motion state
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2022 ◽  
Vol 2022 ◽  
pp. 1-21
Author(s):  
Ruibin Zhang ◽  
Yingshi Guo ◽  
Yunze Long ◽  
Yang Zhou ◽  
Chunyan Jiang

A vehicle motion state prediction algorithm integrating point cloud timing multiview features and multitarget interaction information is proposed in this work to effectively predict the motion states of traffic participants around intelligent vehicles in complex scenes. The algorithm analyzes the characteristics of object motion that are affected by the surrounding environment and the interaction of nearby objects and is based on the complex traffic environment perception dual multiline light detection and ranging (LiDAR) technology. The time sequence aerial view map and time sequence front view depth map are obtained using real-time point cloud information perceived by the LiDAR. Time sequence high-level abstract combination features in the multiview scene are then extracted by an improved VGG19 network model and are fused with the potential spatiotemporal interaction of the multitarget operation state data extraction features detected by the laser radar by using a one-dimensional convolution neural network. A temporal feature vector is constructed as the input data of the bidirectional long-term and short-term memory (BiLSTM) network, and the desired input-output mapping relationship is trained to predict the motion state of traffic participants. According to the test results, the proposed BiLSTM model based on point cloud multiview and vehicle interaction information is better than other methods in predicting the state of target vehicles. The results can provide support for the research to evaluate the risk of intelligent vehicle operation environment.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bu Chang

Heart rate monitoring is becoming more and more important in the development of modern health industry. At present, wireless sensor network equipment is mainly used to realize the real-time or periodic monitoring of human heart rate, so as to realize the health management of specific people. At the same time, the monitoring and analysis technology of heart rate is also widely used in special competitive sports. Through the real-time monitoring and analysis of athletes’ heart rate, we can feedback and analyze their corresponding competitive state in real time, so as to monitor the sudden state of athletes, and also provide a basis for the improvement of athletes’ later sports level. Based on this, this paper will use a single-chip microcomputer as the central data processing unit of the monitoring system at the hardware level, and inertial sensor and heart rate sensor at the sensor level. The system will design data acquisition module, motion positioning module, low-power module, athlete heart rate acquisition module, and motion state recognition module. Aiming at the low accuracy of traditional heart rate acceleration motion wireless sensor in competitive sports athletes’ heart rate recognition and motion state recognition, this paper innovatively proposes an athlete heart rate recognition algorithm based on acceleration signal, which extracts the frequency-domain characteristics of motion signal. The time-domain and time-frequency characteristics of athletes’ acceleration signal are used to realize the recognition of athletes’ sports state, and the power spectrum cancellation technology is used to realize the accurate detection of athletes’ heart rate. In order to verify the advantages of the hardware system and algorithm in this paper, three sports with quiet, dynamic, and random dynamic characteristics are selected for experimental verification. The experimental results show that the software algorithm proposed in this paper has obvious accuracy advantages in quiet and dynamic competitive sports compared with the traditional algorithm.


2021 ◽  
pp. 381-389
Author(s):  
Juan Wang ◽  
Longfei Zhang
Keyword(s):  

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shijie Pan ◽  
Youtang Li ◽  
Juane Wang

Aiming at the problem of pitch error of helical gear pair in engineering practice, the influence of pitch error on vibration, bifurcation, and chaos characteristics of the helical gear pair system is mainly studied. Due to the periodic time-varying nature of pitch error, a method of simulating the pitch error as a sine function is proposed to calculate pitch error. A nonlinear dynamic model of bending-torsion-shaft coupling of the helical gear pair system is established considering the effect of pitch error. The influence of pitch error on the vibration, bifurcation, and chaos characteristics of the system is analyzed by the Runge–Kutta numerical integration method. The research results show that the introduction of pitch error has the most significant impact on the torsional vibration of the system. With the increase in pitch error, the system exhibits rich bifurcation and chaos characteristics in the torsional direction. Moreover, it is also found that the vibration response in the torsional orientation of the system increases or decreases to the same degree when the system is in a periodic motion state, and the pitch error varies by the same extent. Therefore, the impact of pitch error on the dynamic performance of the helical gear pair system should be considered in engineering practice.


2021 ◽  
pp. 2102036
Author(s):  
Yao Ni ◽  
Jiulong Feng ◽  
Jiaqi Liu ◽  
Hang Yu ◽  
Huanhuan Wei ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jihua Liu

With the increase of people’s exercise in today’s society, how to exercise scientifically and healthily has attracted much attention. Therefore, sports injury risk assessment and monitoring system has attracted more and more attention in real-time, flexibility, intelligence, and other aspects. To solve the above problems, this paper proposes a sports injury risk assessment based on blockchain and Internet of Things. By introducing computational power weight, a computational power balance D-H algorithm based on Internet of Things blockchain network architecture is proposed. It can provide a secure and trusted interactive environment for the Internet of Things. On the basis of blockchain and Internet of Things, a multisensor data fusion algorithm is proposed to be applied to the analysis and evaluation of sports injury. A variety of physiological parameters of human motion state are collected through multisensor, the collected physiological parameters are processed by data fusion, and finally, sports injury risk assessment is carried out. The built system takes the embedded esp8266wifi module as the hardware processing core and uses body temperature sensor, blood pressure sensor, EMG sensor, and pulse sensor to form wearable devices. By wearing wearable devices, four human physiological parameters such as body temperature, blood pressure, electromyography, and pulse can be collected. In the process of decision level fusion, different weights are set for the focal elements causing information conflict, and the optimized D-S evidence theory algorithm is used. Thus, according to the data detected by multisensor, the injury risk of user motion state is evaluated.


ACS Nano ◽  
2021 ◽  
Author(s):  
Zixuan Song ◽  
Xiaosong Zhang ◽  
Zheng Wang ◽  
Tao Ren ◽  
Wei Long ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jiaoyi Hou ◽  
Pengwei Guo ◽  
Aoyu Xu ◽  
Dayong Ning ◽  
Shengtao Chen ◽  
...  

The acoustic signal generated by mechanical motion contains the information of its motion state, but when the signal-to-noise ratio (SNR) is low, the accuracy of real-time monitoring mechanical motion state by the acoustic signal is low. This study proposes an adaptive noise reduction method based on the dislocation superposition method (DSM), which can realize the adaptive noise reduction and the extraction of fault a component from the automobile engine abnormal noise signal of low SNR. Firstly, the wavelet coefficients of engine abnormal noise signal are obtained by continuous wavelet transform (CWT), and the fault feature points of the abnormal noise signal in each period are extracted by setting hard threshold function, window function, and feature points extraction algorithm. Then, the signal segments containing fault components are obtained by using the position of feature points to extend the length of the fault component forward and backward, respectively, and Pearson’s correlation is calculated by traversal to determine the starting superposition point of each signal segment containing fault components. Finally, the signal segments of the odd group and even group are selected for superposition calculation. When the superposition stop condition is not satisfied, the number of superpositions increased until the stop condition is satisfied, and the superposition signal can be used as a fault component. The experimental results show that, compared with the improved DSM, this method has a good effect on the noise reduction and extraction of fault components of automobile engine cylinder knocking fault, and the effectiveness of this method is verified. This method is used to reduce the noise and extract the fault components of automobile engine cylinder missing fault and knock fault, and good results are obtained.


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