Following the publication of Zeng et al.(2023),an inadvertent error was recently identified in Figure 1B and Supplementary Figure S3.To ensure the accuracy and integrity of our published work,we formally request a cor...Following the publication of Zeng et al.(2023),an inadvertent error was recently identified in Figure 1B and Supplementary Figure S3.To ensure the accuracy and integrity of our published work,we formally request a correction to address this issue and apologize for any confusion this error may have caused.For details,please refer to the modified Supplementary Materials.展开更多
Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was...Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.展开更多
Quantum error correction is essential for realizing fault-tolerant quantum computing,where both the efficiency and accuracy of the decoding algorithms play critical roles.In this work,we introduce the implementation o...Quantum error correction is essential for realizing fault-tolerant quantum computing,where both the efficiency and accuracy of the decoding algorithms play critical roles.In this work,we introduce the implementation of the PLANAR algorithm,a software framework designed for fast and exact decoding of quantum codes with a planar structure.The algorithm first converts the optimal decoding of quantum codes into a partition function computation problem of an Ising spin glass model.Then it utilizes the exact Kac–Ward formula to solve it.In this way,PLANAR offers the exact maximum likelihood decoding in polynomial complexity for quantum codes with a planar structure,including the surface code with independent code-capacity noise and the quantum repetition code with circuit-level noise.Unlike traditional minimumweight decoders such as minimum-weight perfect matching(MWPM),PLANAR achieves theoretically optimal performance while maintaining polynomial-time efficiency.In addition,to demonstrate its capabilities,we exemplify the implementation using the rotated surface code,a commonly used quantum error correction code with a planar structure,and show that PLANAR achieves a threshold of approximately p_(uc)≈0.109 under the depolarizing error model,with a time complexity scaling of O(N^(0.69)),where N is the number of spins in the Ising model.展开更多
Errors inevitably exist in numerical weather prediction （NWP） due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the pred...Errors inevitably exist in numerical weather prediction （NWP） due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model （model error, denoted as ME）. In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System （GRAPES-GFS） for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature （SST） were both based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.展开更多
By using error correction model, I conduct co-integration analysis on the research of the relationship between the per capita practical consumption and per capita practical disposable income of urban residents in Huna...By using error correction model, I conduct co-integration analysis on the research of the relationship between the per capita practical consumption and per capita practical disposable income of urban residents in Hunan Province from 1978 to 2009. The results show that there is a co-integration relationship between the per capita practical consumption and the practical per capita disposable income of urban residents, and based on these, the corresponding error correction model is established. Finally, corresponding countermeasures and suggestions are put forward as follows: broaden the income channel of urban residents; create goods consuming environment; perfect socialist security system.展开更多
An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors （MEs） in past intervals. Given t...An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors （MEs） in past intervals. Given the analyses, the ME in each interval （6 h） between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year （2001-2005） GRAPES- GFS （Global Forecast System of the Global and Regional Assimilation and Prediction System） error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.展开更多
The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correct...The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correction variable, which represents the short-run discrepancy from the equilibrium state in a cointegrated system, plays an important role in the ECM. It is natural to ask how the error-correction mechanism works, or equivalently, how the short-run discrepancy affects the development of the cointegrated system? This paper examines the effect or local influence on the error-correction variable in an error-correction model. Following the argument of the second-order approach to local influence suggested by reference [5], we develop a diagnostic statistic to examine the local influence on the estimation of the parameter associated with the error-correction variable in an ECM. An empirical example is presented to illustrate the application of the proposed diagnostic. We find that the short-run discre pancy may have strong influence on the estimation of the parameter associated with the error-correction model. It is the error-correction variable that the short-run discrepancies can be incorporated through the error-correction mechanism.展开更多
In this paper, we conduct research on the large precision instrument error correction model under the perspectives of stability androbustness. It is one of the effective methods to improve the instruments accuracy usi...In this paper, we conduct research on the large precision instrument error correction model under the perspectives of stability androbustness. It is one of the effective methods to improve the instruments accuracy using error correction technology, but at present, a lot of errorcorrection is limited to the system error modifi cation, only a small number of the instruments to an error in the dynamic error correction timely,device on the instrument precision sensors, apparently complicate the instrument structure. To fully system error correction that will affect theprecision of instrument mainly random error. Instrument is the main task of error correction is to use a certain method to compensate separableinstruments each component part of a deterministic system error, so the key problems of error correction as is the requirement of equipmentstructure stability is good, with this to ensure that the instrument error of the uncertainty, so that the fundamental fl aw. Under this basis, this paperproposes the novel countermeasure of the issues that is innovative.展开更多
This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characte...This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.展开更多
In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can eff...In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.展开更多
The integrated energy systems(IESs)offer a practical solution for achieving low-carbon targets in residential buildings.However,IES encounters several challenges related to increased energy consumption and costs due t...The integrated energy systems(IESs)offer a practical solution for achieving low-carbon targets in residential buildings.However,IES encounters several challenges related to increased energy consumption and costs due to fluctuations in renewable energy generation.Leveraging building flexibility to address these power fluctuations within IES is a promising strategy,which requires coordinated control between air-conditioning systems and other IES components.This study proposes a cross-time-scale control framework that contains optimal scheduling and on-the-fly flexible control to reduce the cost impacts of a residential IES system equipped with photovoltaic(PV)panels,batteries,a heat pump,and a domestic hot water tank.The method involves three key steps:solar irradiance prediction,day-ahead optimal scheduling of energy storage,and intra-day flexible control of the heat pump.The method is validated through a high-fidelity residential building model with actual weather and energy usage data in Frankfurt,Germany.Results reveal that the proposed method limits the cost increase to just 2.67% compared to the day-ahead schedule,whereas the cost could increase by 7.39% without the flexible control.Additionally,computational efficiency is enhanced by transforming the mixed-integer programming(MIP)into nonlinear programming(NLP)problem via introducing action-exclusive constraints.This approach offers valuable support for residential IES operations.展开更多
Heilongjiang is a large agriculture province.Problems of agriculture,rural areas and farmers are urgent to be solved.The development of agriculture needs the support of agricultural credits,because finance is the cent...Heilongjiang is a large agriculture province.Problems of agriculture,rural areas and farmers are urgent to be solved.The development of agriculture needs the support of agricultural credits,because finance is the center of agriculture economy.However,the low comparative advantage in agriculture and pursuit of the capital interests which aggravate the conflicts of supply and demand of agricultural funds.Lacking of fund is the main factor that constrains the development of agricultural economy.In order to analyze the economic effect of agricultural credits on agricultural economy,an error correction model was set up to research the relationship between them,which based on the least square methods.Through the study of the contribution from agricultural credits to total value of agricultural out-put,the empirical evidence for developing the rural financial vigorously was provided,in order to promote the agricultura leconomic development.展开更多
According to the data of the total trade of agricultural products between China and the United States from 2009 to 2018 and the general description of agriculture in China,this paper adopts the method of econometric m...According to the data of the total trade of agricultural products between China and the United States from 2009 to 2018 and the general description of agriculture in China,this paper adopts the method of econometric model to make a detailed analysis of the agricultural trade between China and the United States by using cointegration analysis,Granger causality test and error correction model in order to explore the impact of agricultural trade between China and the United States on China’s agricultural development. The results of empirical analysis show that there is a balanced relationship between the trade of agricultural products between China and the United States and the development of agriculture in China. The total trade of agricultural products between China and the United States affects the development of China’s agriculture.In addition,in the short term,if the short-term fluctuation deviates from the long-term equilibrium,then the error correction term will reverse it with strength of 0. 378,so that the non-equilibrium state will gradually return to the equilibrium state.展开更多
Machine learning models were used to improve the accuracy of China Meteorological Administration Multisource Precipitation Analysis System(CMPAS)in complex terrain areas by combining rain gauge precipitation with topo...Machine learning models were used to improve the accuracy of China Meteorological Administration Multisource Precipitation Analysis System(CMPAS)in complex terrain areas by combining rain gauge precipitation with topographic factors like altitude,slope,slope direction,slope variability,surface roughness,and meteorological factors like temperature and wind speed.The results of the correction demonstrated that the ensemble learning method has a considerably corrective effect and the three methods(Random Forest,AdaBoost,and Bagging)adopted in the study had similar results.The mean bias between CMPAS and 85%of automatic weather stations has dropped by more than 30%.The plateau region displays the largest accuracy increase,the winter season shows the greatest error reduction,and decreasing precipitation improves the correction outcome.Additionally,the heavy precipitation process’precision has improved to some degree.For individual stations,the revised CMPAS error fluctuation range is significantly reduced.展开更多
The"state and domain phenomenon"emerged at the modeling period of the crowd system,and with the development of cities,communities are extruded by the multivariate space and the crowd system,forming the socia...The"state and domain phenomenon"emerged at the modeling period of the crowd system,and with the development of cities,communities are extruded by the multivariate space and the crowd system,forming the social need of a basic guarantee of community correction. After the expansion of communities,the functional integration period begins,which leads to the period of urban transition,when community and the jurisprudential basis begin to coincide with each other,bringing the community correction model. In the industrialized period,the urban population made great contribution to the community correction of cities. Fromthe perspective of globalization,urban integration will experience the anguish of community correction,and crime and punishment will enter communities of the whole society,forming normal community correction model,so the service systemof community correction begins with the formation of the community-based management system,the crowd-based management range,the fundamental organization structure and the coincidence of the organizing function.展开更多
In order to investigate the existence of a stable long-run equilibrium relationship between economic growth and consumption in China, the relationship between the gross domestic product （GDP） and consumption in Chin...In order to investigate the existence of a stable long-run equilibrium relationship between economic growth and consumption in China, the relationship between the gross domestic product （GDP） and consumption in China was investigated by the cointegration analysis method. Using the Engle-Granger （EG） test and considering the possibility of structural changes, the impact of external economic shocks on the long-run equilibrium relationship between economic growth and consumption in China was analyzed. Analysis results show that without considering structural changes, the EG test cannot detect cointegration in the series subjected to structural changes; in considering structural changes, cointegration is successfully detected by specifying the dummy variable. In addition, the error correction models were constructed in different periods. This study verifies the existence of a long-run equilibrium relationship between economic growth and consumption in China, and this relationship has significantly changed in 1989 and 1997, respectively.展开更多
Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use o...Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use of the vertical section information, it does not agree with the actual propagation path. The atmospheric refraction error correction method of the Longley-Rice channel model has been improved. The improved method makes use of the vertical section information sufficiently and maps the distance between the receiver and transmitter to the radio wave propagation distance, It can exactly reflect the infection of propagation distance for the radio wave propagation loss. It is predicted to be more close to the experimental results by simulation in comparison with the measured data. The effectiveness of improved methods is proved by simulation.展开更多
This paper utilizes cointegration theory,error correcting model and Granger causality testing theory to make an empirical research on the relation between urbanization and GDP in China,and also implements a comparativ...This paper utilizes cointegration theory,error correcting model and Granger causality testing theory to make an empirical research on the relation between urbanization and GDP in China,and also implements a comparative analysis to the relation between three industries and degree of urbanization,the related coeffecient is 0.97,0.95,0.97,0.97.And the result shows a long-term balance between these two factors,and the promoting effect to tertiary industry by urbanization is more obvious.Urbanization and economic growth are the long-term balanced relations.In the long-term balance,every 1%increment of urbanization can make 4.82%increment of GDP;In short-term balance,if the balance depart from the long-term balance at the i-th term,the model will take automatic reversal adjustment with-0.06 adjusting strength at the(i+1)th term,to make it move to the long-term balance.The economic growth onto urbanization is one-way causality relationship,the primary and secondary industry onto urbanization is also one-way causality relationship.However,the tertiary industry onto urbanization is both-way causality relationship.展开更多
文摘Following the publication of Zeng et al.(2023),an inadvertent error was recently identified in Figure 1B and Supplementary Figure S3.To ensure the accuracy and integrity of our published work,we formally request a correction to address this issue and apologize for any confusion this error may have caused.For details,please refer to the modified Supplementary Materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2242213,U2142213,42305167,42175105)。
文摘Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.
基金supported by the National Natural Science Foundation of China(Grant Nos.12325501,12047503,and 12247104)the Chinese Academy of Sciences(Grant No.ZDRW-XX-2022-3-02)P.Z.is partially supported by the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301900).
文摘Quantum error correction is essential for realizing fault-tolerant quantum computing,where both the efficiency and accuracy of the decoding algorithms play critical roles.In this work,we introduce the implementation of the PLANAR algorithm,a software framework designed for fast and exact decoding of quantum codes with a planar structure.The algorithm first converts the optimal decoding of quantum codes into a partition function computation problem of an Ising spin glass model.Then it utilizes the exact Kac–Ward formula to solve it.In this way,PLANAR offers the exact maximum likelihood decoding in polynomial complexity for quantum codes with a planar structure,including the surface code with independent code-capacity noise and the quantum repetition code with circuit-level noise.Unlike traditional minimumweight decoders such as minimum-weight perfect matching(MWPM),PLANAR achieves theoretically optimal performance while maintaining polynomial-time efficiency.In addition,to demonstrate its capabilities,we exemplify the implementation using the rotated surface code,a commonly used quantum error correction code with a planar structure,and show that PLANAR achieves a threshold of approximately p_(uc)≈0.109 under the depolarizing error model,with a time complexity scaling of O(N^(0.69)),where N is the number of spins in the Ising model.
基金funded by the National Natural Science Foundation Science Fund for Youth (Grant No.41405095)the Key Projects in the National Science and Technology Pillar Program during the Twelfth Fiveyear Plan Period (Grant No.2012BAC22B02)the National Natural Science Foundation Science Fund for Creative Research Groups (Grant No.41221064)
文摘Errors inevitably exist in numerical weather prediction （NWP） due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model （model error, denoted as ME）. In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System （GRAPES-GFS） for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature （SST） were both based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.
基金Supported by the Scientific Research Subject of Department of Education in Hunan Province(10C0556)
文摘By using error correction model, I conduct co-integration analysis on the research of the relationship between the per capita practical consumption and per capita practical disposable income of urban residents in Hunan Province from 1978 to 2009. The results show that there is a co-integration relationship between the per capita practical consumption and the practical per capita disposable income of urban residents, and based on these, the corresponding error correction model is established. Finally, corresponding countermeasures and suggestions are put forward as follows: broaden the income channel of urban residents; create goods consuming environment; perfect socialist security system.
基金funded by the National Natural Science Foundation Science Fund for Youth (Grant No.41405095)the Key Projects in the National Science and Technology Pillar Program during the Twelfth Fiveyear Plan Period (Grant No.2012BAC22B02)the National Natural Science Foundation Science Fund for Creative Research Groups (Grant No.41221064)
文摘An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors （MEs） in past intervals. Given the analyses, the ME in each interval （6 h） between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year （2001-2005） GRAPES- GFS （Global Forecast System of the Global and Regional Assimilation and Prediction System） error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.
基金This project was supported by the National Natural Science Foundation (No. 79800012 and No. 79400014).
文摘The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correction variable, which represents the short-run discrepancy from the equilibrium state in a cointegrated system, plays an important role in the ECM. It is natural to ask how the error-correction mechanism works, or equivalently, how the short-run discrepancy affects the development of the cointegrated system? This paper examines the effect or local influence on the error-correction variable in an error-correction model. Following the argument of the second-order approach to local influence suggested by reference [5], we develop a diagnostic statistic to examine the local influence on the estimation of the parameter associated with the error-correction variable in an ECM. An empirical example is presented to illustrate the application of the proposed diagnostic. We find that the short-run discre pancy may have strong influence on the estimation of the parameter associated with the error-correction model. It is the error-correction variable that the short-run discrepancies can be incorporated through the error-correction mechanism.
文摘In this paper, we conduct research on the large precision instrument error correction model under the perspectives of stability androbustness. It is one of the effective methods to improve the instruments accuracy using error correction technology, but at present, a lot of errorcorrection is limited to the system error modifi cation, only a small number of the instruments to an error in the dynamic error correction timely,device on the instrument precision sensors, apparently complicate the instrument structure. To fully system error correction that will affect theprecision of instrument mainly random error. Instrument is the main task of error correction is to use a certain method to compensate separableinstruments each component part of a deterministic system error, so the key problems of error correction as is the requirement of equipmentstructure stability is good, with this to ensure that the instrument error of the uncertainty, so that the fundamental fl aw. Under this basis, this paperproposes the novel countermeasure of the issues that is innovative.
基金The National Natural Science Foundation of China(No.61273236)the Natural Science Foundation of Jiangsu Province(No.BK2010239)the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861061)
文摘This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 40575036 and 40325015）.Acknowledgement The authors thank Drs Zhang Pei-Qun and Bao Ming very much for their valuable comments on the present paper.
文摘In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.
基金supported by the National Key Research and Development Program of China(2022YFB4200902)。
文摘The integrated energy systems(IESs)offer a practical solution for achieving low-carbon targets in residential buildings.However,IES encounters several challenges related to increased energy consumption and costs due to fluctuations in renewable energy generation.Leveraging building flexibility to address these power fluctuations within IES is a promising strategy,which requires coordinated control between air-conditioning systems and other IES components.This study proposes a cross-time-scale control framework that contains optimal scheduling and on-the-fly flexible control to reduce the cost impacts of a residential IES system equipped with photovoltaic(PV)panels,batteries,a heat pump,and a domestic hot water tank.The method involves three key steps:solar irradiance prediction,day-ahead optimal scheduling of energy storage,and intra-day flexible control of the heat pump.The method is validated through a high-fidelity residential building model with actual weather and energy usage data in Frankfurt,Germany.Results reveal that the proposed method limits the cost increase to just 2.67% compared to the day-ahead schedule,whereas the cost could increase by 7.39% without the flexible control.Additionally,computational efficiency is enhanced by transforming the mixed-integer programming(MIP)into nonlinear programming(NLP)problem via introducing action-exclusive constraints.This approach offers valuable support for residential IES operations.
基金Supported by the Fund for Heilongjiang Province Philosophy and Social Sciences Project (08E015)Social Sciences Fund of the Heilongjiang Provincial Education Department (11542014)Scientific Research Fund of Northeast Agricultural University
文摘Heilongjiang is a large agriculture province.Problems of agriculture,rural areas and farmers are urgent to be solved.The development of agriculture needs the support of agricultural credits,because finance is the center of agriculture economy.However,the low comparative advantage in agriculture and pursuit of the capital interests which aggravate the conflicts of supply and demand of agricultural funds.Lacking of fund is the main factor that constrains the development of agricultural economy.In order to analyze the economic effect of agricultural credits on agricultural economy,an error correction model was set up to research the relationship between them,which based on the least square methods.Through the study of the contribution from agricultural credits to total value of agricultural out-put,the empirical evidence for developing the rural financial vigorously was provided,in order to promote the agricultura leconomic development.
文摘According to the data of the total trade of agricultural products between China and the United States from 2009 to 2018 and the general description of agriculture in China,this paper adopts the method of econometric model to make a detailed analysis of the agricultural trade between China and the United States by using cointegration analysis,Granger causality test and error correction model in order to explore the impact of agricultural trade between China and the United States on China’s agricultural development. The results of empirical analysis show that there is a balanced relationship between the trade of agricultural products between China and the United States and the development of agriculture in China. The total trade of agricultural products between China and the United States affects the development of China’s agriculture.In addition,in the short term,if the short-term fluctuation deviates from the long-term equilibrium,then the error correction term will reverse it with strength of 0. 378,so that the non-equilibrium state will gradually return to the equilibrium state.
基金Program of Science and Technology Department of Sichuan Province(2022YFS0541-02)Program of Heavy Rain and Drought-flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province(SCQXKJQN202121)Innovative Development Program of the China Meteorological Administration(CXFZ2021Z007)。
文摘Machine learning models were used to improve the accuracy of China Meteorological Administration Multisource Precipitation Analysis System(CMPAS)in complex terrain areas by combining rain gauge precipitation with topographic factors like altitude,slope,slope direction,slope variability,surface roughness,and meteorological factors like temperature and wind speed.The results of the correction demonstrated that the ensemble learning method has a considerably corrective effect and the three methods(Random Forest,AdaBoost,and Bagging)adopted in the study had similar results.The mean bias between CMPAS and 85%of automatic weather stations has dropped by more than 30%.The plateau region displays the largest accuracy increase,the winter season shows the greatest error reduction,and decreasing precipitation improves the correction outcome.Additionally,the heavy precipitation process’precision has improved to some degree.For individual stations,the revised CMPAS error fluctuation range is significantly reduced.
文摘The"state and domain phenomenon"emerged at the modeling period of the crowd system,and with the development of cities,communities are extruded by the multivariate space and the crowd system,forming the social need of a basic guarantee of community correction. After the expansion of communities,the functional integration period begins,which leads to the period of urban transition,when community and the jurisprudential basis begin to coincide with each other,bringing the community correction model. In the industrialized period,the urban population made great contribution to the community correction of cities. Fromthe perspective of globalization,urban integration will experience the anguish of community correction,and crime and punishment will enter communities of the whole society,forming normal community correction model,so the service systemof community correction begins with the formation of the community-based management system,the crowd-based management range,the fundamental organization structure and the coincidence of the organizing function.
文摘In order to investigate the existence of a stable long-run equilibrium relationship between economic growth and consumption in China, the relationship between the gross domestic product （GDP） and consumption in China was investigated by the cointegration analysis method. Using the Engle-Granger （EG） test and considering the possibility of structural changes, the impact of external economic shocks on the long-run equilibrium relationship between economic growth and consumption in China was analyzed. Analysis results show that without considering structural changes, the EG test cannot detect cointegration in the series subjected to structural changes; in considering structural changes, cointegration is successfully detected by specifying the dummy variable. In addition, the error correction models were constructed in different periods. This study verifies the existence of a long-run equilibrium relationship between economic growth and consumption in China, and this relationship has significantly changed in 1989 and 1997, respectively.
文摘Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use of the vertical section information, it does not agree with the actual propagation path. The atmospheric refraction error correction method of the Longley-Rice channel model has been improved. The improved method makes use of the vertical section information sufficiently and maps the distance between the receiver and transmitter to the radio wave propagation distance, It can exactly reflect the infection of propagation distance for the radio wave propagation loss. It is predicted to be more close to the experimental results by simulation in comparison with the measured data. The effectiveness of improved methods is proved by simulation.
文摘This paper utilizes cointegration theory,error correcting model and Granger causality testing theory to make an empirical research on the relation between urbanization and GDP in China,and also implements a comparative analysis to the relation between three industries and degree of urbanization,the related coeffecient is 0.97,0.95,0.97,0.97.And the result shows a long-term balance between these two factors,and the promoting effect to tertiary industry by urbanization is more obvious.Urbanization and economic growth are the long-term balanced relations.In the long-term balance,every 1%increment of urbanization can make 4.82%increment of GDP;In short-term balance,if the balance depart from the long-term balance at the i-th term,the model will take automatic reversal adjustment with-0.06 adjusting strength at the(i+1)th term,to make it move to the long-term balance.The economic growth onto urbanization is one-way causality relationship,the primary and secondary industry onto urbanization is also one-way causality relationship.However,the tertiary industry onto urbanization is both-way causality relationship.
