The compressed sensing (CS) of acceleration data has been drawing increasing attention in gait telemonitoring application. In such application, there still exist some challenging issues including high energy consumpti...The compressed sensing (CS) of acceleration data has been drawing increasing attention in gait telemonitoring application. In such application, there still exist some challenging issues including high energy consumption of body-worn device for acceleration data acquisition and the poor reconstruction performance due to nonsparsity of acceleration data. Thus, the novel scheme of compressive sensing of acceleration data is needed urgently for solutions that are found to these issues.展开更多
A motion information analysis system based on the acceleration data is proposed in this paper,consisting of filtering,feature extraction and classification.The Kalman filter is adopted to eliminate the noise.With the ...A motion information analysis system based on the acceleration data is proposed in this paper,consisting of filtering,feature extraction and classification.The Kalman filter is adopted to eliminate the noise.With the time-domain and frequency-domain analysis,acceleration features like the amplitude,the period and the acceleration region values are obtained.Furthermore,the accuracy of the motion classification is improved by using the k-nearest neighbor （KNN） algorithm.展开更多
Artificial intelligence(AI)has become an increasingly important propellant for energy materials and energy chemistry research,such as accelerating advanced energy materials discovery[1],analyzing vast amounts of data ...Artificial intelligence(AI)has become an increasingly important propellant for energy materials and energy chemistry research,such as accelerating advanced energy materials discovery[1],analyzing vast amounts of data from both experiments and computations[2],process optimization for materials syntheses,management and monitoring of energy storage devices such as lithium batteries,and algorithm-optimized grid load forecasting.Looking back at recent pioneering works of AI-driven energy chemistry research,constructing a dataset with both large quantity and high quality is almost the first step and largely determines the following success of training AI models and figuring out corresponding scientific issues.展开更多
文摘The compressed sensing (CS) of acceleration data has been drawing increasing attention in gait telemonitoring application. In such application, there still exist some challenging issues including high energy consumption of body-worn device for acceleration data acquisition and the poor reconstruction performance due to nonsparsity of acceleration data. Thus, the novel scheme of compressive sensing of acceleration data is needed urgently for solutions that are found to these issues.
基金supported by the In-shoe Triaxial Pressure Measurement (Grant No.07DZ12077)and the Shanghai Innovation Project
文摘A motion information analysis system based on the acceleration data is proposed in this paper,consisting of filtering,feature extraction and classification.The Kalman filter is adopted to eliminate the noise.With the time-domain and frequency-domain analysis,acceleration features like the amplitude,the period and the acceleration region values are obtained.Furthermore,the accuracy of the motion classification is improved by using the k-nearest neighbor （KNN） algorithm.
基金supported by the National Key Research and Development Program of China(2021YFB2500300)the National Natural Science Foundation of China(T2322015,92472101,22393903,22393900,52394170)the Beijing Municipal Natural Science Foundation(L247015,L233004)。
文摘Artificial intelligence(AI)has become an increasingly important propellant for energy materials and energy chemistry research,such as accelerating advanced energy materials discovery[1],analyzing vast amounts of data from both experiments and computations[2],process optimization for materials syntheses,management and monitoring of energy storage devices such as lithium batteries,and algorithm-optimized grid load forecasting.Looking back at recent pioneering works of AI-driven energy chemistry research,constructing a dataset with both large quantity and high quality is almost the first step and largely determines the following success of training AI models and figuring out corresponding scientific issues.
