In recent years, robot technology has developed rapidly, and robots represented by small, high-precision and flexible robotic arms have become the research focus of enterprises and research institutions. However, exce...
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It is crucial to predict future mechanical behaviors for the prevention of structural *** for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to ...
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It is crucial to predict future mechanical behaviors for the prevention of structural *** for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex *** that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring ***,the proposed model was formalized on multiple time series of strain monitoring ***,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction *** the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of *** a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.
Single-cell RNA sequencing(scRNA-seq)technology has become an effective tool for high-throughout transcriptomic study,which circumvents the averaging artifacts corresponding to bulk RNA-seq technology,yielding new per...
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Single-cell RNA sequencing(scRNA-seq)technology has become an effective tool for high-throughout transcriptomic study,which circumvents the averaging artifacts corresponding to bulk RNA-seq technology,yielding new perspectives on the cellular diversity of potential superficially homogeneous *** various sequencing techniques have decreased the amplification bias and improved capture efficiency caused by the low amount of starting material,the technical noise and biological variation are inevitably introduced into experimental process,resulting in high dropout events,which greatly hinder the downstream *** the bimodal expression pattern and the right-skewed characteristic existed in normalized scRNA-seq data,we propose a customized autoencoder based on a twopart-generalized-gamma distribution(AE-TPGG)for scRNAseq data analysis,which takes mixed discrete-continuous random variables of scRNA-seq data into account using a twopart model and utilizes the generalized gamma(GG)distribution,for fitting the positive and right-skewed continuous *** adopted autoencoder enables AE-TPGG to captures the inherent relationship between *** addition to the ability of achieving low-dimensional representation,the AETPGG model also provides a denoised imputation according to statistical characteristic of gene *** on real datasets demonstrate that our proposed model is competitive to current imputation methods and ameliorates a diverse set of typical scRNA-seq data analyses.
Fatigue driving is one of the main causes of traffic accidents. Effective fatigue driving detection technology can reduce traffic accidents caused by fatigue driving. Traditional fatigue driving detection methods usua...
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Most of the research conducted in action recognition is mainly focused on general human action recognition, and most of the available datasets support studies in general human action recognition. In more specific cont...
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Association rule mining plays an important role in the field of data mining, which is used to discover hidden relationships. However, as data volumes increase, traditional association rule mining methods are constrain...
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Association rule mining plays an important role in the field of data mining, which is used to discover hidden relationships. However, as data volumes increase, traditional association rule mining methods are constrained to single-machine computing when processing large-scale data. These methods are unable to leverage the advantages of modern distributed computing frameworks, resulting in more significant performance bottlenecks when processing large-scale datasets. Therefore, research on how to combine distributed computing technology with association rule mining has become the key to improving efficiency and scalability. To this end, the study introduced a parallel frequent itemset mining technique, FiDoop DP, which used the MapReduce programming paradigm for data partitioning on Hadoop clusters and integrates an improved k-means++ algorithm for data preprocessing to provide better data processing results. The findings indicated that the enhanced k-means++ clustering method achieved a Davies-Bouldin index of 0.642 for performance validation, while its CalinskiHarabasz score reached 5186. The improved k-means++ clustering technique showed advantageous clustering results, while the data partitioning method based on frequent item set parallel mining shown a notable performance advantage. With 60 seed points, the execution time for the frequent item set parallel mining technique was just 683 s, the mining duration was only 402 s, and the shuffling expenditure amounted to 2280GB. This indicates that the FiDoop DP method proposed by the study has significant importance in modern cluster environments. By combining the distributed computing capabilities of Hadoop clusters with the improved k-means++ clustering algorithm, this method effectively solves the scalability problem in processing large datasets and significantly improves the efficiency of clustering analysis and frequent itemset mining.
Due to the limitations of current spectral imaging equipment in acquiring high-resolution hyperspectral images (HR-HSIs), a common approach is to fuse low-resolution hyperspectral images (LR-HSIs) with high-resolution...
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With the increasing prevalence of virtual assistants, multimodal conversational recommendation systems (multimodal CRS) becomes essential for boosting customer engagement, improving conversion rates, and enhancing use...
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Evolutionary Algorithms (EAs) can not handle expensive optimization problems (EOPs) well due to the limited function evaluations in EOPs. To address this challenge, surrogate-assisted evolutionary algorithms (SAEAs) h...
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Recently, graph neural networks(GNNs) have played a key crucial in many recommendation situations. In particular, contrastive learning-based hypergraph neural networks (HGNNs) are gradually becoming a research focus f...
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