Agriculture 5.0 is an emerging concept where sensors,big data,Internet-of-Things(IoT),robots,and Artificial In-telligence(AI)are used for agricultural purposes.Different from Agriculture 4.0,robots and AI become the f...Agriculture 5.0 is an emerging concept where sensors,big data,Internet-of-Things(IoT),robots,and Artificial In-telligence(AI)are used for agricultural purposes.Different from Agriculture 4.0,robots and AI become the focus of the implementation in Agriculture 5.0.One of the applications of Agriculture 5.0 is weed management where robots are used to discriminate weeds from the crops or plants so that proper action can be performed to remove the weeds.This paper discusses an in-depth review of Machine Learning(ML)techniques used for discriminating weeds from crops or plants.We specifically present a detailed explanation of five steps required in using ML algorithms to distinguish between weeds and plants.展开更多
Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation pipe.Its complex problem to detect the sensors health and ...Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation pipe.Its complex problem to detect the sensors health and abnormality as the sensor reading would reflect the various states of the compressor.In ideal situation,sensor readings offer vast amounts of information on compressor health and could possibly indicate early fault of machines.Furthermore,due to harsh site and process operating conditions,sensors are often found to have drifted or failed,and there is no standard methodology to predict abnormality apart from applying emerging industrial smart sensor technologies.In this paper,we investigate a minimalist approach for detecting abnormality of compressor's shaft's RPM sensor.As the sensors in the compressor are correlated,we first use the outputs of other sensors to predict the shaft's RPM using regression-based models(neural networks and multiple linear regression).Second,we calculate the histogram of residuals by taking the difference between the predicted sensor value and the actual sensor value plus the abnormality in terms of bias/miscalibration and noise.The histogram of residuals can be used for sensor abnormality monitoring.In general,sensor states can be monitored by observing the shifting of the mean in the histogram of residuals.The sensor readings contaminated with noise can be seen by a shifted mean whose value is between the normal condition mean and the biased condition mean.This method is compact and would be relevant to monitor irregularity of the sensors.展开更多
Compatmental pandemic models have become a significant tool in the battle against disease outbreaks.Despite this,pandemic models sometimes require extensive modification to accuately reflect the actual epidemic condit...Compatmental pandemic models have become a significant tool in the battle against disease outbreaks.Despite this,pandemic models sometimes require extensive modification to accuately reflect the actual epidemic condition The Susceptble-Infectious-Removed(SIR)model,in particular,contains two primary parameters:the infectious rate parameter ρ and the removal rate parameter r,in addition to additional unknowns such as the initial infectious population.Adding to the complexity,there is an obvious challenge to tack the evolution of these parameters,especially ρ and γ,over time which leads to the estimation of the reproduction number for the paticular time window,^(κ)T.This reproduction mumber may provide better understanding on the efectiveness of isolation or control measures.The changing ^(κ)T values(evolving over time window)will lead to even more possible parameter scenanios.Given the present Coronavirus Disease 2019(COVID-19)pandemic,a stochastic optimization stategy is proposed to ft the model on the basis of parameter changes over timne.Solutions are encoded to reflet the changing parameters of Tρ and γT,alwing the changing ^(κ)T to be estimated.In our approach,an Adaptive Differential Evolution(ADE)and Paricle Swam Optimization(PSO)are used to ft the curves into previously recorded data.ADE eliminates the need to tume the parameters of the Differential Evolution(DE)to balance the exploitation and exploration in the solution space.Results show that the proposed optimized model can generally ft the cuves well albeit high variance in the solutions.展开更多
基金ASEAN-India Collaborative R&D scheme under ASEAN-India S&T Development Fund(AISTDF)File Number:CRD/2020/000248 and partly by Universitas Indonesia's Inter-national Indexed Publication(PUTI)Q2 Grant,year 2023,number:NKB-803/UN2.RST/HKP.05.00/2023.
文摘Agriculture 5.0 is an emerging concept where sensors,big data,Internet-of-Things(IoT),robots,and Artificial In-telligence(AI)are used for agricultural purposes.Different from Agriculture 4.0,robots and AI become the focus of the implementation in Agriculture 5.0.One of the applications of Agriculture 5.0 is weed management where robots are used to discriminate weeds from the crops or plants so that proper action can be performed to remove the weeds.This paper discusses an in-depth review of Machine Learning(ML)techniques used for discriminating weeds from crops or plants.We specifically present a detailed explanation of five steps required in using ML algorithms to distinguish between weeds and plants.
文摘Compressors play an important role in day-to-day operation in most oil and gas platforms,especially in the case for maintaining gas pressure in transportation pipe.Its complex problem to detect the sensors health and abnormality as the sensor reading would reflect the various states of the compressor.In ideal situation,sensor readings offer vast amounts of information on compressor health and could possibly indicate early fault of machines.Furthermore,due to harsh site and process operating conditions,sensors are often found to have drifted or failed,and there is no standard methodology to predict abnormality apart from applying emerging industrial smart sensor technologies.In this paper,we investigate a minimalist approach for detecting abnormality of compressor's shaft's RPM sensor.As the sensors in the compressor are correlated,we first use the outputs of other sensors to predict the shaft's RPM using regression-based models(neural networks and multiple linear regression).Second,we calculate the histogram of residuals by taking the difference between the predicted sensor value and the actual sensor value plus the abnormality in terms of bias/miscalibration and noise.The histogram of residuals can be used for sensor abnormality monitoring.In general,sensor states can be monitored by observing the shifting of the mean in the histogram of residuals.The sensor readings contaminated with noise can be seen by a shifted mean whose value is between the normal condition mean and the biased condition mean.This method is compact and would be relevant to monitor irregularity of the sensors.
文摘Compatmental pandemic models have become a significant tool in the battle against disease outbreaks.Despite this,pandemic models sometimes require extensive modification to accuately reflect the actual epidemic condition The Susceptble-Infectious-Removed(SIR)model,in particular,contains two primary parameters:the infectious rate parameter ρ and the removal rate parameter r,in addition to additional unknowns such as the initial infectious population.Adding to the complexity,there is an obvious challenge to tack the evolution of these parameters,especially ρ and γ,over time which leads to the estimation of the reproduction number for the paticular time window,^(κ)T.This reproduction mumber may provide better understanding on the efectiveness of isolation or control measures.The changing ^(κ)T values(evolving over time window)will lead to even more possible parameter scenanios.Given the present Coronavirus Disease 2019(COVID-19)pandemic,a stochastic optimization stategy is proposed to ft the model on the basis of parameter changes over timne.Solutions are encoded to reflet the changing parameters of Tρ and γT,alwing the changing ^(κ)T to be estimated.In our approach,an Adaptive Differential Evolution(ADE)and Paricle Swam Optimization(PSO)are used to ft the curves into previously recorded data.ADE eliminates the need to tume the parameters of the Differential Evolution(DE)to balance the exploitation and exploration in the solution space.Results show that the proposed optimized model can generally ft the cuves well albeit high variance in the solutions.
