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268 articles for “error model”
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Optimization of Liquid Metal Nanocomposites and Biogas Addition Rate Using ANN-GA
Abstract: In this study, the liquid metal nanocomposites were investigated using artificial neural network (ANN) prediction capabilities for Compression Ignition (CI) engine performance. The independent input variables selected were load (20-100%), Liquid-metal nanocomposites Doped Rate (NDR, 0-50 ppm), and Biogas Flow Rate (BFR, 0.5-1.0 kg/h). The Central Composite Face-Centered Design (CCFCD) was used in conjunction with the selected input variables and output parameters to assist in the preparation of the Design …
Published in Journal of Polymer & Composites · Vol. 11, Issue 11, 2023 · pp. 12–27 Read article
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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
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AI-integrated Responsive Polymer Composites for Controlled Drug Delivery
Abstract: In the current biomedical engineering, it has been established that the development of smart drug delivery systems has become a paramount of relevance especially in ensuring precise, controlled and targeted therapeutic effects. This paper proposes a responsive polymer composite architecture with built-in AI, which is used to deliver drugs in a controlled manner and involves the development of advanced material design and predictive modeling based on data. Biocompatible materials and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Artificial Neural Network Based Prediction of Impact Loads and Thickness in CFRP and GFRP Composite Laminates
Abstract: Recent technological advancements, particularly the integration of neural networks, have facilitated a predictive approach to complex engineering problems, especially those involving composite materials with directional properties. The scarcity of literature on predicting impact damage using experimental and ultrasonic flaw detection data motivated this study. Experimental assessment of impact damage on carbon fiber/epoxy (CFRP) and glass fiber/epoxy (GFRP) composites was conducted using low-velocity drop weight impact testing. Damage assessment employed an …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 2, Issue 1, 2024 · pp. 34–45 Read article
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Role of Satellite Data Assimilation on ERA-Interim and ERA5 Wave Parameter Ratios – A Case Study based on Year-long In-Situ Observations in the Bay of Bengal
Abstract: The rapid decline in the energy resources forced mankind to tap other forms of natural energy resources in the light of exponential increase in the demand due to over-population. The energy from ocean waves is one of the cleanest sources of energy available perennially that changes seasonally and is site-specific. To assess the wave power potential at any site, knowledge of the wave parameters such as wave height, period and …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 1, 2025 · pp. 1–20 Read article
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Role of Artificial Intelligence in Structural Health Monitoring-A Brief Evaluation
Abstract: Artificial intelligence (AI) refers to the capacity of a machine or a computer to ‘think’ or reason in the way a human would, utilizing experience, learned facts, and flexible rules to solve problems that may not fit the standard outlines for a normal algorithm. From this follows the utilization of AI in various sectors, such as the information technology (IT) industry, media, healthcare and medicine, logistics, environmental sustainability, finance, business, …
Published in Journal of Structural Engineering and Management · Vol. 13, Issue 1, 2026 · pp. 34–39 Read article
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article
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Thermodynamic modelling of the effects of high temperatures on brine fluid density
Abstract: Current trends in oil discoveries points to deep water zones characterized by temperatures ranging from 150oC to 350oC which needs good prediction of the fracture pressure and pore pressure hence imperative to continually understand temperature-density relationships of the fluids which are pumped downhole for well control. This paper seeks to propose a new, cost effective and easier method of estimating the reduction in density of brine completion fluid as it …
Published in Journal of Petroleum Engineering & Technology · Vol. 9, Issue 1, 2019 · pp. 26–35 Read article
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BER Improvement in Fading Conditions Using QAM
Abstract: AbstractThe future mobile network requires huge capacity, coverage, energy efficiency and spectral efficiency. In different fading conditions, like Gaussian noise channel, Rician fading and Rayleigh fading conditions, higher order QAM can improve spectral efficiency by improving bit error rate. Additive white Gaussian noise model describes the effect of random processes in nature. Rayleigh distribution has static time varying nature on the receiver side. The envelope of Rician fading is small …
Published in Trends in Opto-electro & Optical Communication · Vol. 7, Issue 2, 2017 · pp. 1–6 Read article
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Graph Neural Networks for Molecular Scale Property Prediction and Inverse Design of Thermoset Polymer Nanocomposites: A Computational Framework
Abstract: Thermoset polymer nanocomposites exhibit properties that are highly sensitive to molecular scale formulation decisions, yet the vast design space remains largely unexplored because of the high cost of experimental characterisation and fully atomistic simulation. This paper presents TNC GNN, a dual mode graph neural network framework developed for the computational design of thermoset nanocomposite formulations. The forward module employs an attention augmented Message Passing Neural Network with 3D geometric encoding …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Machine Learning Approach to Predict the Performability and Emissions of Diesel Engine Fueled with Doped Biodiesel Blend
Abstract: Enhancing the performability and emission characteristics of diesel engines has been a difficult task in light of growing concerns about global warming and other negative effects, as diesel accounts for 70% of global energy demand. In this study, engine performance and exhaust emissions for various fuel blends were thoroughly evaluated using machine learning techniques to predict engine emission and performance behavior. We focused on biodiesel blend and nanoparticle additive concentration …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 1, 2025 · pp. 1–12 Read article
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Harnessing Deep Learning to Explore Microbial Community Structure and Carbon Storage Capacity in Mangrove Ecosystems: A Framework for Computationally
Abstract: Mangrove ecosystems represent one of the most efficient natural carbon sinks on Earth, functioning as critical blue carbon habitats that sustain diverse microbial communities responsible for biogeochemical cycling and long-term carbon storage. Despite their global ecological significance, accurately quantifying and predicting carbon sequestration in mangrove systems remains challenging due to the complex interactions between microbial diversity, sediment chemistry, and environmental drivers. This study presents a comprehensive and sustainable artificial intelligence …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 41–49 Read article
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Physics-Adaptive Digital Twin with Neural-Operator Reduced-Order Modelling
Abstract: This study proposes a novel Physics-Adaptive Digital Twin with Neural-Operator Reduced-Order Modelling (PADT-NO) framework for predictive modelling of complex, nonlinear, and multiscale fluid flows. The proposed mathematical framework integrates fundamental conservation laws, Navier–Stokes dynamics, physics-constrained neural operators, adaptive reduced-order modelling, and uncertainty-aware state estimation within a unified computational architecture. Unlike conventional computational fluid dynamics and purely data-driven approaches, the proposed model dynamically couples high-fidelity physical information with a low-dimensional latent …
Published in Recent Trends in Fluid Mechanics · Vol. 13, Issue 2, 2026 · pp. 89–103 Read article
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Design of a Closed Loop Boost Converter with Parametric Variation Analysis of PI Controller for Constant Output Voltage Applications
Abstract: The DC-DC converters have an unregulated input dc voltage and a constant or regulated output dc voltage. Switching DC-DC voltage converters have two elements: A controller and a power stage. The power stage regulates the switching elements and converts input voltage to output voltage. The controller controls the switching operation to regulate the output voltage. The two systems are linked by a feedback loop that compares the actual output voltage …
Published in Journal of Power Electronics and Power Systems · Vol. 6, Issue 3, 2016 · pp. 1–13 Read article
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The Application of Python Programming Language in the Automation of a Mathematical Model for Well Trajectory Selection Optimisation for a Given Field
Abstract: Directional drilling plays a major role in the exploration and recovery of hydrocarbons. There are three basic trajectories that a well may follow. They include; Build-and-Hold, Build-Hold-andDrop and Continuous-Build Trajectories. The selection of a trajectory is usually based on experience/trial and error approach and may be associated with errors. This work has therefore developed a mathematical model that optimised the automatic selection of a well trajectory type for a given …
Published in Journal of Petroleum Engineering & Technology · Vol. 12, Issue 3, 2022 · pp. 1–9 Read article
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Rainfall-runoff Modeling using HEC-HMS Hydrologic Model for Guder River Watershed, Blue Nile Basin, Ethiopia
Abstract: Rainfall-runoff modeling is important for a number of hydrologic applications including flood forecasting, water resource planning and management. This study presents the result of a watershed rainfall-runoff modeling for Guder river watershed having area of 6597 km 2 using Hydrologic Engineering Center-Hydrologic Modeling Systems (HEC-HMS). The watershed runoff is varying spatially and temporally due to manmade factors and natural factors on the watershed. These issue needs efficient water resource planning …
Published in Journal of Water Resource Engineering and Management · Vol. 8, Issue 2, 2021 · pp. 62–77 Read article
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Prediction of Excitation Current of Synchronous Machines Based on Neural Network Model
Abstract: There are several difficulties found to estimate the excitation current & and optimum input parameters of synchronous motors. Heuristic methods are frequently used to weightt the problem's parameters or optimum coefficients. As a result, a neural network model is modified in this study to explore the best parameters and estimate the excitation current of a synchronous motor with minimal prediction errors for both the testing dataset and cross validation. Excitation …
Published in Recent Trends in Electronics Communication Systems · Vol. 10, Issue 1, 2023 · pp. 28–33 Read article
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Performance Analysis of Wireless Propagation Model to Improve Signal Strength and BER for Wireless Mobile Network
Abstract: AbstractWhenever we setup a network, environment properties such as fading, scattering etc. plays a very important role in choosing spectrum and method of propagation. The spectrum deals with frequency range suitable for communication in a particular environment, and the propagation method deal with channel selection, channel sensing and to perform quality communication throughout the network. To achieve quality output from the network, it is necessary to define network under different …
Published in Journal of Communication Engineering & Systems · Vol. 5, Issue 2, 2015 · pp. 1–7 Read article
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Electric Vehicle Induction Motor Automated Drive System with Smart Battery Monitoring Performance for Range Exchanger
Abstract: This study describes the development of a model of an electric vehicle (EV) with a smart battery-powered inverter-controlled induction machine drive system. The computer simulation model in MATLAB Simulink is used to estimate the energy and power requirements of vehicles over standard driving cycles under various driving conditions. Here, using smart logic for battery performance, factors affecting range and energy use, helps to optimize maximum efficiency of battery and motor …
Published in Current Trends in Signal Processing · Vol. 10, Issue 3, 2020 · pp. 1–7 Read article
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Deep Learning Enhanced Compressive Sensing for Wireless IoT Data Optimization and Weather Monitoring.
Abstract: This research explores the application of deep learning and compressive sensing in order to optimize data traffic in non-orthogonal multiple access (NOMA)-based wireless internet of things (IoT) networks and weather monitoring. Such a framework would be very effective and overcome pilot attacks and reconstruction losses for secure data transmission. In this regard, a strong communication model has been adopted based on power-domain NOMA for simultaneous wireless transmission by multiple IoT …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 20–36 Read article