We evaluate different Neural Radiance Field(NeRF)techniques for the 3D reconstruction of plants in varied environments,from indoor settings to outdoor *** methods usually fail to capture the complex geometric details ...
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We evaluate different Neural Radiance Field(NeRF)techniques for the 3D reconstruction of plants in varied environments,from indoor settings to outdoor *** methods usually fail to capture the complex geometric details of plants,which is crucial for phenotyping and breeding *** evaluate the reconstruction fidelity of NeRFs in 3 scenarios with increasing complexity and compare the results with the point cloud obtained using light detection and ranging as ground *** the most realistic field scenario,the NeRF models achieve a 74.6％F1 score after 30 min of training on the graphics processing unit,highlighting the efficacy of NeRFs for 3D reconstruction in challenging ***,we propose an early stopping technique for NeRF training that almost halves the training time while achieving only a reduction of 7.4％in the average F1 *** optimization process substantially enhances the speed and efficiency of 3D reconstruction using *** findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.
In real-world materials research,machine learning(ML)models are usually expected to predict and discover novel exceptional materials that deviate from the known *** is thus a pressing question to provide an objective ...
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In real-world materials research,machine learning(ML)models are usually expected to predict and discover novel exceptional materials that deviate from the known *** is thus a pressing question to provide an objective evaluation ofMLmodel performances in property prediction of out-ofdistribution(OOD)materials that are different fromthe training *** performance evaluation of materials property prediction models through the random splitting of the dataset frequently results in artificially high-performance assessments due to the inherent redundancy of typical material datasets.
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.
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
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
This research work explores the effects of dry, liquid N2-based cryogenic cooling and cryogenic plus MQL hybrid strategy on surface roughness, rake surface temperature, principal cutting-edge temperature, auxiliary cu...
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In the competitive landscape of globalised markets, businesses must prioritise cost reduction for sustained competitiveness. This study delves into the dynamic facility layout problem (DFLP) within a cable production ...
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The primary promoters for the advancement of NFRCs (Natural fibre reinforced composites) in many industries are the imperative need to decrease energy consumption and mitigate environmental consequences. Natural fibre...
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In industrial inspection, the detection of surface defects - such as scratches, dents, or other defects - is crucial for ensuring product quality. However, the limited availability of annotated images of such defects ...
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This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-tim...
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