Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the ...ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the performances of these methods in detecting oceanic features for both noise free and noise contaminated AVHRR (Advanced Very High Resolution Radiometer) IR image with Kuroshio. Also, practical experiments in detecting the eddy of Kuroshio with these methods are carried out for comparison. Results show that the ICSED algorithm has more advantages than other methods in detecting mesoscale features of ocean. Finally, the effectiveness of window size of ICSED method to oceanic features detection is quantitatively discussed.展开更多
This paper proposes a model to analyze the massive data of electricity.Feature subset is determined by the correla-tion-based feature selection and the data-driven methods.The attribute season can be classified succes...This paper proposes a model to analyze the massive data of electricity.Feature subset is determined by the correla-tion-based feature selection and the data-driven methods.The attribute season can be classified successfully through five classi-fiers using the selected feature subset,and the best model can be determined further.The effects on analyzing electricity consump-tion of the other three attributes,including months,businesses,and meters,can be estimated using the chosen model.The data used for the project is provided by Beijing Power Supply Bureau.We use WEKA as the machine learning tool.The models we built are promising for electricity scheduling and power theft detection.展开更多
With the development of large scale text processing, the dimension of text feature space has become larger and larger, which has added a lot of difficulties to natural language processing. How to reduce the dimension...With the development of large scale text processing, the dimension of text feature space has become larger and larger, which has added a lot of difficulties to natural language processing. How to reduce the dimension has become a practical problem in the field. Here we present two clustering methods, i.e. concept association and concept abstract, to achieve the goal. The first refers to the keyword clustering based on the co occurrence of展开更多
Methods for extracting features from time series data using deep learning have been widely studied,but they still suffer from problems of severe loss of feature information across different network layers and paramete...Methods for extracting features from time series data using deep learning have been widely studied,but they still suffer from problems of severe loss of feature information across different network layers and parameter redun-dancy.Therefore,a new time-series data feature extraction model(CNN-CBAM)that integrates convolutional neural networks(CNN)and convolutional attention mechanisms(CBAM)is proposed.First,the parameters of the CNN and BiGRU prediction models are optimized through uniform design methods.Next,the CNN is used to extract features from the time series data,outputting multiple feature maps.These feature maps are then subjected to feature re-extraction by the CBAM attention mechanism at both the spatial and channel levels.Finally,the feature maps are input into the BiGRU model for prediction.Experimental results show that after CNN-CBAM processing,the stability and accuracy of the BiGRU pre-diction model improved by 77.6%and 76.3%,respectively,outperforming other feature extraction methods.Meanwhile,the training time of the model has only increased by 7.1%,demonstrating excellent time efficiency.展开更多
In this paper, we target a similarity search among data supply chains, which plays an essential role in optimizing the supply chain and extending its value. This problem is very challenging for application-oriented da...In this paper, we target a similarity search among data supply chains, which plays an essential role in optimizing the supply chain and extending its value. This problem is very challenging for application-oriented data supply chains because the high complexity of the data supply chain makes the computation of similarity extremely complex and inefficient. In this paper, we propose a feature space representation model based on key points,which can extract the key features from the subsequences of the original data supply chain and simplify it into a feature vector form. Then, we formulate the similarity computation of the subsequences based on the multiscale features. Further, we propose an improved hierarchical clustering algorithm for a similarity search over the data supply chains. The main idea is to separate the subsequences into disjoint groups such that each group meets one specific clustering criteria; thus, the cluster containing the query object is the similarity search result. The experimental results show that the proposed approach is both effective and efficient for data supply chain retrieval.展开更多
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
文摘ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the performances of these methods in detecting oceanic features for both noise free and noise contaminated AVHRR (Advanced Very High Resolution Radiometer) IR image with Kuroshio. Also, practical experiments in detecting the eddy of Kuroshio with these methods are carried out for comparison. Results show that the ICSED algorithm has more advantages than other methods in detecting mesoscale features of ocean. Finally, the effectiveness of window size of ICSED method to oceanic features detection is quantitatively discussed.
基金Supported by the National Earthquake Major Project of China (201008007)the Fundamental Research Funds for Central University of China (216275645)
文摘This paper proposes a model to analyze the massive data of electricity.Feature subset is determined by the correla-tion-based feature selection and the data-driven methods.The attribute season can be classified successfully through five classi-fiers using the selected feature subset,and the best model can be determined further.The effects on analyzing electricity consump-tion of the other three attributes,including months,businesses,and meters,can be estimated using the chosen model.The data used for the project is provided by Beijing Power Supply Bureau.We use WEKA as the machine learning tool.The models we built are promising for electricity scheduling and power theft detection.
文摘With the development of large scale text processing, the dimension of text feature space has become larger and larger, which has added a lot of difficulties to natural language processing. How to reduce the dimension has become a practical problem in the field. Here we present two clustering methods, i.e. concept association and concept abstract, to achieve the goal. The first refers to the keyword clustering based on the co occurrence of
文摘Methods for extracting features from time series data using deep learning have been widely studied,but they still suffer from problems of severe loss of feature information across different network layers and parameter redun-dancy.Therefore,a new time-series data feature extraction model(CNN-CBAM)that integrates convolutional neural networks(CNN)and convolutional attention mechanisms(CBAM)is proposed.First,the parameters of the CNN and BiGRU prediction models are optimized through uniform design methods.Next,the CNN is used to extract features from the time series data,outputting multiple feature maps.These feature maps are then subjected to feature re-extraction by the CBAM attention mechanism at both the spatial and channel levels.Finally,the feature maps are input into the BiGRU model for prediction.Experimental results show that after CNN-CBAM processing,the stability and accuracy of the BiGRU pre-diction model improved by 77.6%and 76.3%,respectively,outperforming other feature extraction methods.Meanwhile,the training time of the model has only increased by 7.1%,demonstrating excellent time efficiency.
基金partly supported by the National Natural Science Foundation of China(Nos.61532012,61370196,and 61672109)
文摘In this paper, we target a similarity search among data supply chains, which plays an essential role in optimizing the supply chain and extending its value. This problem is very challenging for application-oriented data supply chains because the high complexity of the data supply chain makes the computation of similarity extremely complex and inefficient. In this paper, we propose a feature space representation model based on key points,which can extract the key features from the subsequences of the original data supply chain and simplify it into a feature vector form. Then, we formulate the similarity computation of the subsequences based on the multiscale features. Further, we propose an improved hierarchical clustering algorithm for a similarity search over the data supply chains. The main idea is to separate the subsequences into disjoint groups such that each group meets one specific clustering criteria; thus, the cluster containing the query object is the similarity search result. The experimental results show that the proposed approach is both effective and efficient for data supply chain retrieval.
