Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples acco...Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.展开更多
Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as re...Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may occur.Therefore,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is required.In this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data.Furthermore,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them.The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs.In the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.展开更多
Tyre Pressure Monitoring Systems(TPMS)are installed in automobiles to monitor the pressure of the tyres.Tyre pressure is an important parameter for the comfort of the travelers and the safety of the passengers.Many me...Tyre Pressure Monitoring Systems(TPMS)are installed in automobiles to monitor the pressure of the tyres.Tyre pressure is an important parameter for the comfort of the travelers and the safety of the passengers.Many methods have been researched and reported for TPMS.Amongst them,vibration-based indirect TPMS using machine learning techniques are the recent ones.The literature reported the results for a perfectly balanced wheel.However,if there is a small unbalance,which is very common in automobile wheels,‘What will be the effect on the classification accuracy?’is the question on hand.This paper attempts to study the effect of unbalance of the wheel on the classification accuracy of an indirect TPMS system.The tyres filled with air are considered with different pressure values to represent puncture,normal,under pressure and overpressure conditions.The vibration signals of each condition were acquired and processed using machine learning techniques.The procedure is carried out with perfectly balanced wheels and known unbalanced wheels.The results are compared and presented.展开更多
Tyre Pressure Monitoring Systems(TPMS)are installed in automobiles to monitor the pressure of the tyres.Tyre pressure is an important parameter for the comfort of the travelers and the safety of the passengers.Many me...Tyre Pressure Monitoring Systems(TPMS)are installed in automobiles to monitor the pressure of the tyres.Tyre pressure is an important parameter for the comfort of the travelers and the safety of the passengers.Many methods have been researched and reported for TPMS.Amongst them,vibration-based indirect TPMS using machine learning techniques are the recent ones.The literature reported the results for a perfectly balanced wheel.However,if there is a small unbalance,which is very common in automobile wheels,‘What will be the effect on the classification accuracy?’is the question on hand.This paper attempts to study the effect of unbalance of the wheel on the classification accuracy of an indirect TPMS system.The tyres filled with air are considered with different pressure values to represent puncture,normal,under pressure and overpressure conditions.The vibration signals of each condition were acquired and processed using machine learning techniques.The procedure is carried out with perfectly balanced wheels and known unbalanced wheels.The results are compared and presented.展开更多
基金Supported by the National High Technology Research and Development Program of China (2006AA040309)National BasicResearch Program of China (2007CB714000)
文摘Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.
基金This work was supported by a research grant from Seoul Women’s University(2023-0183).
文摘Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream.However,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may occur.Therefore,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is required.In this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data.Furthermore,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them.The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs.In the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.
文摘Tyre Pressure Monitoring Systems(TPMS)are installed in automobiles to monitor the pressure of the tyres.Tyre pressure is an important parameter for the comfort of the travelers and the safety of the passengers.Many methods have been researched and reported for TPMS.Amongst them,vibration-based indirect TPMS using machine learning techniques are the recent ones.The literature reported the results for a perfectly balanced wheel.However,if there is a small unbalance,which is very common in automobile wheels,‘What will be the effect on the classification accuracy?’is the question on hand.This paper attempts to study the effect of unbalance of the wheel on the classification accuracy of an indirect TPMS system.The tyres filled with air are considered with different pressure values to represent puncture,normal,under pressure and overpressure conditions.The vibration signals of each condition were acquired and processed using machine learning techniques.The procedure is carried out with perfectly balanced wheels and known unbalanced wheels.The results are compared and presented.
文摘Tyre Pressure Monitoring Systems(TPMS)are installed in automobiles to monitor the pressure of the tyres.Tyre pressure is an important parameter for the comfort of the travelers and the safety of the passengers.Many methods have been researched and reported for TPMS.Amongst them,vibration-based indirect TPMS using machine learning techniques are the recent ones.The literature reported the results for a perfectly balanced wheel.However,if there is a small unbalance,which is very common in automobile wheels,‘What will be the effect on the classification accuracy?’is the question on hand.This paper attempts to study the effect of unbalance of the wheel on the classification accuracy of an indirect TPMS system.The tyres filled with air are considered with different pressure values to represent puncture,normal,under pressure and overpressure conditions.The vibration signals of each condition were acquired and processed using machine learning techniques.The procedure is carried out with perfectly balanced wheels and known unbalanced wheels.The results are compared and presented.
