The types of myocardial ischemia can be revealed by electrocardiographic(ECG)ST segment.Effective mea-surement and electrocardiographic analysis of ST as well as calculation of displacement and shape change of ST segm...The types of myocardial ischemia can be revealed by electrocardiographic(ECG)ST segment.Effective mea-surement and electrocardiographic analysis of ST as well as calculation of displacement and shape change of ST segment can help doctors diagnose coronary heart disease and myocar-dial ischemia,especially for asymptomatic myocardial isch-emia.Therefore,it is a very important subject in clinical practice to measure and classify the ECG ST segment.In this paper,we introduce a computerized automatic identification method of the electrocardiographic ST segment shape with radial basis function neural network based on adaptive fuzzy system,which has a better effect than other methods.It helps to analyze the reason of the ST segment change and confirm the position of myocardial ischemia,and is useful for doctor diagnosis.展开更多
Particles suspended in air are often non-spherical shapes, giving rise to shape-dependent complex dynamical processes. Suspended non-spherical particles are associated with a wide array of engineering and scientific s...Particles suspended in air are often non-spherical shapes, giving rise to shape-dependent complex dynamical processes. Suspended non-spherical particles are associated with a wide array of engineering and scientific scenarios, embodying both their microscopic and macroscopic dynamical behaviors. A comprehensive understanding of the dynamical behaviors of non-spherical particles in air hinges on the accurate identification and description of particle shape, the development of shape-specific models for the forces and torques acting on these particles, and the subsequent micro- and macroscopic phenomena that emerge as a result. This review surveys the latest advancements in the field of non-spherical particles, spanning from shape identification to the characterization of their dynamical properties. An emphasis is placed on establishing a connection between the micro- and macroscopic dynamical behaviors of non-spherical particles. The shape-induced features encompass periodic rotation and preferential orientation, which result in an oscillating migration path and lead to distinctive macroscopic characteristics. The macroscopic features of non-spherical particles are elucidated based on the preceding analysis of forces, torques, and particle-flow interactions. The future perspectives are also discussed in this review.展开更多
Indicator diagram plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in shape iden...Indicator diagram plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in shape identification. This paper illuminates ANN realization in identifying fault kinds of indicator diagrams, including a back-propagation algorithm, characteristics of the indicator diagram and some examples. It is concluded that the buildup of a neural network and the abstract of indicator diagrams are important to successful application.展开更多
It is widely accepted that urban plant leaves can capture airborne particles. Previous studies on the particle capture capacity of plant leaves have mostly focused on particle mass and/or size distribution. Fewer stud...It is widely accepted that urban plant leaves can capture airborne particles. Previous studies on the particle capture capacity of plant leaves have mostly focused on particle mass and/or size distribution. Fewer studies, however, have examined the particle density, and the size and shape characteristics of particles, which may have important implications for evaluating the particle capture efficiency of plants, and identifying the particle sources. In addition, the role of different vegetation types is as yet unclear. Here, we chose three species of different vegetation types, and firstly applied an object-based classification approach to automatically identify the particles from scanning electron microscope（SEM）micrographs. We then quantified the particle capture efficiency, and the major sources of particles were identified. We found（1） Rosa xanthina Lindl（shrub species） had greater retention efficiency than Broussonetia papyrifera（broadleaf species） and Pinus bungeana Zucc.（coniferous species）, in terms of particle number and particle area cover.（2） 97.9% of the identified particles had diameter ≤10 μm, and 67.1% of them had diameter ≤2.5 μm. 89.8% of the particles had smooth boundaries, with 23.4% of them being nearly spherical.（3） 32.4%–74.1% of the particles were generated from bare soil and construction activities, and 15.5%–23.0% were mainly from vehicle exhaust and cooking fumes.展开更多
文摘The types of myocardial ischemia can be revealed by electrocardiographic(ECG)ST segment.Effective mea-surement and electrocardiographic analysis of ST as well as calculation of displacement and shape change of ST segment can help doctors diagnose coronary heart disease and myocar-dial ischemia,especially for asymptomatic myocardial isch-emia.Therefore,it is a very important subject in clinical practice to measure and classify the ECG ST segment.In this paper,we introduce a computerized automatic identification method of the electrocardiographic ST segment shape with radial basis function neural network based on adaptive fuzzy system,which has a better effect than other methods.It helps to analyze the reason of the ST segment change and confirm the position of myocardial ischemia,and is useful for doctor diagnosis.
文摘Particles suspended in air are often non-spherical shapes, giving rise to shape-dependent complex dynamical processes. Suspended non-spherical particles are associated with a wide array of engineering and scientific scenarios, embodying both their microscopic and macroscopic dynamical behaviors. A comprehensive understanding of the dynamical behaviors of non-spherical particles in air hinges on the accurate identification and description of particle shape, the development of shape-specific models for the forces and torques acting on these particles, and the subsequent micro- and macroscopic phenomena that emerge as a result. This review surveys the latest advancements in the field of non-spherical particles, spanning from shape identification to the characterization of their dynamical properties. An emphasis is placed on establishing a connection between the micro- and macroscopic dynamical behaviors of non-spherical particles. The shape-induced features encompass periodic rotation and preferential orientation, which result in an oscillating migration path and lead to distinctive macroscopic characteristics. The macroscopic features of non-spherical particles are elucidated based on the preceding analysis of forces, torques, and particle-flow interactions. The future perspectives are also discussed in this review.
文摘Indicator diagram plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in shape identification. This paper illuminates ANN realization in identifying fault kinds of indicator diagrams, including a back-propagation algorithm, characteristics of the indicator diagram and some examples. It is concluded that the buildup of a neural network and the abstract of indicator diagrams are important to successful application.
基金supported by the “One-Hundred Talents” program of the Chinese Academy of Sciences (No. N234)the National Natural Science Foundation of China(Nos. 41430638 and 41301199)the project “Major Special Project-The China High-Resolution Earth Observation System”
文摘It is widely accepted that urban plant leaves can capture airborne particles. Previous studies on the particle capture capacity of plant leaves have mostly focused on particle mass and/or size distribution. Fewer studies, however, have examined the particle density, and the size and shape characteristics of particles, which may have important implications for evaluating the particle capture efficiency of plants, and identifying the particle sources. In addition, the role of different vegetation types is as yet unclear. Here, we chose three species of different vegetation types, and firstly applied an object-based classification approach to automatically identify the particles from scanning electron microscope（SEM）micrographs. We then quantified the particle capture efficiency, and the major sources of particles were identified. We found（1） Rosa xanthina Lindl（shrub species） had greater retention efficiency than Broussonetia papyrifera（broadleaf species） and Pinus bungeana Zucc.（coniferous species）, in terms of particle number and particle area cover.（2） 97.9% of the identified particles had diameter ≤10 μm, and 67.1% of them had diameter ≤2.5 μm. 89.8% of the particles had smooth boundaries, with 23.4% of them being nearly spherical.（3） 32.4%–74.1% of the particles were generated from bare soil and construction activities, and 15.5%–23.0% were mainly from vehicle exhaust and cooking fumes.
