It is important to understand the dynamics of malaria vectors in implementing malaria control strategies. Six villages were selected from different sections in the Three Gorges Reservoir fc,r exploring the relationshi...It is important to understand the dynamics of malaria vectors in implementing malaria control strategies. Six villages were selected from different sections in the Three Gorges Reservoir fc,r exploring the relationship between the climatic ｜：actors and its malaria vector density from 1997 to 2007 using the auto-regressive linear model regressi^n method. The result indicated that both temperature and precipitation were better modeled as quadratic rather than linearly related to the density of Anopheles sinensis.展开更多
The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovol...The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovoltaic(PV)market,thereby making the management and maintenance of solar photovoltaic(SPV)panels a new area of business as neglecting it may lead to significant financial losses and failure to combat climate change and the energy crisis.SPV panels face many risks that may degrade their power generation performance,damage their structures,or even cause the complete loss of their power generation capacity during their long service life.It is hoped that these problems can be identified and resolved as soon as possible.However,this is a challenging task as a solar power plant(SPP)may contain hundreds even thousands of SPV panels.To provide a potential solution for this issue,a smart drone-based SPV panel condition monitoring(CM)technique has been studied in this paper.In the study,the U-Net neural network(UNNN),which is ideal for undertaking image segmentation tasks and good at handling small sample size problem,is adopted to automatically create mask images from the collected true color thermal infrared images.The support vector machine(SVM),which performs very well in highdimensional feature spaces and is therefore good at image recognition,is employed to classifying the mask images generated by the UNNN.The research result has shown that with the aid of the UNNN and SVM,the thermal infrared images that are remotely collected by drones from SPPs can be automatically and effectively processed,analyzed,and classified with reasonable accuracy(over 80%).Particularly,the mask images produced by the trained UNNN,which contain less interference items than true color thermal infrared images,significantly benefit the assessing accuracy of the health state of SPV panels.It is anticipated that the technical approach presented in this paper will serve as an inspiration for the exploration of more advanced and dependable smart asset management techniques within the solar power industry.展开更多
Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the...Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the model using the reference data set collected from the healthy structure and employing the trained model to identify outlier conditions representing the damaged state.In this paper,the coefficients and the residuals of the autoregressive model with exogenous input created using only the measured output signals are extracted as damage features.These features obtained at the baseline state for each sensor cluster are then utilized to train the one class support vector machine,an unsupervised classifier generating a decision function using only patterns belonging to this baseline state.Structural damage,once detected by the trained machine,a damage index based on comparison of the residuals between the trained class and the outlier state is implemented for localizing damage.The two-step damage assessment framework is first implemented on an eight degree-of-freedom numerical model with the effects of measurement noise integrated.Subsequently,vibration data collected from a one-story one-bay reinforced concrete frame inflicted with progressive levels of damage have been utilized to verify the accuracy and robustness of the proposed methodology.展开更多
To design a more effective blade pitch adjustment mechanism,research was done on changes to the hydrodynamic characteristics of VVPs(Variable Vector Propeller) caused by different rules for changing pitch angle. A mat...To design a more effective blade pitch adjustment mechanism,research was done on changes to the hydrodynamic characteristics of VVPs(Variable Vector Propeller) caused by different rules for changing pitch angle. A mathematical method for predicting the hydrodynamic characteristics of a VVP under unsteady conditions is presented based on the panel method. Mathematical models for evaluation based on potential flow theory and the Green theorem are also presented. The hydrodynamic characteristics are numerically predicted. To avoid gaps between panels,hyperboloidal quadrilateral panels were used. The pressure Kutta condition on the trailing edge of the VVP blade was satisfied by the Newton-Raphson iterative procedure. The influence coefficients of the panels were calculated by Morino's analytical formulations to improve numerical calculation speed,and the method developed by Yanagizawa was used to eliminate the point singularity on derivation calculus while determining the velocities on propeller surfaces. The calculation results show that it's best for the hydrodynamic characteristics of the VVP that pitch angle changes follow the sine rule.展开更多
基金funded by the Public Project(20080219)of the Ministry of Science and Technology,PRC
文摘It is important to understand the dynamics of malaria vectors in implementing malaria control strategies. Six villages were selected from different sections in the Three Gorges Reservoir fc,r exploring the relationship between the climatic ｜：actors and its malaria vector density from 1997 to 2007 using the auto-regressive linear model regressi^n method. The result indicated that both temperature and precipitation were better modeled as quadratic rather than linearly related to the density of Anopheles sinensis.
基金the Efficiency and Performance Engineering Network International Collaboration Fund(award No.of TEPEN-ICF2021-05).
文摘The exploitation of renewable energy has become a pressing task due to climate change and the recent energy crisis caused by regional conflicts.This has further accelerated the rapid development of the global photovoltaic(PV)market,thereby making the management and maintenance of solar photovoltaic(SPV)panels a new area of business as neglecting it may lead to significant financial losses and failure to combat climate change and the energy crisis.SPV panels face many risks that may degrade their power generation performance,damage their structures,or even cause the complete loss of their power generation capacity during their long service life.It is hoped that these problems can be identified and resolved as soon as possible.However,this is a challenging task as a solar power plant(SPP)may contain hundreds even thousands of SPV panels.To provide a potential solution for this issue,a smart drone-based SPV panel condition monitoring(CM)technique has been studied in this paper.In the study,the U-Net neural network(UNNN),which is ideal for undertaking image segmentation tasks and good at handling small sample size problem,is adopted to automatically create mask images from the collected true color thermal infrared images.The support vector machine(SVM),which performs very well in highdimensional feature spaces and is therefore good at image recognition,is employed to classifying the mask images generated by the UNNN.The research result has shown that with the aid of the UNNN and SVM,the thermal infrared images that are remotely collected by drones from SPPs can be automatically and effectively processed,analyzed,and classified with reasonable accuracy(over 80%).Particularly,the mask images produced by the trained UNNN,which contain less interference items than true color thermal infrared images,significantly benefit the assessing accuracy of the health state of SPV panels.It is anticipated that the technical approach presented in this paper will serve as an inspiration for the exploration of more advanced and dependable smart asset management techniques within the solar power industry.
基金funding provided by the Scientific and Technological Research Council of Türkiye(TÜBİTAK).
文摘Machine learning algorithms operating in an unsupervised fashion has emerged as promising tools for detecting structural damage in an automated fashion.Its essence relies on selecting appropriate features to train the model using the reference data set collected from the healthy structure and employing the trained model to identify outlier conditions representing the damaged state.In this paper,the coefficients and the residuals of the autoregressive model with exogenous input created using only the measured output signals are extracted as damage features.These features obtained at the baseline state for each sensor cluster are then utilized to train the one class support vector machine,an unsupervised classifier generating a decision function using only patterns belonging to this baseline state.Structural damage,once detected by the trained machine,a damage index based on comparison of the residuals between the trained class and the outlier state is implemented for localizing damage.The two-step damage assessment framework is first implemented on an eight degree-of-freedom numerical model with the effects of measurement noise integrated.Subsequently,vibration data collected from a one-story one-bay reinforced concrete frame inflicted with progressive levels of damage have been utilized to verify the accuracy and robustness of the proposed methodology.
文摘To design a more effective blade pitch adjustment mechanism,research was done on changes to the hydrodynamic characteristics of VVPs(Variable Vector Propeller) caused by different rules for changing pitch angle. A mathematical method for predicting the hydrodynamic characteristics of a VVP under unsteady conditions is presented based on the panel method. Mathematical models for evaluation based on potential flow theory and the Green theorem are also presented. The hydrodynamic characteristics are numerically predicted. To avoid gaps between panels,hyperboloidal quadrilateral panels were used. The pressure Kutta condition on the trailing edge of the VVP blade was satisfied by the Newton-Raphson iterative procedure. The influence coefficients of the panels were calculated by Morino's analytical formulations to improve numerical calculation speed,and the method developed by Yanagizawa was used to eliminate the point singularity on derivation calculus while determining the velocities on propeller surfaces. The calculation results show that it's best for the hydrodynamic characteristics of the VVP that pitch angle changes follow the sine rule.
