During the whole service lifetime of aircraft structures with composite materials,impacts are inevitable and can usually cause severe but barely visible damages.Since the occurrences of impact are random and unpredict...During the whole service lifetime of aircraft structures with composite materials,impacts are inevitable and can usually cause severe but barely visible damages.Since the occurrences of impact are random and unpredictable,it is a hotspot direction to develop an online impact monitoring system that can meet strict limitations of aerospace applications including small size,light weight,and low power consumption.Piezoelectric(PZT)sensor,being able to generate impact response signals with no external power and cover a large-scale structure with only a small amount of them,is a promising choice.Meanwhile,for real systems,networks with multiple nodes are normally required to monitor large-scale structures in a global way to identify any impact localization confliction,yet the existing studies are mostly evaluated with single nodes instead of networks.Therefore,in this paper,based on a new low-power node designed,a Bluetooth-based digital impact monitoring PZT sensor network is proposed for the first time with its global confliction-solving impact localization method.Evaluations of the system as a network are researched and analyzed on a complex real aircraft wing box for a global confliction-solving impact localization,showing a satisfying high accuracy.展开更多
In this paper,we propose a new algorithm to extend support vector machine(SVM)for binary classification to multicategory classification.The proposed method is based on a sequential binary classification algorithm.We f...In this paper,we propose a new algorithm to extend support vector machine(SVM)for binary classification to multicategory classification.The proposed method is based on a sequential binary classification algorithm.We first classify a target class by excluding the possibility of labeling as any other classes using a forward step of sequential SVM;we then exclude the already classified classes and repeat the same procedure for the remaining classes in a backward step.The proposed algorithm relies on SVM for each binary classification and utilizes only feasible data in each step;therefore,the method guarantees convergence and entails light computational burden.We prove Fisher consistency of the proposed forward–backward SVM(FB-SVM)and obtain a stochastic bound for the predicted misclassification rate.We conduct extensive simulations and analyze real-world data to demonstrate the superior performance of FB-SVM,for example,FB-SVM achieves a classification accuracy much higher than the current standard for predicting conversion from mild cognitive impairment to Alzheimer’s disease.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51921003,51975292 and 52275153)the Outstanding Youth Foundation of Jiangsu Province of China(No.BK20211519)+2 种基金the Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures,China(Nanjing University of Aeronautics and Astronautics,No.MCMS-I-0521K01)the Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronautics,Chinathe Priority Academic Program Development of Jiangsu Higher Education Institutions,China。
文摘During the whole service lifetime of aircraft structures with composite materials,impacts are inevitable and can usually cause severe but barely visible damages.Since the occurrences of impact are random and unpredictable,it is a hotspot direction to develop an online impact monitoring system that can meet strict limitations of aerospace applications including small size,light weight,and low power consumption.Piezoelectric(PZT)sensor,being able to generate impact response signals with no external power and cover a large-scale structure with only a small amount of them,is a promising choice.Meanwhile,for real systems,networks with multiple nodes are normally required to monitor large-scale structures in a global way to identify any impact localization confliction,yet the existing studies are mostly evaluated with single nodes instead of networks.Therefore,in this paper,based on a new low-power node designed,a Bluetooth-based digital impact monitoring PZT sensor network is proposed for the first time with its global confliction-solving impact localization method.Evaluations of the system as a network are researched and analyzed on a complex real aircraft wing box for a global confliction-solving impact localization,showing a satisfying high accuracy.
基金This work is supported by NIH Grants R01GM124104,NS073671,NS082062,NUL1 RR025747Alzheimer’s Disease Neuroimaging Initiative(ADNI)(U01 AG024904,DOD ADNI,W81XWH-12-2-0012),and a pilot award from the Gillings Innovation Lab at the University of North Carolina.The authors acknowledge the investigators within the ADNI who contributed to the design and implementation of ADNI.
文摘In this paper,we propose a new algorithm to extend support vector machine(SVM)for binary classification to multicategory classification.The proposed method is based on a sequential binary classification algorithm.We first classify a target class by excluding the possibility of labeling as any other classes using a forward step of sequential SVM;we then exclude the already classified classes and repeat the same procedure for the remaining classes in a backward step.The proposed algorithm relies on SVM for each binary classification and utilizes only feasible data in each step;therefore,the method guarantees convergence and entails light computational burden.We prove Fisher consistency of the proposed forward–backward SVM(FB-SVM)and obtain a stochastic bound for the predicted misclassification rate.We conduct extensive simulations and analyze real-world data to demonstrate the superior performance of FB-SVM,for example,FB-SVM achieves a classification accuracy much higher than the current standard for predicting conversion from mild cognitive impairment to Alzheimer’s disease.
