Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is ba...Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.展开更多
目的探究个体化饮食指导在糖尿病骨性关节炎患者中的应用价值。方法选取晋江市医院(上海市第六人民医院福建医院)于2023年2—10月收治的90例糖尿病骨性关节炎患者为研究对象,采用密闭信封法分为对照组和研究组,各45例。对照组实施常规干...目的探究个体化饮食指导在糖尿病骨性关节炎患者中的应用价值。方法选取晋江市医院(上海市第六人民医院福建医院)于2023年2—10月收治的90例糖尿病骨性关节炎患者为研究对象,采用密闭信封法分为对照组和研究组,各45例。对照组实施常规干预,研究组结合个体化饮食指导。于干预前及干预2个月后,检测两组空腹血糖(Fasting Plasma Glucose,FPG)、餐后2 h血糖(2 h Postprandial Plasma Glucose,2 hPG)水平。对比两组负性情绪及生活质量简表(World Health Organization Quality of Life 100,WHOQOL-100)评分。结果干预2个月后,研究组FPG水平为(5.34±0.51)mmol/L,2 hPG水平为(6.73±0.56)mmol/L,均低于对照组,差异有统计学意义(t=5.348、10.807,P均<0.05)。研究组负性情绪评分低于对照组,WHOQOL-100各维度评分均高于对照组,差异有统计学意义(P均<0.05)。结论糖尿病骨性关节炎患者行个体化饮食指导干预,可有效降低血糖水平,缓解负性情绪,从而提高患者生活质量。展开更多
This work describes an improved feature extractor algorithm to extract the peripheral features of point x(ti,fj) using a nonlinear algorithm to compute the nonlinear time spectrum (NL-TS) pattern. The algo- rithm ob...This work describes an improved feature extractor algorithm to extract the peripheral features of point x(ti,fj) using a nonlinear algorithm to compute the nonlinear time spectrum (NL-TS) pattern. The algo- rithm observes n×n neighborhoods of the point in all directions, and then incorporates the peripheral fea- tures using the Mel frequency cepstrum components (MFCCs)-based feature extractor of the Tsinghua elec- tronic engineering speech processing (THEESP) for Mandarin automatic speech recognition (MASR) sys- tem as replacements of the dynamic features with different feature combinations. In this algorithm, the or- thogonal bases are extracted directly from the speech data using discrite cosime transformation (DCT) with 3×3 blocks on an NL-TS pattern as the peripheral features. The new primal bases are then selected and simplified in the form of the ?dp- operator in the time direction and the ?dp- operator in the frequency di- t f rection. The algorithm has 23.29% improvements of the relative error rate in comparison with the standard MFCC feature-set and the dynamic features in tests using THEESP with the duration distribution-based hid- den Markov model (DDBHMM) based on MASR system.展开更多
Action recognition is important for understanding the human behaviors in the video,and the video representation is the basis for action recognition.This paper provides a new video representation based on convolution n...Action recognition is important for understanding the human behaviors in the video,and the video representation is the basis for action recognition.This paper provides a new video representation based on convolution neural networks（CNN）.For capturing human motion information in one CNN,we take both the optical flow maps and gray images as input,and combine multiple convolutional features by max pooling across frames.In another CNN,we input single color frame to capture context information.Finally,we take the top full connected layer vectors as video representation and train the classifiers by linear support vector machine.The experimental results show that the representation which integrates the optical flow maps and gray images obtains more discriminative properties than those which depend on only one element.On the most challenging data sets HMDB51 and UCF101,this video representation obtains competitive performance.展开更多
基金supported by the Prominent Youth Fund of the National Natural Science Foundation of China (61025006)
文摘Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar （ISAR） target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding （NNSC） and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis （PCA） and non-negative matrix factorization （NMF） via simulations.
文摘目的探究个体化饮食指导在糖尿病骨性关节炎患者中的应用价值。方法选取晋江市医院(上海市第六人民医院福建医院)于2023年2—10月收治的90例糖尿病骨性关节炎患者为研究对象,采用密闭信封法分为对照组和研究组,各45例。对照组实施常规干预,研究组结合个体化饮食指导。于干预前及干预2个月后,检测两组空腹血糖(Fasting Plasma Glucose,FPG)、餐后2 h血糖(2 h Postprandial Plasma Glucose,2 hPG)水平。对比两组负性情绪及生活质量简表(World Health Organization Quality of Life 100,WHOQOL-100)评分。结果干预2个月后,研究组FPG水平为(5.34±0.51)mmol/L,2 hPG水平为(6.73±0.56)mmol/L,均低于对照组,差异有统计学意义(t=5.348、10.807,P均<0.05)。研究组负性情绪评分低于对照组,WHOQOL-100各维度评分均高于对照组,差异有统计学意义(P均<0.05)。结论糖尿病骨性关节炎患者行个体化饮食指导干预,可有效降低血糖水平,缓解负性情绪,从而提高患者生活质量。
基金This work was supported by the National Natural Science Foundation of China(Grant No.60172033)Excellent Ph.D Paper Author Foundation of China(Grant No.200036).
基金Supported by the National High-Tech Research and Development (863) Program of China (No. 200/AA/14)
文摘This work describes an improved feature extractor algorithm to extract the peripheral features of point x(ti,fj) using a nonlinear algorithm to compute the nonlinear time spectrum (NL-TS) pattern. The algo- rithm observes n×n neighborhoods of the point in all directions, and then incorporates the peripheral fea- tures using the Mel frequency cepstrum components (MFCCs)-based feature extractor of the Tsinghua elec- tronic engineering speech processing (THEESP) for Mandarin automatic speech recognition (MASR) sys- tem as replacements of the dynamic features with different feature combinations. In this algorithm, the or- thogonal bases are extracted directly from the speech data using discrite cosime transformation (DCT) with 3×3 blocks on an NL-TS pattern as the peripheral features. The new primal bases are then selected and simplified in the form of the ?dp- operator in the time direction and the ?dp- operator in the frequency di- t f rection. The algorithm has 23.29% improvements of the relative error rate in comparison with the standard MFCC feature-set and the dynamic features in tests using THEESP with the duration distribution-based hid- den Markov model (DDBHMM) based on MASR system.
基金Supported by the National High Technology Research and Development Program of China(863 Program,2015AA016306)National Nature Science Foundation of China(61231015)+2 种基金Internet of Things Development Funding Project of Ministry of Industry in 2013(25)Technology Research Program of Ministry of Public Security(2016JSYJA12)the Nature Science Foundation of Hubei Province(2014CFB712)
文摘Action recognition is important for understanding the human behaviors in the video,and the video representation is the basis for action recognition.This paper provides a new video representation based on convolution neural networks（CNN）.For capturing human motion information in one CNN,we take both the optical flow maps and gray images as input,and combine multiple convolutional features by max pooling across frames.In another CNN,we input single color frame to capture context information.Finally,we take the top full connected layer vectors as video representation and train the classifiers by linear support vector machine.The experimental results show that the representation which integrates the optical flow maps and gray images obtains more discriminative properties than those which depend on only one element.On the most challenging data sets HMDB51 and UCF101,this video representation obtains competitive performance.
