Determining trip purpose is an important link to explore travel rules. In this paper,we takea utomobile users in urban areas as the research object,combine unsupervised learning and supervised learningm ethods to anal...Determining trip purpose is an important link to explore travel rules. In this paper,we takea utomobile users in urban areas as the research object,combine unsupervised learning and supervised learningm ethods to analyze their travel characteristics,and focus on the classification and prediction of automobileu sers’trip purposes. However,previous studies on trip purposes mainly focused on questionnaires and GPSd ata,which cannot well reflect the characteristics of automobile travel. In order to avoid the multi-dayb ehavior variability and unobservable heterogeneity of individual characteristics ignored in traditional traffic questionnaires,traffic monitoring data from the Northern District of Qingdao are used,and the K-meansc lustering method is applied to estimate the trip purposes of automobile users. Then,Adaptive Boosting(AdaBoost)and Random Forest(RF)methods are used to classify and predict trip purposes. Finally,ther esult shows:(1)the purpose of automobile users can be mainly divided into four clusters,which includeC ommuting trips,Flexible life demand travel in daytime,Evening entertainment and leisure shopping,andT axi-based trips for the first three types of purposes,respectively;(2)the Random Forest method performss ignificantly better than AdaBoost in trip purpose prediction for higher accuracy;(3)the average predictiona ccuracy of Random Forest under hyper-parameters optimization reaches96.25%,which proves the feasibilitya nd rationality of the above clustering results.展开更多
At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the p...At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.展开更多
The fatigue of concrete structures will gradually appear after being subjected to alternating loads for a long time,and the accidents caused by fatigue failure of bridge structures also appear from time to time.Aiming...The fatigue of concrete structures will gradually appear after being subjected to alternating loads for a long time,and the accidents caused by fatigue failure of bridge structures also appear from time to time.Aiming at the problem of degradation of long-span continuous rigid frame bridges due to fatigue and environmental effects,this paper suggests a method to analyze the fatigue degradation mechanism of this type of bridge,which combines long-term in-site monitoring data collected by the health monitoring system(HMS)and fatigue theory.In the paper,the authors mainly carry out the research work in the following aspects:First of all,a long-span continuous rigid frame bridge installed with HMS is used as an example,and a large amount of health monitoring data have been acquired,which can provide efficient information for fatigue in terms of equivalent stress range and cumulative number of stress cycles;next,for calculating the cumulative fatigue damage of the bridge structure,fatigue stress spectrum got by rain flow counting method,S-N curves and damage criteria are used for fatigue damage analysis.Moreover,it was considered a linear accumulation damage through the Palmgren-Miner rule for the counting of stress cycles.The health monitoring data are adopted to obtain fatigue stress data and the rain flow counting method is used to count the amplitude varying fatigue stress.The proposed fatigue reliability approach in the paper can estimate the fatigue damage degree and its evolution law of bridge structures well,and also can help bridge engineers do the assessment of future service duration.展开更多
Initial residual stress is the main reason causing machining deformation of the workpiece,which has been deemed as one of the most important aspects of machining quality issues.The inference of the distribution of ini...Initial residual stress is the main reason causing machining deformation of the workpiece,which has been deemed as one of the most important aspects of machining quality issues.The inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation control.Due to the principle error of existing residual stress detection methods,there are still challenges in practical applications.Aiming at the detection problem of the initial residual stress field,an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this paper.Monitoring data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress,and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish;the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism model.Finally,the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism model.Verification results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.展开更多
A sufficient sample size of monitoring data becomes a key factor for describing aircraft engines state.Generative adversarial nets(GAN)can be used to expand the sample size based on the existing state monitoring infor...A sufficient sample size of monitoring data becomes a key factor for describing aircraft engines state.Generative adversarial nets(GAN)can be used to expand the sample size based on the existing state monitoring information.In the paper,a GAN model is introduced to design an algorithm for generating the monitoring data of aircraft engines.This feasibility of the method is illustrated by an example.The experimental results demonstrate that the probability density distribution of generated data after a large number of network training iterations is consistent with the probability density distribution of monitoring data.The proposed method also effectively demonstrates the generated monitoring data of aircraft engine are in a reasonable range.The method can effectively solve the problem of inaccurate performance degradation evaluation caused by the small amount of aero?engine condition monitoring data.展开更多
Smart grid puts forward higher requirements for power quality.Power quality evaluation can provide a decision-making basis for quality improvement.Based on power quality monitoring data,a grey target method is propose...Smart grid puts forward higher requirements for power quality.Power quality evaluation can provide a decision-making basis for quality improvement.Based on power quality monitoring data,a grey target method is proposed for power quality evaluation.The grey target is composed of power quality evaluation standard and power quality monitoring data to be evaluated.Combining with the characteristics of each power quality evaluation index,the target center of the whole grey target system is found.Then,the power quality monitoring data to be evaluated and the power quality standard mode are compared and analyzed to construct the power quality grey correlation difference information space.Finally,the target center coefficient and target degree of power quality are calculated to realize the comprehensive evaluation of power quality,and the evaluation grade of power quality monitoring data to be evaluated is obtained.Compared with the evaluation results of the existing literature,the effectiveness of the proposed method is verified,which shows that grey target theory is reasonable in the comprehensive evaluation of power quality.展开更多
Many countries are paying more and more attention to the protection of water resources at present,and how to protect water resources has received extensive attention from society.Water quality monitoring is the key wo...Many countries are paying more and more attention to the protection of water resources at present,and how to protect water resources has received extensive attention from society.Water quality monitoring is the key work to water resources protection.How to efficiently collect and analyze water quality monitoring data is an important aspect of water resources protection.In this paper,python programming tools and regular expressions were used to design a web crawler for the acquisition of water quality monitoring data from Global Freshwater Quality Database(GEMStat)sites,and the multi-thread parallelism was added to improve the efficiency in the process of downloading and parsing.In order to analyze and process the crawled water quality data,Pandas and Pyecharts are used to visualize the water quality data to show the intrinsic correlation and spatiotemporal relationship of the data.展开更多
Air quality monitoring is effective for timely understanding of the current air quality status of a region or city.Currently,the huge volume of environmental monitoring data,which has reasonable real-time performance,...Air quality monitoring is effective for timely understanding of the current air quality status of a region or city.Currently,the huge volume of environmental monitoring data,which has reasonable real-time performance,provides strong support for in-depth analysis of air pollution characteristics and causes.However,in the era of big data,to meet current demands for fine management of the atmospheric environment,it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality.This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period,and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas(i.e.,the“2+26”region)during the period of the three-year action plan to fight air pollution.We suggest that air quality should be comprehensively,deeply,and scientifically evaluated from the aspects of air pollution characteristics,causes,and influences of meteorological conditions and anthropogenic emissions.It is also suggested that a threeyear moving average be introduced as one of the evaluation indexes of long-term change of pollutants.Additionally,both temporal and spatial differences should be considered when removing confounding meteorological factors.展开更多
Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the eff...Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.展开更多
The study PV/diesel system is a stand-alone microgrid powered by the PV/diesel combination without production storage. The study focused on optimising PV/diesel production by monitoring data. It also referred to a com...The study PV/diesel system is a stand-alone microgrid powered by the PV/diesel combination without production storage. The study focused on optimising PV/diesel production by monitoring data. It also referred to a comparison of sensitive factors in PV/diesel production. This study implemented structural and non-structural factors of the said system. A literature search was conducted to determine the factors involved. So, factors such as system autonomy, energy quality, system stability and data monitoring were considered for the study. Thus, after a detailed presentation of the data monitoring, a comparison based on the method, Analysis of Failure Modes, their Effects and Criticalities (FMEA) was carried out. At the end of the comparison, a hierarchy of parameters in the exploitation of the energy production of autonomous microgrids was made. From its results, it emerges a good consideration of the factor “data monitoring” in the management of the system studied. The results obtained confirm the importance of data monitoring for a better optimization of energy production. A monitoring program or procedure has been developed according to the originality that the present study has identified. The study also made it possible to evaluate the performance of data monitoring for the energy production of photovoltaic systems in general and hybrid PV/diesel systems in particular.展开更多
The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq....The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq. km.) on the Loess Plateau. It sums up Remote sensing (RS), Geographical Information System (GIS) and Expert System (ES) and consists of a integrated system. As a basic level information system of Loess Plateau, its perfection and psreading will bring about a great advance in resources exploitation and management of Loess Plateau.展开更多
In order to improve the quality of automatic monitoring data of pollution sources and apply the automatic monitoring data to verify the environmental tax,Shandong Province took the lead in adopting the Internet of Thi...In order to improve the quality of automatic monitoring data of pollution sources and apply the automatic monitoring data to verify the environmental tax,Shandong Province took the lead in adopting the Internet of Things technology and drawing on the successful experience of air automatic monitoring stations and surface water automatic monitoring stations in management,and developed a dynamic management and control system for automatic monitoring equipment of pollution sources to improve and strengthen the quality audit of automatic monitoring data,further improve the quality of automatic monitoring data and better provide a basis for environmental management and decision making.The system realizes the simultaneous monitoring of monitoring data,running state and parameters of the automatic monitoring equipment,eliminates the phenomenon of falsification by modifying equipment parameters,and judges the validity of the collected data by acquiring the working state of the equipment remotely and randomly.After the actual operation test of the Department of Ecological Environment of Shandong Province,the system is proved to have the characteristics of practicality,real time and high efficiency,and be able to make up for low frequency and narrow coverage of manual inspection,with good application prospect in the field of environment and pollution source monitoring.展开更多
We carried out time-lapse analysis in a producing Niger Delta X-field, by first investigating the response and sensitivity of rock properties/attributes to lithology and pore fill in 3-D cross plot domain and by Gassm...We carried out time-lapse analysis in a producing Niger Delta X-field, by first investigating the response and sensitivity of rock properties/attributes to lithology and pore fill in 3-D cross plot domain and by Gassmann’s fluid substitution modeling. Furthermore, 4-D seismic data were inverted into acoustic impedance volumes through model based inversion scheme. This served as input into a multi-attribute neural network algorithm for the extraction of rock attribute volumes based on the results of the petrophysical log analysis. Subsequently, horizon slices of rock properties/ attributes were extracted from the inverted seismic data and analyzed. In this way, we mapped hydrocarbon depleted wells in the field, and identified probable by-passed hydrocarbon zones. Thus, the integration of well and time lapse seismic (4-D) data in reservoir studies has remarkably improved information on the reservoir economic potential, and enhanced hydrocarbon recovery factor.展开更多
In this paper we have developed a data logging and monitoring system, we validated the system by comparing the result from it with the existing one and found that the system performs slightly better than the existing ...In this paper we have developed a data logging and monitoring system, we validated the system by comparing the result from it with the existing one and found that the system performs slightly better than the existing work in the same area. This implies that the data logger and monitoring system is good and can be used to monitor solar energy variables even at the comfort of our homes. We fitted a model to the generated data and found that the meteorological variables considered accounted for 99.88% of the power output in the rainy seasons while 0.12% of the variation was not explained due to other factors. Solar panels inclined at an angle of 5° (Tilt) and facing South Pole perform optimally.展开更多
In light of the complex and dynamic mechanical properties of evolving weak strata in open-pit mines,and the consequent difficulty of determining their mechanical parameters,this study uses the ultimate balance theory,...In light of the complex and dynamic mechanical properties of evolving weak strata in open-pit mines,and the consequent difficulty of determining their mechanical parameters,this study uses the ultimate balance theory,along with the back analysis method combined with monitoring data on field displacement,to carry out parameter inversion using the FLAC3D numerical simulation software.The edge slope of a working pit of the Weijiamao open-pit mine was used as research object to this end.As the results obtained by the constitutive model were consistent with the field monitoring data,the evolving weak strata in the slope and the position of the landslide in the mine could be obtained.The landslide was directed northeast.The mechanism of the edge slope of the working pit was identified as unloading shear failure,and the feasibility of the method of parameter inversion was verified.The internal friction angle φand cohesion C of evolving weak strata in the slope of the open-pit mine were also obtained,where this compensated for the deficiency of laboratory tests and enabled the transformation from qualitative to quantitative analysis.This can provide a reliable basis for the safe operation of open-pit mines.展开更多
Although China’s urban floating population is mainly concentrated in developed cities,from the central and western cities to the eastern developed cities,but the characteristics of the floating population in differen...Although China’s urban floating population is mainly concentrated in developed cities,from the central and western cities to the eastern developed cities,but the characteristics of the floating population in different cities are significantly different.This paper systematically investigates the spatiotemporal characteristics and influencing factors of the floating population in different levels of cities.The results show that the regional imbalance to further strengthen,accumulation and dispersion trend has become increasingly obvious,liquidity is positively correlated and city level scale,and urban agglomeration and the core city is still polarization center of floating population.Flow range is closely related to urban hierarchy:the higher the intra-urban grade,the more tend to inter-provincial flow;the lower the city grade,the more tend to intra-urban mobility.Short-term(1-2 years)and long-term(more than 7 years)flow-time coexist.The short-term liquidity increases with the city grade,and the long-term liquidity decreases with the city level.Farmers are still the main body of the floating population.Younger age,lower education level,low-skilled,high gender ratio employees are the most basic demographic characteristics of the floating population,although there are differences between different cities.The main reason for affecting the floating population is seeking jobs and doing business.展开更多
A volcano can be defined as a complex system, not least for the hidden clues related to its internal nature. Innovative models grounded in the Artificial Sciences, have been proposed for a novel pattern recognition an...A volcano can be defined as a complex system, not least for the hidden clues related to its internal nature. Innovative models grounded in the Artificial Sciences, have been proposed for a novel pattern recognition analysis at Mt. Etna volcano. The reference monitoring dataset dealt with real data of 28 parameters collected between January 2001 and April 2005, during which the volcano underwent the July-August 2001, October 2002-January 2003 and September 2004-April 2005 flank eruptions. There were 301 eruptive days out of an overall number of 1581 investigated days. The analysis involved successive steps. First, the TWIST algorithm was used to select the most predictive attributes associated with the flank eruption target. During his work, the algorithm TWIST selected 11 characteristics of the input vector: among them SO<sub>2</sub> and CO<sub>2</sub> emissions, and also many other attributes whose linear correlation with the target was very low. A 5 × 2 Cross Validation protocol estimated the sensitivity and specificity of pattern recognition algorithms. Finally, different classification algorithms have been compared to understand if this pattern recognition task may have suitable results and which algorithm performs best. Best results (higher than 97% accuracy) have been obtained after performing advanced Artificial Neural Networks, with a sensitivity and specificity estimates over 97% and 98%, respectively. The present analysis highlights that a suitable monitoring dataset inferred hidden information about volcanic phenomena, whose highly non-linear processes are enhanced.展开更多
Since the late 20th century,global change issues have attracted lots of attention.As a key component of global changes,land cover and land use information has been increasingly important for improved understanding of ...Since the late 20th century,global change issues have attracted lots of attention.As a key component of global changes,land cover and land use information has been increasingly important for improved understanding of global environmental changes and feedbacks between social and environmental systems(Verburg et al.,2015).A set of national and global scale land cover/use products with higher spatial and temporal resolutions have been developed to fill this gap.In China,existing efforts include China’s展开更多
In recent years,with the increase of traffic construction in mountainous areas in China,road slope traffic accidents have become more and more common.In addition,natural disasters such as landslides,collapses and subg...In recent years,with the increase of traffic construction in mountainous areas in China,road slope traffic accidents have become more and more common.In addition,natural disasters such as landslides,collapses and subgrade settlements caused by rainfall,driving load,seasonal variation and groundwater distribution are frequent.In particular,rainfall is one of the most common factors leading to slope instability(landslide).Therefore,this paper proposes the seep module based on the application software Geo-studio,and analyzes the slope soil parameters and slope stability under five types of rainfall conditions:light rain(10 mm/d),moderate rain(25 mm/d),heavy rain(50 mm/d),rainstorm(100 mm/d),and torrential rain(250 mm/d).The critical safety factor under rainfall intensity is fitted with nonlinear curve by sine function.The results show that the fitting curve of rainfall intensity and safety factor on the upper slope is excellent.The residual points are evenly distributed in the belt area of±0.1,and the data basically conform to the nonlinear sine model,indicating that the curve plays an essential role in slope health diagnosis.展开更多
基金Sponsored by the National Key R&D Program of China(Grant No.2020YFB1600500)the National Natural Science Foundation of China(GrantN o.52272319)。
文摘Determining trip purpose is an important link to explore travel rules. In this paper,we takea utomobile users in urban areas as the research object,combine unsupervised learning and supervised learningm ethods to analyze their travel characteristics,and focus on the classification and prediction of automobileu sers’trip purposes. However,previous studies on trip purposes mainly focused on questionnaires and GPSd ata,which cannot well reflect the characteristics of automobile travel. In order to avoid the multi-dayb ehavior variability and unobservable heterogeneity of individual characteristics ignored in traditional traffic questionnaires,traffic monitoring data from the Northern District of Qingdao are used,and the K-meansc lustering method is applied to estimate the trip purposes of automobile users. Then,Adaptive Boosting(AdaBoost)and Random Forest(RF)methods are used to classify and predict trip purposes. Finally,ther esult shows:(1)the purpose of automobile users can be mainly divided into four clusters,which includeC ommuting trips,Flexible life demand travel in daytime,Evening entertainment and leisure shopping,andT axi-based trips for the first three types of purposes,respectively;(2)the Random Forest method performss ignificantly better than AdaBoost in trip purpose prediction for higher accuracy;(3)the average predictiona ccuracy of Random Forest under hyper-parameters optimization reaches96.25%,which proves the feasibilitya nd rationality of the above clustering results.
基金the National Natural Science Foundation of China(No.51775185)Natural Science Foundation of Hunan Province(No.2022JJ90013)+1 种基金Intelligent Environmental Monitoring Technology Hunan Provincial Joint Training Base for Graduate Students in the Integration of Industry and Education,and Hunan Normal University University-Industry Cooperation.the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open Project,Grant Number 20181901CRP04.
文摘At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.
文摘The fatigue of concrete structures will gradually appear after being subjected to alternating loads for a long time,and the accidents caused by fatigue failure of bridge structures also appear from time to time.Aiming at the problem of degradation of long-span continuous rigid frame bridges due to fatigue and environmental effects,this paper suggests a method to analyze the fatigue degradation mechanism of this type of bridge,which combines long-term in-site monitoring data collected by the health monitoring system(HMS)and fatigue theory.In the paper,the authors mainly carry out the research work in the following aspects:First of all,a long-span continuous rigid frame bridge installed with HMS is used as an example,and a large amount of health monitoring data have been acquired,which can provide efficient information for fatigue in terms of equivalent stress range and cumulative number of stress cycles;next,for calculating the cumulative fatigue damage of the bridge structure,fatigue stress spectrum got by rain flow counting method,S-N curves and damage criteria are used for fatigue damage analysis.Moreover,it was considered a linear accumulation damage through the Palmgren-Miner rule for the counting of stress cycles.The health monitoring data are adopted to obtain fatigue stress data and the rain flow counting method is used to count the amplitude varying fatigue stress.The proposed fatigue reliability approach in the paper can estimate the fatigue damage degree and its evolution law of bridge structures well,and also can help bridge engineers do the assessment of future service duration.
基金National Natural Science Foundation of China(Grant No.51775278)National Science Fund of China for Distinguished Young Scholars(Grant No.51925505).
文摘Initial residual stress is the main reason causing machining deformation of the workpiece,which has been deemed as one of the most important aspects of machining quality issues.The inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation control.Due to the principle error of existing residual stress detection methods,there are still challenges in practical applications.Aiming at the detection problem of the initial residual stress field,an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this paper.Monitoring data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress,and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish;the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism model.Finally,the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism model.Verification results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.
基金supported by the National Science Foundation for Young Scientists of China (No. 71401073)
文摘A sufficient sample size of monitoring data becomes a key factor for describing aircraft engines state.Generative adversarial nets(GAN)can be used to expand the sample size based on the existing state monitoring information.In the paper,a GAN model is introduced to design an algorithm for generating the monitoring data of aircraft engines.This feasibility of the method is illustrated by an example.The experimental results demonstrate that the probability density distribution of generated data after a large number of network training iterations is consistent with the probability density distribution of monitoring data.The proposed method also effectively demonstrates the generated monitoring data of aircraft engine are in a reasonable range.The method can effectively solve the problem of inaccurate performance degradation evaluation caused by the small amount of aero?engine condition monitoring data.
文摘Smart grid puts forward higher requirements for power quality.Power quality evaluation can provide a decision-making basis for quality improvement.Based on power quality monitoring data,a grey target method is proposed for power quality evaluation.The grey target is composed of power quality evaluation standard and power quality monitoring data to be evaluated.Combining with the characteristics of each power quality evaluation index,the target center of the whole grey target system is found.Then,the power quality monitoring data to be evaluated and the power quality standard mode are compared and analyzed to construct the power quality grey correlation difference information space.Finally,the target center coefficient and target degree of power quality are calculated to realize the comprehensive evaluation of power quality,and the evaluation grade of power quality monitoring data to be evaluated is obtained.Compared with the evaluation results of the existing literature,the effectiveness of the proposed method is verified,which shows that grey target theory is reasonable in the comprehensive evaluation of power quality.
基金This research was funded by the National Natural Science Foundation of China(No.51775185)Scientific Research Fund of Hunan Province Education Department(18C0003)+2 种基金Research project on teaching reform in colleges and universities of Hunan Province Education Department(20190147)Innovation and Entrepreneurship Training Program for College Students in Hunan Province(2021-1980)Hunan Normal University University-Industry Cooperation.This work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open project,Grant Number 20181901CRP04.
文摘Many countries are paying more and more attention to the protection of water resources at present,and how to protect water resources has received extensive attention from society.Water quality monitoring is the key work to water resources protection.How to efficiently collect and analyze water quality monitoring data is an important aspect of water resources protection.In this paper,python programming tools and regular expressions were used to design a web crawler for the acquisition of water quality monitoring data from Global Freshwater Quality Database(GEMStat)sites,and the multi-thread parallelism was added to improve the efficiency in the process of downloading and parsing.In order to analyze and process the crawled water quality data,Pandas and Pyecharts are used to visualize the water quality data to show the intrinsic correlation and spatiotemporal relationship of the data.
基金supported by the National Key Research and Development Program of China(No.2019YFC0214800)。
文摘Air quality monitoring is effective for timely understanding of the current air quality status of a region or city.Currently,the huge volume of environmental monitoring data,which has reasonable real-time performance,provides strong support for in-depth analysis of air pollution characteristics and causes.However,in the era of big data,to meet current demands for fine management of the atmospheric environment,it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality.This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period,and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas(i.e.,the“2+26”region)during the period of the three-year action plan to fight air pollution.We suggest that air quality should be comprehensively,deeply,and scientifically evaluated from the aspects of air pollution characteristics,causes,and influences of meteorological conditions and anthropogenic emissions.It is also suggested that a threeyear moving average be introduced as one of the evaluation indexes of long-term change of pollutants.Additionally,both temporal and spatial differences should be considered when removing confounding meteorological factors.
基金supported by National Natural Science Foundation of China(NSFC)under Grant Number T2350710232.
文摘Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.
文摘The study PV/diesel system is a stand-alone microgrid powered by the PV/diesel combination without production storage. The study focused on optimising PV/diesel production by monitoring data. It also referred to a comparison of sensitive factors in PV/diesel production. This study implemented structural and non-structural factors of the said system. A literature search was conducted to determine the factors involved. So, factors such as system autonomy, energy quality, system stability and data monitoring were considered for the study. Thus, after a detailed presentation of the data monitoring, a comparison based on the method, Analysis of Failure Modes, their Effects and Criticalities (FMEA) was carried out. At the end of the comparison, a hierarchy of parameters in the exploitation of the energy production of autonomous microgrids was made. From its results, it emerges a good consideration of the factor “data monitoring” in the management of the system studied. The results obtained confirm the importance of data monitoring for a better optimization of energy production. A monitoring program or procedure has been developed according to the originality that the present study has identified. The study also made it possible to evaluate the performance of data monitoring for the energy production of photovoltaic systems in general and hybrid PV/diesel systems in particular.
文摘The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq. km.) on the Loess Plateau. It sums up Remote sensing (RS), Geographical Information System (GIS) and Expert System (ES) and consists of a integrated system. As a basic level information system of Loess Plateau, its perfection and psreading will bring about a great advance in resources exploitation and management of Loess Plateau.
文摘In order to improve the quality of automatic monitoring data of pollution sources and apply the automatic monitoring data to verify the environmental tax,Shandong Province took the lead in adopting the Internet of Things technology and drawing on the successful experience of air automatic monitoring stations and surface water automatic monitoring stations in management,and developed a dynamic management and control system for automatic monitoring equipment of pollution sources to improve and strengthen the quality audit of automatic monitoring data,further improve the quality of automatic monitoring data and better provide a basis for environmental management and decision making.The system realizes the simultaneous monitoring of monitoring data,running state and parameters of the automatic monitoring equipment,eliminates the phenomenon of falsification by modifying equipment parameters,and judges the validity of the collected data by acquiring the working state of the equipment remotely and randomly.After the actual operation test of the Department of Ecological Environment of Shandong Province,the system is proved to have the characteristics of practicality,real time and high efficiency,and be able to make up for low frequency and narrow coverage of manual inspection,with good application prospect in the field of environment and pollution source monitoring.
文摘We carried out time-lapse analysis in a producing Niger Delta X-field, by first investigating the response and sensitivity of rock properties/attributes to lithology and pore fill in 3-D cross plot domain and by Gassmann’s fluid substitution modeling. Furthermore, 4-D seismic data were inverted into acoustic impedance volumes through model based inversion scheme. This served as input into a multi-attribute neural network algorithm for the extraction of rock attribute volumes based on the results of the petrophysical log analysis. Subsequently, horizon slices of rock properties/ attributes were extracted from the inverted seismic data and analyzed. In this way, we mapped hydrocarbon depleted wells in the field, and identified probable by-passed hydrocarbon zones. Thus, the integration of well and time lapse seismic (4-D) data in reservoir studies has remarkably improved information on the reservoir economic potential, and enhanced hydrocarbon recovery factor.
文摘In this paper we have developed a data logging and monitoring system, we validated the system by comparing the result from it with the existing one and found that the system performs slightly better than the existing work in the same area. This implies that the data logger and monitoring system is good and can be used to monitor solar energy variables even at the comfort of our homes. We fitted a model to the generated data and found that the meteorological variables considered accounted for 99.88% of the power output in the rainy seasons while 0.12% of the variation was not explained due to other factors. Solar panels inclined at an angle of 5° (Tilt) and facing South Pole perform optimally.
基金the State Key Laboratory for Geomechanics and Deep Underground Engineering,China University of Mining and Technology,Beijing(No.SKLGDUEK1923)Special fund for basic scientific research of central universities(Grant no:2013QZ04).
文摘In light of the complex and dynamic mechanical properties of evolving weak strata in open-pit mines,and the consequent difficulty of determining their mechanical parameters,this study uses the ultimate balance theory,along with the back analysis method combined with monitoring data on field displacement,to carry out parameter inversion using the FLAC3D numerical simulation software.The edge slope of a working pit of the Weijiamao open-pit mine was used as research object to this end.As the results obtained by the constitutive model were consistent with the field monitoring data,the evolving weak strata in the slope and the position of the landslide in the mine could be obtained.The landslide was directed northeast.The mechanism of the edge slope of the working pit was identified as unloading shear failure,and the feasibility of the method of parameter inversion was verified.The internal friction angle φand cohesion C of evolving weak strata in the slope of the open-pit mine were also obtained,where this compensated for the deficiency of laboratory tests and enabled the transformation from qualitative to quantitative analysis.This can provide a reliable basis for the safe operation of open-pit mines.
文摘Although China’s urban floating population is mainly concentrated in developed cities,from the central and western cities to the eastern developed cities,but the characteristics of the floating population in different cities are significantly different.This paper systematically investigates the spatiotemporal characteristics and influencing factors of the floating population in different levels of cities.The results show that the regional imbalance to further strengthen,accumulation and dispersion trend has become increasingly obvious,liquidity is positively correlated and city level scale,and urban agglomeration and the core city is still polarization center of floating population.Flow range is closely related to urban hierarchy:the higher the intra-urban grade,the more tend to inter-provincial flow;the lower the city grade,the more tend to intra-urban mobility.Short-term(1-2 years)and long-term(more than 7 years)flow-time coexist.The short-term liquidity increases with the city grade,and the long-term liquidity decreases with the city level.Farmers are still the main body of the floating population.Younger age,lower education level,low-skilled,high gender ratio employees are the most basic demographic characteristics of the floating population,although there are differences between different cities.The main reason for affecting the floating population is seeking jobs and doing business.
文摘A volcano can be defined as a complex system, not least for the hidden clues related to its internal nature. Innovative models grounded in the Artificial Sciences, have been proposed for a novel pattern recognition analysis at Mt. Etna volcano. The reference monitoring dataset dealt with real data of 28 parameters collected between January 2001 and April 2005, during which the volcano underwent the July-August 2001, October 2002-January 2003 and September 2004-April 2005 flank eruptions. There were 301 eruptive days out of an overall number of 1581 investigated days. The analysis involved successive steps. First, the TWIST algorithm was used to select the most predictive attributes associated with the flank eruption target. During his work, the algorithm TWIST selected 11 characteristics of the input vector: among them SO<sub>2</sub> and CO<sub>2</sub> emissions, and also many other attributes whose linear correlation with the target was very low. A 5 × 2 Cross Validation protocol estimated the sensitivity and specificity of pattern recognition algorithms. Finally, different classification algorithms have been compared to understand if this pattern recognition task may have suitable results and which algorithm performs best. Best results (higher than 97% accuracy) have been obtained after performing advanced Artificial Neural Networks, with a sensitivity and specificity estimates over 97% and 98%, respectively. The present analysis highlights that a suitable monitoring dataset inferred hidden information about volcanic phenomena, whose highly non-linear processes are enhanced.
基金supported by the Key Research Program of Frontier Sciences, the Chinese Academy of Sciences (Grant No. QYZDB-SSW-DQC005)the Thousand Youth Talents Plan
文摘Since the late 20th century,global change issues have attracted lots of attention.As a key component of global changes,land cover and land use information has been increasingly important for improved understanding of global environmental changes and feedbacks between social and environmental systems(Verburg et al.,2015).A set of national and global scale land cover/use products with higher spatial and temporal resolutions have been developed to fill this gap.In China,existing efforts include China’s
基金Received on August 27th,2020revised on September 22th,2020.This research was jointly supported by grants from the National Natural Science Foundation of China(41501062)the Applied Basic Research Foundation of Qinghai Province(2018-ZJ-784).
文摘In recent years,with the increase of traffic construction in mountainous areas in China,road slope traffic accidents have become more and more common.In addition,natural disasters such as landslides,collapses and subgrade settlements caused by rainfall,driving load,seasonal variation and groundwater distribution are frequent.In particular,rainfall is one of the most common factors leading to slope instability(landslide).Therefore,this paper proposes the seep module based on the application software Geo-studio,and analyzes the slope soil parameters and slope stability under five types of rainfall conditions:light rain(10 mm/d),moderate rain(25 mm/d),heavy rain(50 mm/d),rainstorm(100 mm/d),and torrential rain(250 mm/d).The critical safety factor under rainfall intensity is fitted with nonlinear curve by sine function.The results show that the fitting curve of rainfall intensity and safety factor on the upper slope is excellent.The residual points are evenly distributed in the belt area of±0.1,and the data basically conform to the nonlinear sine model,indicating that the curve plays an essential role in slope health diagnosis.
