Differential privacy (DP) provides a principled approach to synthesizing data (e.g., loads) from real-world power systems while limiting the exposure of sensitive information. However, adversaries may exploit syntheti...
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Artificial intelligence(AI)is shifting the paradigm of two-phase heat transfer *** innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that ha...
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Artificial intelligence(AI)is shifting the paradigm of two-phase heat transfer *** innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not been addressed in the past,for making their insights available to other domains,and for solving for physical quantities based on first principles for phasechange thermofluidic *** review outlines core ideas of current AI technologies connected to thermal energy science to illustrate how they can be used to push the limit of our knowledge boundaries about boiling and condensation *** technologies for meta-analysis,data extraction,and data stream analysis are described with their potential challenges,opportunities,and alternative ***,we offer outlooks and perspectives regarding physics-centered machine learning,sustainable cyberinfrastructures,and multidisciplinary efforts that will help foster the growing trend of AI for phase-change heat and mass transfer.
Developing manufacturing methods for flexible electronics will enable and improve the large-scale production of flexible, spatially efficient, and lightweight devices. Laser sintering is a promising postprocessing met...
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Vision sensors are versatile and can capture a wide range of visual cues, such as color, texture, shape, and depth. This versatility, along with the relatively inexpensive availability of machine vision cameras, playe...
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Vision sensors are versatile and can capture a wide range of visual cues, such as color, texture, shape, and depth. This versatility, along with the relatively inexpensive availability of machine vision cameras, played an important role in adopting vision-based environment perception systems in autonomous vehicles (AVs). However, vision-based perception systems can be easily affected by glare in the presence of a bright source of light, such as the sun or the headlights of the oncoming vehicle at night or simply by light reflecting off snow or ice-covered surfaces;scenarios encountered frequently during driving. In this paper, we investigate various glare reduction techniques, including the proposed saturated pixel-aware glare reduction technique for improved performance of the computer vision (CV) tasks employed by the perception layer of AVs. We evaluate these glare reduction methods based on various performance metrics of the CV algorithms used by the perception layer. Specifically, we considered object detection, object recognition, object tracking, depth estimation, and lane detection which are crucial for autonomous driving. The experimental findings validate the efficacy of the proposed glare reduction approach, showcasing enhanced performance across diverse perception tasks and remarkable resilience against varying levels of glare. IEEE
This paper addresses the passive source localization problem using hybrid angle-of-arrival (AOA) and time-difference-of-arrival (TDOA) measurements observed by single stationary receiver at several time intervals, whe...
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Remote sensing is of great importance for analyzing and studying various phenomena occurrence and development on *** is possible to extract features specific to various fields of application with the application of mo...
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Remote sensing is of great importance for analyzing and studying various phenomena occurrence and development on *** is possible to extract features specific to various fields of application with the application of modern machine learning techniques,such as Convolutional Neural Networks(CNN)on MultiSpectral Images(MSI).This systematic review examines the application of 1D-,2D-,3D-,and 4D-CNNs to MSI,following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)*** review addresses three Research Questions(RQ):RQ1:“In which application domains different CNN models have been successfully applied for processing MSI data?”,RQ2:“What are the commonly utilized MSI datasets for training CNN models in the context of processing multispectral satellite imagery?”,and RQ3:“How does the degree of CNN complexity impact the performance of classification,regression or segmentation tasks for multispectral satellite imagery?”.Publications are selected from three databases,Web of science,IEEE Xplore,and *** on the obtained results,the main conclusions are:(1)The majority of studies are applied in the field of agriculture and are using Sentinel-2 satellite data;(2)Publications implementing 1D-,2D-,and 3D-CNNs mostly utilize *** 4D-CNN,there are limited number of studies,and all of them use segmentation;(3)This study shows that 2D-CNNs prevail in all application domains,but 3D-CNNs prove to be better for spatio-temporal pattern recognition,more specifically in agricultural and environmental monitoring applications.1D-CNNs are less common compared to 2D-CNNs and 3D-CNNs,but they show good performance in spectral analysis tasks.4D-CNNs are more complex and still underutilized,but they have potential for complex data *** details about metrics according to each CNN are provided in the text and supplementary files,offering a comprehensive overview of the evaluation metrics for each type of machine learning technique
Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injec...
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Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for *** overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning *** study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced *** to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard *** the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.
Faults in an Induction Motor (IM) can lead to unexpected downtime, resulting in considerable economic and productivity losses. From existing literature, conventional fault diagnosis approaches in an IM struggle to rel...
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The need for renewable energy access has led to the use of variable input converter approaches because renewable energy sources often generate electricity in an unpredictable manner. A high-performance multi-input boo...
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The need for renewable energy access has led to the use of variable input converter approaches because renewable energy sources often generate electricity in an unpredictable manner. A high-performance multi-input boost converter is developed to provide the necessary output voltage and power while accommodating variations in input sources. This converter is specifically designed for the efficient usage of renewable energy. The proposed architecture integrates three separate unidirectional input power sources: photovoltaics, fuel cells, and storage system batteries. The architecture has five switches, and the implementation of each switch in the converter is achieved by applying the calculated duty ratios in various operating states. The closed-loop response of the converter with a proportional-integral (PI) controller-based switching system is examined by analyzing the Matlab-Simulink model utilizing a proportional-integral derivative (PID) tuner. The controller can deliver the desired output voltage of 400 V and an average power of 2 kW while exhibiting low switching transient effects. Therefore, the proposed multi-input interleaved boost converter demonstrates robust results for real-time applications by effectively harnessing renewable power sources.
Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distri...
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Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.
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