With the further advancement of industrial technology, the data generated by sensors is gradually becoming more complex. Deep learning approaches have made notable strides in the domain of anomaly detection, especiall...
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Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential ***,gene expressio...
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Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential ***,gene expression data are prone to significant fluctuations due to noise interference in topological *** this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise *** then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression *** the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential *** strategy exhibited a high recognition rate and good *** validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy *** with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and *** also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene *** experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.
Through computer vision and image processing techniques, a set of images from a scene can be reconstructed in 3D to recover a 3D model of the scene, in which dense reconstruction is a crucial part, and most existing a...
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This paper investigates the problems of invariant set analysis and control synthesis for multi-equilibrium switched systems under control constraints. A control strategy based on the invariant set method is proposed, ...
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Dear Editor,This letter focuses on leveraging the object information in images to improve the performance of the U-Net based change *** detection is fundamental to many computer vision *** existing solutions based on ...
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Dear Editor,This letter focuses on leveraging the object information in images to improve the performance of the U-Net based change *** detection is fundamental to many computer vision *** existing solutions based on deep neural networks are able to achieve impressive results.
This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a coll...
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This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging *** augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization *** of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point ***,there has been a lack of focus on making the most of the numerous existing augmentation *** this deficiency,this research investigates the possibility of combining two fundamental data augmentation *** paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named *** of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or *** innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or *** crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data *** results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation *** is achieved without compromising computational *** examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point *** data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the ro
Flow dynamics of binary particles are investigated to realize the monitoring and optimization of fluidized *** is a challenge to accurately classify the mass fraction of mixed biomass,considering the limitations of ex...
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Flow dynamics of binary particles are investigated to realize the monitoring and optimization of fluidized *** is a challenge to accurately classify the mass fraction of mixed biomass,considering the limitations of existing *** data collected from an electrostatic sensor array is *** correlation,empirical mode decomposition(EMD),Hilbert-Huang transform(HHT)are applied to process the *** a higher mass fraction of the wood sawdust,the segregation behavior occurs,and the high energy region of HHT spectrum ***,two data-driven models are trained based on a hybrid wavelet scattering transform and bidirectional long short-term memory(ST-BiLSTM)network and a EMD and BiLSTM(EMD-BiLSTM)network to identify the mass fractions of the mixed biomass,with accuracies of 92%and 99%.The electrostatic sensing combined with the EMD-BiLSTM model is effective to classify the mass fraction of the mixed biomass.
In this research, a fuzzy adaptive PD control approach is introduced for managing the coupled indoor temperature and humidity system. Initially, the mathematical framework of indoor temperature and humidity is analyze...
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We consider word-of-mouth social learning involving m Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measure...
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This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specificat...
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