This paper proposes an iterative deep variational approach for image segmentation in a fusion manner: it is not only able to realize selective segmentation, but can also alleviate the issue of parameter/initialization...
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The increase in the number of visually impaired people has led to active research on artificial retinas. In our laboratory, we study a fully-implantable retinal prosthesis using a 3D-stacked artificial retina chip, wh...
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This article implements the multi-objective grey wolf optimizer (MOGWO) in the tuning process of the gains of the Fractional-Order Proportional-Integral-Derivative (FOPID) controller applied in the control of a boiler...
This article implements the multi-objective grey wolf optimizer (MOGWO) in the tuning process of the gains of the Fractional-Order Proportional-Integral-Derivative (FOPID) controller applied in the control of a boiler system. For a sequence of 100 runs, this application had compared and analyzed with other implementation that uses MOGWO to optimize the gains of the classical Proportional-Integral-Derivative (PID) controller. In the computational simulation, the value hypervolume metric had used to analyze the performance of the controllers. In the results, the implementation of the FOPID showed superior to PID, where the comparison had validated by a hypothesis test. Despite the higher computational cost concerning the tuning process of the PID controller, this study proved that the FOPID controller can be advantageous for industrial applications.
Smart power grids, pivotal to modern infrastructure, face increased vulnerability due to their complex and interconnected nature, often leading to disruptions that can result in widespread power outages and significan...
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ISBN:
(纸本)9798350367744
Smart power grids, pivotal to modern infrastructure, face increased vulnerability due to their complex and interconnected nature, often leading to disruptions that can result in widespread power outages and significant economic losses. Traditional grid monitoring systems frequently fall short in both predicting and mitigating these disruptions, creating a critical need for more advanced predictive methods that can foresee and interpret the progression of these anomalies into distinct failure modes. This paper introduces a transformer-based prognostic model based on Phasor Measurement Unit (PMU) data to enhance the predictive monitoring of smart grids. The method capitalizes on the advanced capabilities of transformer models to manage high-dimensional time-series data with complex dependencies. The methodology, validated rigorously against historical data, progresses through three main phases: (1) PMU data acquisition and preprocessing, (2) PMU-based feature engineering, and (3) prognostic modeling. In the data acquisition and preprocessing phase, high-resolution PMU data were collected, encompassing parameters such as voltage and current magnitudes, frequency, and phase angles. Next, we extracted 43 distinct features across four temporal windows, which were essential in characterizing the dynamic behavior of the grid. The prognostic modeling phase employed a transformer architecture with a sophisticated self-attention mechanism that efficiently handles the sequential PMU data, identifying subtle patterns indicative of potential grid failures. The prognostic model had a superior performance in predicting different types of grid failures-severe weather, lightning, and failed AC circuit equipment, which achieved an accuracy of up to 84.07% with low variability using five-fold cross-validation. This prognostic model enhances the predictive capabilities of smart grid monitoring systems and offers a robust framework for proactive management of grid health, potentially red
Proton Exchange Membrane Fuel Cells (PEMFC) generate substantial heat during operation. This heat constitutes over 40% of the total energy produced from the cells' hydrogen fuel, making reusing this energy a focal...
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Benefits of a failure friendly culture, e.g., learning from failure, are widely known in occupational settings. Validated scales have been developed to measure organizational failure culture and individuals' mind-...
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Safety concerns during the operation of legged robots must be addressed to enable their widespread use. Machine learning-based control methods that use model-based constraints provide promising means to improve robot ...
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In this paper, we propose a general distributionally robust framework for performative optimization, where the selected decision can influence the probabilistic distribution of uncertain parameters. Our framework faci...
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We propose a node clustering method for time-varying graphs based on the assumption that the cluster labels are changed smoothly over time. Clustering is one of the fundamental tasks in many science and engineering fi...
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Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer...
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Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.
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