To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
In dynamic scenes,the pose estimation and map consistency of visual simultaneous localisation and mapping(visual SLAM)are affected by intermittent changes in object motion states.An adaptive motion-state estimation an...In dynamic scenes,the pose estimation and map consistency of visual simultaneous localisation and mapping(visual SLAM)are affected by intermittent changes in object motion states.An adaptive motion-state estimation and feature-reuse mechanism is proposed which restores features once objects become stationary.Camera ego-motion is com-pensated via projection-based point-to-point red-green-blue-depth(RGB-D)Iterative Closest Point;the alignment residual yields a short-term jitter score.An Extended Kalman Filter fuses the centre-pixel trajectory and depth of the object,using depth innovation as strong evidence to suppress false triggers.Applied adaptive decision thresholds involve resolution,ego-motion intensity,jitter,and reference depth,and are combined with dual/single triggering and hysteresis to achieve robust switching.When an object is considered static,its feature points are reused.On the Bonn RGB-D Dynamic Dataset(BONN)and TUM RGB-D SLAM Dataset and Benchmark(TUM),the proposed method matches or exceeds baselines:In intermittent-motion-dominated BONN sequences Placing_non_box,it re-duces the root-mean-square of the absolute trajectory error(ATE-RMSE)by 27%relative to the baseline,remains comparable to Ellipsoid-SLAM on TUM,and consistently outperforms ORB-SLAM3 in dynamic scenes.The hysteresis counter reading on Placing_non_box2 shows that the proposed method can reduce the motion-state misclassification rate by nearly 40%.From the ablation experiment results,we confirm that adaptive thresholds yield the most significant optimisation effect.The approach improves robustness and map completeness in dynamic environments without degrading performance in low-dynamic settings.展开更多
Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonline...Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonlinearity,leading to delays in detecting time-varying data features.Additionally,the uncertain kernel function and kernel parameters limit the ability of the extracted features to express process characteristics,resulting in poor fault detection performance.To alleviate the above problems,a novel randomized auto-regressive dynamic slow feature analysis(RRDSFA)method is proposed to simultaneously monitor the operating point deviations and process dynamic faults,enabling real-time monitoring of data features in industrial processes.Firstly,the proposed Random Fourier mappingbased method achieves more effective nonlinear transformation,contrasting with the current kernelbased RDSFA algorithm that may lead to significant computational complexity.Secondly,a randomized RDSFA model is developed to extract nonlinear dynamic slow features.Furthermore,a Bayesian inference-based overall fault monitoring model including all RRDSFA sub-models is developed to overcome the randomness of random Fourier mapping.Finally,the superiority and effectiveness of the proposed monitoring method are demonstrated through a numerical case and a simulation of continuous stirred tank reactor.展开更多
Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently d...Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.展开更多
Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressi...Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressive(PVAR) model that combines the human development approach with the global indicator framework for Sustainable Development Goals(SDGs) to identify the poverty-causing and the poverty-reducing factors in China. The aim is to measure the multidimensional poverty index(MPI) of China’s provinces from 2007 to 2017, and use the exploratory spatio-temporal data analysis(ESTDA) method to reveal the characteristics of the spatio-temporal dynamics of multidimensional poverty. The results show the following:(1) The poverty-causing factors in China include the high social gross dependency ratio and crop-to-disaster ratio, and the poverty-reducing factors include the high per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 people, rate of participation in the new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita research and development(R&D) expenditure, and funding per capita for cultural undertakings.(2) From 2007 to 2017, provincial income poverty(IP), health poverty(HP), cultural poverty(CP), and multidimensional poverty have been significantly reduced in China, and the overall national poverty has dropped by 5.67% annually. there is a differentiation in poverty along different dimensions in certain provinces.(3) During the study period, the local spatial pattern of multidimensional poverty between provinces showed strong spatial dynamics, and a trend of increase from the eastern to the central and western regions was noted. The MPI among provinces exhibited a strong spatial dependence over time to form a pattern of decrease from northwestern and northeastern China to the surrounding areas.(4) The spatio-temporal networks of multidimensional poverty in adjacent provinces were mainly negatively correlated, with only Shaanxi and Henan, Shaanxi and Ningxia, Qinghai and Gansu, Hubei and Anhui, Sichuan and Guizhou, and Hainan and Guangdong forming spatially strong cooperative poverty reduction relationships. These results have important reference value for the implementation of China’s poverty alleviation strategy.展开更多
Most of the exist action recognition methods mainly utilize spatio-temporal descriptors of single interest point while ignoring their potential integral information, such as spatial distribution information. By combin...Most of the exist action recognition methods mainly utilize spatio-temporal descriptors of single interest point while ignoring their potential integral information, such as spatial distribution information. By combining local spatio-temporal feature and global positional distribution information(PDI) of interest points, a novel motion descriptor is proposed in this paper. The proposed method detects interest points by using an improved interest point detection method. Then, 3-dimensional scale-invariant feature transform(3D SIFT) descriptors are extracted for every interest point. In order to obtain a compact description and efficient computation, the principal component analysis(PCA) method is utilized twice on the 3D SIFT descriptors of single frame and multiple frames. Simultaneously, the PDI of the interest points are computed and combined with the above features. The combined features are quantified and selected and finally tested by using the support vector machine(SVM) recognition algorithm on the public KTH dataset. The testing results have showed that the recognition rate has been significantly improved and the proposed features can more accurately describe human motion with high adaptability to scenarios.展开更多
It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast...It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast mining areas in the semi-arid areas.Long-time series MODIS NDVI data are widely used to simulate the vegetation cover to reflect the disturbance and restoration of local ecosystems.In this study, both qualitative(linear regression method and coefficient of variation(CoV)) and quantitative(spatial buffer analysis, and change amplitude and the rate of change in the average NDVI) analyses were conducted to analyze the spatio-temporal dynamics of vegetation during 2000–2017 in Jungar Banner of Inner Mongolia Autonomous Region, China, at the large(Jungar Banner and three mine groups) and small(three types of functional areas: opencast coal mining excavation areas, reclamation areas and natural areas) scales.The results show that the rates of change in the average NDVI in the reclamation areas(20%–60%) and opencast coal mining excavation areas(10%–20%) were considerably higher than that in the natural areas(<7%).The vegetation in the reclamation areas experienced a trend of increase(3–5 a after reclamation)-decrease(the sixth year of reclamation)-stability.The vegetation in Jungar Banner has a spatial heterogeneity under the influences of mining and reclamation activities.The ratio of vegetation improvement area to vegetation degradation area in the west, southwest and east mine groups during 2000–2017 was 8:1, 20:1 and 33:1, respectively.The regions with the high CoV of NDVI above 0.45 were mainly distributed around the opencast coal mining excavation areas, and the regions with the CoV of NDVI above 0.25 were mostly located in areas with low(28.8%) and medium-low(10.2%) vegetation cover.The average disturbance distances of mining activities on vegetation in the three mine groups(west, southwest and east) were 800, 800 and 1000 m, respectively.The greater the scale of mining, the farther the disturbance distances of mining activities on vegetation.We conclude that vegetation reclamation will certainly compensate for the negative impacts of opencast coal mining activities on vegetation.Sufficient attention should be paid to the proportional allocation of plant species(herbs and shrubs) in the reclamation areas, and the restored vegetation in these areas needs to be protected for more than 6 a.Then, as the repair time increased, the vegetation condition of the reclamation areas would exceed that of the natural areas.展开更多
It is urgent and important to explore the dynamic evolution in comprehensive transportation green efficiency(CTGE)in the context of green development.We constructed a social development index that reflects the social ...It is urgent and important to explore the dynamic evolution in comprehensive transportation green efficiency(CTGE)in the context of green development.We constructed a social development index that reflects the social benefits of transportation services,and incorporated it into the comprehensive transportation efficiency evaluation framework as an expected output.Based on the panel data of 30 regions in China from 2003-2018,the CTGE in China was measured using the slacks-based measure-data envelopment analysis(SBM-DEA)model.Further,the dynamic evolution trends of CTGE were determined using the spatial Markov model and exploratory spatio-temporal data analysis(ESTDA)technique from a spatio-temporal perspective.The results showed that the CTGE shows a U-shaped change trend but with an overall low level and significant regional differences.The state transition of CTGE has a strong spatial dependence,and there exists the phenomenon of“club convergence”.Neighbourhood background has a significant impact on the CTGE transition types,and the spatial spillover effect is pronounced.The CTGE has an obvious positive correlation and spatial agglomeration characteristics.The geometric characteristics of the LISA time path show that the evolution process of local spatial structure and local spatial dependence of China’s CTGE is stable,but the integration of spatial evolution is weak.The spatio-temporal transition results of LISA indicate that the CTGE has obvious transfer inertness and has certain path-dependence and spatial locking characteristics,which will become the major difficulty in improving the CTGE.展开更多
Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role...Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role in the cooking oil market of China. The Jianghan Plain and Dongting Lake Plain (JPDLP) are major agricultural production areas in China. Essential changes in winter rape distribution have taken place in this area during the 21st century. However, the pattern of these changes remains unknown. In this study, the spatial and temporal dynamics of winter rape from 2000 to 2017 on the JPDLP were analyzed. An artificial neural network (ANN)-based classification method was proposed to map fractional winter rape distribution by fusing moderate resolution imaging spectrometer (MODIS) data and high-resolution imagery. The results are as follows:(1) The total winter rape acreages on the JPDLP dropped significantly, especially on the Jianghan Plain with a decline of about 45% during 2000 and 2017.(2) The winter rape abundance keeps changing with about 20–30% croplands changing their abundance drastically in every two consecutive observation years.(3) The winter rape has obvious regional differentiation for the trend of its change at the county level, and the decreasing trend was observed more strongly in the traditionally dominant agricultural counties.展开更多
In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the po...In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the potential of robotic applications.Compared to standard SLAM under the static world assumption,dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly.Therefore,dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments.Additionally,to meet the demands of some high-level tasks,dynamic SLAM can be integrated with multiple object tracking.This article presents a survey on dynamic SLAM from the perspective of feature choices.A discussion of the advantages and disadvantages of different visual features is provided in this article.展开更多
We evaluated the dynamics of land use in the Bouba Ndjidda National Park (BNNP) and adjacent areas, in northern Cameroon. Using a maximum likelihood supervised classification of satellite images from 1990 to 2016, cou...We evaluated the dynamics of land use in the Bouba Ndjidda National Park (BNNP) and adjacent areas, in northern Cameroon. Using a maximum likelihood supervised classification of satellite images from 1990 to 2016, coupled with field and a socio-economic survey, we performed a robust land-use classification. Between 1990 and 2016, the area included eight classes of land use, with the largest in 1990 being the woody savannah (42.9%) followed by the gallery forest (20.2%) and the clear forest (16.3%). Between 1990 and 1999, the gallery forest lost 64.8% of its area mostly to the benefit of woody savannahs. Between 1999 and 2016, the largest loss of area was that of the clear forest, which decreased generally by 43.2% in favor of woody savannah. Rates of increase of crop field areas were 59.6% and 78.8% respectively for the periods of 1990 to 1999 and 1999 to 2016 to the detriment of woody savannahs. We attribute the changes in land use observed mainly to the increasing human population and associated agriculture, overgrazing, fuelwood harvesting and bush fires. The exploitation of non-timber forest products and climatic factors may also have changed the vegetation cover. We recommend the implementation of farming techniques with low impact on the environment such as agroforestry.展开更多
Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-maki...Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-making. With the long-term Defense Meteorological Satellite Program’s Operational Linescan System(DMSP/OLS) nighttime light images, a pixel level assessment of urbanization of China from 1992 to 2013 was conducted in this study, and the spatio-temporal dynamics and future trends of urban development were fully detected. The results showed that the urbanization and urban dynamics of China experienced drastic fluctuations from 1992 to 2013, especially for those in the coastal and metropolitan areas. From a regional perspective, it was found that the urban dynamics and increasing trends in North Coast China, East Coast China and South Coast China were much more stable and significant than that in other regions. Moreover, with the sustainability estimating of nighttime light dynamics, the regional agglomeration trends of urban regions were also detected. The light intensity in nearly 50% of lighted pixels may continuously decrease in the future, indicating a severe situation of urbanization within these regions. In this study, The results revealed in this study can provided a new insight in long time urbanization detecting and is thus beneficial to the better understanding of trends and dynamics of urban development.展开更多
The paper discusses the features of active tectonics,seismicity and neotectonic environment in the Northwestern Yunnan extensional region.The intensity of both tectonic activity and seismicity is strong near the south...The paper discusses the features of active tectonics,seismicity and neotectonic environment in the Northwestern Yunnan extensional region.The intensity of both tectonic activity and seismicity is strong near the south and north boundaries in the areas,but weak in the middle.The distribution of the strongest subsided areas,lacustrine terrace and Quaternary fold is characterized by the diagonal symmetry.Formation of extensional tectonics in the Northwestern Yunnan can be explained by passive model,experiencing the action of compressional force in the N-S direction and shear force in the SW-NE direction,and classified as a special pull-apart tectonics.The direction of the composite force is NNE,which is coincided with the results acquired by the methods of water-compressed rupture and physical modelling.展开更多
Recently,video based flame detection has become an important approach for early detection of fire under complex circumstances.However,the detection accuracy of most existing methods remains unsatisfactory.In this pape...Recently,video based flame detection has become an important approach for early detection of fire under complex circumstances.However,the detection accuracy of most existing methods remains unsatisfactory.In this paper,we develop a new algorithm that can significantly improve the accuracy of flame detection in video images.The algorithm segments a video image and obtains areas that may contain flames by combining a two-step clustering based approach with the RGB color model.A few new dynamic and hierarchical features associated with the suspected regions,including the flicker frequency of flames,are then extracted and analyzed.The algorithm determines whether a suspected region contains flames or not by processing the color and dynamic features of the area altogether with a classifier,which can be a BP neural network,a k nearest neighbor classifier or a support vector machine.Testing results show that this algorithm is robust and efficient,and is able to significantly reduce the probability of false alarms.展开更多
Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning mode...Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.展开更多
This paper is devoted to the two-dimensional nonlinear modeling of the fluid-solid interaction (FSI) between fabric and air flow, which is based on the Automatic Incremental Dynamic Nonlinear Analysis (AIDNA)-FSI prog...This paper is devoted to the two-dimensional nonlinear modeling of the fluid-solid interaction (FSI) between fabric and air flow, which is based on the Automatic Incremental Dynamic Nonlinear Analysis (AIDNA)-FSI program in order to study the dynamic bending features of fabrics in a specific air flow filed. The computational fluid dynamics (CFD) model for flow and the finite element model (FEM) for fabric was set up to constitute an FSI model in which the geometric nonlinear behavior and the dynamic stress-strain variation of the relatively soft fabric material were taken into account. Several FSI cases with different time-dependent wind load and the model frequency analysis for fabric were carried out. The dynamic response of fabric and the distribution of fluid variables were investigated. The results of numerical simulation and experiments fit quite well. Hence, this work contributes to the research of modeling the dynamic bending behavior of fabrics in air field.展开更多
In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the ...In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.展开更多
Warm compression deformation of Fe-13.5%Cr-4.7%Al-2.0%Mo-0.70%Nb-0.40Ta(wt%)(FeCrAl)and Fe-13.5%Cr-4.7%Al-2.0%Mo-0.45%Nb-0.40Ta-0.11Zr(wt%)(FeCrAl-Zr)ferritic stainless steel was performed by a thermal simulation mach...Warm compression deformation of Fe-13.5%Cr-4.7%Al-2.0%Mo-0.70%Nb-0.40Ta(wt%)(FeCrAl)and Fe-13.5%Cr-4.7%Al-2.0%Mo-0.45%Nb-0.40Ta-0.11Zr(wt%)(FeCrAl-Zr)ferritic stainless steel was performed by a thermal simulation machine Gleeble 3800 at 600°C and strain rates of 0.01-10 s^(-1).Before deformation,all the samples were solution-annealed for 2 h at 1150°C for FeCrAl alloy and 1250°C for FeCrAl-Zr alloy.The strain rate has little or no effect on peak stress,and the precipitates in matrix or grain boundary precipitates(GBPs)have no difference in the samples deformed at the strain rate 0.01 s^(-1)and 1 s^(-1)both in FeCrAl and FeCrAl-Zr alloys.The addition of Zr increased the proportion of low-angle grain boundaries(LAGBs).The Laves phase in FeCrAl alloy precipitated uniform in the matrix,while in FeCrAl-Zr alloy Laves phase precipitated at grain boundary and formed GBP.The LAGBs andΣ3 coincident site lattice(CSL)grain boundary both increased in FeCrAl-Zr alloy,which possessed some beneficial properties such as high-temperature creep resistance to the Fe-Cr-Al alloy.More interesting,twins were created by warm deformation,which was difficult in typical bcc ferrite alloy.These results could be expected to provide guidance for subsequent warm working processes for the alloy.展开更多
Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for locationbased services continues to increase.Channel state information(CSI)can be...Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for locationbased services continues to increase.Channel state information(CSI)can be used as location feature information in fingerprint-based positioning systems because it can reflect the characteristics of the signal on multiple subcarriers.However,the random noise contained in the raw CSI information increases the likelihood of confusion when matching fingerprint data.In this paper,the Dynamic Fusion Feature(DFF)is proposed as a new fingerprint formation method to remove the noise and improve the feature resolution of the system,which combines the pre-processed amplitude and phase data.Then,the improved edit distance on real sequence(IEDR)is used as a similarity metric for fingerprint matching.Based on the above studies,we propose a new indoor fingerprint positioning method,named DFF-EDR,for improving positioning performance.During the experimental stage,data were collected and analyzed in two typical indoor environments.The results show that the proposed localization method in this paper effectively improves the feature resolution of the system in terms of both fingerprint features and similarity measures,has good anti-noise capability,and effectively reduces the localization errors.展开更多
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
文摘In dynamic scenes,the pose estimation and map consistency of visual simultaneous localisation and mapping(visual SLAM)are affected by intermittent changes in object motion states.An adaptive motion-state estimation and feature-reuse mechanism is proposed which restores features once objects become stationary.Camera ego-motion is com-pensated via projection-based point-to-point red-green-blue-depth(RGB-D)Iterative Closest Point;the alignment residual yields a short-term jitter score.An Extended Kalman Filter fuses the centre-pixel trajectory and depth of the object,using depth innovation as strong evidence to suppress false triggers.Applied adaptive decision thresholds involve resolution,ego-motion intensity,jitter,and reference depth,and are combined with dual/single triggering and hysteresis to achieve robust switching.When an object is considered static,its feature points are reused.On the Bonn RGB-D Dynamic Dataset(BONN)and TUM RGB-D SLAM Dataset and Benchmark(TUM),the proposed method matches or exceeds baselines:In intermittent-motion-dominated BONN sequences Placing_non_box,it re-duces the root-mean-square of the absolute trajectory error(ATE-RMSE)by 27%relative to the baseline,remains comparable to Ellipsoid-SLAM on TUM,and consistently outperforms ORB-SLAM3 in dynamic scenes.The hysteresis counter reading on Placing_non_box2 shows that the proposed method can reduce the motion-state misclassification rate by nearly 40%.From the ablation experiment results,we confirm that adaptive thresholds yield the most significant optimisation effect.The approach improves robustness and map completeness in dynamic environments without degrading performance in low-dynamic settings.
基金supported by the Program of National Natural Science Foundation of China(U23A20329,62163036)Youth Academic and Technical Leaders Reserve Talent Training project(202105AC160094)Industrial Innovation Talent Special Project of Xingdian Talent Support Program(XDYC-CYCX-2022-0010).
文摘Kernel-based slow feature analysis(SFA)methods have been successfully applied in the industrial process fault detection field.However,kernel-based SFA methods have high computational complexity as dealing with nonlinearity,leading to delays in detecting time-varying data features.Additionally,the uncertain kernel function and kernel parameters limit the ability of the extracted features to express process characteristics,resulting in poor fault detection performance.To alleviate the above problems,a novel randomized auto-regressive dynamic slow feature analysis(RRDSFA)method is proposed to simultaneously monitor the operating point deviations and process dynamic faults,enabling real-time monitoring of data features in industrial processes.Firstly,the proposed Random Fourier mappingbased method achieves more effective nonlinear transformation,contrasting with the current kernelbased RDSFA algorithm that may lead to significant computational complexity.Secondly,a randomized RDSFA model is developed to extract nonlinear dynamic slow features.Furthermore,a Bayesian inference-based overall fault monitoring model including all RRDSFA sub-models is developed to overcome the randomness of random Fourier mapping.Finally,the superiority and effectiveness of the proposed monitoring method are demonstrated through a numerical case and a simulation of continuous stirred tank reactor.
基金supported in part by the National Science Fund for Distinguished Young Scholars of China(62225303)the National Natural Science Fundation of China(62303039,62433004)+2 种基金the China Postdoctoral Science Foundation(BX20230034,2023M730190)the Fundamental Research Funds for the Central Universities(buctrc202201,QNTD2023-01)the High Performance Computing Platform,College of Information Science and Technology,Beijing University of Chemical Technology
文摘Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.
基金National Natural Science Foundation of China,No.71974070, No.41501593National Key R&D Project,No.2016YFA0602500Humanities and Social Sciences Foundation of Ministry of Education of China,No.19YJCZH068。
文摘Exploring the spatio-temporal dynamics of poverty is important for research on sustainable poverty reduction in China. Based on the perspective of development geography, this paper proposes a panel vector autoregressive(PVAR) model that combines the human development approach with the global indicator framework for Sustainable Development Goals(SDGs) to identify the poverty-causing and the poverty-reducing factors in China. The aim is to measure the multidimensional poverty index(MPI) of China’s provinces from 2007 to 2017, and use the exploratory spatio-temporal data analysis(ESTDA) method to reveal the characteristics of the spatio-temporal dynamics of multidimensional poverty. The results show the following:(1) The poverty-causing factors in China include the high social gross dependency ratio and crop-to-disaster ratio, and the poverty-reducing factors include the high per capita GDP, per capita social security expenditure, per capita public health expenditure, number of hospitals per 10,000 people, rate of participation in the new rural cooperative medical scheme, vegetation coverage, per capita education expenditure, number of universities, per capita research and development(R&D) expenditure, and funding per capita for cultural undertakings.(2) From 2007 to 2017, provincial income poverty(IP), health poverty(HP), cultural poverty(CP), and multidimensional poverty have been significantly reduced in China, and the overall national poverty has dropped by 5.67% annually. there is a differentiation in poverty along different dimensions in certain provinces.(3) During the study period, the local spatial pattern of multidimensional poverty between provinces showed strong spatial dynamics, and a trend of increase from the eastern to the central and western regions was noted. The MPI among provinces exhibited a strong spatial dependence over time to form a pattern of decrease from northwestern and northeastern China to the surrounding areas.(4) The spatio-temporal networks of multidimensional poverty in adjacent provinces were mainly negatively correlated, with only Shaanxi and Henan, Shaanxi and Ningxia, Qinghai and Gansu, Hubei and Anhui, Sichuan and Guizhou, and Hainan and Guangdong forming spatially strong cooperative poverty reduction relationships. These results have important reference value for the implementation of China’s poverty alleviation strategy.
基金supported by National Natural Science Foundation of China(No.61103123)Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry
文摘Most of the exist action recognition methods mainly utilize spatio-temporal descriptors of single interest point while ignoring their potential integral information, such as spatial distribution information. By combining local spatio-temporal feature and global positional distribution information(PDI) of interest points, a novel motion descriptor is proposed in this paper. The proposed method detects interest points by using an improved interest point detection method. Then, 3-dimensional scale-invariant feature transform(3D SIFT) descriptors are extracted for every interest point. In order to obtain a compact description and efficient computation, the principal component analysis(PCA) method is utilized twice on the 3D SIFT descriptors of single frame and multiple frames. Simultaneously, the PDI of the interest points are computed and combined with the above features. The combined features are quantified and selected and finally tested by using the support vector machine(SVM) recognition algorithm on the public KTH dataset. The testing results have showed that the recognition rate has been significantly improved and the proposed features can more accurately describe human motion with high adaptability to scenarios.
基金supported by the National Key Research and Development Program of China (2016YFC0501107)the Project of Ordos Science and Technology Program (2017006)the Special Project of Science and Technology Basic Work of Ministry of Science and Technology of China (2014FY110800)
文摘It is known that the exploitation of opencast coal mines has seriously damaged the environments in the semi-arid areas.Vegetation status can reliably reflect the ecological degeneration and restoration in the opencast mining areas in the semi-arid areas.Long-time series MODIS NDVI data are widely used to simulate the vegetation cover to reflect the disturbance and restoration of local ecosystems.In this study, both qualitative(linear regression method and coefficient of variation(CoV)) and quantitative(spatial buffer analysis, and change amplitude and the rate of change in the average NDVI) analyses were conducted to analyze the spatio-temporal dynamics of vegetation during 2000–2017 in Jungar Banner of Inner Mongolia Autonomous Region, China, at the large(Jungar Banner and three mine groups) and small(three types of functional areas: opencast coal mining excavation areas, reclamation areas and natural areas) scales.The results show that the rates of change in the average NDVI in the reclamation areas(20%–60%) and opencast coal mining excavation areas(10%–20%) were considerably higher than that in the natural areas(<7%).The vegetation in the reclamation areas experienced a trend of increase(3–5 a after reclamation)-decrease(the sixth year of reclamation)-stability.The vegetation in Jungar Banner has a spatial heterogeneity under the influences of mining and reclamation activities.The ratio of vegetation improvement area to vegetation degradation area in the west, southwest and east mine groups during 2000–2017 was 8:1, 20:1 and 33:1, respectively.The regions with the high CoV of NDVI above 0.45 were mainly distributed around the opencast coal mining excavation areas, and the regions with the CoV of NDVI above 0.25 were mostly located in areas with low(28.8%) and medium-low(10.2%) vegetation cover.The average disturbance distances of mining activities on vegetation in the three mine groups(west, southwest and east) were 800, 800 and 1000 m, respectively.The greater the scale of mining, the farther the disturbance distances of mining activities on vegetation.We conclude that vegetation reclamation will certainly compensate for the negative impacts of opencast coal mining activities on vegetation.Sufficient attention should be paid to the proportional allocation of plant species(herbs and shrubs) in the reclamation areas, and the restored vegetation in these areas needs to be protected for more than 6 a.Then, as the repair time increased, the vegetation condition of the reclamation areas would exceed that of the natural areas.
基金National Key Research and Development Program of China(2019YFB1600400)National Natural Science Foundation of China(72174035)+2 种基金National Natural Science Foundation of China(71774018)Liaoning Revitalization Talents Program(XLYC2008030)Liaoning Provincial Natural Science Foundation Shipping Joint Foundation Program(2020-HYLH-20)。
文摘It is urgent and important to explore the dynamic evolution in comprehensive transportation green efficiency(CTGE)in the context of green development.We constructed a social development index that reflects the social benefits of transportation services,and incorporated it into the comprehensive transportation efficiency evaluation framework as an expected output.Based on the panel data of 30 regions in China from 2003-2018,the CTGE in China was measured using the slacks-based measure-data envelopment analysis(SBM-DEA)model.Further,the dynamic evolution trends of CTGE were determined using the spatial Markov model and exploratory spatio-temporal data analysis(ESTDA)technique from a spatio-temporal perspective.The results showed that the CTGE shows a U-shaped change trend but with an overall low level and significant regional differences.The state transition of CTGE has a strong spatial dependence,and there exists the phenomenon of“club convergence”.Neighbourhood background has a significant impact on the CTGE transition types,and the spatial spillover effect is pronounced.The CTGE has an obvious positive correlation and spatial agglomeration characteristics.The geometric characteristics of the LISA time path show that the evolution process of local spatial structure and local spatial dependence of China’s CTGE is stable,but the integration of spatial evolution is weak.The spatio-temporal transition results of LISA indicate that the CTGE has obvious transfer inertness and has certain path-dependence and spatial locking characteristics,which will become the major difficulty in improving the CTGE.
基金supported by the Natural Science Foundation of Hubei Province, China (2017CFB434)the National Natural Science Foundation of China (41506208 and 61501200)the Basic Research Funds for Yellow River Institute of Hydraulic Research, China (HKYJBYW-2016-06)
文摘Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role in the cooking oil market of China. The Jianghan Plain and Dongting Lake Plain (JPDLP) are major agricultural production areas in China. Essential changes in winter rape distribution have taken place in this area during the 21st century. However, the pattern of these changes remains unknown. In this study, the spatial and temporal dynamics of winter rape from 2000 to 2017 on the JPDLP were analyzed. An artificial neural network (ANN)-based classification method was proposed to map fractional winter rape distribution by fusing moderate resolution imaging spectrometer (MODIS) data and high-resolution imagery. The results are as follows:(1) The total winter rape acreages on the JPDLP dropped significantly, especially on the Jianghan Plain with a decline of about 45% during 2000 and 2017.(2) The winter rape abundance keeps changing with about 20–30% croplands changing their abundance drastically in every two consecutive observation years.(3) The winter rape has obvious regional differentiation for the trend of its change at the county level, and the decreasing trend was observed more strongly in the traditionally dominant agricultural counties.
基金This work was supported by National Natural Science Foundation of China,Nos.62002359 and 61836015the Beijing Advanced Discipline Fund,No.115200S001.
文摘In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the potential of robotic applications.Compared to standard SLAM under the static world assumption,dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly.Therefore,dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments.Additionally,to meet the demands of some high-level tasks,dynamic SLAM can be integrated with multiple object tracking.This article presents a survey on dynamic SLAM from the perspective of feature choices.A discussion of the advantages and disadvantages of different visual features is provided in this article.
文摘We evaluated the dynamics of land use in the Bouba Ndjidda National Park (BNNP) and adjacent areas, in northern Cameroon. Using a maximum likelihood supervised classification of satellite images from 1990 to 2016, coupled with field and a socio-economic survey, we performed a robust land-use classification. Between 1990 and 2016, the area included eight classes of land use, with the largest in 1990 being the woody savannah (42.9%) followed by the gallery forest (20.2%) and the clear forest (16.3%). Between 1990 and 1999, the gallery forest lost 64.8% of its area mostly to the benefit of woody savannahs. Between 1999 and 2016, the largest loss of area was that of the clear forest, which decreased generally by 43.2% in favor of woody savannah. Rates of increase of crop field areas were 59.6% and 78.8% respectively for the periods of 1990 to 1999 and 1999 to 2016 to the detriment of woody savannahs. We attribute the changes in land use observed mainly to the increasing human population and associated agriculture, overgrazing, fuelwood harvesting and bush fires. The exploitation of non-timber forest products and climatic factors may also have changed the vegetation cover. We recommend the implementation of farming techniques with low impact on the environment such as agroforestry.
基金Under the auspices of State Scholarship Fund of China Scholarship Council(No.201706320300)。
文摘Understanding the dynamics of urbanization is essential to the sustainable development of cities. Meanwhile the analysis of urban development can also provide scientifically and effective information for decision-making. With the long-term Defense Meteorological Satellite Program’s Operational Linescan System(DMSP/OLS) nighttime light images, a pixel level assessment of urbanization of China from 1992 to 2013 was conducted in this study, and the spatio-temporal dynamics and future trends of urban development were fully detected. The results showed that the urbanization and urban dynamics of China experienced drastic fluctuations from 1992 to 2013, especially for those in the coastal and metropolitan areas. From a regional perspective, it was found that the urban dynamics and increasing trends in North Coast China, East Coast China and South Coast China were much more stable and significant than that in other regions. Moreover, with the sustainability estimating of nighttime light dynamics, the regional agglomeration trends of urban regions were also detected. The light intensity in nearly 50% of lighted pixels may continuously decrease in the future, indicating a severe situation of urbanization within these regions. In this study, The results revealed in this study can provided a new insight in long time urbanization detecting and is thus beneficial to the better understanding of trends and dynamics of urban development.
文摘The paper discusses the features of active tectonics,seismicity and neotectonic environment in the Northwestern Yunnan extensional region.The intensity of both tectonic activity and seismicity is strong near the south and north boundaries in the areas,but weak in the middle.The distribution of the strongest subsided areas,lacustrine terrace and Quaternary fold is characterized by the diagonal symmetry.Formation of extensional tectonics in the Northwestern Yunnan can be explained by passive model,experiencing the action of compressional force in the N-S direction and shear force in the SW-NE direction,and classified as a special pull-apart tectonics.The direction of the composite force is NNE,which is coincided with the results acquired by the methods of water-compressed rupture and physical modelling.
文摘Recently,video based flame detection has become an important approach for early detection of fire under complex circumstances.However,the detection accuracy of most existing methods remains unsatisfactory.In this paper,we develop a new algorithm that can significantly improve the accuracy of flame detection in video images.The algorithm segments a video image and obtains areas that may contain flames by combining a two-step clustering based approach with the RGB color model.A few new dynamic and hierarchical features associated with the suspected regions,including the flicker frequency of flames,are then extracted and analyzed.The algorithm determines whether a suspected region contains flames or not by processing the color and dynamic features of the area altogether with a classifier,which can be a BP neural network,a k nearest neighbor classifier or a support vector machine.Testing results show that this algorithm is robust and efficient,and is able to significantly reduce the probability of false alarms.
基金Youth Innovation Promotion Association CAS,Grant/Award Number:2021103Strategic Priority Research Program of Chinese Academy of Sciences,Grant/Award Number:XDC02060500。
文摘Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.
基金National Natural Science Foundations of China(No.50803010,No.60904056)
文摘This paper is devoted to the two-dimensional nonlinear modeling of the fluid-solid interaction (FSI) between fabric and air flow, which is based on the Automatic Incremental Dynamic Nonlinear Analysis (AIDNA)-FSI program in order to study the dynamic bending features of fabrics in a specific air flow filed. The computational fluid dynamics (CFD) model for flow and the finite element model (FEM) for fabric was set up to constitute an FSI model in which the geometric nonlinear behavior and the dynamic stress-strain variation of the relatively soft fabric material were taken into account. Several FSI cases with different time-dependent wind load and the model frequency analysis for fabric were carried out. The dynamic response of fabric and the distribution of fluid variables were investigated. The results of numerical simulation and experiments fit quite well. Hence, this work contributes to the research of modeling the dynamic bending behavior of fabrics in air field.
基金supported by the National Natural Science Foundation of China under Grant 52325402, 52274057, 52074340 and 51874335the National Key R&D Program of China under Grant 2023YFB4104200+1 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028。
文摘In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.
基金This work was financially supported by the National Natural Science Foundation of China(No.U1867201)the Key Project of Nuclear Safety and Advanced Nuclear Technology(No.2019YFB1901002)"the Project supported by State Key Laboratory of Powder Metallurgy",Central South University,Changsha.China.
文摘Warm compression deformation of Fe-13.5%Cr-4.7%Al-2.0%Mo-0.70%Nb-0.40Ta(wt%)(FeCrAl)and Fe-13.5%Cr-4.7%Al-2.0%Mo-0.45%Nb-0.40Ta-0.11Zr(wt%)(FeCrAl-Zr)ferritic stainless steel was performed by a thermal simulation machine Gleeble 3800 at 600°C and strain rates of 0.01-10 s^(-1).Before deformation,all the samples were solution-annealed for 2 h at 1150°C for FeCrAl alloy and 1250°C for FeCrAl-Zr alloy.The strain rate has little or no effect on peak stress,and the precipitates in matrix or grain boundary precipitates(GBPs)have no difference in the samples deformed at the strain rate 0.01 s^(-1)and 1 s^(-1)both in FeCrAl and FeCrAl-Zr alloys.The addition of Zr increased the proportion of low-angle grain boundaries(LAGBs).The Laves phase in FeCrAl alloy precipitated uniform in the matrix,while in FeCrAl-Zr alloy Laves phase precipitated at grain boundary and formed GBP.The LAGBs andΣ3 coincident site lattice(CSL)grain boundary both increased in FeCrAl-Zr alloy,which possessed some beneficial properties such as high-temperature creep resistance to the Fe-Cr-Al alloy.More interesting,twins were created by warm deformation,which was difficult in typical bcc ferrite alloy.These results could be expected to provide guidance for subsequent warm working processes for the alloy.
基金This work was financially supported by the National Key Research&Development Program of China under Grant No.2020YFC1511702the Beijing Municipal Natural Science Foundation under Grant No.L191003.
文摘Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for locationbased services continues to increase.Channel state information(CSI)can be used as location feature information in fingerprint-based positioning systems because it can reflect the characteristics of the signal on multiple subcarriers.However,the random noise contained in the raw CSI information increases the likelihood of confusion when matching fingerprint data.In this paper,the Dynamic Fusion Feature(DFF)is proposed as a new fingerprint formation method to remove the noise and improve the feature resolution of the system,which combines the pre-processed amplitude and phase data.Then,the improved edit distance on real sequence(IEDR)is used as a similarity metric for fingerprint matching.Based on the above studies,we propose a new indoor fingerprint positioning method,named DFF-EDR,for improving positioning performance.During the experimental stage,data were collected and analyzed in two typical indoor environments.The results show that the proposed localization method in this paper effectively improves the feature resolution of the system in terms of both fingerprint features and similarity measures,has good anti-noise capability,and effectively reduces the localization errors.
