In this paper,a cooperative localization algorithm for autonomous underwater vehicles(AUVs)is proposed.A"parallel"model is adopted to describe the cooperative localization problem instead of the traditional&...In this paper,a cooperative localization algorithm for autonomous underwater vehicles(AUVs)is proposed.A"parallel"model is adopted to describe the cooperative localization problem instead of the traditional"leader-follower"model,and a linear programming associated with convex optimization method is used to deal with the problem.After an unknown-but-bounded model for sensor noise is assumed,bearing and range measurements can be modeled as linear constraints on the configuration space of the AUVs.Merging these constraints induces a convex polyhedron representing the set of all configurations consistent with the sensor measurements.Estimates for the uncertainty in the position of a single AUV or the relative positions of two or more nodes can then be obtained by projecting this polyhedron onto appropriate subspaces of the configuration space.Two different optimization algorithms are given to recover the uncertainty region according to the number of the AUVs.Simulation results are presented for a typical localization example of the AUV formation.The results show that our positioning method offers a good localization accuracy,although a small number of low-cost sensors are needed for each vehicle,and this validates that it is an economical and practical positioning approach compared with the traditional approach.展开更多
Diabetes is a highly prevalent disease that was initially simplified into three major types:Type 1,type 2 and gestational diabetes.With the global rise in incidence of acute pancreatitis(AP),a lesser-known type of dia...Diabetes is a highly prevalent disease that was initially simplified into three major types:Type 1,type 2 and gestational diabetes.With the global rise in incidence of acute pancreatitis(AP),a lesser-known type of diabetes referred to as diabetes of the exocrine pancreas(DEP)is becoming more recognized.However,there is a poor understanding of the inherent relationship between diabetes and AP.There is established data about certain diseases affecting the exocrine function of the pancreas which can lead to diabetes.More specifically,there are well established guidelines for diagnosis and management of DEP caused be chronic pancreatitis.Conversely,the sequelae of AP leading to diabetes has limited recognition and data.The purpose of this review is to provide a comprehensive summary of the prevalence,epidemiology,pathophysiology and future research aims of APrelated diabetes.In addition,we propose a screening and diagnostic algorithm to aid clinicians in providing better care for their patients.展开更多
CO_(2)huff and puff technology can enhance the recovery of heavy oil in high-water-cut stages.However,the effectiveness of this method varies significantly under different geological and fluid conditions,which leads t...CO_(2)huff and puff technology can enhance the recovery of heavy oil in high-water-cut stages.However,the effectiveness of this method varies significantly under different geological and fluid conditions,which leads to a high-dimensional and small-sample(HDSS)dataset.It is difficult for conventional techniques that identify key factors that influence CO_(2)huff and puff effects,such as fuzzy mathematics,to manage HDSS datasets,which often contain nonlinear and irremovable abnormal data.To accurately pinpoint the primary control factors for heavy oil CO_(2)huff and puff,four machine learning classification algorithms were adopted.These algorithms were selected to align with the characteristics of HDSS datasets,taking into account algorithmic principles and an analysis of key control factors.The results demonstrated that logistic regression encounters difficulties when dealing with nonlinear data,whereas the extreme gradient boosting and gradient boosting decision tree algorithms exhibit greater sensitivity to abnormal data.By contrast,the random forest algorithm proved to be insensitive to outliers and provided a reliable ranking of factors that influence CO_(2)huff and puff effects.The top five control factors identified were the distance between parallel wells,cumulative gas injection volume,liquid production rate of parallel wells,huff and puff timing,and heterogeneous Lorentz coefficient.These research find-ings not only contribute to the precise implementation of heavy oil CO_(2)huff and puff but also offer valuable insights into selecting classification algorithms for typical HDSS data.展开更多
基金Supported by National High Technology Research and Development Program of China(863 Program)(2007AA809502C)National Natural Science Foundation of China(50979093)Program for New Century Excellent Talents in University(NCET-06-0877)
文摘In this paper,a cooperative localization algorithm for autonomous underwater vehicles(AUVs)is proposed.A"parallel"model is adopted to describe the cooperative localization problem instead of the traditional"leader-follower"model,and a linear programming associated with convex optimization method is used to deal with the problem.After an unknown-but-bounded model for sensor noise is assumed,bearing and range measurements can be modeled as linear constraints on the configuration space of the AUVs.Merging these constraints induces a convex polyhedron representing the set of all configurations consistent with the sensor measurements.Estimates for the uncertainty in the position of a single AUV or the relative positions of two or more nodes can then be obtained by projecting this polyhedron onto appropriate subspaces of the configuration space.Two different optimization algorithms are given to recover the uncertainty region according to the number of the AUVs.Simulation results are presented for a typical localization example of the AUV formation.The results show that our positioning method offers a good localization accuracy,although a small number of low-cost sensors are needed for each vehicle,and this validates that it is an economical and practical positioning approach compared with the traditional approach.
文摘Diabetes is a highly prevalent disease that was initially simplified into three major types:Type 1,type 2 and gestational diabetes.With the global rise in incidence of acute pancreatitis(AP),a lesser-known type of diabetes referred to as diabetes of the exocrine pancreas(DEP)is becoming more recognized.However,there is a poor understanding of the inherent relationship between diabetes and AP.There is established data about certain diseases affecting the exocrine function of the pancreas which can lead to diabetes.More specifically,there are well established guidelines for diagnosis and management of DEP caused be chronic pancreatitis.Conversely,the sequelae of AP leading to diabetes has limited recognition and data.The purpose of this review is to provide a comprehensive summary of the prevalence,epidemiology,pathophysiology and future research aims of APrelated diabetes.In addition,we propose a screening and diagnostic algorithm to aid clinicians in providing better care for their patients.
基金supported by the Science Foundation of China University of Petroleum(2462019YJRC013).
文摘CO_(2)huff and puff technology can enhance the recovery of heavy oil in high-water-cut stages.However,the effectiveness of this method varies significantly under different geological and fluid conditions,which leads to a high-dimensional and small-sample(HDSS)dataset.It is difficult for conventional techniques that identify key factors that influence CO_(2)huff and puff effects,such as fuzzy mathematics,to manage HDSS datasets,which often contain nonlinear and irremovable abnormal data.To accurately pinpoint the primary control factors for heavy oil CO_(2)huff and puff,four machine learning classification algorithms were adopted.These algorithms were selected to align with the characteristics of HDSS datasets,taking into account algorithmic principles and an analysis of key control factors.The results demonstrated that logistic regression encounters difficulties when dealing with nonlinear data,whereas the extreme gradient boosting and gradient boosting decision tree algorithms exhibit greater sensitivity to abnormal data.By contrast,the random forest algorithm proved to be insensitive to outliers and provided a reliable ranking of factors that influence CO_(2)huff and puff effects.The top five control factors identified were the distance between parallel wells,cumulative gas injection volume,liquid production rate of parallel wells,huff and puff timing,and heterogeneous Lorentz coefficient.These research find-ings not only contribute to the precise implementation of heavy oil CO_(2)huff and puff but also offer valuable insights into selecting classification algorithms for typical HDSS data.
