Considering the effects of aging on the existing Inner Drift Chamber （IDC） of BESIII, a GEM-based inner tracker, the Cylindrical-GEM Inner Tracker （CGEM-IT）, is proposed to be designed and constructed as an upgrad...Considering the effects of aging on the existing Inner Drift Chamber （IDC） of BESIII, a GEM-based inner tracker, the Cylindrical-GEM Inner Tracker （CGEM-IT）, is proposed to be designed and constructed as an upgrade candidate for the IDC. This paper introduces a full simulation package for the CGEM-IT with a simplified digitization model, and describes the development of software for cluster reconstruction and track fitting, using a track fitting algorithm based on the Kalman filter method. Preliminary results for the reconstruction algorithms which are obtained using a Monte Carlo sample of single muon events in the CGEM-IT, show that the CGEM-IT has comparable momentum resolution and transverse vertex resolution to the IDC, and a better z-direction resolution than the IDC.展开更多
Purpose-Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research.In order to determine the evolving disease on the patient and ...Purpose-Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research.In order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing methodologies.Hence deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation cost.Design/methodology/approach-Multifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the architecture.Classifier is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new variant.The performance of disease progression learning progress utilizes the precision of the constituent classifiers,which usually has larger generalization benefits than those optimized classifiers.Findings-Deep learning architecture uses weight function,bias function on input layers and max pooling.Outcome of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease,and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected network.Experimental results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and accuracy.Originality/value-Proposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient trajectory.Then,the generative output layer of the proposed model produces the progression outcome of the disease of the particular patient trajectory.The model tries to produce the accurate prognosis outcomes by employing data conditional probability function.The originality of the work defines 70%and comparisons of the previous methods the method of values are accurate and increased analysis of the predictions.展开更多
基金Supported by National Key Basic Research Program of China(2015CB856700)National Natural Science Foundation of China(11205184,11205182)Joint Funds of National Natural Science Foundation of China(U1232201)
文摘Considering the effects of aging on the existing Inner Drift Chamber （IDC） of BESIII, a GEM-based inner tracker, the Cylindrical-GEM Inner Tracker （CGEM-IT）, is proposed to be designed and constructed as an upgrade candidate for the IDC. This paper introduces a full simulation package for the CGEM-IT with a simplified digitization model, and describes the development of software for cluster reconstruction and track fitting, using a track fitting algorithm based on the Kalman filter method. Preliminary results for the reconstruction algorithms which are obtained using a Monte Carlo sample of single muon events in the CGEM-IT, show that the CGEM-IT has comparable momentum resolution and transverse vertex resolution to the IDC, and a better z-direction resolution than the IDC.
文摘Purpose-Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research.In order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing methodologies.Hence deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation cost.Design/methodology/approach-Multifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the architecture.Classifier is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new variant.The performance of disease progression learning progress utilizes the precision of the constituent classifiers,which usually has larger generalization benefits than those optimized classifiers.Findings-Deep learning architecture uses weight function,bias function on input layers and max pooling.Outcome of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease,and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected network.Experimental results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and accuracy.Originality/value-Proposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient trajectory.Then,the generative output layer of the proposed model produces the progression outcome of the disease of the particular patient trajectory.The model tries to produce the accurate prognosis outcomes by employing data conditional probability function.The originality of the work defines 70%and comparisons of the previous methods the method of values are accurate and increased analysis of the predictions.
