According to the mining method for Dongguashan Copper Mine and Tongkeng Mine in China, and with the help of the cavity monitoring system（CMS） and mining software Surpac, the 3D cavity models were established exactly...According to the mining method for Dongguashan Copper Mine and Tongkeng Mine in China, and with the help of the cavity monitoring system（CMS） and mining software Surpac, the 3D cavity models were established exactly. A series of correlative techniques for calculating stope over-excavation and under-excavation, stope dilution and ore loss rates, and the blasting design of the pillar with complicated irregular boundaries were developed. These techniques were applied in Dongguashan Copper Mine and Tongkeng Mine successfully. Using these techniques, the dilution rates of stopes 52-2^#, 52-6^#, 52-8^#and 52-10^# of Dongguashan Copper Mine are calculated to be 2.12%, 8.46%, 12-67% and 10.68%, respectively, and the ore loss rates of stopes 52-6^# and 5-8^# are 4.41% and 3.70%, severally. Furthermore, according to the design accomplished by the technique for a pillar of Tongkeng Mine with irregular boundary, the volume, total length of boreholes and the dynamite quantity of the pillar are computed to be 1.2 ×10^4 m^3, 2.98 km and 10.97 t, correspondingly.展开更多
Based on the physical meaning of sensitivity,a new finite element(FE) model updating method was proposed. In this method,a three-dimensional FE model of the Nanjing Yangtze River Bridge(NYRB) with ANSYS program was es...Based on the physical meaning of sensitivity,a new finite element(FE) model updating method was proposed. In this method,a three-dimensional FE model of the Nanjing Yangtze River Bridge(NYRB) with ANSYS program was established and updated by modifying some design parameters. To further validate the updated FE model,the analytical stress-time histories responses of main members induced by a moving train were compared with the measured ones. The results show that the relative error of maximum stress is 2.49% and the minimum relative coefficient of analytical stress-time histories responses is 0.793. The updated model has a good agreement between the calculated data and the tested data,and provides a current baseline FE model for long-term health monitoring and condition assessment of the NYRB. At the same time,the model is validated by stress-time histories responses to be feasible and practical for railway steel bridge model updating.展开更多
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo...[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.展开更多
The basic difference non-equal interval model GM(1,1) in grey theory was used to fit and forecast data series with non-equal lengths and different inertias, acquired from oil monitoring of internal combustion engines....The basic difference non-equal interval model GM(1,1) in grey theory was used to fit and forecast data series with non-equal lengths and different inertias, acquired from oil monitoring of internal combustion engines. The fitted and forecasted results show that the length or inertia of a sequence affects its precision very much, i.e. the bigger the inertia of a sequence is, or the shorter the length of a series is, the less the errors of fitted and forecasted results are. Based on the research results, it is suggested that short series should be applied to be fitted and forecasted; for longer series, the newer datum should be applied instead of the older datum to be analyzed by non- equalinterval GM(1,1) to improve the forecasted and fitted precision, and that data sequence should be verified to satisfy the conditions of grey forecasting.展开更多
The structural health status of Hunan Road Bridge during its two-year service period from April 2015 to April 2017 was studied based on monitored data.The Hunan Road Bridge is the widest concrete self-anchored suspens...The structural health status of Hunan Road Bridge during its two-year service period from April 2015 to April 2017 was studied based on monitored data.The Hunan Road Bridge is the widest concrete self-anchored suspension bridge in China at present.Its structural changes and safety were evaluated using the health monitoring data,which included deformations,detailed stresses,and vibration characteristics.The influences of the single and dual effects comprising the ambient temperature changes and concrete shrinkage and creep(S&C)were analyzed based on the measured data.The ANSYS beam finite element model was established and validated by the measured bridge completion state.The comparative analyses of the prediction results of long-term concrete S&C effects were conducted using CEB-FIP 90 and B3 prediction models.The age-adjusted effective modulus method was adopted to simulate the aging behavior of concrete.Prestress relaxation was considered in the stepwise calculation.The results show that the transverse deviations of the towers are noteworthy.The spatial effect of the extra-wide girder is significant,as the compressive stress variations at the girder were uneven along the transverse direction.General increase and decrease in the girder compressive stresses were caused by seasonal ambient warming and cooling,respectively.The temperature gradient effects in the main girder were significant.Comparisons with the measured data showed that more accurate prediction results were obtained with the B3 prediction model,which can consider the concrete material parameters,than with the CEB-FIP 90 model.Significant deflection of the midspan girder in the middle region will be caused by the deviations of the cable anchoring positions at the girder ends and tower tops toward the midspan due to concrete S&C.The increase in the compressive stresses at the top plate and decrease in the stresses at the bottom plate at the middle midspan will be significant.The pre-deviations of the towers toward the sidespan and pre-lift of the midspan girder can reduce the adverse influences of concrete S&C on the structural health of the self-anchored suspension bridge with extra-wide concrete girder.展开更多
Groundwater is an important water resource in Haihe River basin,North China.The number of aquifers that appear to be declining under conditions of groundwater overdraft is increasing.To effectively manage the water re...Groundwater is an important water resource in Haihe River basin,North China.The number of aquifers that appear to be declining under conditions of groundwater overdraft is increasing.To effectively manage the water resources,there is a strong scientific need to analyze the net use of this important water resource and to quantify the water rights allocation for improved understanding of the future water展开更多
Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the hea...Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.展开更多
Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew back...Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.展开更多
基金Projects(2007BAK22B04, 2006BAB02B05) supported by the National 11th Five-Year Science and Technology Supporting Plan of ChinaProject(50490274) supported by the National Natural Science Foundation of China
文摘According to the mining method for Dongguashan Copper Mine and Tongkeng Mine in China, and with the help of the cavity monitoring system（CMS） and mining software Surpac, the 3D cavity models were established exactly. A series of correlative techniques for calculating stope over-excavation and under-excavation, stope dilution and ore loss rates, and the blasting design of the pillar with complicated irregular boundaries were developed. These techniques were applied in Dongguashan Copper Mine and Tongkeng Mine successfully. Using these techniques, the dilution rates of stopes 52-2^#, 52-6^#, 52-8^#and 52-10^# of Dongguashan Copper Mine are calculated to be 2.12%, 8.46%, 12-67% and 10.68%, respectively, and the ore loss rates of stopes 52-6^# and 5-8^# are 4.41% and 3.70%, severally. Furthermore, according to the design accomplished by the technique for a pillar of Tongkeng Mine with irregular boundary, the volume, total length of boreholes and the dynamite quantity of the pillar are computed to be 1.2 ×10^4 m^3, 2.98 km and 10.97 t, correspondingly.
基金Project(2001G025) supported by the Foundation of the Science and Technology Section of Ministry of Railway of ChinaProject(2006FJ4233) supported by Hunan Postdoctoral Scientific Program of ChinaProject(2006) supported by the Postdoctoral Foundation of Central South University,China
文摘Based on the physical meaning of sensitivity,a new finite element(FE) model updating method was proposed. In this method,a three-dimensional FE model of the Nanjing Yangtze River Bridge(NYRB) with ANSYS program was established and updated by modifying some design parameters. To further validate the updated FE model,the analytical stress-time histories responses of main members induced by a moving train were compared with the measured ones. The results show that the relative error of maximum stress is 2.49% and the minimum relative coefficient of analytical stress-time histories responses is 0.793. The updated model has a good agreement between the calculated data and the tested data,and provides a current baseline FE model for long-term health monitoring and condition assessment of the NYRB. At the same time,the model is validated by stress-time histories responses to be feasible and practical for railway steel bridge model updating.
文摘[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.
文摘The basic difference non-equal interval model GM(1,1) in grey theory was used to fit and forecast data series with non-equal lengths and different inertias, acquired from oil monitoring of internal combustion engines. The fitted and forecasted results show that the length or inertia of a sequence affects its precision very much, i.e. the bigger the inertia of a sequence is, or the shorter the length of a series is, the less the errors of fitted and forecasted results are. Based on the research results, it is suggested that short series should be applied to be fitted and forecasted; for longer series, the newer datum should be applied instead of the older datum to be analyzed by non- equalinterval GM(1,1) to improve the forecasted and fitted precision, and that data sequence should be verified to satisfy the conditions of grey forecasting.
基金Project(201606090050)supported by China Scholarship CouncilProject(51278104)supported by the National Natural Science Foundation of China+2 种基金Project(2011Y03)supported by Jiangsu Province Transportation Scientific Research Programs,ChinaProject(20133204120015)supported by the Research Fund for the Doctoral Program of Higher Education of ChinaProject(12KJB560003)supported by Jiangsu Province Universities Natural Science Foundation,China
文摘The structural health status of Hunan Road Bridge during its two-year service period from April 2015 to April 2017 was studied based on monitored data.The Hunan Road Bridge is the widest concrete self-anchored suspension bridge in China at present.Its structural changes and safety were evaluated using the health monitoring data,which included deformations,detailed stresses,and vibration characteristics.The influences of the single and dual effects comprising the ambient temperature changes and concrete shrinkage and creep(S&C)were analyzed based on the measured data.The ANSYS beam finite element model was established and validated by the measured bridge completion state.The comparative analyses of the prediction results of long-term concrete S&C effects were conducted using CEB-FIP 90 and B3 prediction models.The age-adjusted effective modulus method was adopted to simulate the aging behavior of concrete.Prestress relaxation was considered in the stepwise calculation.The results show that the transverse deviations of the towers are noteworthy.The spatial effect of the extra-wide girder is significant,as the compressive stress variations at the girder were uneven along the transverse direction.General increase and decrease in the girder compressive stresses were caused by seasonal ambient warming and cooling,respectively.The temperature gradient effects in the main girder were significant.Comparisons with the measured data showed that more accurate prediction results were obtained with the B3 prediction model,which can consider the concrete material parameters,than with the CEB-FIP 90 model.Significant deflection of the midspan girder in the middle region will be caused by the deviations of the cable anchoring positions at the girder ends and tower tops toward the midspan due to concrete S&C.The increase in the compressive stresses at the top plate and decrease in the stresses at the bottom plate at the middle midspan will be significant.The pre-deviations of the towers toward the sidespan and pre-lift of the midspan girder can reduce the adverse influences of concrete S&C on the structural health of the self-anchored suspension bridge with extra-wide concrete girder.
文摘Groundwater is an important water resource in Haihe River basin,North China.The number of aquifers that appear to be declining under conditions of groundwater overdraft is increasing.To effectively manage the water resources,there is a strong scientific need to analyze the net use of this important water resource and to quantify the water rights allocation for improved understanding of the future water
文摘Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.
基金This project was supported by the foundation of the Visual and Auditory Information Processing Laboratory of BeijingUniversity of China (0306) and the National Science Foundation of China (60374031).
文摘Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.
基金Supported by National Basic Research Program of China（973 Program）（2013CB035500） National Natural Science Foundation of China（61233004,61221003,61074061）+1 种基金 International Cooperation Program of Shanghai Science and Technology Commission （12230709600） the Higher Education Research Fund for the Doctoral Program of China（20120073130006）
基金Supported by National Natural Science Foundation Of China （60873235, 60473099）, Science-Technology Development Key Project of Jilin Province of China （20080318）, and Program of New Century Excellent Talents in University of China （NCET-06-0300）
