Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the clou...Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.展开更多
Data organization requires high efficiency for large amount of data applied in the digital mine system. A new method of storing massive data of block model is proposed to meet the characteristics of the database, incl...Data organization requires high efficiency for large amount of data applied in the digital mine system. A new method of storing massive data of block model is proposed to meet the characteristics of the database, including ACID-compliant, concurrency support, data sharing, and efficient access. Each block model is organized by linear octree, stored in LMDB（lightning memory-mapped database）. Geological attribute can be queried at any point of 3D space by comparison algorithm of location code and conversion algorithm from address code of geometry space to location code of storage. The performance and robustness of querying geological attribute at 3D spatial region are enhanced greatly by the transformation from 3D to 2D and the method of 2D grid scanning to screen the inner and outer points. Experimental results showed that this method can access the massive data of block model, meeting the database characteristics. The method with LMDB is at least 3 times faster than that with etree, especially when it is used to read. In addition, the larger the amount of data is processed, the more efficient the method would be.展开更多
On 21 May 2021(UTC),an MW 7.4 earthquake jolted the east Bayan Har block in the Tibetan Plateau.The earthquake received widespread attention as it is the largest event in the Tibetan Plateau and its surroundings since...On 21 May 2021(UTC),an MW 7.4 earthquake jolted the east Bayan Har block in the Tibetan Plateau.The earthquake received widespread attention as it is the largest event in the Tibetan Plateau and its surroundings since the 2008 Wenchuan earthquake,and especially in proximity to the seismic gaps on the east Kunlun fault.Here we use satellite interferometric synthetic aperture radar data and subpixel offset observations along the range directions to characterize the coseismic deformation of the earthquake.Range offset displacements depict clear surface ruptures with a total length of~170 km involving two possible activated fault segments in the earthquake.Coseismic modeling results indicate that the earthquake was dominated by left-lateral strike-slip motions of up to 7 m within the top 12 km of the crust.The well-resolved slip variations are characterized by five major slip patches along strike and 64%of shallow slip deficit,suggesting a young seismogenic structure.Spatial-temporal changes of the postseismic deformation are mapped from early 6-day and 24-day InSAR observations,and are well explained by time-dependent afterslip models.Analysis of Global Navigation Satellite System(GNSS)velocity profiles and strain rates suggests that the eastward extrusion of plateau is diffusely distributed across the east Bayan Har block,but exhibits significant lateral heterogeneities,as evidenced by magnetotelluric observations.The block-wide distributed deformation of the east Bayan Har block along with the significant co-and post-seismic stress loadings from the Madoi earthquake imply high seismic risks along regional faults,especially the Tuosuo Lake and Maqên-Maqu segments of the Kunlun fault that are known as seismic gaps.展开更多
With the reversible data hiding method based on pixel-value-ordering,data are embedded through the modification of the maximum and minimum values of a block.A significant relationship exists between the embedding perf...With the reversible data hiding method based on pixel-value-ordering,data are embedded through the modification of the maximum and minimum values of a block.A significant relationship exists between the embedding performance and the block size.Traditional pixel-value-ordering methods utilize pixel blocks with a fixed size to embed data;the smaller the pixel blocks,greater is the embedding capacity.However,it tends to result in the deterioration of the quality of the marked image.Herein,a novel reversible data hiding method is proposed by incorporating a block merging strategy into Li et al.’s pixel-value-ordering method,which realizes the dynamic control of block size by considering the image texture.First,the cover image is divided into non-overlapping 2×2 pixel blocks.Subsequently,according to their complexity,similarity and thresholds,these blocks are employed for data embedding through the pixel-value-ordering method directly or after being emerged into 2×4,4×2,or 4×4 sized blocks.Hence,smaller blocks can be used in the smooth region to create a high embedding capacity and larger blocks in the texture region to maintain a high peak signal-to-noise ratio.Experimental results prove that the proposed method is superior to the other three advanced methods.It achieves a high embedding capacity while maintaining low distortion and improves the embedding performance of the pixel-value-ordering algorithm.展开更多
In this paper we study the problem of locating multiple facilities in convex sets with fuzzy parameters. This problem asks to find the location of new facilities in the given convex sets such that the sum of weighted ...In this paper we study the problem of locating multiple facilities in convex sets with fuzzy parameters. This problem asks to find the location of new facilities in the given convex sets such that the sum of weighted distances between new facilities and existing facilities is minimized. We present a linear programming model for this problem with block norms, then we use it for problems with fuzzy data. We also do this for rectilinear and infinity norms as special cases of block norms.展开更多
In this paper, we present a distributed multi-level cache system based on cloud storage, which is aimed at the low access efficiency of small spatio-temporal data files in information service system of Smart City. Tak...In this paper, we present a distributed multi-level cache system based on cloud storage, which is aimed at the low access efficiency of small spatio-temporal data files in information service system of Smart City. Taking classification attribute of small spatio-temporal data files in Smart City as the basis of cache content selection, the cache system adopts different cache pool management strategies in different levels of cache. The results of experiment in prototype system indicate that multi-level cache in this paper effectively increases the access bandwidth of small spatio-temporal files in Smart City and greatly improves service quality of multiple concurrent access in system.展开更多
At present,the process of digital image information fusion has the problems of low data cleaning unaccuracy and more repeated data omission,resulting in the unideal information fusion.In this regard,a visualized multi...At present,the process of digital image information fusion has the problems of low data cleaning unaccuracy and more repeated data omission,resulting in the unideal information fusion.In this regard,a visualized multicomponent information fusion method for big data based on radar map is proposed in this paper.The data model of perceptual digital image is constructed by using the linear regression analysis method.The ID tag of the collected image data as Transactin Identification(TID)is compared.If the TID of two data is the same,the repeated data detection is carried out.After the test,the data set is processed many times in accordance with the method process to improve the precision of data cleaning and reduce the omission.Based on the radar images,hierarchical visualization of processed multi-level information fusion is realized.The experiments show that the method can clean the redundant data accurately and achieve the efficient fusion of multi-level information of big data in the digital image.展开更多
Compared with the study of single point motion of landslides,studying landslide block movement based on data from multiple monitoring points is of great significance for improving the accurate identification of landsl...Compared with the study of single point motion of landslides,studying landslide block movement based on data from multiple monitoring points is of great significance for improving the accurate identification of landslide deformation.Based on the study of landslide block,this paper regarded the landslide block as a rigid body in particle swarm optimization algorithm.The monitoring data were organized to achieve the optimal state of landslide block,and the 6-degree of freedom pose of the landslide block was calculated after the regularization.Based on the characteristics of data from multiple monitoring points of landslide blocks,a prediction equation for the motion state of landslide blocks was established.By using Kalman filtering data assimilation method,the parameters of prediction equation for landslide block motion state were adjusted to achieve the optimal prediction.This paper took the Baishuihe landslide in the Three Gorges reservoir area as the research object.Based on the block segmentation of the landslide,the monitoring data of the Baishuihe landslide block were organized,6-degree of freedom pose of block B was calculated,and the Kalman filtering data assimilation method was used to predict the landslide block movement.The research results showed that the proposed prediction method of the landslide movement state has good prediction accuracy and meets the expected goal.This paper provides a new research method and thinking angle to study the motion state of landslide block.展开更多
Under the background of urban renewal, this paper re-explores the tourism development modeof Nanluoguxiang historical and cultural block based on online review data, and puts forward correspondingdevelopment strategie...Under the background of urban renewal, this paper re-explores the tourism development modeof Nanluoguxiang historical and cultural block based on online review data, and puts forward correspondingdevelopment strategies. As a cultural label of a city, historical and cultural blocks should be updated first inorder to achieve sustainable development. By using multi-source big data review and qualitative researchmethods, the perception evaluation of tourists in Nanluoguxiang is obtained, and the shortcomings ofcurrent tourism development mode are analyzed. Furthermore, corresponding improvement strategies andsuggestions are put forward, in order to provide some effective ideas for the sustainable development ofhistorical and cultural blocks in the future.展开更多
Sharing economy as an important product of the Internet Era,its essence is to rely on Internet technology,especially big data technology to reuse the idle resources of society and creating new market value.However,wit...Sharing economy as an important product of the Internet Era,its essence is to rely on Internet technology,especially big data technology to reuse the idle resources of society and creating new market value.However,with the increasing scope of the sharing economy,the problems of data security,circulation,sharing and privacy protection are gradually emerging,and big data technology has become the biggest bottleneck for further development of shared economy.The block chain technology is composed of a variety of technology and communication protocol to form a new Internet architecture,it through cryptographic sharing,distributed books and other feathers to provide new methods and ideas for data distribution and sharing and complementary with big data technology.Therefore,through the combination of block chain technology and big data technology,they can subvert the traditional shared economic business model and provide a new opportunity for sharing economic development.展开更多
Based on the arrangement of the across-fault measurement data along the northern edge of the Qinghai-Xizang block,we divide the deformation into different types and probe the nature of various fault movements based on...Based on the arrangement of the across-fault measurement data along the northern edge of the Qinghai-Xizang block,we divide the deformation into different types and probe the nature of various fault movements based on these types.The recent situation of tectonic movement of main structural belts and seismicity in this area are expounded.From the above,it is concluded that across-fault measurement can reflect not only the conditions of fault movement nearby but also the change of regional stress fields; not only is this a method to obtain regional seismogenic information and to conduct short-term prediction but it is also involved with large scale space-time prediction of moderate and strong earthquakes on the basis of the macro characteristics of fractures.展开更多
In this paper,a novel secret data-driven carrier-free(semi structural formula)visual secret sharing(VSS)scheme with(2,2)threshold based on the error correction blocks of QR codes is investigated.The proposed scheme is...In this paper,a novel secret data-driven carrier-free(semi structural formula)visual secret sharing(VSS)scheme with(2,2)threshold based on the error correction blocks of QR codes is investigated.The proposed scheme is to search two QR codes that altered to satisfy the secret sharing modules in the error correction mechanism from the large datasets of QR codes according to the secret image,which is to embed the secret image into QR codes based on carrier-free secret sharing.The size of secret image is the same or closest with the region from the coordinate of(7,7)to the lower right corner of QR codes.In this way,we can find the QR codes combination of embedding secret information maximization with secret data-driven based on Big data search.Each output share is a valid QR code which can be decoded correctly utilizing a QR code reader and it may reduce the likelihood of attracting the attention of potential attackers.The proposed scheme can reveal secret image visually with the abilities of stacking and XOR decryptions.The secret image can be recovered by human visual system(HVS)without any computation based on stacking.On the other hand,if the light-weight computation device is available,the secret image can be lossless revealed based on XOR operation.In addition,QR codes could assist alignment for VSS recovery.The experimental results show the effectiveness of our scheme.展开更多
随着5G、物联网(Internet of Things,IoT)、人工智能(Artificial Intelligence,AI)等技术的蓬勃发展,高密度、大带宽、超低时延的业务需求激增,边缘计算作为应对这些挑战的关键范式应运而生。边缘数据中心作为边缘计算的核心物理载体,...随着5G、物联网(Internet of Things,IoT)、人工智能(Artificial Intelligence,AI)等技术的蓬勃发展,高密度、大带宽、超低时延的业务需求激增,边缘计算作为应对这些挑战的关键范式应运而生。边缘数据中心作为边缘计算的核心物理载体,其建设模式与性能直接影响边缘计算的落地效果。本文深入剖析了当前基于传统汇聚节点机房和通信基站改造的边缘数据中心建设模式在能效、空间利用率、部署速度、适应性及投资效率等方面存在的显著问题。创新性地提出并详细阐述了CMCC-BLOCK(中国移动边缘算力处理单元)一体化数据中心的设计理念、技术架构与实现方案。为5G及未来网络时代的边缘计算提供高效、可靠、敏捷的基础设施支撑,具有广阔的推广前景。展开更多
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
Real-time monitoring of wellbore stability during drilling is crucial for the early detection of instability and timely interventions.The cause and type of wellbore instability can be identified by analyzing the dropp...Real-time monitoring of wellbore stability during drilling is crucial for the early detection of instability and timely interventions.The cause and type of wellbore instability can be identified by analyzing the dropped blocks brought to the surface by the drilling fluid,enabling preventive measures to be taken.In this study,an image capture system with fully automated sorting and 3D scanning was developed to obtain the complete 3D point cloud data of dropping blocks.The raw data obtained were preprocessed using methods such as format conversion,down sampling,coordinate transformation,statistical filtering,and clustering.Feature extraction algorithms,including the principal component analysis bounding box method,triangular meshing method,triaxial projection method,local curvature method,and model segmentation projection method,were employed,which resulted in the extraction of 32 feature parameters from the point cloud data.An optimal machine learning algorithm was developed by training it with 10 machine learning algorithms and the block data collected in the field.The XGBoost algorithm was then used to optimize the feature parameters and improve the classification model.An intelligent,fully automated feature parameter extraction and classification system was developed and applied to classify the types of falling blocks in 12 sets of drilling field and laboratory experiments and to identify the causes of wellbore instability.An average accuracy of 93.9%was achieved.This system can thus enable the timely diagnosis and implementation of preventive and control measures for wellbore instability in the field.展开更多
基金Supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R896).
文摘Deep neural networks have achieved excellent classification results on several computer vision benchmarks.This has led to the popularity of machine learning as a service,where trained algorithms are hosted on the cloud and inference can be obtained on real-world data.In most applications,it is important to compress the vision data due to the enormous bandwidth and memory requirements.Video codecs exploit spatial and temporal correlations to achieve high compression ratios,but they are computationally expensive.This work computes the motion fields between consecutive frames to facilitate the efficient classification of videos.However,contrary to the normal practice of reconstructing the full-resolution frames through motion compensation,this work proposes to infer the class label from the block-based computed motion fields directly.Motion fields are a richer and more complex representation of motion vectors,where each motion vector carries the magnitude and direction information.This approach has two advantages:the cost of motion compensation and video decoding is avoided,and the dimensions of the input signal are highly reduced.This results in a shallower network for classification.The neural network can be trained using motion vectors in two ways:complex representations and magnitude-direction pairs.The proposed work trains a convolutional neural network on the direction and magnitude tensors of the motion fields.Our experimental results show 20×faster convergence during training,reduced overfitting,and accelerated inference on a hand gesture recognition dataset compared to full-resolution and downsampled frames.We validate the proposed methodology on the HGds dataset,achieving a testing accuracy of 99.21%,on the HMDB51 dataset,achieving 82.54%accuracy,and on the UCF101 dataset,achieving 97.13%accuracy,outperforming state-of-the-art methods in computational efficiency.
基金Projects(41572317,51374242)supported by the National Natural Science Foundation of ChinaProject(2015CX005)supported by the Innovation Driven Plan of Central South University,China
文摘Data organization requires high efficiency for large amount of data applied in the digital mine system. A new method of storing massive data of block model is proposed to meet the characteristics of the database, including ACID-compliant, concurrency support, data sharing, and efficient access. Each block model is organized by linear octree, stored in LMDB（lightning memory-mapped database）. Geological attribute can be queried at any point of 3D space by comparison algorithm of location code and conversion algorithm from address code of geometry space to location code of storage. The performance and robustness of querying geological attribute at 3D spatial region are enhanced greatly by the transformation from 3D to 2D and the method of 2D grid scanning to screen the inner and outer points. Experimental results showed that this method can access the massive data of block model, meeting the database characteristics. The method with LMDB is at least 3 times faster than that with etree, especially when it is used to read. In addition, the larger the amount of data is processed, the more efficient the method would be.
基金supported by the Natural Science Foundation of Jiangsu Province(Grant No.SBK2020043202)by Key Laboratory of Geospace Environment and Geodesy,Ministry of Education,Wuhan University(No.19-01-08).
文摘On 21 May 2021(UTC),an MW 7.4 earthquake jolted the east Bayan Har block in the Tibetan Plateau.The earthquake received widespread attention as it is the largest event in the Tibetan Plateau and its surroundings since the 2008 Wenchuan earthquake,and especially in proximity to the seismic gaps on the east Kunlun fault.Here we use satellite interferometric synthetic aperture radar data and subpixel offset observations along the range directions to characterize the coseismic deformation of the earthquake.Range offset displacements depict clear surface ruptures with a total length of~170 km involving two possible activated fault segments in the earthquake.Coseismic modeling results indicate that the earthquake was dominated by left-lateral strike-slip motions of up to 7 m within the top 12 km of the crust.The well-resolved slip variations are characterized by five major slip patches along strike and 64%of shallow slip deficit,suggesting a young seismogenic structure.Spatial-temporal changes of the postseismic deformation are mapped from early 6-day and 24-day InSAR observations,and are well explained by time-dependent afterslip models.Analysis of Global Navigation Satellite System(GNSS)velocity profiles and strain rates suggests that the eastward extrusion of plateau is diffusely distributed across the east Bayan Har block,but exhibits significant lateral heterogeneities,as evidenced by magnetotelluric observations.The block-wide distributed deformation of the east Bayan Har block along with the significant co-and post-seismic stress loadings from the Madoi earthquake imply high seismic risks along regional faults,especially the Tuosuo Lake and Maqên-Maqu segments of the Kunlun fault that are known as seismic gaps.
文摘With the reversible data hiding method based on pixel-value-ordering,data are embedded through the modification of the maximum and minimum values of a block.A significant relationship exists between the embedding performance and the block size.Traditional pixel-value-ordering methods utilize pixel blocks with a fixed size to embed data;the smaller the pixel blocks,greater is the embedding capacity.However,it tends to result in the deterioration of the quality of the marked image.Herein,a novel reversible data hiding method is proposed by incorporating a block merging strategy into Li et al.’s pixel-value-ordering method,which realizes the dynamic control of block size by considering the image texture.First,the cover image is divided into non-overlapping 2×2 pixel blocks.Subsequently,according to their complexity,similarity and thresholds,these blocks are employed for data embedding through the pixel-value-ordering method directly or after being emerged into 2×4,4×2,or 4×4 sized blocks.Hence,smaller blocks can be used in the smooth region to create a high embedding capacity and larger blocks in the texture region to maintain a high peak signal-to-noise ratio.Experimental results prove that the proposed method is superior to the other three advanced methods.It achieves a high embedding capacity while maintaining low distortion and improves the embedding performance of the pixel-value-ordering algorithm.
文摘In this paper we study the problem of locating multiple facilities in convex sets with fuzzy parameters. This problem asks to find the location of new facilities in the given convex sets such that the sum of weighted distances between new facilities and existing facilities is minimized. We present a linear programming model for this problem with block norms, then we use it for problems with fuzzy data. We also do this for rectilinear and infinity norms as special cases of block norms.
基金Supported by the Natural Science Foundation of Hubei Province(2012FFC034,2014CFC1100)
文摘In this paper, we present a distributed multi-level cache system based on cloud storage, which is aimed at the low access efficiency of small spatio-temporal data files in information service system of Smart City. Taking classification attribute of small spatio-temporal data files in Smart City as the basis of cache content selection, the cache system adopts different cache pool management strategies in different levels of cache. The results of experiment in prototype system indicate that multi-level cache in this paper effectively increases the access bandwidth of small spatio-temporal files in Smart City and greatly improves service quality of multiple concurrent access in system.
基金2018 National Grade Innovation and Entrepreneurship Training Program for College Students,China(No.201811562005)Research Project of Gansu University,China(No.2016A-105)Innovation and Entrepreneurship Education Project of Gansu Province in 2019,China(No.2019024)。
文摘At present,the process of digital image information fusion has the problems of low data cleaning unaccuracy and more repeated data omission,resulting in the unideal information fusion.In this regard,a visualized multicomponent information fusion method for big data based on radar map is proposed in this paper.The data model of perceptual digital image is constructed by using the linear regression analysis method.The ID tag of the collected image data as Transactin Identification(TID)is compared.If the TID of two data is the same,the repeated data detection is carried out.After the test,the data set is processed many times in accordance with the method process to improve the precision of data cleaning and reduce the omission.Based on the radar images,hierarchical visualization of processed multi-level information fusion is realized.The experiments show that the method can clean the redundant data accurately and achieve the efficient fusion of multi-level information of big data in the digital image.
基金supported by National Natural Science Foundation of China(Grant Nos.42090054,52027814 and 41772376)the Open Fund of the Technology Innovation Center for Automated Geological Disaster Monitoring,Ministry of Natural Resources(Grant No.2022058014)。
文摘Compared with the study of single point motion of landslides,studying landslide block movement based on data from multiple monitoring points is of great significance for improving the accurate identification of landslide deformation.Based on the study of landslide block,this paper regarded the landslide block as a rigid body in particle swarm optimization algorithm.The monitoring data were organized to achieve the optimal state of landslide block,and the 6-degree of freedom pose of the landslide block was calculated after the regularization.Based on the characteristics of data from multiple monitoring points of landslide blocks,a prediction equation for the motion state of landslide blocks was established.By using Kalman filtering data assimilation method,the parameters of prediction equation for landslide block motion state were adjusted to achieve the optimal prediction.This paper took the Baishuihe landslide in the Three Gorges reservoir area as the research object.Based on the block segmentation of the landslide,the monitoring data of the Baishuihe landslide block were organized,6-degree of freedom pose of block B was calculated,and the Kalman filtering data assimilation method was used to predict the landslide block movement.The research results showed that the proposed prediction method of the landslide movement state has good prediction accuracy and meets the expected goal.This paper provides a new research method and thinking angle to study the motion state of landslide block.
基金National Social Science Foundation Project of China(21FYSB048)Humanities and Social Sciences Research Project of the Ministry of Education(20YJAZH1010).
文摘Under the background of urban renewal, this paper re-explores the tourism development modeof Nanluoguxiang historical and cultural block based on online review data, and puts forward correspondingdevelopment strategies. As a cultural label of a city, historical and cultural blocks should be updated first inorder to achieve sustainable development. By using multi-source big data review and qualitative researchmethods, the perception evaluation of tourists in Nanluoguxiang is obtained, and the shortcomings ofcurrent tourism development mode are analyzed. Furthermore, corresponding improvement strategies andsuggestions are put forward, in order to provide some effective ideas for the sustainable development ofhistorical and cultural blocks in the future.
基金National Natural Science Foundation of China(71563031)Inner Mongolia Natural Science Fund Projects(2015MS0714)+1 种基金Philosophy and social science planning project inInner Mongolia(2017NDB067)Erdos Science and TechnologyPlan Projects(YY201610045).
文摘Sharing economy as an important product of the Internet Era,its essence is to rely on Internet technology,especially big data technology to reuse the idle resources of society and creating new market value.However,with the increasing scope of the sharing economy,the problems of data security,circulation,sharing and privacy protection are gradually emerging,and big data technology has become the biggest bottleneck for further development of shared economy.The block chain technology is composed of a variety of technology and communication protocol to form a new Internet architecture,it through cryptographic sharing,distributed books and other feathers to provide new methods and ideas for data distribution and sharing and complementary with big data technology.Therefore,through the combination of block chain technology and big data technology,they can subvert the traditional shared economic business model and provide a new opportunity for sharing economic development.
文摘Based on the arrangement of the across-fault measurement data along the northern edge of the Qinghai-Xizang block,we divide the deformation into different types and probe the nature of various fault movements based on these types.The recent situation of tectonic movement of main structural belts and seismicity in this area are expounded.From the above,it is concluded that across-fault measurement can reflect not only the conditions of fault movement nearby but also the change of regional stress fields; not only is this a method to obtain regional seismogenic information and to conduct short-term prediction but it is also involved with large scale space-time prediction of moderate and strong earthquakes on the basis of the macro characteristics of fractures.
文摘In this paper,a novel secret data-driven carrier-free(semi structural formula)visual secret sharing(VSS)scheme with(2,2)threshold based on the error correction blocks of QR codes is investigated.The proposed scheme is to search two QR codes that altered to satisfy the secret sharing modules in the error correction mechanism from the large datasets of QR codes according to the secret image,which is to embed the secret image into QR codes based on carrier-free secret sharing.The size of secret image is the same or closest with the region from the coordinate of(7,7)to the lower right corner of QR codes.In this way,we can find the QR codes combination of embedding secret information maximization with secret data-driven based on Big data search.Each output share is a valid QR code which can be decoded correctly utilizing a QR code reader and it may reduce the likelihood of attracting the attention of potential attackers.The proposed scheme can reveal secret image visually with the abilities of stacking and XOR decryptions.The secret image can be recovered by human visual system(HVS)without any computation based on stacking.On the other hand,if the light-weight computation device is available,the secret image can be lossless revealed based on XOR operation.In addition,QR codes could assist alignment for VSS recovery.The experimental results show the effectiveness of our scheme.
文摘随着5G、物联网(Internet of Things,IoT)、人工智能(Artificial Intelligence,AI)等技术的蓬勃发展,高密度、大带宽、超低时延的业务需求激增,边缘计算作为应对这些挑战的关键范式应运而生。边缘数据中心作为边缘计算的核心物理载体,其建设模式与性能直接影响边缘计算的落地效果。本文深入剖析了当前基于传统汇聚节点机房和通信基站改造的边缘数据中心建设模式在能效、空间利用率、部署速度、适应性及投资效率等方面存在的显著问题。创新性地提出并详细阐述了CMCC-BLOCK(中国移动边缘算力处理单元)一体化数据中心的设计理念、技术架构与实现方案。为5G及未来网络时代的边缘计算提供高效、可靠、敏捷的基础设施支撑,具有广阔的推广前景。
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.
基金supported by the Scientific research and technology development projects of CNPC“Research on Key Technologies and Equipment for Drilling and Completion of 10000-m Ultra-deep Oil and Gas Resources”(No.2022ZG06)“Development of a Complete Set of 70 MPa Intelligent Managed Pressure Drilling Equipment”(No.2024ZG35).
文摘Real-time monitoring of wellbore stability during drilling is crucial for the early detection of instability and timely interventions.The cause and type of wellbore instability can be identified by analyzing the dropped blocks brought to the surface by the drilling fluid,enabling preventive measures to be taken.In this study,an image capture system with fully automated sorting and 3D scanning was developed to obtain the complete 3D point cloud data of dropping blocks.The raw data obtained were preprocessed using methods such as format conversion,down sampling,coordinate transformation,statistical filtering,and clustering.Feature extraction algorithms,including the principal component analysis bounding box method,triangular meshing method,triaxial projection method,local curvature method,and model segmentation projection method,were employed,which resulted in the extraction of 32 feature parameters from the point cloud data.An optimal machine learning algorithm was developed by training it with 10 machine learning algorithms and the block data collected in the field.The XGBoost algorithm was then used to optimize the feature parameters and improve the classification model.An intelligent,fully automated feature parameter extraction and classification system was developed and applied to classify the types of falling blocks in 12 sets of drilling field and laboratory experiments and to identify the causes of wellbore instability.An average accuracy of 93.9%was achieved.This system can thus enable the timely diagnosis and implementation of preventive and control measures for wellbore instability in the field.
