Due to its special observation principle, GPS remote sensing atmospheric precipitation has the advantages of high time resolution and no weather conditions, and has been widely used in the research field of atmospheri...Due to its special observation principle, GPS remote sensing atmospheric precipitation has the advantages of high time resolution and no weather conditions, and has been widely used in the research field of atmospheric precipitation. Using ground-based GPS precipitate water vapor data (GPS-PWV) and radiosonde-precipitate water vapor data (RS-PWV) that integrated by Radiosonde data, the error between GPS-PWV and RS-PWV in Tengchong is analyzed on its distribution of wet and dry seasons, also the difference between 00:00 UTC and 12:00 UTC. Results show that the RMSE of GPS-PWV and RS-PWV on both 00:00 UTC and 12:00 UTC are less than 5 mm, they correspond with each other well and their correlation coefficient is above 0.95, additionally, GPS-PWV value is stable than RS-PWV value. On the whole, the value of GPS-PWV is slightly larger than RS-PWV. And the mean absolute error between them has higher values, 4.5 mm in 2011 and 4.7 mm in 2012 from May to October (local rainy season) and lower values, 2.8 mm in 2011 and 3.1 mm in 2012 in November to April (local dry season). Besides, the mean absolute error in the morning seems has a difference with its component in the evening. Specifically, it is bigger on 12:00 UTC than on 00:00 UTC and the mean absolute errors on 12:00 UTC of two years are 27% and 11% larger than errors on 00:00 UTC respectively. The correlation of mean absolute error and surface vapor pressure, surface air temperature is examined in this study as well. We achieved that the correlation coefficient between mean absolute error and surface vapor pressure, surface air temperature equals 0.32, 0.37 separately. Diverse characters of mean absolute error under different precipitation conditions are also discussed. The outcome is that the mean absolute error has a higher value on rainy days and a lower value on clear days. However, during the precipitation periods, it appears that the mean absolute error and the rainfall situation don’t agree with each other well, it is likely to change randomly.展开更多
In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep lea...In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep learning process has also been widely considered in these genomics data processing system.In this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods.Moeover,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)approach.The suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage error.Proposed DBNJZZ gives better performance than previously available methods.展开更多
For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of sing...For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output.Utilizing a variety of non-linear techniques,a SISO FS is simulated.The results of FS experiments conducted in comparable conditions are then compared.The simulated results and the results of the experimental setup agree fairly well.The findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control accuracy.By using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs.The proposed fuzzy system performed far better than other modern days approaches available in the literature.展开更多
Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby communities.Electrical circuits,arson,and neglected charcoal stoves are the major causes of these...Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby communities.Electrical circuits,arson,and neglected charcoal stoves are the major causes of these fires.Previous methods,i.e.,satellites,are expensive to maintain and cause unnecessary delays.Also,unit-smoke detectors are highly prone to false alerts.In this paper,an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach.A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy model.Two crisp input parameters,namely:FIT and FIG��are used.Results show that the Interval Type-2 model achieved an accuracy value of FIO�=98.2%,MAE=1.3010,MSE=1.6938 and RMSE=1.3015 using regression analysis.The study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire danger.As a result,the proposed solution can be fully implemented in low-cost,low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false alerts.Through informed decision-making in low-cost fire detection devices,early warning notifications can be provided to aid in the rapid evacuation of people,thereby improving fire safety surveillance,management,and protection for the market community.展开更多
文摘Due to its special observation principle, GPS remote sensing atmospheric precipitation has the advantages of high time resolution and no weather conditions, and has been widely used in the research field of atmospheric precipitation. Using ground-based GPS precipitate water vapor data (GPS-PWV) and radiosonde-precipitate water vapor data (RS-PWV) that integrated by Radiosonde data, the error between GPS-PWV and RS-PWV in Tengchong is analyzed on its distribution of wet and dry seasons, also the difference between 00:00 UTC and 12:00 UTC. Results show that the RMSE of GPS-PWV and RS-PWV on both 00:00 UTC and 12:00 UTC are less than 5 mm, they correspond with each other well and their correlation coefficient is above 0.95, additionally, GPS-PWV value is stable than RS-PWV value. On the whole, the value of GPS-PWV is slightly larger than RS-PWV. And the mean absolute error between them has higher values, 4.5 mm in 2011 and 4.7 mm in 2012 from May to October (local rainy season) and lower values, 2.8 mm in 2011 and 3.1 mm in 2012 in November to April (local dry season). Besides, the mean absolute error in the morning seems has a difference with its component in the evening. Specifically, it is bigger on 12:00 UTC than on 00:00 UTC and the mean absolute errors on 12:00 UTC of two years are 27% and 11% larger than errors on 00:00 UTC respectively. The correlation of mean absolute error and surface vapor pressure, surface air temperature is examined in this study as well. We achieved that the correlation coefficient between mean absolute error and surface vapor pressure, surface air temperature equals 0.32, 0.37 separately. Diverse characters of mean absolute error under different precipitation conditions are also discussed. The outcome is that the mean absolute error has a higher value on rainy days and a lower value on clear days. However, during the precipitation periods, it appears that the mean absolute error and the rainfall situation don’t agree with each other well, it is likely to change randomly.
文摘In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep learning process has also been widely considered in these genomics data processing system.In this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods.Moeover,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)approach.The suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage error.Proposed DBNJZZ gives better performance than previously available methods.
文摘For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output.Utilizing a variety of non-linear techniques,a SISO FS is simulated.The results of FS experiments conducted in comparable conditions are then compared.The simulated results and the results of the experimental setup agree fairly well.The findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control accuracy.By using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs.The proposed fuzzy system performed far better than other modern days approaches available in the literature.
文摘Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby communities.Electrical circuits,arson,and neglected charcoal stoves are the major causes of these fires.Previous methods,i.e.,satellites,are expensive to maintain and cause unnecessary delays.Also,unit-smoke detectors are highly prone to false alerts.In this paper,an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach.A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy model.Two crisp input parameters,namely:FIT and FIG��are used.Results show that the Interval Type-2 model achieved an accuracy value of FIO�=98.2%,MAE=1.3010,MSE=1.6938 and RMSE=1.3015 using regression analysis.The study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire danger.As a result,the proposed solution can be fully implemented in low-cost,low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false alerts.Through informed decision-making in low-cost fire detection devices,early warning notifications can be provided to aid in the rapid evacuation of people,thereby improving fire safety surveillance,management,and protection for the market community.
