The application of real-time three-dimensional echocardiography （RT 3DE） in the diagnosis of double orifice mitral valve （DOMV） was explored. Five cases of DOMV were examined by using 2-dimensional echocardiograp...The application of real-time three-dimensional echocardiography （RT 3DE） in the diagnosis of double orifice mitral valve （DOMV） was explored. Five cases of DOMV were examined by using 2-dimensional echocardiography （2DE） and RT 3DE. The spatial morphology of malformed mitral valve and its change in hemodynamics were observed. DOMV associated with partial atrioventricular septal defect was found in 3 cases （in which 2 cases had cleft mitral valve） and isolated DOMV in 2 cases; and moderate to severe mitral regurgitation was detected in 3 cases, and mild mitral regurgitation in 1, and no regurgitation in 1 case; 1 case had complicated rhumatic heart disease. Three cases were preoperatively discovered by 2DE, while 2 missed （1 case was discovered postoperatively）. Four cases were diagnosed by RT 3DE preoperatively, and 1 case was diagnosed postoperatively （not examined by RT 3DE preoperatively）. It was suggested that RT 3DE is a reliable technique in the diagnosis of DOMV; it permitted comprehensive and noninvasive assessment of mitral valve and may supplement 2D TTE in the assessment of DOMV.展开更多
μC/OS-Ⅱ is an open source real-time kernel adopting priority preemptive schedule strategy. Aiming at the problem of μC/OS-Ⅱ failing to support homology priority tasks scheduling, an approach for solution is propos...μC/OS-Ⅱ is an open source real-time kernel adopting priority preemptive schedule strategy. Aiming at the problem of μC/OS-Ⅱ failing to support homology priority tasks scheduling, an approach for solution is proposed. The basic idea is adding round-robin scheduling strategy in its original scheduler in order to schedule homology priority tasks through time slice roundrobin. Implementation approach is given in detail. Firstly, the Task Control Block （TCB） is extended. And then, a new priority index table is created, in which each index pointer points to a set of homology priority tasks. Eventually, on the basis of reconstructing μC/OS-Ⅱ real-time kernel, task scheduling module is rewritten. Otherwise, schedulability of homology task supported by modified kernel had been analyzed, and deadline formula of created homology tasks is given. By theoretical analysis and experiment verification, the modified kernel can support homology priority tasks scheduling, meanwhile, it also remains preemptive property of original μC/OS-Ⅱ.展开更多
In this paper, the normal approximation rate and the random weighting approximation rate of error distribution of the kernel estimator of conditional density function f(y|x) are studied. The results may be used to...In this paper, the normal approximation rate and the random weighting approximation rate of error distribution of the kernel estimator of conditional density function f(y|x) are studied. The results may be used to construct the confidence interval of f(y|x) .展开更多
Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors an...Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.展开更多
文摘The application of real-time three-dimensional echocardiography （RT 3DE） in the diagnosis of double orifice mitral valve （DOMV） was explored. Five cases of DOMV were examined by using 2-dimensional echocardiography （2DE） and RT 3DE. The spatial morphology of malformed mitral valve and its change in hemodynamics were observed. DOMV associated with partial atrioventricular septal defect was found in 3 cases （in which 2 cases had cleft mitral valve） and isolated DOMV in 2 cases; and moderate to severe mitral regurgitation was detected in 3 cases, and mild mitral regurgitation in 1, and no regurgitation in 1 case; 1 case had complicated rhumatic heart disease. Three cases were preoperatively discovered by 2DE, while 2 missed （1 case was discovered postoperatively）. Four cases were diagnosed by RT 3DE preoperatively, and 1 case was diagnosed postoperatively （not examined by RT 3DE preoperatively）. It was suggested that RT 3DE is a reliable technique in the diagnosis of DOMV; it permitted comprehensive and noninvasive assessment of mitral valve and may supplement 2D TTE in the assessment of DOMV.
基金Supported by the "Chunhui" Plan of Ministry of Education of China (Z2005-2-11013)
文摘μC/OS-Ⅱ is an open source real-time kernel adopting priority preemptive schedule strategy. Aiming at the problem of μC/OS-Ⅱ failing to support homology priority tasks scheduling, an approach for solution is proposed. The basic idea is adding round-robin scheduling strategy in its original scheduler in order to schedule homology priority tasks through time slice roundrobin. Implementation approach is given in detail. Firstly, the Task Control Block （TCB） is extended. And then, a new priority index table is created, in which each index pointer points to a set of homology priority tasks. Eventually, on the basis of reconstructing μC/OS-Ⅱ real-time kernel, task scheduling module is rewritten. Otherwise, schedulability of homology task supported by modified kernel had been analyzed, and deadline formula of created homology tasks is given. By theoretical analysis and experiment verification, the modified kernel can support homology priority tasks scheduling, meanwhile, it also remains preemptive property of original μC/OS-Ⅱ.
基金Supported by Natural Science Foundation of Beijing City and National Natural Science Foundation ofChina(2 2 30 4 1 0 0 1 30 1
文摘In this paper, the normal approximation rate and the random weighting approximation rate of error distribution of the kernel estimator of conditional density function f(y|x) are studied. The results may be used to construct the confidence interval of f(y|x) .
基金supported by the National Key R&D Program of China(No.2022YFA1005204l)。
文摘Silicone material extrusion(MEX)is widely used for processing liquids and pastes.Owing to the uneven linewidth and elastic extrusion deformation caused by material accumulation,products may exhibit geometric errors and performance defects,leading to a decline in product quality and affecting its service life.This study proposes a process parameter optimization method that considers the mechanical properties of printed specimens and production costs.To improve the quality of silicone printing samples and reduce production costs,three machine learning models,kernel extreme learning machine(KELM),support vector regression(SVR),and random forest(RF),were developed to predict these three factors.Training data were obtained through a complete factorial experiment.A new dataset is obtained using the Euclidean distance method,which assigns the elimination factor.It is trained with Bayesian optimization algorithms for parameter optimization,the new dataset is input into the improved double Gaussian extreme learning machine,and finally obtains the improved KELM model.The results showed improved prediction accuracy over SVR and RF.Furthermore,a multi-objective optimization framework was proposed by combining genetic algorithm technology with the improved KELM model.The effectiveness and reasonableness of the model algorithm were verified by comparing the optimized results with the experimental results.
