Fiber Bragg grating(FBG)sensors are often used in monitoring activities and to ensure that environmental parameters satisfy industrial requirements.They offer crucial safety measures in the early detection of hazards ...Fiber Bragg grating(FBG)sensors are often used in monitoring activities and to ensure that environmental parameters satisfy industrial requirements.They offer crucial safety measures in the early detection of hazards due to their greatly reduced size,low weight,flexibility,and immunity to electromagnetic interference.These characteristics make FBGs suitable also for use in relation to the human body for in vivo measurements and long-term monitoring.In this study,recent developments are presented with regard to the utilization of these sensors to measure the so-called post-mortem interval(PMI).Such developments rely on numerical simulations based on the Matlab software and monitoring of the rectal temperature,which is one of the main parameters for estimating the PMI.First,the Matlab software is used to solve the Henssge equation for different ambient temperatures and for different body masses;then a Bragg grating sensors is used for post-mortem dating.The results and their accuracy are discussed.展开更多
Plants diseases have a detrimental effect on the quality but also on the quantity of agricultural production.However,the prediction of these diseases is proving the effect on crop quality and on reducing the risk of p...Plants diseases have a detrimental effect on the quality but also on the quantity of agricultural production.However,the prediction of these diseases is proving the effect on crop quality and on reducing the risk of production losses.Indeed,the detection of plant diseases-either with a naked eye or using traditional methods-is largely a cumbersome process in terms of time,availability and results with a high-risk error.The present work introduces a depth study of various CNN architectures with different optimization algorithms carried out for olive disease detection using classification techniques that recommend the best model for constructing an effective disease detector.This study presents a dataset of 5571 olive leaf images collected manually on real conditions from different regions of Morocco,that also includes healthy class to detect olive diseases.Further,one of the goals of this research was to study the correlation effects between CNN architectures and optimization algorithms evaluated by the accuracy and other performance metrics.The highest rate in trained models was 100%,while the highest rate in experiments without data augmentation was 92,59%.Another subject of this study is the influence of the optimization algorithms on neuronal network performance.As a result of the experiments carried out,the MobileNet architecture using Rmsprop algorithms outperformed the others combinations in terms of performance and efficiency of disease detector.展开更多
文摘Fiber Bragg grating(FBG)sensors are often used in monitoring activities and to ensure that environmental parameters satisfy industrial requirements.They offer crucial safety measures in the early detection of hazards due to their greatly reduced size,low weight,flexibility,and immunity to electromagnetic interference.These characteristics make FBGs suitable also for use in relation to the human body for in vivo measurements and long-term monitoring.In this study,recent developments are presented with regard to the utilization of these sensors to measure the so-called post-mortem interval(PMI).Such developments rely on numerical simulations based on the Matlab software and monitoring of the rectal temperature,which is one of the main parameters for estimating the PMI.First,the Matlab software is used to solve the Henssge equation for different ambient temperatures and for different body masses;then a Bragg grating sensors is used for post-mortem dating.The results and their accuracy are discussed.
文摘Plants diseases have a detrimental effect on the quality but also on the quantity of agricultural production.However,the prediction of these diseases is proving the effect on crop quality and on reducing the risk of production losses.Indeed,the detection of plant diseases-either with a naked eye or using traditional methods-is largely a cumbersome process in terms of time,availability and results with a high-risk error.The present work introduces a depth study of various CNN architectures with different optimization algorithms carried out for olive disease detection using classification techniques that recommend the best model for constructing an effective disease detector.This study presents a dataset of 5571 olive leaf images collected manually on real conditions from different regions of Morocco,that also includes healthy class to detect olive diseases.Further,one of the goals of this research was to study the correlation effects between CNN architectures and optimization algorithms evaluated by the accuracy and other performance metrics.The highest rate in trained models was 100%,while the highest rate in experiments without data augmentation was 92,59%.Another subject of this study is the influence of the optimization algorithms on neuronal network performance.As a result of the experiments carried out,the MobileNet architecture using Rmsprop algorithms outperformed the others combinations in terms of performance and efficiency of disease detector.
