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268 articles for “error model”
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Intelligent Failure Detection in Biomedical Composite Materials Using Machine Vision
Abstract: The biomedical composite materials are intelligent failure-detecting, which is necessary to ensure the reliability, safety, and durability of the current healthcare equipment. This paper describes a machine vision design, which incorporates convolutional neural networks, transformer models, and ensemble learning to correctly detect and localize material defects. The proposed system takes advantage of the capabilities of high-resolution imaging, advanced preprocessing software, and deep feature learning in the identification of the intricate …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Flood Frequency Analysis of Big Akaki River, Awash Basin, Ethiopia
Abstract: Flood is one of the most dangerous naturally occurring catastrophic hazards now days displacing and killing people and destroying properties. During high flow, the Big Akaki River flooded the vicinity of the area and causes loss of cultivated land and life. Flood water inundates the floodplain areas and cause vast damages to life and property. The main objective of this paper is to analyze the inundation area along the Big …
Published in Journal of Water Resource Engineering and Management · Vol. 8, Issue 2, 2021 · pp. 35–48 Read article
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Dynamic Performance Enhancement of Polymer Composites through Metaheuristic machinining optimization
Abstract: This work aims to provide an optimization of meta-heuristic algorithms in order to improve the dynamic behavior of composite materials utilized in various practical engineering tasks. Based on the Comprehensive literature review it has been observed that composite sandwich panels with PVC foam cores accomplished mechanical characteristics superior than those ones that were produced on PU foam core mainly in flexural, compression, and impact tests Thus the study establishes the …
Published in Journal of Polymer & Composites · Vol. 12, Issue 2, 2024 · pp. 114–129 Read article
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A Survey on Ensemble Technique for Enhanced Cyberattack Detection
Abstract: It is now more difficult than ever to safeguard enterprises against cyberattacks due to their fast growth and growing sophistication. Stronger cyberattack detection systems are becoming more and more necessary as hostile strategies continue to evolve in order to safeguard information, preserve corporate trust, and protect sensitive data. An overview of contemporary detection techniques is given in this study, with a focus on integrating machine learning (ML) to increase efficacy. …
Published in Journal Of Network security · Vol. 13, Issue 3, 2025 · pp. 50–54 Read article
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Strategic Integration of Machine Learning in Polymer Composite Development: A Framework for R&D Portfolio Management and Technological Adoption
Abstract: The progress of advanced polymer composites is slow, costly and unpredictable due to traditional methods of trial-and-error research. As materials informatics and data-driven modeling speed up the process of discovering technology, there exists a huge disconnect between computational predictions on one hand and strategic decision-making on the other in research and development (R&D). To solve this issue, this paper presents the Agile Materials-Intelligence (AMI) Framework, a systematic combined methodology that …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1272–2286 Read article
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GHS Label Detection Using Color Segmentation
Abstract: AbstractThis research presented the Globally Harmonized System (GHS) label detection by color segmentation approach. The researcher adapts this approach for identify boundary of each color pixels in Different environment image set. Experimental result shown the detection model have high accuracy. However, the mainly cause of error processing is asymmetrical GHS label in sample image that.Keywords: Color Segmentation, Image Processing, GHS Label Detection, Computer VisionCite this ArticleSansanee Hiranchan. GHS Label Detection …
Published in Current Trends in Signal Processing · Vol. 10, Issue 2, 2020 · pp. 26–32 Read article
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A CON: EVO Reduction Technique for Uncertain Interval Systems Using Differential Evolution Algorithm
Abstract: In this paper advantage of evolutionary technique is used in combination with a reliable conventionaltechnique to reduce the dimension of high order uncertain linear interval systems into low order intervalsystems. The proposed technique is depicted by a name CON: EVO, which stands for combination ofconventional & evolutionary technique. In evolutionary method recently proposed Differential Evolution(DE) optimization technique is used with and without powerful conventional Routh approximation techniqueto minimize the integral …
Published in Journal of Control & Instrumentation · Vol. 1, Issue 1-2-3, 2011 · pp. 1–11 Read article
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Optimizing Performance Characteristics, Thermal Stability, and Manufacturing Performance of Polymer Nanocomposites Using Artificial Intelligence
Abstract: Artificial intelligence (AI) has proven an efficient method to optimize the design and manufacture of polymer nanocomposites, allowing the proper prediction of the behavior of the materials and the results of the processing. This work proposes an AI-based framework to enhance the performance characteristics, thermal stability and manufacturing performance of advanced polymer nanocomposites. The input variables of the proposed framework are the material composition, the nanoparticle concentration, the particle size, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Machine Learning Assisted Optimization of Nanoscale MOSFET Parameters Using TCAD Simulation
Abstract: This paper presents a machine learning (ML) assisted framework for the multi-objective optimization of nanoscale bulk n-channel metal-oxide-semiconductor field-effect transistors (nMOSFETs) with a 10 nm physical gate length, high-k HfO₂ gate dielectric, and TiN metal gate. Technology computer-aided design (TCAD) simulations employing drift-diffusion transport, Shockley-Read-Hall recombination, Lombardi mobility degradation, and density- gradient quantum correction models are used to generate a parametric dataset of 2,400 device configurations spanning gate length (L), …
Published in Journal of Microelectronics and Solid State Devices · Vol. 13, Issue 1, 2026 · pp. 10–19 Read article
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Analysis of Cooperative Communication Techniques
Abstract: The scope of this study is to analyze cooperative communication techniques by measuring the symbol error rate vs. signal to noise ratio and analyzing the merits and demerits of those techniques. Unless stated otherwise, the channel is modelled as Rayleigh fading and noise is modeled as white Gaussian. The results of this study can be used to understand the benefits of cooperative protocols and their necessity in future communication systems. …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 7, Issue 3, 2020 · pp. 1–9 Read article
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Enzyme Stability Prediction using BERT and CNN-A Deep Learning Approach for Enhanced Biocatalysis
Abstract: An important factor in determining the efficacy of industrial enzymes used in various biotechnological applications is their stability. The goal of this study is to develop a predictive model for industrial enzyme stability, which is essential to the efficiency of these enzymes in biotechnological applications. The research takes a comprehensive strategy to comprehend the parameters affecting enzyme stability by combining statistical analysis, deep learning algorithms (BERT and CNN), and molecular …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 2, 2024 · pp. 19–35 Read article
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Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation
Abstract: Accurate estimation of the State of Charge (SOC) and State of Health (SOH) of a battery is key to safe and efficient management of batteries in electric vehicles and energy-storage systems. However, it is challenging due to high nonlinearity, varying operating conditions, measurement noise, and limited access to comprehensive electrochemical parameters. Traditional data-driven models often generalize poorly and require heavy tuning, which can produce unstable predictions. To address these problems, …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 53–64 Read article
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Modeling of Sintering Process for the Preparation of Magnetic Abrasives by RSM and ANN Models
Abstract: In this study, a modeling has been done for the prediction of the sintering process, as sintering process is one of the best processes to prepare magnetic abrasives. The sintering process is modelled by using RSM and ANN techniques. The ANN model has been developed using a multilayer feed-forward neural network and trained with the help of an error backpropagation learning algorithm based on the generalized delta rule. The indication …
Published in Journal of Experimental & Applied Mechanics · Vol. 10, Issue 3, 2019 · pp. 5–12 Read article
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Comparison Between Reed–Solomon and BCH Code with Various Modulation Schemes Over Coding Gain and Coding Rate
Abstract: The main objective of this research paper is to make a comparison between the performance of Reed-Solomon (RS) and Bose–Chaudhuri–Hocquenghem (BCH) codes across different modulation schemes concerning coding gain and coding rate within an additive white Gaussian noise (AWGN) channel system, while maintaining a constant transmission bandwidth. In this paper bit error rate (BER) versus signal/noise (S/N) performance of a Simulink model is validated with MATLAB results for a RS …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 12, Issue 1, 2024 · pp. 32–50 Read article
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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
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Model based Tuning of PID Controller
Abstract: In this paper PID controller with Internal Model Control tuning method (IMC-PID) is presented for robust operation. The implementation of IMC is simplified in large class of industrial applications as the process dynamics can be adequately characterized by a First Order plus Delay Time (FOPDT) model. Tuning of the controller is easily done by adjusting the filter time constant which provides improved performance and robustness of the closed loop system. …
Published in Journal of Control & Instrumentation · Vol. 4, Issue 1, 2013 · pp. 16–22 Read article
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Investigation on Open Loop Position Control of Amplified Piezoactuator
Abstract: Amplified piezoelectric actuators (APAs) consist of stacked piezoelectric actuators inside a flexural mechanical amplifier which helps in amplifying the displacements of the piezostacks in static and dynamic conditions. Accounting for their precision movement and dynamic characteristics, these actuators are extensively used in micropositioning, active damping, and microactuation applications. In this paper, FEM analysis of mechanical flexural amplifier of APA230L piezoactuator was performed to estimate its stiffness in the horizontal direction …
Published in Journal of Mechatronics and Automation · Vol. 2, Issue 1, 2015 · pp. 16–24 Read article
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Study of an Improved Quantum Particle Swarm Optimization-Based Framework for Neural Network Optimization in Modelling of Polymer Data
Abstract: The accurate forecasting of polymer viscosity at various physicochemical conditions has been quite critical due to the nonlinear interactions and interrelations between the variables. This paper suggests a better hybrid modelling framework, which involves the use of Artificial Neural Networks (ANN) and more advanced versions of Quantum Particle Swarm Optimization (QPSO) to better predict polymer viscosity. The input parameters taken are, namely, log (shear rate), polymer concentration, NaCl concentration, Ca …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 282–297 Read article
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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 · pp. 31–41 Read article
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AI-Optimized Biodegradable Polymer Composites for Medical Applications
Abstract: The value of biodegradable polymer composites in the medical practice has been massive as the composites may be deployed to provide temporary structural support, and they are also safe to degrade within the human body. However, the conventional material design process is trial and error, which is ineffective and inefficient. The article proposes a hybrid model involving experimental characterization, as well as an artificial intelligence (AI)-based model, to optimize biodegradable …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article