The Euler angle estimation is a calibration method for vector data measured by the magnetometer on a satellite.It is used to find the relative rotation between the coordinate system of the magnetometer and the satelli...The Euler angle estimation is a calibration method for vector data measured by the magnetometer on a satellite.It is used to find the relative rotation between the coordinate system of the magnetometer and the satellite(usually determined by Star Imagers).Before launch of the low-orbit,low-inclination Macao Science Satellite-1(known as MSS-1),we simulated the estimation of Euler angles by using the magnetic measurements of the in-orbit Swarm and China Seismo-Electromagnetic Satellite(noted as CSES),with various data combinations.In this study,11 data sets were designed to analyze the estimation results for the MSS-1 orbit by using a joint estimation method of the geomagnetic field model parameters and Euler angles.For the model results,we found that all the spatial power spectral lines showed behavior consistent with that of the CHAOS-7.8 model at low degrees(corresponding to large-scale magnetic signals).The spectra of models without global data coverage deviated much more(by a maximum of~10^(4) nT^(2))from those of the CHAOS-7.8 model at higher degrees.For models with global data coverage and with various data combinations,the spectral lines were distributed similarly.Moreover,the models with accordant power spectral distributions demonstrated different Euler angle estimations.As more vector data at higher latitudes were included,the estimated Euler angles varied monotonically in all three directions.The models with vector data in the same latitude range showed similar Euler angle results,regardless of whether the poleward scalar data were included.The largest value difference was found between the models using vector data within±40°latitudes and those using vector data within±60°latitudes,which reached to~28″.Therefore,we concluded that the inversion of the spherical harmonic Gauss coefficients in our tests was mainly affected by the spatial coverage range of the data,whereas the estimation of Euler angles largely depended on the latitude range where the vector data could be obtained.These results can be used for future in-flight data testing.We expect the estimation of Euler angles to improve as other methods are adopted.展开更多
Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory.Bluetooth low-energy(BLE)beacons have the advantages of low price,small size and l...Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory.Bluetooth low-energy(BLE)beacons have the advantages of low price,small size and low energy consumption,which make them a promising solution for sample position estimation in the automated laboratory.Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength(RSS)data.However,most of the research depends on intensive beacon installation.Proximity estimation,which depends entirely on one beacon,is more suitable for sample position estimation in large automated laboratories.The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model(PLM),which is a widely used radio wave propagation model-based proximity estimation method.In this paper,BLE sensing devices for sample position estimation are proposed.The BLE beacon-based proximity estimation is discussed in the framework of machine learning,in which the support vector regression(SVR)is utilized to model the nonlinear relationship between the RSS data and distance,and the Kalman filter is utilized to decrease the RSS data deviation.The experimental results over different environments indicate that the SVR outperforms the PLM significantly,and provides 1 m absolute errors for more than 95%of the testing samples.The Kalman filter brings benefits to stable distance predictions.Apart from proximity-based sample position estimation,the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.展开更多
The heavy-tailed distributions are very useful and play a major role in actuary and financial management problems.Actuaries are often searching for such distributions to provide the best fit to financial and economic ...The heavy-tailed distributions are very useful and play a major role in actuary and financial management problems.Actuaries are often searching for such distributions to provide the best fit to financial and economic data sets.In the current study,a prominent method to generate new distributions useful for modeling heavy-tailed data is considered.The proposed family is introduced using trigonometric function and can be named as the Arcsine-X family of distri-butions.For the purposes of the demonstration,a specific sub-model of the proposed family,called the Arcsine-Weibull distribution is considered.The max-imum likelihood estimation method is adopted for estimating the parameters of the Arcsine-X distributions.The resultant estimators are evaluated in a detailed Monte Carlo simulation study.To illustrate the Arcsine-Weibull two insurance data sets are analyzed.Comparison of the Arcsine-Weibull model is done with the well-known two parameters and four parameters competitors.The competitive models including the Weibull,Lomax,Burr-XII and beta Weibull models.Different goodness of fit measures are taken into account to determine the useful-ness of the Arcsine-Weibull and other considered models.Data analysis shows that the Arcsine-Weibull distribution works much better than competing models in financial data analysis.展开更多
Nordmann's Greenshank(Tringa guttifer)is a globally endangered species that has received little research attention.It is threatened by rapid habitat loss,an incomplete network of protected sites,and lack of long-t...Nordmann's Greenshank(Tringa guttifer)is a globally endangered species that has received little research attention.It is threatened by rapid habitat loss,an incomplete network of protected sites,and lack of long-term data on population dynamics.Citizen science data can be combined with survey data to support population estimation and conservation gap analysis.From 2020 to 2021,Nordmann's Greenshank was surveyed in Tiaozini,Xiaoyangkou,and Dongling on the southern coast of Jiangsu Province,China,and the global population of the species was re-evaluated using the data obtained.We integrated citizen science data from eBird and the China Bird Report from 2000 to 2020 with the survey results to identify important habitats harboring over 1%of its total population,and compared this data with existing protected areas to identify gaps in its global conservation.Our survey found that Tiaozini supported at least 1194 individuals.Consequently,its global population was reestimated to be 1500-2000.Moreover,45 important habitats were identified based on citizen data and survey results.Although 44.4%and 50.0%of the priority sites in the world and China,respectively,are located outside protected areas,the Conservation Effectiveness Index(C)is 68.4%and 71.1%,respectively,showing that the current coverage of protected areas for this part of its range is reasonable.This study presents the most complete and recent population data to date.Tiaozini is the most important migration stopover site for Nordmann's Greenshanks.The species is under threat in terms of breeding,wintering,and stopover sites.Therefore,we suggest improving monitoring,establishing new protected sites to complete the habitat protection network,and improving the effectiveness of existing habitat protection strategies,including further developing high tide roosting sites.展开更多
The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of ...The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of charge(SOC)is one of the important parameters.The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years.However,a common problem with these models is that their estimation performances are not always stable,which makes them difficult to use in practical applications.To address this problem,an optimized radial basis function neural network(RBF-NN)that combines the concepts of Golden Section Method(GSM)and Sparrow Search Algorithm(SSA)is proposed in this paper.Specifically,GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model,and its parameters such as radial base center,connection weights and so on are optimized by SSA,which greatly improve the performance of RBF-NN in SOC estimation.In the experiments,data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model,and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.展开更多
基金funded by the Macao Foundation,the pre-research project of Civil Aerospace Technologies(Nos.D020308 and D020303)funded by the China National Space Administration,Macao Science and Technology Development Fund(FDCT+1 种基金No.0001/2019/A1)the opening fund of the State Key Laboratory of Lunar and Planetary Sciences(Macao University of Science and Technology,Macao FDCT No.119/2017/A3)。
文摘The Euler angle estimation is a calibration method for vector data measured by the magnetometer on a satellite.It is used to find the relative rotation between the coordinate system of the magnetometer and the satellite(usually determined by Star Imagers).Before launch of the low-orbit,low-inclination Macao Science Satellite-1(known as MSS-1),we simulated the estimation of Euler angles by using the magnetic measurements of the in-orbit Swarm and China Seismo-Electromagnetic Satellite(noted as CSES),with various data combinations.In this study,11 data sets were designed to analyze the estimation results for the MSS-1 orbit by using a joint estimation method of the geomagnetic field model parameters and Euler angles.For the model results,we found that all the spatial power spectral lines showed behavior consistent with that of the CHAOS-7.8 model at low degrees(corresponding to large-scale magnetic signals).The spectra of models without global data coverage deviated much more(by a maximum of~10^(4) nT^(2))from those of the CHAOS-7.8 model at higher degrees.For models with global data coverage and with various data combinations,the spectral lines were distributed similarly.Moreover,the models with accordant power spectral distributions demonstrated different Euler angle estimations.As more vector data at higher latitudes were included,the estimated Euler angles varied monotonically in all three directions.The models with vector data in the same latitude range showed similar Euler angle results,regardless of whether the poleward scalar data were included.The largest value difference was found between the models using vector data within±40°latitudes and those using vector data within±60°latitudes,which reached to~28″.Therefore,we concluded that the inversion of the spherical harmonic Gauss coefficients in our tests was mainly affected by the spatial coverage range of the data,whereas the estimation of Euler angles largely depended on the latitude range where the vector data could be obtained.These results can be used for future in-flight data testing.We expect the estimation of Euler angles to improve as other methods are adopted.
基金the Synergy Project ADAM(Autonomous Discovery of Advanced Materials)funded by the European Research Council(Grant No.856405).
文摘Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory.Bluetooth low-energy(BLE)beacons have the advantages of low price,small size and low energy consumption,which make them a promising solution for sample position estimation in the automated laboratory.Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength(RSS)data.However,most of the research depends on intensive beacon installation.Proximity estimation,which depends entirely on one beacon,is more suitable for sample position estimation in large automated laboratories.The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model(PLM),which is a widely used radio wave propagation model-based proximity estimation method.In this paper,BLE sensing devices for sample position estimation are proposed.The BLE beacon-based proximity estimation is discussed in the framework of machine learning,in which the support vector regression(SVR)is utilized to model the nonlinear relationship between the RSS data and distance,and the Kalman filter is utilized to decrease the RSS data deviation.The experimental results over different environments indicate that the SVR outperforms the PLM significantly,and provides 1 m absolute errors for more than 95%of the testing samples.The Kalman filter brings benefits to stable distance predictions.Apart from proximity-based sample position estimation,the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.
文摘The heavy-tailed distributions are very useful and play a major role in actuary and financial management problems.Actuaries are often searching for such distributions to provide the best fit to financial and economic data sets.In the current study,a prominent method to generate new distributions useful for modeling heavy-tailed data is considered.The proposed family is introduced using trigonometric function and can be named as the Arcsine-X family of distri-butions.For the purposes of the demonstration,a specific sub-model of the proposed family,called the Arcsine-Weibull distribution is considered.The max-imum likelihood estimation method is adopted for estimating the parameters of the Arcsine-X distributions.The resultant estimators are evaluated in a detailed Monte Carlo simulation study.To illustrate the Arcsine-Weibull two insurance data sets are analyzed.Comparison of the Arcsine-Weibull model is done with the well-known two parameters and four parameters competitors.The competitive models including the Weibull,Lomax,Burr-XII and beta Weibull models.Different goodness of fit measures are taken into account to determine the useful-ness of the Arcsine-Weibull and other considered models.Data analysis shows that the Arcsine-Weibull distribution works much better than competing models in financial data analysis.
基金funded by the National Natural Science Foundation of China(No.31971400)the"Saving Spoon-billed Sandpiper"of Shenzhen Mangrove Wetlands Conservation Foundation(MCF)the Fundamental Research Funds for the Central Universities(No.BLX202144)。
文摘Nordmann's Greenshank(Tringa guttifer)is a globally endangered species that has received little research attention.It is threatened by rapid habitat loss,an incomplete network of protected sites,and lack of long-term data on population dynamics.Citizen science data can be combined with survey data to support population estimation and conservation gap analysis.From 2020 to 2021,Nordmann's Greenshank was surveyed in Tiaozini,Xiaoyangkou,and Dongling on the southern coast of Jiangsu Province,China,and the global population of the species was re-evaluated using the data obtained.We integrated citizen science data from eBird and the China Bird Report from 2000 to 2020 with the survey results to identify important habitats harboring over 1%of its total population,and compared this data with existing protected areas to identify gaps in its global conservation.Our survey found that Tiaozini supported at least 1194 individuals.Consequently,its global population was reestimated to be 1500-2000.Moreover,45 important habitats were identified based on citizen data and survey results.Although 44.4%and 50.0%of the priority sites in the world and China,respectively,are located outside protected areas,the Conservation Effectiveness Index(C)is 68.4%and 71.1%,respectively,showing that the current coverage of protected areas for this part of its range is reasonable.This study presents the most complete and recent population data to date.Tiaozini is the most important migration stopover site for Nordmann's Greenshanks.The species is under threat in terms of breeding,wintering,and stopover sites.Therefore,we suggest improving monitoring,establishing new protected sites to complete the habitat protection network,and improving the effectiveness of existing habitat protection strategies,including further developing high tide roosting sites.
基金This work was supported by the Fundamental Research Funds for the Central Universities(2022MS015)。
文摘The safety and reliability of battery storage systems are critical to the mass roll-out of electrified transportation and new energy generation.To achieve safe management and optimal control of batteries,the state of charge(SOC)is one of the important parameters.The machine-learning based SOC estimation methods of lithium-ion batteries have attracted substantial interests in recent years.However,a common problem with these models is that their estimation performances are not always stable,which makes them difficult to use in practical applications.To address this problem,an optimized radial basis function neural network(RBF-NN)that combines the concepts of Golden Section Method(GSM)and Sparrow Search Algorithm(SSA)is proposed in this paper.Specifically,GSM is used to determine the optimum number of neurons in hidden layer of the RBF-NN model,and its parameters such as radial base center,connection weights and so on are optimized by SSA,which greatly improve the performance of RBF-NN in SOC estimation.In the experiments,data collected from different working conditions are used to demonstrate the accuracy and generalization ability of the proposed model,and the results of the experiment indicate that the maximum error of the proposed model is less than 2%.
