Composite structures are commonly used in complex applicationssuch as automotive and aerospace due to their high strength-to-weight ratio. Although strictly supervised and inspected, they are often subject to dynamic...
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Composite structures are commonly used in complex applicationssuch as automotive and aerospace due to their high strength-to-weight ratio. Although strictly supervised and inspected, they are often subject to dynamic events during their useful life that can cause invisible failures that extend and severely compromise their performance over time. Detecting these defects preventively and repairing them could avoid dramatic accidents. Here, we present a deep learning-based method for the non-destructive detection of defects in composite samplesbased on a laser ultrasonic system (LUT). Laser ultrasonic technology is a promising non-destructive testing (NDT) method for detecting inner defects in a non-contact way, as it does not require liquid coupling media. We investigated a composite laminate specimen containing six programmed defects as a test sample. We show that training deep learning-based models as autoencoders makes it possible to extract features that can be used to discern defective areas from non-defective ones in the Us C-scan maps. The results demonstrate high detection accuracies (above 90% balanced accuracy and 75% F-1-score), indicating a promising and effective approach to NDT on composite materials.
Contrastive unsupervised learning has made significant progress, but there isstill potential for improvement by capturing finer details in input data. In this letter, we present PGMoCo, a Progressive Growth-based Mom...
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Language acquisition is an integral part of early schooling, but young English language learnersstruggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching ...
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Lactobacillus delbrueckii ***(***)and streptococcus thermophilus(***)are commonly used starters in milk *** experiments revealed that *** interactions(Lb st I)substantially impact dairy product quality and *** biologi...
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Lactobacillus delbrueckii ***(***)and streptococcus thermophilus(***)are commonly used starters in milk *** experiments revealed that *** interactions(Lb st I)substantially impact dairy product quality and *** biological humidity experiments are time-consuming and labor-intensive in screening interaction combinations,an artificial intelligence-based method for screening interactive starter combinations is ***,in the current research on artificial intelligence based interaction prediction in the field of bioinformatics,most successful models adopt supervised learning methods,and there is a lack of research on interaction prediction with only a small number of labeled ***,thisstudy aimed to develop a semi-supervised learning framework for predicting Lb st I using genomic data from 362 isolates(181per species).The framework consisted of a two-part model:a co-clustering prediction model(based on the Kyoto Encyclopedia of Genes and Genomes(KEGG)dataset)and a Laplacian regularized least squares prediction model(based on K-mer analysis and gene composition of all isolates datasets).To enhance accuracy,we integrated the separate outcomes produced by each component of the two-part model to generate the ultimate Lb st I prediction results,which were verified through milk fermentation *** through milk fermentation experiments confirmed a high precision rate of 85%(17/20;validated with 20 randomly selected combinations of expected interacting isolates).Our data suggest that the biosynthetic pathways of cysteine,riboflavin,teichoic acid,and exopolysaccharides,as well as the ATP-binding cassette transport systems,contribute to the mutualistic relationship between these starter bacteria during milk ***,this finding requires further experimental *** presented model and data are valuable resources for academics an
computer vision is the science that aims to enable computers to emulate human visual perception, and it encompasses various techniques and methods for extracting and interpreting information from two-dimensional image...
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The issues of automated generation of mnemonic diagrams for automated workstations used by the operators of electrical installations are considered. The algorithm is implemented based on the substation configuration d...
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Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity co...
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Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long short-Term Memory(LsTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LsTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LsTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMsE and an 18.8%improvement in MAE over conventional unidirectional LsTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including sVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMsE of 0.2213 during testing,significantly outperforming these *** results highlight the model’ssuperior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,thisresearch contributes to the development of intelligent,sustainable urban environments.
Multi-agent Reinforcement learning(MARL)has become one of the best methods in Adaptive Traffic signal Control(ATsC).Traffic flow is a very regular traffic volume,which is highly critical to signal control ***,dynamic ...
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Multi-agent Reinforcement learning(MARL)has become one of the best methods in Adaptive Traffic signal Control(ATsC).Traffic flow is a very regular traffic volume,which is highly critical to signal control ***,dynamic control policies will directly affect traffic flow formation,and it is impossible to provide observation through the original traffic flow *** paper proposes a method for estimating traffic flow according to the time window in Reinforcement learning(RL)***,it is verified on both the regular road network and the real road *** method further reduces the intersection delay and queue length compared with the original method.
Knowledge distillation typically involves transferring knowledge from large-scale teacher models to student models, garnering widespread attention in the fields of model compression and knowledge transfer. However, ex...
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Deep learning’s rapid development is generating significant interest in its potential to improve medical imaging. It hasshown promising results in detecting malignant lymphoma in histopathology medical images. Image...
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