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268 articles for “error model”
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A Suboptimal Nonlinear Duty-cycle Modulation Scheme
Abstract: The DCM (duty-cycle modulation) technique is increasingly used in industrial electronics applications, including instrumentation systems, interfacing drivers and signal transmission chains. However, in existing research works related to new applications of DCM technique, the linear approximation policy is used for the sake of structural simplicity and low implementation cost, at the expends of rigorous analysis and low approximation errors. In this paper, a suboptimal nonlinear DCM scheme is developed. It …
Published in Journal of Electronic Design Technology · Vol. 7, Issue 1, 2016 · pp. 22–31 Read article
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Comparative Study of Aggregate and Disaggregate Traffic Forecasting Technique for Industrial Corridor: Case Study of Vadodara District
Abstract: AbstractTransportation occupies a prominent place in modern life and its impact is spread in all domains of life. Transport planning is a discipline to study problems rising while planning transport facilities at urban, regional or national level and to prepare efficient basis for providing such facilities. Aim of transport planning at regional level is provision of connectivity and circuity with other regions as well as for expansion of existing facility …
Published in Trends in Transport Engineering and Applications · Vol. 5, Issue 1, 2018 · pp. 14–21 Read article
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Study of Stainless Steel’s Properties for Radiation Safety using Artificial Neural Networks
Abstract: Several general components of reactor are made from stainless steel. Most of the container used for storing nuclear waste is made from stainless steel. In this work, an artificial neural networks (ANNs) model is used to improve the properties of stainless steel during its manufacturing process. The main job of ANNs is determining the layer’s thickness and predicting the influence of different parameters on the growth kinetic of the process. …
Published in Journal of Nuclear Engineering & Technology · Vol. 8, Issue 3, 2018 · pp. 31–40 Read article
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Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials
Abstract: The real-time prediction of mechanical properties in polymeric materials is essential for ensuring quality, consistency, and operational efficiency in modern manufacturing systems. As industrial processes become increasingly complex, traditional trial-and-error approaches to material characterization are no longer sufficient to meet the demands of high-throughput production environments. This study introduces a digital twin-integrated machine learning approach for the real-time estimation of tensile strength in polymeric materials by combining simulation-driven insights with …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 246–257 Read article
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AI-Assisted Gain Scheduling for Real-Time Temperature Control in Chemical Reactors
Abstract: Temperature control in continuous stirred-tank reactors (CSTR) represents a critical challenge in chemical process industries due to inherent nonlinearities, time-varying dynamics, and parametric uncertainties. Conventional proportional-integral-derivative (PID) controllers with fixed gains often fail to maintain optimal performance across varying operating conditions, leading to temperature excursions that compromise product quality and safety. This paper presents a novel AI-assisted gain scheduling framework that integrates artificial neural networks (ANN) with adaptive PID control …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 1, 2026 · pp. 24–33 Read article
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Understanding Sentiment Trends Through Zero-Shot and Few-Shot Learning Models
Abstract: The requirement for large, manually labeled datasets is one of the main barriers to applying sentiment analysis algorithms in specialized or rapidly evolving disciplines in the present natural language processing (NLP) landscape. This work investigates a paradigm shift from traditional fully supervised learning to data-efficient methods, specifically zero-shot learning (ZSL) and few-shot learning (FSL). This study uses the advanced capabilities of instruction-tuned large language models (LLMs), like GPT-4, to assess …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 01–08 Read article
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House Price Estimation Using Linear Regression: A Machine Learning Perspective
Abstract: House price prediction plays a crucial role in the real estate industry, helping buyers, sellers, and investors make well-informed decisions. Accurate estimation of property values enables stakeholders to assess market trends, plan investments, and minimize financial risks. This study focuses on the application of linear regression, a fundamental and widely used machine learning algorithm, to predict house prices based on multiple influencing factors. These factors include location, property size, number …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 1, 2026 · pp. 21–29 Read article
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Structure–Property Modeling of Cement-Based Multi-Component Composites Using Ensemble Machine Learning and Explainable Feature Attribution
Abstract: Accurate prediction of compressive strength is central to structure–property optimization, quality control, and sustainability-driven design in cement-based composite materials. Cementitious systems represent heterogeneous multi-phase composites composed of reactive binder matrices and dispersed aggregate phases, whose macroscopic mechanical performance emerges from complex nonlinear interactions among constituents and curing-dependent microstructural evolution. This study develops a data-driven structure–property modeling framework to quantify the nonlinear dependence of compressive strength on multi-component composite composition and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 112–131 Read article
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Self-Compensation of Commutation Phase Error for Brushless DC Motor
Abstract: BLDC motors are synchronous motors powered by DC electricity via an inverter which produces an AC electric current to drive each phase of the motor via a closed loop controller. Electronic commutation is the on-and-off of the semiconductor switches at appropriate time to turn stator windings. Commutation phase ripple is generated due to the different exchange rate between outgoing phase current and incoming phase current of inverter during commutation period. …
Published in Trends in Electrical Engineering · Vol. 8, Issue 3, 2018 · pp. 72–81 Read article
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Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches
Abstract: Stock price prediction is a crucial task in financial analysis, aiding investors and traders in making informed decisions. This study investigates the use of deep learning methods, particularly Long Short-Term Memory (LSTM) networks, for predicting stock prices based on historical market data. The dataset, sourced from Yahoo Finance, consists of time-series stock price data, which is preprocessed, feature-engineered, and visualized to improve prediction accuracy. The model's performance is assessed using …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 29–40 Read article
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Artificial Intelligence-Assisted Multi-Objective Optimization of Agricultural Biomass-Reinforced Polymer Composites
Abstract: Agricultural biomass can reduce the environmental burden of polymer composites, yet its heterogeneous structure creates competing effects on strength, moisture resistance, density, and process ability. This study developed an artificial intelligence-assisted framework for balanced composite formulation. Experimental data of agricultural biomass reinforced polymer composites were gathered, harmonized and validated using leakage-controlled validation. The mechanical and physical properties were predicted by artificial neural networks and conventional regression models. Explainable analysis gave …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 202–222 Read article
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Performance Comparison of IRT Method with NNMPC Method
Abstract: The continuous stirred tank reactor (CSTR) system was used for experimentation and the improved relay tuning (IRT) method was applied for design of optimum PID settings. The closed loop response was obtained using these optimum PID settings and compared with that of neural network model predictive control (NNMPC) method. The integral absolute error (IAE) criterion for the performance evaluation was used and it was found that the integral error has …
Published in Journal of Control & Instrumentation · Vol. 7, Issue 2, 2016 · pp. 1–4 Read article
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Temperature Control of Heat Exchanger using Sliding Mode Control Law
Abstract: Shell and tube heat exchanger system is widely used in chemical plants as it sustains wide range of temperature and pressure. The main purpose of heat exchanger is to transfer heat from a hot fluid to a cooler fluid, so temperature control of outlet fluid is having prime importance. To control the temperature of outlet fluid of shell and tube heat exchanger, a conventional PID controller can be used. But …
Published in Journal of Control & Instrumentation · Vol. 6, Issue 1, 2015 · pp. 14–26 Read article
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Digital Twin Assisted Intelligent Prediction of Polymer Composite Degradation Under Environmental Exposure
Abstract: Polymer matrix composites (PMCs) deployed in aerospace, marine, automotive, and renewable-energy structures are continuously subjected to coupled environmental stressors — ultraviolet (UV) radiation, moisture ingress, thermal cycling, and mechanical loading — that progressively degrade their mechanical performance. Conventional accelerated ageing tests and empirical lifetime models are time-consuming, destructive, and poorly suited to in-service, asset-specific degradation forecasting. This paper proposes a Digital Twin (DT) assisted intelligent prediction framework that fuses a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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INTEGRATION OF REVIT TO MICROSOFT PROJECT FOR CONSTRUCTION PROJECT MANAGEMENT: A COMPARATIVE ANALYSIS OF SCHEDULING AND RESOURCE ALLOCATION
Abstract: Building Information Modelling (BIM) has revolutionized the construction sector by having architectural, structural, and mechanical design on one 3D platform. Autodesk Revit application enables real-time updates and changes without increasing errors and omissions. Its parametric nature allows users to develop intelligent models, enhancing decision-making, resource allocation, and cost estimation. Revit's visual representation allows effective coordination and communication with stakeholders, promoting a higher degree of client satisfaction. The application and benefits …
Published in Journal of Construction Engineering, Technology & Management · Vol. 15, Issue 3, 2025 · pp. 13–46 Read article
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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
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Hybrid Machine Learning and Finite Element Framework for Predicting Damage Behavior in Fiber-Reinforced Polymer Composites
Abstract: Fiber Reinforced Polymer (FRP) composites have broad spread use in aerospace, automotive, marine and structural applications due to its high specific strength, stiffness and corrosion resistance. The various damage mechanisms such as matrix cracking, fiber breakage, delamination and interfacial failure, however, make the forecasting of damage particularly complex. In this work, a hybrid machine learning (ML) and finite element (FE) system is proposed for predicting the damage behavior of FRP …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Blind Image Quality Assessment: An Overview
Abstract: We have develop an efficient model for improving image quality using IQA and NSS based on blind image Quality Assessment.. This algorithm does computation for the parameters which user expect at output. The certain extracted features approach relies on a simple Bayesian inference model to predict image quality scores. The project features are based on statistic scenes of discrete cosine transform for images. The estimated parameters of the model are …
Published in Journal of Electronic Design Technology · Vol. 12, Issue 3, 2021 · pp. 1–3 Read article
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Blind Image Quality Assessment using NSS Approach in the DCT Domain
Abstract: We have develop an efficient model for improving image quality using IQA and NSS based on blind image Quality Assessment.This algorithm does computation for the parameters which user expect at output. The certain extracted features approach depends on a simple Bayesian inference model to dipict image quality scores. The project features are based on statistic scenes of discrete cosine transform for images. The resultant parameters of the model are used …
Published in Recent Trends in Electronics Communication Systems · Vol. 8, Issue 3, 2021 · pp. 1–6 Read article
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Blind Image Quality Assessment using NSS Approach in the DCT Domain
Abstract: We have develop an efficient model for improving image quality using IQA and NSS based on blind image Quality Assessment. This algorithm does computation for the parameters which user expect at output. The certain extracted features approach depends on a simple Bayesian inference model to dipict image quality scores. The project features are based on statistic scenes of discrete cosine transform for images. The resultant parameters of the model are …
Published in Recent Trends in Electronics Communication Systems Read article