A direct numerical modeling method for parachute is proposed firstly, and a model for the star-shaped folded parachute with detailed structures is established. The simplified arbitrary Lagrangian-Eulerian fluid struct...A direct numerical modeling method for parachute is proposed firstly, and a model for the star-shaped folded parachute with detailed structures is established. The simplified arbitrary Lagrangian-Eulerian fluid structure interaction （SALE/FSI） method is used to simulate the infla- tion process of a folded parachute, and the flow field calculation is mainly based on operator split- ting technique. By using this method, the dynamic variations of related parameters such as flow field and structure are obtained, and the load jump appearing at the end of initial inflation stage is cap- tured. Numerical results including opening load, drag characteristics, swinging angle, etc. are well consistent with wind tunnel tests. In addition, this coupled method can get more space-time detailed information such as geometry shape, structure, motion, and flow field. Compared with previous inflation time method, this method is a completely theoretical analysis approach without relying on empirical coefficients, which can provide a reference for material selection, performance optimi- zation during parachute design.展开更多
This paper presents an empirical likelihood estimation procedure for parameters of the discretely sampled process of Ornstein-Uhlenbeck type. The proposed procedure is based on the condi- tional characteristic functio...This paper presents an empirical likelihood estimation procedure for parameters of the discretely sampled process of Ornstein-Uhlenbeck type. The proposed procedure is based on the condi- tional characteristic function, and the maximum empirical likelihood estimator is proved to be consistent and asymptotically normal. Moreover, this estimator is shown to be asymptotically efficient under some mild conditions. When the background driving Lévy process is of type A or B, we show that the intensity parameter can be exactly recovered, and we study the maximum empirical likelihood estimator with the plug-in estimated intensity parameter. Testing procedures based on the empirical likelihood ratio statistic are developed for parameters and for estimating equations, respectively. Finally, Monte Carlo simulations are conducted to demonstrate the performance of proposed estimators.展开更多
The sour gas sweetening is one of the main processes in gas industries. Gas sweetening is done through chemical processes. Therefore, it requires high cost and energy. The results show that increasing the operating te...The sour gas sweetening is one of the main processes in gas industries. Gas sweetening is done through chemical processes. Therefore, it requires high cost and energy. The results show that increasing the operating temperature increases the mass transfer coefficient and increases the mass transfer rate. Theoretical and experimental data show that sulfur removal in 4.5 W magnetic field is desirable. The increase in sulfur removal percentage in the magnetic field of 4.5 W and 6.75 W is about 16.4% and 15.2%, respectively. According to the obtained results, the effect of temperature increase from 18.8°C to 23.4°C is more evident than the effect of temperature change from 23.4°C to 32.2°C. Because more thermal energy is needed to provide higher temperatures. Therefore, the temperature of 23.4°C is reported as the optimal temperature. The results of this research show that the percentage of sulfur removal is also high at this temperature.展开更多
Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generali...Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem thus;the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed.In contrast,this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders(KDNN-SAE)that computes the disease before the exact heart rate by combining features from multiple ECG Signals.Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.This work contained Training and testing stages,in the preparation part at first the Adaptive Filter Enthalpy-based Empirical Mode Decomposition(EMD)is utilized to eliminate the motion artifact in the signal.At that point,the robotic process automation(RPA)learning part extracts the effective features are extracted,and normalized the value of the feature then estimated utilizing the RPA loss function.At last KDNN-SAE prepared training for the data stored in the dataset.In the subsequent stage,input signal compute motion artifact and RPA Learning the evaluation part determines the detection of Heartbeat.So early diagnosis of heart failures is an essential factor.The results of the experiments show that our proposed method has a high score outcome of 0.9997.Comparable to the CIF,which reaches 0.9990.The CNN and Artificial Neural Network(ANN)had less score 0.95115 and 0.90147.展开更多
In this paper, we discuss the problem of testing the hypothesis that the underlying regression isa partial linear model. A test statistic, which is based on the quadratic form of a cusum process of residuals,is propos...In this paper, we discuss the problem of testing the hypothesis that the underlying regression isa partial linear model. A test statistic, which is based on the quadratic form of a cusum process of residuals,is proposed. The asymptotic distributions of the test statistic under null hypothesis and the local alternativehypothesis are given. The number simulation shows that the test is available.展开更多
基金co-supported by the National Natural Science Foundation of China (No. 11172137)the Aeronautical Science Foundation of China (No. 20122910001)
文摘A direct numerical modeling method for parachute is proposed firstly, and a model for the star-shaped folded parachute with detailed structures is established. The simplified arbitrary Lagrangian-Eulerian fluid structure interaction （SALE/FSI） method is used to simulate the infla- tion process of a folded parachute, and the flow field calculation is mainly based on operator split- ting technique. By using this method, the dynamic variations of related parameters such as flow field and structure are obtained, and the load jump appearing at the end of initial inflation stage is cap- tured. Numerical results including opening load, drag characteristics, swinging angle, etc. are well consistent with wind tunnel tests. In addition, this coupled method can get more space-time detailed information such as geometry shape, structure, motion, and flow field. Compared with previous inflation time method, this method is a completely theoretical analysis approach without relying on empirical coefficients, which can provide a reference for material selection, performance optimi- zation during parachute design.
基金supported by National Natural Science Foundation of China (Grant No. 10671037)the Science Foundation of Shanghai Educational Department (Grant No. 06FZ035)
文摘This paper presents an empirical likelihood estimation procedure for parameters of the discretely sampled process of Ornstein-Uhlenbeck type. The proposed procedure is based on the condi- tional characteristic function, and the maximum empirical likelihood estimator is proved to be consistent and asymptotically normal. Moreover, this estimator is shown to be asymptotically efficient under some mild conditions. When the background driving Lévy process is of type A or B, we show that the intensity parameter can be exactly recovered, and we study the maximum empirical likelihood estimator with the plug-in estimated intensity parameter. Testing procedures based on the empirical likelihood ratio statistic are developed for parameters and for estimating equations, respectively. Finally, Monte Carlo simulations are conducted to demonstrate the performance of proposed estimators.
文摘The sour gas sweetening is one of the main processes in gas industries. Gas sweetening is done through chemical processes. Therefore, it requires high cost and energy. The results show that increasing the operating temperature increases the mass transfer coefficient and increases the mass transfer rate. Theoretical and experimental data show that sulfur removal in 4.5 W magnetic field is desirable. The increase in sulfur removal percentage in the magnetic field of 4.5 W and 6.75 W is about 16.4% and 15.2%, respectively. According to the obtained results, the effect of temperature increase from 18.8°C to 23.4°C is more evident than the effect of temperature change from 23.4°C to 32.2°C. Because more thermal energy is needed to provide higher temperatures. Therefore, the temperature of 23.4°C is reported as the optimal temperature. The results of this research show that the percentage of sulfur removal is also high at this temperature.
文摘Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem thus;the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed.In contrast,this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders(KDNN-SAE)that computes the disease before the exact heart rate by combining features from multiple ECG Signals.Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.This work contained Training and testing stages,in the preparation part at first the Adaptive Filter Enthalpy-based Empirical Mode Decomposition(EMD)is utilized to eliminate the motion artifact in the signal.At that point,the robotic process automation(RPA)learning part extracts the effective features are extracted,and normalized the value of the feature then estimated utilizing the RPA loss function.At last KDNN-SAE prepared training for the data stored in the dataset.In the subsequent stage,input signal compute motion artifact and RPA Learning the evaluation part determines the detection of Heartbeat.So early diagnosis of heart failures is an essential factor.The results of the experiments show that our proposed method has a high score outcome of 0.9997.Comparable to the CIF,which reaches 0.9990.The CNN and Artificial Neural Network(ANN)had less score 0.95115 and 0.90147.
基金This work was partly supported by the Knowledge Innovation Project of ChineseAcademy of Sciences (Grant No. KZCX2-SW-118) and the National Natural Science Foundation of China (Grant No. 19791093).
文摘In this paper, we discuss the problem of testing the hypothesis that the underlying regression isa partial linear model. A test statistic, which is based on the quadratic form of a cusum process of residuals,is proposed. The asymptotic distributions of the test statistic under null hypothesis and the local alternativehypothesis are given. The number simulation shows that the test is available.
