Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by...Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by environmental interference and sensor drift,highlighting the need for effective calibration methods to improve data reliability.This study proposes a data correction method based on Bayesian Optimization Support Vector Regression(BO-SVR),which combines the nonlinear modeling capability of Support Vector Regression(SVR)with the efficient global hyperparameter search of Bayesian Optimization.By introducing cross-validation loss as the optimization objective and using Gaussian process modeling with an Expected Improvement acquisition strategy,the approach automatically determines optimal hyperparameters for accurate pollutant concentration prediction.Experiments on real-world micro-sensor datasets demonstrate that BO-SVR outperforms traditional SVR,grid search SVR,and random forest(RF)models across multiple pollutants,including PM_(2.5),PM_(10),CO,NO_(2),SO_(2),and O_(3).The proposed method achieves lower prediction residuals,higher fitting accuracy,and better generalization,offering an efficient and practical solution for enhancing the quality of micro-sensor air monitoring data.展开更多
In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental ...In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.展开更多
HY-2 satellite is the first marine dynamic environment satellite of China.In this study,global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from...HY-2 satellite is the first marine dynamic environment satellite of China.In this study,global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from April 1 to 30,2014.The algorithm of evaporation and water vapor transport is discussed in detail,and results are compared with other reanalysis data.The sea surface temperature of HY-2 is in good agreement with the ARGO buoy data.Two clusters are shown in the scatter plot of HY-2 and OAFlux evaporation due to the uneven global distribution of evaporation.To improve the calculation accuracy,we compared the different parameterization schemes and adopted the method of calibrating HY-2 precipitation data by SSM/I and Global Precipitation Climatology Project(GPCP)data.In calculating the water vapor transport,the adjustment scheme is proposed to match the balance of the water cycle for data in the low latitudes.展开更多
Post-training quantization(PTQ)can reduce the memory footprint and latency of deep model inference while still preserving the accuracy of model,with only a small unlabeled calibration set and without the retraining on...Post-training quantization(PTQ)can reduce the memory footprint and latency of deep model inference while still preserving the accuracy of model,with only a small unlabeled calibration set and without the retraining on full training set.To calibrate a quantized model,current PTQ methods usually randomly select some unlabeled data from the training set as calibration data.However,we show the random data selection would result in performance instability and degradation due to the activation distribution mismatch.In this paper,we attempt to solve the crucial task on appropriate calibration data selection,and propose a novel one-shot calibration data selection method termed SelectQ,which selects specific data for calibration via dynamic clustering.The setting of our SelectQ uses the statistic information of activation and performs layer-wise clustering to learn an activation distribution on training set.For that purpose,a new metric called knowledge distance is proposed to calculate the distances of the activation statistics to centroids.Finally,after calibration with the selected data,quantization noise can be alleviated by mitigating the distribution mismatch within activations.Extensive experiments on ImageNet dataset show that our SelectQ increases the top-1 accuracy of ResNet18 over 15% in 4-bit quantization,compared to randomly sampled calibration data.It's noteworthy that SelectQ does not involve both the backward propagation and batch normalization parameters,which means that it has fewer limitations in practical applications.展开更多
This paper presents a relative flux calibration method for the Guoshoujing Telescope （LAMOST）, which may be applied to connect a blue spectrum to a red spectrum to build the whole spectrum across the total wavelengt...This paper presents a relative flux calibration method for the Guoshoujing Telescope （LAMOST）, which may be applied to connect a blue spectrum to a red spectrum to build the whole spectrum across the total wavelength range （3700 ～ 9000 A）. In each spectrograph, we estimate the effective temperatures of selected stars using a grid of spectral line indices in the blue spectral range and a comparison with stellar atmosphere models. For each spectrograph, stars of types A and F are selected as pseudo-standard stars, and the theoretical spectra are used to calibrate both the blue （3700 ～ 5900 A） and red spectrograph arms （5700 ～ 9000 A）. Then the spectral response function for these pseudo-standard stars could be used to correct the raw spectra provided by the other fibers of the spectrograph, after a fiber efficiency function has been derived from twilight flat-field exposures. A key problem in this method is the fitting of a pseudo stellar continuum, so we also give a detailed description of this step. The method is tested by comparing a small sample of LAMOST spectra calibrated in this way on stars also observed by the Sloan Digital Sky Survey. The result shows that the T eff estimation and relative flux calibration method are adequate.展开更多
Butterfly spring-relief valve, a crucial safety attachment of pressure vessel, is used to prevent pressuresystem from exceeding allowable limit value. Safe, expeditious and accurate calibration of safety valves is con...Butterfly spring-relief valve, a crucial safety attachment of pressure vessel, is used to prevent pressuresystem from exceeding allowable limit value. Safe, expeditious and accurate calibration of safety valves is consequentlyof vital importance to safe and economic operation of generating units. NSH CALIBRATOR could complete, not only theon-line performance and parameter tests of safety valves within two to five seconds with opening pressure of safetyvalves and steam medium pressure automatically recorded, but also could complete the on-line adjustment of safetyvalves verified unqualified. It saves energy consumption, decreases noise pollution and improves accuracy and efficiencyof safety valve calibration.[展开更多
Traffic modeling is a key step in several intelligent transportation systems（ITS） applications. This paper regards the traffic modeling through the enhancement of the cell transmission model. It considers the traffi...Traffic modeling is a key step in several intelligent transportation systems（ITS） applications. This paper regards the traffic modeling through the enhancement of the cell transmission model. It considers the traffic flow as a hybrid dynamic system and proposes a piecewise switched linear traffic model. The latter allows an accurate modeling of the traffic flow in a given section by considering its geometry. On the other hand, the piecewise switched linear traffic model handles more than one congestion wave and has the advantage to be modular. The measurements at upstream and downstream boundaries are also used in this model in order to decouple the traffic flow dynamics of successive road portions. Finally, real magnetic sensor data, provided by the performance measurement system on a portion of the Californian SR60-E highway are used to validate the proposed model.展开更多
The China Seismo-Electromagnetic Satellite(CSES) is the first platform of China's earthquake observation system in space and the first satellite of China's geophysical field detection missions. The high precis...The China Seismo-Electromagnetic Satellite(CSES) is the first platform of China's earthquake observation system in space and the first satellite of China's geophysical field detection missions. The high precision magnetometer(HPM), which contains two fluxgate sensors and a coupled dark state magnetometer(CDSM), measures the vector of the Earth's magnetic field with a bandwidth from DC to 15 Hz. The two fluxgate sensors are in a gradiometer configuration in order to reduce satellite interferences. Additionally, the CDSM sensor measures the scalar value of the magnetic field with higher accuracy and stability.Several data processing and calibration methods have been prepared to get accurate vector magnetic field data. This includes the calibration of each of the three sensors, the absolute vector correction algorithm, the spacecraft magnetic interference elimination and the coordinate transformation method. Also the instrument performances based on ground calibration activities are shown in this article.展开更多
文摘Miniature air quality sensors are widely used in urban grid-based monitoring due to their flexibility in deployment and low cost.However,the raw data collected by these devices often suffer from low accuracy caused by environmental interference and sensor drift,highlighting the need for effective calibration methods to improve data reliability.This study proposes a data correction method based on Bayesian Optimization Support Vector Regression(BO-SVR),which combines the nonlinear modeling capability of Support Vector Regression(SVR)with the efficient global hyperparameter search of Bayesian Optimization.By introducing cross-validation loss as the optimization objective and using Gaussian process modeling with an Expected Improvement acquisition strategy,the approach automatically determines optimal hyperparameters for accurate pollutant concentration prediction.Experiments on real-world micro-sensor datasets demonstrate that BO-SVR outperforms traditional SVR,grid search SVR,and random forest(RF)models across multiple pollutants,including PM_(2.5),PM_(10),CO,NO_(2),SO_(2),and O_(3).The proposed method achieves lower prediction residuals,higher fitting accuracy,and better generalization,offering an efficient and practical solution for enhancing the quality of micro-sensor air monitoring data.
基金supported by the National Basic Research Program of China (the 973 Program,Grant No.2010CB951102)the National Supporting Plan Program of China (Grants No.2007BAB28B01 and 2008BAB42B03)the National Natural Science Foundation of China (Grant No. 50709042),and the Regional Water Theme in the Water for a Healthy Country Flagship
文摘In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.
基金the financial support from the National Natural Science Foundation of China (No. 4197 6017)the Ministry of Science and Technology of China (No. 2016YFC1401405)the National Natural Science Foundation of China (No. U1406401)
文摘HY-2 satellite is the first marine dynamic environment satellite of China.In this study,global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from April 1 to 30,2014.The algorithm of evaporation and water vapor transport is discussed in detail,and results are compared with other reanalysis data.The sea surface temperature of HY-2 is in good agreement with the ARGO buoy data.Two clusters are shown in the scatter plot of HY-2 and OAFlux evaporation due to the uneven global distribution of evaporation.To improve the calculation accuracy,we compared the different parameterization schemes and adopted the method of calibrating HY-2 precipitation data by SSM/I and Global Precipitation Climatology Project(GPCP)data.In calculating the water vapor transport,the adjustment scheme is proposed to match the balance of the water cycle for data in the low latitudes.
基金partially supported by the National Natural Science Foundation of China(Nos.62072151,62376236,61932009)Anhui Provincial Natural Science Fund for the Distinguished Young Scholars,China(No.2008085J30)+2 种基金Open Foundation of Yunnan Key Laboratory of Software Engineering,China(No.2023SE103)CCF-Baidu Open Fund,CAAI-Huawei MindSpore Open Fund,Shenzhen Science and Technology Program,China(No.ZDSYS20230626091302006)Key Project of Science and Technology of Guangxi,China(No.AB22035022-2021AB20147).
文摘Post-training quantization(PTQ)can reduce the memory footprint and latency of deep model inference while still preserving the accuracy of model,with only a small unlabeled calibration set and without the retraining on full training set.To calibrate a quantized model,current PTQ methods usually randomly select some unlabeled data from the training set as calibration data.However,we show the random data selection would result in performance instability and degradation due to the activation distribution mismatch.In this paper,we attempt to solve the crucial task on appropriate calibration data selection,and propose a novel one-shot calibration data selection method termed SelectQ,which selects specific data for calibration via dynamic clustering.The setting of our SelectQ uses the statistic information of activation and performs layer-wise clustering to learn an activation distribution on training set.For that purpose,a new metric called knowledge distance is proposed to calculate the distances of the activation statistics to centroids.Finally,after calibration with the selected data,quantization noise can be alleviated by mitigating the distribution mismatch within activations.Extensive experiments on ImageNet dataset show that our SelectQ increases the top-1 accuracy of ResNet18 over 15% in 4-bit quantization,compared to randomly sampled calibration data.It's noteworthy that SelectQ does not involve both the backward propagation and batch normalization parameters,which means that it has fewer limitations in practical applications.
基金funded by the National Natural Science Foundation of China (Grant No.10973021)
文摘This paper presents a relative flux calibration method for the Guoshoujing Telescope （LAMOST）, which may be applied to connect a blue spectrum to a red spectrum to build the whole spectrum across the total wavelength range （3700 ～ 9000 A）. In each spectrograph, we estimate the effective temperatures of selected stars using a grid of spectral line indices in the blue spectral range and a comparison with stellar atmosphere models. For each spectrograph, stars of types A and F are selected as pseudo-standard stars, and the theoretical spectra are used to calibrate both the blue （3700 ～ 5900 A） and red spectrograph arms （5700 ～ 9000 A）. Then the spectral response function for these pseudo-standard stars could be used to correct the raw spectra provided by the other fibers of the spectrograph, after a fiber efficiency function has been derived from twilight flat-field exposures. A key problem in this method is the fitting of a pseudo stellar continuum, so we also give a detailed description of this step. The method is tested by comparing a small sample of LAMOST spectra calibrated in this way on stars also observed by the Sloan Digital Sky Survey. The result shows that the T eff estimation and relative flux calibration method are adequate.
文摘Butterfly spring-relief valve, a crucial safety attachment of pressure vessel, is used to prevent pressuresystem from exceeding allowable limit value. Safe, expeditious and accurate calibration of safety valves is consequentlyof vital importance to safe and economic operation of generating units. NSH CALIBRATOR could complete, not only theon-line performance and parameter tests of safety valves within two to five seconds with opening pressure of safetyvalves and steam medium pressure automatically recorded, but also could complete the on-line adjustment of safetyvalves verified unqualified. It saves energy consumption, decreases noise pollution and improves accuracy and efficiencyof safety valve calibration.[
文摘Traffic modeling is a key step in several intelligent transportation systems（ITS） applications. This paper regards the traffic modeling through the enhancement of the cell transmission model. It considers the traffic flow as a hybrid dynamic system and proposes a piecewise switched linear traffic model. The latter allows an accurate modeling of the traffic flow in a given section by considering its geometry. On the other hand, the piecewise switched linear traffic model handles more than one congestion wave and has the advantage to be modular. The measurements at upstream and downstream boundaries are also used in this model in order to decouple the traffic flow dynamics of successive road portions. Finally, real magnetic sensor data, provided by the performance measurement system on a portion of the Californian SR60-E highway are used to validate the proposed model.
文摘The China Seismo-Electromagnetic Satellite(CSES) is the first platform of China's earthquake observation system in space and the first satellite of China's geophysical field detection missions. The high precision magnetometer(HPM), which contains two fluxgate sensors and a coupled dark state magnetometer(CDSM), measures the vector of the Earth's magnetic field with a bandwidth from DC to 15 Hz. The two fluxgate sensors are in a gradiometer configuration in order to reduce satellite interferences. Additionally, the CDSM sensor measures the scalar value of the magnetic field with higher accuracy and stability.Several data processing and calibration methods have been prepared to get accurate vector magnetic field data. This includes the calibration of each of the three sensors, the absolute vector correction algorithm, the spacecraft magnetic interference elimination and the coordinate transformation method. Also the instrument performances based on ground calibration activities are shown in this article.
