AIM： To figure out the contributed factors of the hospitalization expenses of senile cataract patients（HECP） and build up an area-specified senile cataract diagnosis related group（DRG） of Shanghai thereby formula...AIM： To figure out the contributed factors of the hospitalization expenses of senile cataract patients（HECP） and build up an area-specified senile cataract diagnosis related group（DRG） of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund.METHODS： The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector（E-CHAID） model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc.RESULTS： The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases.CONCLUSION： The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.展开更多
The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA...The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA)has been widely used to solve binary problems in the real world.The classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain work.However,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant attributes.Ignoring filtered features and applying expertise may not produce an optimal classification result.To overcome these limitations,this study models feature selection and signal categorization into feature grouping problems.This study hybridizes Grouping Genetic Algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search process.The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically.In this study,the data coding and operators of GGA are redefined for grouping tasks.The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.展开更多
It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optima...It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.展开更多
In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Mo...In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.展开更多
Convolutional neural networks （CNNs） have been applied in state-of-the-art visual tracking tasks to represent the target. However, most existing algorithms treat visual tracking as an object-specific task. Therefore...Convolutional neural networks （CNNs） have been applied in state-of-the-art visual tracking tasks to represent the target. However, most existing algorithms treat visual tracking as an object-specific task. Therefore, the model needs to be retrained for different test video sequences. We propose a branch-activated multi-domain convolutional neural network （BAMDCNN）. In contrast to most existing trackers based on CNNs which require frequent online training, BAMDCNN only needs offine training and online fine-tuning. Specifically, BAMDCNN exploits category-specific features that are more robust against variations. To allow for learning category-specific information, we introduce a group algorithm and a branch activation method. Experimental results on challenging benchmark show that the proposed algorithm outperforms other state-of-the-art methods. What＇s more, compared with CNN based trackers, BAMDCNN increases tracking speed.展开更多
基金Supported by the Key Research and Development Program of Hunan Province(No.2017SK2011)
文摘AIM： To figure out the contributed factors of the hospitalization expenses of senile cataract patients（HECP） and build up an area-specified senile cataract diagnosis related group（DRG） of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund.METHODS： The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector（E-CHAID） model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc.RESULTS： The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases.CONCLUSION： The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.
基金NSFC http://www.nsfc.gov.cn/for the support through Grants No.61877045Fundamental Research Project of Shenzhen Science and Technology Program for the support through Grants No.JCYJ2016042815-3956266.
文摘The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA)has been widely used to solve binary problems in the real world.The classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain work.However,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant attributes.Ignoring filtered features and applying expertise may not produce an optimal classification result.To overcome these limitations,this study models feature selection and signal categorization into feature grouping problems.This study hybridizes Grouping Genetic Algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search process.The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically.In this study,the data coding and operators of GGA are redefined for grouping tasks.The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.
基金supported by the National Natural Science Foundation of China(6107901361079014+4 种基金61403198)the National Natural Science Funds and Civil Aviaiton Mutual Funds(U1533128U1233114)the Programs of Natural Science Foundation of China and China Civil Aviation Joint Fund(60939003)the Natural Science Foundation of Jiangsu Province in China(BK2011737)
文摘It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.
文摘In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.
基金the Innovation Action Plan Foundation of Shanghai(No.16511101200)
文摘Convolutional neural networks （CNNs） have been applied in state-of-the-art visual tracking tasks to represent the target. However, most existing algorithms treat visual tracking as an object-specific task. Therefore, the model needs to be retrained for different test video sequences. We propose a branch-activated multi-domain convolutional neural network （BAMDCNN）. In contrast to most existing trackers based on CNNs which require frequent online training, BAMDCNN only needs offine training and online fine-tuning. Specifically, BAMDCNN exploits category-specific features that are more robust against variations. To allow for learning category-specific information, we introduce a group algorithm and a branch activation method. Experimental results on challenging benchmark show that the proposed algorithm outperforms other state-of-the-art methods. What＇s more, compared with CNN based trackers, BAMDCNN increases tracking speed.
