China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragil...China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty.In this paper,the Liupan Mountain Region of China was used as a case study,and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area.The indicator data for returning to ecological poverty from 2011-2020 were collected and summarized in three dimensions:ecological,economic and social.The autoregressive integrated moving average model(ARIMA)time series and exponential smoothing method(ES)were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties(districts)in the Liupan Mountain Region for 2021-2030.The back propagation neural network(BPNN)and geographic information system(GIS)were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty(RREP index).The results show that 1)ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region.2)The RREP index for the 61 counties(districts)exhibited a downward trend from 2021-2030.The RREP index declined more in medium-and high-risk areas than in low-risk areas.From 2021 to 2025,the RREP index exhibited a slight downward trend.From 2026 to2030,the RREP index was expected to decline faster,especially from 2029-2030.3)Based on the RREP index,it can be roughly divided into three types,namely,the high-risk areas,the medium-risk areas,and the low-risk areas.The natural resource conditions in lowrisk areas of returning to ecological poverty,were better than those in medium-and high-risk areas.展开更多
Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the trai...Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sounding data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF) model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML) algorithms including extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN) are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE) of 2.05 K for temperature,0.67 g m~(-3) for water vapor density(WVD),and 13.98% for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD) algorithm indicate that our algorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observations in global locations.展开更多
Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convi...Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convincing forecasting systems have been established. A prediction model for fashion color forecasting was established by applying an improved back propagation neural network (BPNN) model in this paper. Successive six-year fashion color palettes, released by INTERCOLOR, were used as learning information for the neural network to develop a reliable prediction model. Colors in the palettes were quantified by PANTONE color system. Additionally, performance of the established model was compared with other GM(1, 1) models. Results show that the improved BPNN model is suitable to predict future fashion color trend.展开更多
Random numbers play an increasingly important role in secure wire and wireless communication. Thus the design quality of random number generator(RNG) is significant in information security. A novel pseudo RNG is propo...Random numbers play an increasingly important role in secure wire and wireless communication. Thus the design quality of random number generator(RNG) is significant in information security. A novel pseudo RNG is proposed for improving the security of network communication. The back propagation neural network(BPNN) is nonlinear, which can be used to improve the traditional RNG. The novel pseudo RNG is based on BPNN techniques. The result of test suites standardized by the U.S shows that the RNG can satisfy the security of communication.展开更多
The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic ...The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.展开更多
By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets ...By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets of multi-measurements of the same ESG in different noise environments are ＂mapped＂ into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.展开更多
The small-current grounding fault in distribution network is hard to be located because of its weak fault features.To accurately locate the faults,the transient process is analyzed in this paper.Through the study we t...The small-current grounding fault in distribution network is hard to be located because of its weak fault features.To accurately locate the faults,the transient process is analyzed in this paper.Through the study we take that the main resonant frequency and its corresponding component is related to the fault distance.Based on this,a fault location method based on double-end wavelet energy ratio at the scale corresponding to the main resonant frequency is proposed.And back propagation neural network(BPNN)is selected to fit the non-linear relationship between the wavelet energy ratio and fault distance.The performance of this proposed method has been verified in different scenarios of a simulation model in PSCAD/EMTDC.展开更多
This study aims to examine the feasibility of predicting surface wind pressure induced by conical vortex using a backpropagation neural network(BPNN)combined with proper orthogonal decomposition(POD),in which a 1:150 ...This study aims to examine the feasibility of predicting surface wind pressure induced by conical vortex using a backpropagation neural network(BPNN)combined with proper orthogonal decomposition(POD),in which a 1:150 scaled model with a large-span retractable roof was tested in wind tunnel under both laminar and turbulent flow conditions.The distributions of mean and fluctuating wind pressure coefficients were first described,and the effects of inflow turbulence,wind direction,roof opening were examined separately.For the prediction of wind pressure,the POD-BPNN model was trained using measurement data from adjacent points.The prediction results are overall satisfactory.The root-mean-square-error(RMSE)between test and predicted data lies mostly within 10%.In particular,the prediction of mean wind pressure is found to be better than that of fluctuating wind pressure.The outcomes in this study highlight that the proposed POD-BPNN model can be well used as a useful tool to predict surface wind pressure.展开更多
A new method of artificial intelligence based on a new improved back propagation neural network （BPNN） algorithm is partially applied in the problem of image restoration. In order to over- come the inherited issues ...A new method of artificial intelligence based on a new improved back propagation neural network （BPNN） algorithm is partially applied in the problem of image restoration. In order to over- come the inherited issues in conventional back propagation algorithm i.e. slow convergence rate, longer training time, hard to achieve global minima etc. , different methods have been used including the introduction of dynamic learning rate and dynamic momentum coefficient etc. With the passage of time different techniques has been used to improve the dynamicity of these coefficients. The meth- od applied in this paper improves the effect of learning coefficient η by using a new way to modify the value dynamically during learning process. The experimental results show that this helps in im- proving the efficiency overall both in visual effect and quality analysis.展开更多
A new method of back propagation learning with respect to the problem of image restora- tion which is named as greyscale based learning in back propagation neural networks （BPNN） is in- vestigated. It is observed th...A new method of back propagation learning with respect to the problem of image restora- tion which is named as greyscale based learning in back propagation neural networks （BPNN） is in- vestigated. It is observed that by using this method the value of mean square error （MSE） decreases significantly. In addition, this method also gives good visual results when it is applied in image resto- ration problem. This method is also useful to tackle the inherited drawback of falling into local mini- ma by reducing its effect on overall system by bifurcating the learning locally different for different grey scale values. The performance of this algorithm has been studied in detail with different combi- nations of weights. In short, this algorithm provides much better results especially when compared with the simple back propagation algorithm with any further enhancements and without going for hy- brid solutions.展开更多
We present a novel method to monitor the weld geometry for metal inert gas(MIG)welding process with galvanized steel plates using Bayesian network(BN),and propose an effective method of extracting the weld reinforceme...We present a novel method to monitor the weld geometry for metal inert gas(MIG)welding process with galvanized steel plates using Bayesian network(BN),and propose an effective method of extracting the weld reinforcement and width online.The laser vision sensor is mounted after the welding torch and used to profile the weld.With the extracted weld geometry and the adopted process parameters,a back propagation neural network(BPNN)is constructed offline and used to predict the weld reinforcement and width corresponding to the current parameter settings.A BN from welding experience and tests is presented to implement the decision making of welding current/voltage when the error between the predictive geometry and the actual one occurs.This study can deal with the negative welding tendency to adapt to welding randomness and indicates a valuable application prospect in the welding field.展开更多
In the fifth-generation new radio(5G-NR) high-speed railway(HSR) downlink,a deep learning(DL) based Doppler frequency offset(DFO) estimation scheme is proposed by using the back propagation neural network(BPNN).The pr...In the fifth-generation new radio(5G-NR) high-speed railway(HSR) downlink,a deep learning(DL) based Doppler frequency offset(DFO) estimation scheme is proposed by using the back propagation neural network(BPNN).The proposed method mainly includes pre-training,training,and estimation phases,where the pre-training and training belong to the off-line stage,and the estimation is the online stage.To reduce the performance loss caused by the random initialization,the pre-training method is employed to acquire a desirable initialization,which is used as the initial parameters of the training phase.Moreover,the initial DFO estimation is used as input along with the received pilots to further improve the estimation accuracy.Different from the training phase,the initial DFO estimation in pre-training phase is obtained by the data and pilot symbols.Simulation results show that the mean squared error(MSE) performance of the proposed method is better than those of the available algorithms,and it has acceptable computational complexity.展开更多
Hydropower has made a significant contribution to the economic development of Vietnam,thus it is important to monitor the safety of hydropower dams for the good of the country and the people.In this paper,dam horizont...Hydropower has made a significant contribution to the economic development of Vietnam,thus it is important to monitor the safety of hydropower dams for the good of the country and the people.In this paper,dam horizontal displacement is analyzed and then forecasted using three methods:the multi-regression model,the seasonal integrated auto-regressive moving average(SARIMA)model and the back-propagation neural network(BPNN)merging models.The monitoring data of the Hoa Binh Dam in Vietnam,including horizontal displacement,time,reservoir water level,and air temperature,are used for the experiments.The results indicate that all of these three methods can approximately describe the trend of dam deformation despite their different forecast accuracies.Hence,their short-term forecasts can provide valuable references for the dam safety.展开更多
It is difficult to predict wind speed series accurately due to the instability and randomness of the wind speed series.In order to predict wind speed,authors propose a hybrid model which combines the wavelet transform...It is difficult to predict wind speed series accurately due to the instability and randomness of the wind speed series.In order to predict wind speed,authors propose a hybrid model which combines the wavelet transform technique(WTT),the exponential smoothing(ES)method and the back propagation neural network(BPNN),and is termed as WTT-ES-BPNN.Firstly,WTT is applied to the raw wind speed series for removing the useless information.Secondly,the hybrid model integrating the ES method and the BPNN is used to forecast the de-noising data.Finally,the prediction of raw wind speed series is caught.Real data sets of daily mean wind speed in Hebei Province are used to evaluate the forecasting accuracy of the proposed model.Numerical results indicate that the WTT-ES-BPNN is an effective way to improve the accuracy of wind speed prediction.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.42071230)。
文摘China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty.In this paper,the Liupan Mountain Region of China was used as a case study,and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area.The indicator data for returning to ecological poverty from 2011-2020 were collected and summarized in three dimensions:ecological,economic and social.The autoregressive integrated moving average model(ARIMA)time series and exponential smoothing method(ES)were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties(districts)in the Liupan Mountain Region for 2021-2030.The back propagation neural network(BPNN)and geographic information system(GIS)were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty(RREP index).The results show that 1)ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region.2)The RREP index for the 61 counties(districts)exhibited a downward trend from 2021-2030.The RREP index declined more in medium-and high-risk areas than in low-risk areas.From 2021 to 2025,the RREP index exhibited a slight downward trend.From 2026 to2030,the RREP index was expected to decline faster,especially from 2029-2030.3)Based on the RREP index,it can be roughly divided into three types,namely,the high-risk areas,the medium-risk areas,and the low-risk areas.The natural resource conditions in lowrisk areas of returning to ecological poverty,were better than those in medium-and high-risk areas.
基金Supported by the National Natural Science Foundation of China (42175144)。
文摘Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sounding data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF) model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML) algorithms including extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN) are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE) of 2.05 K for temperature,0.67 g m~(-3) for water vapor density(WVD),and 13.98% for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD) algorithm indicate that our algorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observations in global locations.
文摘Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convincing forecasting systems have been established. A prediction model for fashion color forecasting was established by applying an improved back propagation neural network (BPNN) model in this paper. Successive six-year fashion color palettes, released by INTERCOLOR, were used as learning information for the neural network to develop a reliable prediction model. Colors in the palettes were quantified by PANTONE color system. Additionally, performance of the established model was compared with other GM(1, 1) models. Results show that the improved BPNN model is suitable to predict future fashion color trend.
基金National Natural Science Foundation of China(60363087 ,90104005 and 60473023)
文摘Random numbers play an increasingly important role in secure wire and wireless communication. Thus the design quality of random number generator(RNG) is significant in information security. A novel pseudo RNG is proposed for improving the security of network communication. The back propagation neural network(BPNN) is nonlinear, which can be used to improve the traditional RNG. The novel pseudo RNG is based on BPNN techniques. The result of test suites standardized by the U.S shows that the RNG can satisfy the security of communication.
基金the Research of New Intelligent Integrated Transport Information System,Technical Plan Project of Binhai New District,Tianjin(No.2015XJR21017)
文摘The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.
文摘By combining the distributed Kalman filter （DKF） with the back propagation neural network （BPNN）,a novel method is proposed to identify the bias of electrostatic suspended gyroscope （ESG）. Firstly,the data sets of multi-measurements of the same ESG in different noise environments are ＂mapped＂ into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets. After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias. As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.
基金supported by National Key R&D Program of China(2017YFB0902800)Science and 333 Technology Project of State Grid Corporation of China(52094017003D).
文摘The small-current grounding fault in distribution network is hard to be located because of its weak fault features.To accurately locate the faults,the transient process is analyzed in this paper.Through the study we take that the main resonant frequency and its corresponding component is related to the fault distance.Based on this,a fault location method based on double-end wavelet energy ratio at the scale corresponding to the main resonant frequency is proposed.And back propagation neural network(BPNN)is selected to fit the non-linear relationship between the wavelet energy ratio and fault distance.The performance of this proposed method has been verified in different scenarios of a simulation model in PSCAD/EMTDC.
基金This project was funded by grants from the National Natural Science Foundation of China(No.51778072 and No.51408062)Practice Innovation and Entrepreneurship Enhancement Plan of CSUST(SJCX202021).
文摘This study aims to examine the feasibility of predicting surface wind pressure induced by conical vortex using a backpropagation neural network(BPNN)combined with proper orthogonal decomposition(POD),in which a 1:150 scaled model with a large-span retractable roof was tested in wind tunnel under both laminar and turbulent flow conditions.The distributions of mean and fluctuating wind pressure coefficients were first described,and the effects of inflow turbulence,wind direction,roof opening were examined separately.For the prediction of wind pressure,the POD-BPNN model was trained using measurement data from adjacent points.The prediction results are overall satisfactory.The root-mean-square-error(RMSE)between test and predicted data lies mostly within 10%.In particular,the prediction of mean wind pressure is found to be better than that of fluctuating wind pressure.The outcomes in this study highlight that the proposed POD-BPNN model can be well used as a useful tool to predict surface wind pressure.
基金Supported by the National Natural Science Foundation of China (60772066)Higher Education Commission of Pakistan
文摘A new method of artificial intelligence based on a new improved back propagation neural network （BPNN） algorithm is partially applied in the problem of image restoration. In order to over- come the inherited issues in conventional back propagation algorithm i.e. slow convergence rate, longer training time, hard to achieve global minima etc. , different methods have been used including the introduction of dynamic learning rate and dynamic momentum coefficient etc. With the passage of time different techniques has been used to improve the dynamicity of these coefficients. The meth- od applied in this paper improves the effect of learning coefficient η by using a new way to modify the value dynamically during learning process. The experimental results show that this helps in im- proving the efficiency overall both in visual effect and quality analysis.
基金Supported by the National Natural Science Foundation of China(60772066)Higher Education Commission,Pakistan
文摘A new method of back propagation learning with respect to the problem of image restora- tion which is named as greyscale based learning in back propagation neural networks （BPNN） is in- vestigated. It is observed that by using this method the value of mean square error （MSE） decreases significantly. In addition, this method also gives good visual results when it is applied in image resto- ration problem. This method is also useful to tackle the inherited drawback of falling into local mini- ma by reducing its effect on overall system by bifurcating the learning locally different for different grey scale values. The performance of this algorithm has been studied in detail with different combi- nations of weights. In short, this algorithm provides much better results especially when compared with the simple back propagation algorithm with any further enhancements and without going for hy- brid solutions.
基金the National Natural Science Foundation of China(No.51665037)the Open Fund of the Key Laboratory of Lightweight and High Strength Structural Materials of Jiangxi Province(No.20171BCD40003)the Open Fund of the Key Tahoratory of Nondestructive Testing Ministry of Education,Nanchang Hangkong University of China.(No.EW201980090)。
文摘We present a novel method to monitor the weld geometry for metal inert gas(MIG)welding process with galvanized steel plates using Bayesian network(BN),and propose an effective method of extracting the weld reinforcement and width online.The laser vision sensor is mounted after the welding torch and used to profile the weld.With the extracted weld geometry and the adopted process parameters,a back propagation neural network(BPNN)is constructed offline and used to predict the weld reinforcement and width corresponding to the current parameter settings.A BN from welding experience and tests is presented to implement the decision making of welding current/voltage when the error between the predictive geometry and the actual one occurs.This study can deal with the negative welding tendency to adapt to welding randomness and indicates a valuable application prospect in the welding field.
基金Supported by the National Science Foundation Program of Jiangsu Province(No.BK20191378)the National Science Research Project of Jiangsu Higher Education Institutions(No.18KJB510034)+1 种基金the 11th Batch of China Postdoctoral Science Fund Special Funding Project(No.2018T110530)the National Natural Science Foundation of China(No.61771255)。
文摘In the fifth-generation new radio(5G-NR) high-speed railway(HSR) downlink,a deep learning(DL) based Doppler frequency offset(DFO) estimation scheme is proposed by using the back propagation neural network(BPNN).The proposed method mainly includes pre-training,training,and estimation phases,where the pre-training and training belong to the off-line stage,and the estimation is the online stage.To reduce the performance loss caused by the random initialization,the pre-training method is employed to acquire a desirable initialization,which is used as the initial parameters of the training phase.Moreover,the initial DFO estimation is used as input along with the received pilots to further improve the estimation accuracy.Different from the training phase,the initial DFO estimation in pre-training phase is obtained by the data and pilot symbols.Simulation results show that the mean squared error(MSE) performance of the proposed method is better than those of the available algorithms,and it has acceptable computational complexity.
基金This research was funded by the China Scholarship Council(CSC)and partially supported by the Project 911(Vietnam).The data analysis was carried out as a part of the second author’s PhD studies at the School of Geodesy and Geomatics,Wuhan University,People’s Republic of China[grant number 2011GXZN02].
文摘Hydropower has made a significant contribution to the economic development of Vietnam,thus it is important to monitor the safety of hydropower dams for the good of the country and the people.In this paper,dam horizontal displacement is analyzed and then forecasted using three methods:the multi-regression model,the seasonal integrated auto-regressive moving average(SARIMA)model and the back-propagation neural network(BPNN)merging models.The monitoring data of the Hoa Binh Dam in Vietnam,including horizontal displacement,time,reservoir water level,and air temperature,are used for the experiments.The results indicate that all of these three methods can approximately describe the trend of dam deformation despite their different forecast accuracies.Hence,their short-term forecasts can provide valuable references for the dam safety.
文摘It is difficult to predict wind speed series accurately due to the instability and randomness of the wind speed series.In order to predict wind speed,authors propose a hybrid model which combines the wavelet transform technique(WTT),the exponential smoothing(ES)method and the back propagation neural network(BPNN),and is termed as WTT-ES-BPNN.Firstly,WTT is applied to the raw wind speed series for removing the useless information.Secondly,the hybrid model integrating the ES method and the BPNN is used to forecast the de-noising data.Finally,the prediction of raw wind speed series is caught.Real data sets of daily mean wind speed in Hebei Province are used to evaluate the forecasting accuracy of the proposed model.Numerical results indicate that the WTT-ES-BPNN is an effective way to improve the accuracy of wind speed prediction.
