Electric vehicles, or EVs, have drawn a lot of attention lately as an eco-friendly way to cut carbon emissions and lessen reliance on fossil fuels. This work uses a neural network model called Long Short-Term Memory (...
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Effective preprocessing of image data plays a pivotal role in enhancing the discriminative modeling capabilities in downstream machine learning tasks. This study investigates the significance of adequately mapping ima...
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ISBN:
(纸本)9781510673991;9781510673984
Effective preprocessing of image data plays a pivotal role in enhancing the discriminative modeling capabilities in downstream machine learning tasks. This study investigates the significance of adequately mapping image data into a new feature space during the preprocessing phase, emphasizing its criticality in facilitating more robust and accurate models. While traditional methods such as signal/image processing transforms have been previously explored for this purpose, this study introduces a novel approach leveraging deep learning techniques. Specifically, convolutional and pooling layers are employed to process the image data, offering a more sophisticated and adaptive method for feature extraction and representation. By employing deep learning architectures, the preprocessing phase becomes more flexible and capable of capturing intricate patterns and structures within the data. Through empirical evaluation, our approach demonstrates significant improvements in discriminative modeling across various traditional machine learning approaches. This highlights the effectiveness and versatility of deep learning-based preprocessing in enhancing the performance of downstream tasks, showcasing its potential to advance the field of image dataprocessing and analysis.
Model Predictive control (MPC) is an optimal control technique that employs a dynamic model of the controlled process and an optimization algorithm to determine the control strategy. Nevertheless, the cost and effort ...
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This paper introduces a pioneering approach to optimizing input and output delays in Nonlinear Autoregressive with Exogenous Inputs (NARX) Neural Network (NN) models through the use of Mutual Average Information (MAI)...
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ISBN:
(纸本)9798350385236;9798350385243
This paper introduces a pioneering approach to optimizing input and output delays in Nonlinear Autoregressive with Exogenous Inputs (NARX) Neural Network (NN) models through the use of Mutual Average Information (MAI), enhancing the dynamic modeling of power converters. Our research advances the development of digital twins by employing a data-driven methodology that significantly improves model accuracy for systems under variable loads. The novelty of our work lies in applying the MAI for delays selection in NARX-NN models, a technique that ensures high fidelity in capturing the intricate dynamics of power converters. The validation of our approach is underscored by the use of an advanced simulation model that not only embodies key non-linearities and parasitic components but is also under closed-loop control with a PI compensator. This adds a layer of complexity and realism to our model, illustrating its potential for real-world application. By demonstrating the improved performance of digital twins in power electronics through this refined delay optimization process, our study opens new avenues for future research and application in the field.
Delays in a construction project have been a long-standing dilemma due to their inevitable nature. Consequently, time overrun in construction projects has been the main area of investigation by academic researchers an...
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Delays in a construction project have been a long-standing dilemma due to their inevitable nature. Consequently, time overrun in construction projects has been the main area of investigation by academic researchers and practitioners alike. When projects become more complex, the accuracy of quantifying delays can be an arduous process;without the proper quantification, this leaves contractors subject to the application of liquidated damages or losses. Various reasons for delays in the construction industry can lead to a ripple effect on certain path(s) of activities which cannot be easily traced throughout the lifetime of mega construction project with interdependent disciplines. The current methods within the delay analysis realm all involve a cumbersome process of data collection regarding the delaying events whether it would be utilized in a retroactive or prospective approach. Additionally, lack of proper documentation and records after the event has taken place will lead to an inaccurate delay analysis causing the upheaval of disputes between parties. Therefore, this paper allots for a real-time recording of delaying events through conducting delay analysis using agent-based modeling. This allows for the effect of delaying events to be instantaneously measured in terms of additional time suffered. Not only so, but agent-based modeling avoids the need for delay analysts to investigate the entirety of affected activities to link to the delaying event. A case study was then applied to a path of activities simulated using AnyLogic to validate the agent-based approach for delay analysis.
The article describes the peculiarities of the technological process for temperature control of the educational stand «Thermal Object» using a classical PID controller implemented on Siemens and Schneider co...
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In DC microgrid modeling, power electronic converters generally adopt the state-space average model, which can achieve higher computational efficiency by ignoring higher harmonics. In order to achieve accurate modelin...
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In this paper we present a new approach to determine optimal control sequences for nonlinear systems with imperfect or partial knowledge about their dynamics. In this scenario, classical procedures recommend a more co...
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ISBN:
(纸本)9783907144084
In this paper we present a new approach to determine optimal control sequences for nonlinear systems with imperfect or partial knowledge about their dynamics. In this scenario, classical procedures recommend a more complex analytical modeling design, an effort that may be time consuming due to the acquisition of expertise or possibly not applicable. Our hybrid(1) optimal control design compensates for model errors using Gaussian processes learned from measured system data, thus overcoming the limitations of the classical methodology. The associated hybrid optimal control problem is set up and solved using the unscented transform and the multiple shooting approach, making our developed method flexible, data-efficient, and robust. Relevant practical implementation details are explained and an optimal control is exemplary designed for a fully actuated double pendulum, where we analyze the results and draw a comparison between an application with and without existing prior knowledge.
This research investigates the hard part turning of DC53 tool steel, which is engineered for better mechanical properties compared to AISI D2 tool steel, using Xcel cubic boron nitride. The machining input parameters ...
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This research investigates the hard part turning of DC53 tool steel, which is engineered for better mechanical properties compared to AISI D2 tool steel, using Xcel cubic boron nitride. The machining input parameters such as workpiece hardness (different heat treatments), cutting speed, feed rate, and depth of cut are used to thoroughly evaluate process science across conflicting machinability attributes such as cutting tool life, machined workpiece surface roughness, volume of material removed, machine tool power consumption, and tool-workpiece zone temperature. A full factorial design of experiments with two levels, resulting in 16 experiments, is performed with statistical parametric significance analysis to better controlprocess variability. Multiple artificial neural network (ANN) architectures are generated to accurately model the non-linearity of the process for better prediction of key characteristics. The optimized architectures are used as prediction models to a non-sorting genetic algorithm (NSGA-II) to determine the optimal compromise among all conflicting responses. The significance analysis highlighted that heat treatment is the most influential variable on machinability, with a significance of 74.63% on tool life, 59.03% on roughness, 66.45% on material removed, 38.03% on power consumption, and 29.60% on interaction-zone temperature. The confidence of all ANN architectures is achieved above 0.97 R2 to accurately incorporate parametric relations with physical mechanisms. The compromise against conflicting machinability attributes identified by NSGA-II optimization results in a 92.05% increase in tool life, a 91.83% increase in volume removed, a 33.33% decrease in roughness, a 26.73% decline in power consumption, and a 9.61% reduction in machining temperature. The process variability is thoroughly analyzed using statistical and physical analyses and computational intelligence, which will guide machinists in better decision-making. (c) 2025 Author(s). A
In the intricate process of steel manufacturing, the precise measurement of molten iron is a pivotal procedure, which directly impacts the quality of steel production. Rail weighbridges are commonly deployed in the st...
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