Learning Human Kinematics by Modeling Temporal Correlations between Joints for Video-based Human Pose Estimation

Estimating human poses from videos is critical in human-computer interaction. By precisely estimating human poses, the robot can provide an appropriate response to the human. Most existing approaches use the optical flow, RNNs, or CNNs to extract temporal features from videos. Despite the positive results of these attempts, most of them only straightforwardly integrate features along the temporal dimension, ignoring temporal correlations between joints. In contrast to previous methods, we propose a plug-and-play kinematics modeling module (KMM) based on the domain-cross attention mechanism to model the temporal correlation between joints across different frames explicitly. Specifically, the proposed KMM models the temporal correlation between any two joints by calculating their temporal similarity. In this way, KMM can learn the motion cues of each joint. Using the motion cues (temporal domain) and historical positions of joints (spatial domain), KMM can infer the initial positions of joints in the current frame in advance. In addition, we present a kinematics modeling network (KIMNet) based on the KMM for obtaining the final positions of joints by combining pose features and initial positions of joints. By explicitly modeling temporal correlations between joints, KIMNet can infer the occluded joints at present according to all joints at the previous moment. Furthermore, the KMM is achieved through an attention mechanism, which allows it to maintain the high resolution of features. Therefore, it can transfer rich historical pose information to the current frame, which provides effective pose information for locating occluded joints. Our approach achieves state-of-the-art results on two standard video-based pose estimation benchmarks. Moreover, the proposed KIMNet shows some robustness to the occlusion, demonstrating the effectiveness of the proposed method

The Research on Information Representation of Φ-OTDR Distributed Vibration Signals

This paper mainly focuses on the representable problem of Φ-OTDR distributed vibration signals. The research included a signal extraction part and a signal representation part. Firstly, in order to extract the better Φ-OTDR signal, the time-domain data should be fully preserved. The 2D-TESP method is used to extract data in this paper. There are 29 characters in the traditional TESP method. The characters’ number is reduced from 29 to 13 and the characters’ dimension is expanded from 1 to 2 in the 2D-TESP method. Secondly, in order to represent Φ-OTDR signal better, the characteristics of Φ-OTDR data and damped vibration signals are combined in the paper. The EMD method and the NMF method are combined to form the new method in the paper. Some parameters in the proposed method are optimized and adjusted by GA method. After Φ-OTDR data is represented by the proposed method, there is excellent performance both on the length dimension and on the time dimension. Lastly, some experiments are carried out according to the physical truth in this paper. The experiments are carried out in the semianechoic room. The methods of the paper have better performance. The methods are proved to be effective through these experiments

Efficacy and safety of Danlou tablets in traditional Chinese medicine for coronary heart disease: a systematic review and meta-analysis

BackgroundDanlou tablets exert auxiliary advantages in treating coronary heart disease (CHD), but a summary of evidence-based proof is lacking. This study aims to systematically evaluate Danlou tablets in treating CHD from two aspects, including efficacy and safety.MethodsBy a thorough retrieval of the four English databases, namely, PubMed, The Cochrane Library, Embase, and Web of Science, and the four Chinese databases, namely, CNKI, Wanfang, VIP database, and China Biomedical Literature Service System, we found all randomized controlled trials (RCTs) related to Danlou tablets in treating CHD. The retrieval time was from the construction of the database to April 2022. We engaged two researchers to screen the studies, extract the required data, and assess the risk of bias. We then used RevMan5.3 and STATA.14 software to conduct a meta-analysis. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used to evaluate the quality of outcome indicators.ResultsSeventeen RCTs involving 1,588 patients were included. The meta-analysis results are displayed as follows: clinical treatment effect [risk ratio (RR) = 1.22, 95% confidence interval (CI): 1.16, 1.28, P < 0.00001], angina pectoris duration [MD = −0.2.15, 95% CI: −2.91, −1.04, P < 0.00001], angina pectoris frequency [standard mean difference (SMD) = −2.48, 95% CI: −3.42, −1.54, P < 0.00001], angina pectoris degree [SMD = −0.96, 95% CI: −1.39, −0.53, P < 0.0001], TC [MD = −0.71, 95% CI: −0.92, −0.51, P < 0.00001], TG [MD = −0.38, 95% CI: −0.53, −0.22, P < 0.00001], low-density lipoprotein cholesterol [MD = −0.64, 95% CI: −0.76, −0.51, P < 0.00001], high-density lipoprotein cholesterol [MD = 0.16, 95% CI: 0.11, 0.21, P < 0.00001], and adverse events [RR = 0.46, 95% CI: 0.24, 0.88, P = 0.02].ConclusionThe current evidence suggests that the combination of Danlou tablets and Western medicine can enhance the efficacy of CHD and does not increase adverse events. However, because of the limited number and quality of the included studies, the results of our study should be treated with caution. Further large-scale RCTs are necessary to verify the benefits of this approach

Inferring Weighted Directed Association Networks from Multivariate Time Series with the Small-Shuffle Symbolic Transfer Entropy Spectrum Method

Complex network methodology is very useful for complex system exploration. However, the relationships among variables in complex systems are usually not clear. Therefore, inferring association networks among variables from their observed data has been a popular research topic. We propose a method, named small-shuffle symbolic transfer entropy spectrum (SSSTES), for inferring association networks from multivariate time series. The method can solve four problems for inferring association networks, i.e., strong correlation identification, correlation quantification, direction identification and temporal relation identification. The method can be divided into four layers. The first layer is the so-called data layer. Data input and processing are the things to do in this layer. In the second layer, we symbolize the model data, original data and shuffled data, from the previous layer and calculate circularly transfer entropy with different time lags for each pair of time series variables. Thirdly, we compose transfer entropy spectrums for pairwise time series with the previous layer’s output, a list of transfer entropy matrix. We also identify the correlation level between variables in this layer. In the last layer, we build a weighted adjacency matrix, the value of each entry representing the correlation level between pairwise variables, and then get the weighted directed association network. Three sets of numerical simulated data from a linear system, a nonlinear system and a coupled Rossler system are used to show how the proposed approach works. Finally, we apply SSSTES to a real industrial system and get a better result than with two other methods

Event-Based Communication and Finite-Time Consensus Control of Mobile Sensor Networks for Environmental Monitoring

This paper deals with the problem of environmental monitoring by designing a cooperative control scheme for mobile sensor networks. The proposed cooperative control scheme includes three main modules: a wireless communication module, a direction decision module, and a motion control module. In the wireless communication module, an event-based communication rule is proposed, which means that mobile sensor nodes do not send their positions, velocities, and the data of environmental attributes to the other sensor nodes in real-time for the coordination and control of mobile sensor networks. Due to using the event-based communication rule, the communication bandwidth can be saved. In the direction decision module, a radial basis function network is used to model the monitored environment and is updated in terms of the sampled environmental data and the environmental data from the other sensor nodes by the wireless communication module. The updated environment model is used to guide the mobile sensor network to move towards the region of interest in order to efficiently model the distribution map of environmental attributes, such as temperature, salinity, and pH values for the monitored environment. In the motion control module, a finite-time consensus control approach is proposed to enable the sensor nodes to quickly change their movement directions in light of the gradient information from the environment model. As a result of using the event-based communication rule in the wireless communication module, the proposed control approach can also lower the updating times of the control signal. In particular, the proposed cooperative control scheme is still efficient under the directed wireless communication situation. Finally, the effectiveness of the proposed cooperative control scheme is illustrated for the problem of environmental monitoring

Inferring Weighted Directed Association Networks from Multivariate Time Series with the Small-Shuffle Symbolic Transfer Entropy Spectrum Method

Complex network methodology is very useful for complex system exploration. However, the relationships among variables in complex systems are usually not clear. Therefore, inferring association networks among variables from their observed data has been a popular research topic. We propose a method, named small-shuffle symbolic transfer entropy spectrum (SSSTES), for inferring association networks from multivariate time series. The method can solve four problems for inferring association networks, i.e., strong correlation identification, correlation quantification, direction identification and temporal relation identification. The method can be divided into four layers. The first layer is the so-called data layer. Data input and processing are the things to do in this layer. In the second layer, we symbolize the model data, original data and shuffled data, from the previous layer and calculate circularly transfer entropy with different time lags for each pair of time series variables. Thirdly, we compose transfer entropy spectrums for pairwise time series with the previous layer’s output, a list of transfer entropy matrix. We also identify the correlation level between variables in this layer. In the last layer, we build a weighted adjacency matrix, the value of each entry representing the correlation level between pairwise variables, and then get the weighted directed association network. Three sets of numerical simulated data from a linear system, a nonlinear system and a coupled Rossler system are used to show how the proposed approach works. Finally, we apply SSSTES to a real industrial system and get a better result than with two other methods

Research of the Vibration Source Tracking in Phase-Sensitive Optical Time-Domain Reflectometry Signals Based by Image Processing Method

This paper aims to improve the source tracking efficiency of distributed vibration signals generated by phase-sensitive optical time-domain reflectometry (Φ-OTDR). Considering the two dimensions (time and length) of Φ-OTDR signals, the authors saved and processed these signals as images after particle filtering. The filtering method could save 0.1% of hard drive space without sacrificing the original features of the signals. Then, an integrated feature extraction method was proposed to further process the generated image. The method combines three individual extraction methods, namely, texture feature extraction, shape feature extraction and intrinsic feature extraction. Subsequently, the signal of each frame image was recognized to track the vibration source. To verify the effect of the proposed method, several experiments were carried out to compare it with popular and traditional approaches. The results show that: Hard drive space is greatly conserved by saving the distributed vibration signals as images; the proposed particle filter is a desirable way to screen the vibration signals for monitoring; the integrated feature extraction outperforms the individual extraction methods for texture features, shape features and intrinsic features; the proposed method has a better effect than other popular integrated feature extraction methods; and, the signal source tracking method has little impact on the positioning accuracy of the vibration source. The research findings provide important insights into the source tracking of Φ-OTDR signals

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

In order to realize the multithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A new weight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images. Then the optimal threshold of the image is obtained through searching for the minimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms