure logs play a vital role in ensuring system reliability, performance optimization, fault diagnosis, and security compliance. As applications become increasingly distributed and cloud native, the volume, velocity, and variety of generated log data have grown dramatically. This rapid expansion makes traditional manual log inspection inefficient, error-prone, and largely impractical. To address these challenges, this paper proposes an artificial intelligence (AI) driven log monitoring …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 08–16 Read article
-
Power and Area - Aware Recursive Multiplier Architecture Utilizing Polymer Composites for Neural Network Acceleration
Abstract: Approximate computing is widely applied in error - tolerant systems as an effective technique to enhance circuit performance by deliberately allowing occasional inaccuracies instead of strictly ensuring precise results for every computation. Among the fundamental building blocks of digital systems, multipliers play a crucial role in signal processing, control systems, and machine learning applications; however, they demand significant power, silicon area, and timing resources. Leveraging error - tolerant approximate multipliers …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1320–1337 Read article
-
A study on Parametric and Non-Parametric Statistical Tools
Abstract: This research report provides a brief overview of the most recent research techniques. The field of the research process has seen a resurgence of attention due to recent advancements in academia. Professors, Ph.D. candidates, researchers, investigative officers, and university students comprise the demographic of this research article. The article will become an essential part of your research papers, projects, and reports for citations due to future obligations. This study is …
Published in Journal of Production Research & Management · Vol. 16, Issue 1, 2026 · pp. 10–31 Read article
-
Optimized Machine Learning Framework for Battery State Prediction in Smart Charging Systems
Abstract: Good estimation of battery states, including State-of-Charge (SoC), State-of-Health (SoH), and Remaining Useful Life (RUL), are important in managing energy wisely and controlling the adaptive charging. This work introduces a streamlined machine learning model based on the ability to use multi-dimensional sensor measurements in terms of voltage, current, temperature, and cycle number to forecast battery conditions with high accuracy. Decent preprocessing, such as noise elimination, feature scaling, and calculated features, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 11–23 Read article
-
Hydrodynamic Analysis of Symmetrical Naca Airfoils: A CFD Simulation Approach
Abstract: This study presents a comprehensive numerical investigation of the hydrodynamic characteristics of selected NACA airfoil profiles using advanced computational fluid dynamics (CFD) simulations performed in ANSYS Fluent. The primary objective is to evaluate the fluid–structure interaction behavior of these airfoils when subjected to marine flow conditions relevant to offshore and submerged engineering applications. By modeling the interaction between incompressible fluid flow and various NACA geometries, the study systematically analyzes key …
Published in Journal of Offshore Structure and Technology · Vol. 13, Issue 1, 2026 · pp. 31–38 Read article
-
AI-Driven Topology Optimization of Woven Fiber-Reinforced Composite Chassis Structures for Electric Vehicles Under Crash Loading
Abstract: The structural design of an electric vehicle (EV) chassis represents a unique engineering challenge to achieve minimal weight while meeting occupants' safety requirements during high-energy crash conditions without compromise to the battery housing's integrity or the geometrical constraints of the electric powertrain package. In this paper, a single framework is proposed to integrate physics-based artificial intelligence (AI) surrogate models using PINNs, CNN-accelerated topology optimization, and FEA to design woven fiber-reinforced …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 72–89 Read article
-
SmartTrack : Advanced Attendence System using LR RFID
Abstract: This project presents the design and implementation of a long-range RFID attendance system aimed at transforming how educational institutions track attendance by making the process faster, more accurate, and completely contactless. Tradi- tional methods, whether manual roll calls or short-range RFID scanners, often interrupt class routines and leave room for errors or proxy attendance. To address these issues, the proposed system integrates long-range RFID readers, beam sensors, and ESP32 microcontrollers …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 1, 2026 Read article
-
Machine Learning Assisted Design and Analysis of Polymer Composite Materials for Sustainable Renewable Energy Systems
Abstract: Accurate prediction and optimization of polymer composite properties is of paramount importance in the design of these lightweight, durable, and sustainable materials within renewable energy technologies. This work will provide a holistic machine learning-assisted framework that unites materials informatics with domain-specific features and state-of-the-art ML methodologies in the prediction of the mechanical properties of polymer composites, such as tensile strength. This includes embedding several ensemble models, including Random Forest and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 391–402 Read article
-
Experimental and Polynomial Regression Modelling of Tensile Characteristics of Hybrid Jute/Hemp Fiber Composites
Abstract: The present study aims to develop hybrid jute-hemp fiber/epoxy composites by the hand lay-up process with 60% of fiber reinforcement and 40 % of matrix ratio. To measure the change of tensile properties, prepared hybrid composites were tested for normal tensile strength and edge notch tensile (ENT) test as per the ASTM standards. Findings show that, developed composites with reported tensile strength followed by comparing it with fracture toughness. Experimentally, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 47–58 Read article
-
Bending and Single Edge Notch Bending Test (SENB) Investigation of Natural Fiber- Reinforced Epoxy Composites using Machine Learning
Abstract: The present study aims to determine the behavior of hemp fiber-reinforced epoxy composites in terms of bending behavior and fracture toughness under bending load resembles the substitutive behavior of existing synthetic composites. The fabrication was carried out by hand lay-up assembly of hemp fiber with Lapox-12 epoxy resin volume fraction of 60:40 fiber: matrix volume. Flexural testing revealed an average strength of 93.5 ± 2.8 MPa and SENB testing revealed …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 59–69 Read article
-
Structural Performance Evaluation of Circular Perforated Plates under Mechanical Loading
Abstract: Circular perforated plates are structurally critical components in aerospace, automotive, and marine sectors, where perforation-induced stress concentrations govern failure under mechanical loading. This study conducts a systematic finite element analysis of stress distribution, deformation, and interlaminar behavior in aluminum and glass-epoxy laminates ([−45/45/90/0]S and [−45/45/90/0]AS) under uniform transverse pressure with clamped-free boundary conditions. Four perforation configurations—no hole, central hole, single-series, and 25-hole grid—were evaluated using ANSYS Shell-181, verified against Kirchhoff–Love, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 1–20 Read article
-
Lyapunov-Stable Adaptive Fractional-Order Interval Type-2 Fuzzy Control for Robust Anti-Lock Braking Under Uncertain Road Adhesion Conditions
Abstract: This paper proposes a Lyapunov-stable Adaptive Fractional-Order Interval Type-2 Fuzzy Logic Controller (FO-IT2FLC) for robust anti-lock braking system (ABS) control under nonlinear vehicle dynamics and uncertain road adhesion conditions. The proposed framework integrates fractional-order error dynamics to capture memory-dependent tire–road interaction, interval Type-2 fuzzy inference to model uncertainty via footprint-of-uncertainty representation, and a Lyapunov-based adaptive learning mechanism for real-time parameter tuning. A rigorous stability proof guarantees boundedness of all closed-loop …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 2, 2026 Read article
-
Bias Detection and Accuracy Enhancement in Voice-based Banking Authentication Using Deep Learning
Abstract: Biometric systems have become an integral part of how many people access banking services today, and voice verification systems can be a secure and easy-to-use source of banking authentication that does not require any physical contact with the bank or any other person. From the security perspective, these systems would normally provide an effective means of identifying an individual but frequently exhibit bias with respect to demographics such as the …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 2, 2026 Read article
-
Generative Design of Bioactive Orthopedic Composites for Fracture Repair Using an Integrated Conditional GAN–Transformer Framework: A Multi-Objective Approach
Abstract: Orthopedic composite implants for fracture repair must simultaneously satisfy conflicting mechanical and biological demands: high fracture toughness, sufficient compressive stiffness, and bioactive surface chemistry enabling osteoblast adhesion and mineralization. Existing design approaches rely on trial-and-error experimentation, yielding sub-optimal trade-offs between these objectives. This paper presents an integrated conditional Generative Adversarial Network–Transformer (cGAN-T) framework for fully computational, multi-objective generative design of hydroxyapatite (HA)-reinforced polymer composite microstructures targeting Orthopedic fracture repair. A …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 21–35 Read article
-
Industrial Prognostics via Ensemble Machine Learning: An Uncertainty Aware Framework for RUL Estimation on NASA FD004 Telemetry
Abstract: Estimating the Remaining Useful Life (RUL) of industrial machinery in real-time is now vital for both operational safety and smart resource management. In the aviation industry, turbofan engines deal with constantly shifting flight conditions, making traditional, scheduled maintenance both expensive and prone to error. This paper addresses the flaws in common “point-prediction” AI models, which offer a single failure date without any margin for error, by introducing a new, uncertainty-aware …
Published in Journal of Aerospace Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
-
A Factorial Investigation of Hyperparameter Tuning Strategies for Lasso- Based Genomic Prediction
Abstract: In an earlier comparative study of machine-learning methods for genomic prediction of wheat grain yield, we reported a counter-intuitive result: automated nested-cross-validation tuning of the Lasso regularization penalty reduced mean predictive ability relative to a fixed, arbitrarily chosen penalty (mean Pearson r falling from 0.408 to 0.349 across four environments), the opposite of the expected effect of hyperparameter tuning. We hypothesized two possible explanations at the time — high-variance penalty …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
-
The Overview of Software Testing: Types, Methods, and Levels
Abstract: Software development life cycle (SDLC) is a software development model consisting of different phases i.e., analysis, design, coding, testing, deployment, and maintenance. The analysis phase is the primary phase in which all the requirements are gathered and analysed which is further used for designing and coding. The next phase is testing. Software testing is a process of evaluating software so as to check whether it satisfies the requirements of the …
Published in Journal of Software Engineering Tools & Technology Trends Read article
-
An Efficient Method of Fault Analysis using Artificial Neural Network
Abstract: In the power system, there are many techniques to identify and classify the faults. So, it is utmost important to choose the suitable technique. In this paper, a novel technique based on ANN have been proposed. When abnormal conditions occur in the system, the purposed method identifies and classify the fault to protect the system from the faults and stop from the big hazards. Simulation of purposed Simulink model have …
Published in Current Trends in Signal Processing Read article
-
Simulation of Wavelength Division Multiplexing Based Fiber Optic Link by Using Different Types of Dispersion Compensating Fiber
Abstract: Wavelength division multiplexing (WDM) multiplies various optical signals on a single optical fiber by using different wavelengths of laser light at the transmitter end and demultiplex them into multiple opticalsignal at the receiver end. We want to improve overall system performance and reduced as much as possible the transmission performance influenced by the dispersion, several dispersion compensation technologies are involved. In this paper, we have studied the WDM based fiber …
Published in Trends in Opto-electro & Optical Communication Read article