In order to reveal the rapid increase mechanism of particulate concentration in short time,a notion of short-term cumulative effect of air particulate is defined as the significant increase of pollutant concentration ...In order to reveal the rapid increase mechanism of particulate concentration in short time,a notion of short-term cumulative effect of air particulate is defined as the significant increase of pollutant concentration in a short time under the condition of breeze,stable weather and constant emission caused by human being's activities. Subsequently,an index of short-term cumulative effect is established with air diffusive equation,and then the macro-scale meteorological situation and micro-scale factors of forming the short-term cumulative effect are discussed with the observation data. The macro-scale meteorological variables contain upper-level weather situations and surface weather situations. The micro-scale factors mainly include the boundary-layer height and boundary-layer stability. The analyses show that boundary-layer factors and weather variables have a significant influence on the short-term cumulative effect. The notion of short-term cumulative effect will play an important role in interpreting the severe pollution weather.展开更多
A new short-term warning and integrity monitoring algorithm was proposed for coal mine shaft safety. The Kalman filter (KF) model was used to extract real global positioning system (GPS) kinematic deformation informat...A new short-term warning and integrity monitoring algorithm was proposed for coal mine shaft safety. The Kalman filter (KF) model was used to extract real global positioning system (GPS) kinematic deformation information. The short-term warning model was built by using the two-side cumulative sum (CUSUM) test, which further improves the warning system reliability. Availability (the minimum warning deformation, MWD), false alarm rate (the average run length, ARL), missed rate (the warning delay, WD) and the relationships among them were analyzed and the method choosing warning parameters is given. A test of a deformation simulation platform shows that the warning algorithm can be effectively used for steep deformation warning. A field experiment of the Malan mine shaft in Shanxi coal area illustrates that the proposed algorithm can detect small dynamic changes and the corresponding occurring time. At given warning thresholds (MWD is 15 mm and ARL is 1000),the detected deformations of two consecutive days’ deformation sequences with the algorithm occur at the 705th epoch (705 s) and the 517th epoch (517 s), respectively.展开更多
Parkinson's disease(PD)is a widespread neurodegenerative condition that affects many individuals annually.Early identification and monitoring of disease progression are crucial to effectively managing symptoms and...Parkinson's disease(PD)is a widespread neurodegenerative condition that affects many individuals annually.Early identification and monitoring of disease progression are crucial to effectively managing symptoms and preventing motor complications.This research proposes an automated PD diagnosis and severity-grading model based on time-frequency and fuzzy features using improved uni-directional and bi-directional long short-term memory networks with sensitive hyperparameters optimization.We utilize vertical ground reaction force signals collected from Physionet's publicly available dataset recorded during regular and dual-task clinical trials of walking measurements.Only the cumulative signal of both feet was then utilized and segmented into 30-s windows without further pre-processing.Subsequently,we extracted only four key time-frequency and fuzzy features from each segment,effectively capturing the signal's inherent uncertainty.Bayesian optimization is employed in both detection and grading approaches to fine-tune the two critical hyperparameters:the initial learning rate and the number of hidden units in the network.The detection phase yields an exceptional accuracy of 99.19%,surpassing state-of-the-art studies with the same dataset.In the grading phase,classification based on the unified PD rating scale values achieves an accuracy of 92.28%.The proposed study delves into the potential of cumulative gait signals as a powerful diagnostic tool for PD,aiming to extract precise and intricate information by implementing straightforward and minimal processing endeavors.This method demonstrates significant effi-ciency in terms of complexity,cost,and energy consumption by utilizing a single-dimensional signal,eliminating the need for pre-processing steps,and limiting the features used for training.展开更多
基金Supported by the National Science Foundation of China(41675046)
文摘In order to reveal the rapid increase mechanism of particulate concentration in short time,a notion of short-term cumulative effect of air particulate is defined as the significant increase of pollutant concentration in a short time under the condition of breeze,stable weather and constant emission caused by human being's activities. Subsequently,an index of short-term cumulative effect is established with air diffusive equation,and then the macro-scale meteorological situation and micro-scale factors of forming the short-term cumulative effect are discussed with the observation data. The macro-scale meteorological variables contain upper-level weather situations and surface weather situations. The micro-scale factors mainly include the boundary-layer height and boundary-layer stability. The analyses show that boundary-layer factors and weather variables have a significant influence on the short-term cumulative effect. The notion of short-term cumulative effect will play an important role in interpreting the severe pollution weather.
基金Projects(2013RC16,2012LWB28)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(NCET-13-1019)supported by the Program for New Century Excellent Talents in University,China
文摘A new short-term warning and integrity monitoring algorithm was proposed for coal mine shaft safety. The Kalman filter (KF) model was used to extract real global positioning system (GPS) kinematic deformation information. The short-term warning model was built by using the two-side cumulative sum (CUSUM) test, which further improves the warning system reliability. Availability (the minimum warning deformation, MWD), false alarm rate (the average run length, ARL), missed rate (the warning delay, WD) and the relationships among them were analyzed and the method choosing warning parameters is given. A test of a deformation simulation platform shows that the warning algorithm can be effectively used for steep deformation warning. A field experiment of the Malan mine shaft in Shanxi coal area illustrates that the proposed algorithm can detect small dynamic changes and the corresponding occurring time. At given warning thresholds (MWD is 15 mm and ARL is 1000),the detected deformations of two consecutive days’ deformation sequences with the algorithm occur at the 705th epoch (705 s) and the 517th epoch (517 s), respectively.
文摘Parkinson's disease(PD)is a widespread neurodegenerative condition that affects many individuals annually.Early identification and monitoring of disease progression are crucial to effectively managing symptoms and preventing motor complications.This research proposes an automated PD diagnosis and severity-grading model based on time-frequency and fuzzy features using improved uni-directional and bi-directional long short-term memory networks with sensitive hyperparameters optimization.We utilize vertical ground reaction force signals collected from Physionet's publicly available dataset recorded during regular and dual-task clinical trials of walking measurements.Only the cumulative signal of both feet was then utilized and segmented into 30-s windows without further pre-processing.Subsequently,we extracted only four key time-frequency and fuzzy features from each segment,effectively capturing the signal's inherent uncertainty.Bayesian optimization is employed in both detection and grading approaches to fine-tune the two critical hyperparameters:the initial learning rate and the number of hidden units in the network.The detection phase yields an exceptional accuracy of 99.19%,surpassing state-of-the-art studies with the same dataset.In the grading phase,classification based on the unified PD rating scale values achieves an accuracy of 92.28%.The proposed study delves into the potential of cumulative gait signals as a powerful diagnostic tool for PD,aiming to extract precise and intricate information by implementing straightforward and minimal processing endeavors.This method demonstrates significant effi-ciency in terms of complexity,cost,and energy consumption by utilizing a single-dimensional signal,eliminating the need for pre-processing steps,and limiting the features used for training.
