Search

ObjectiveThe detection of lameness in dairy cows is an important issue that needs to be solved urgently in the process of large-scale dairy farming. Timely detection and effective intervention can reduce the culling rate of young dairy cows, which has important practical significance for increasing the milk production of dairy cows and improving the economic benefits of pastures. Due to the low efficiency and low degree of automation of traditional manual detection and contact sensor detection, the mainstream cow lameness detection method is mainly based on computer vision. The detection perspective of existing computer vision-based cow lameness detection methods is mainly side view, but the side view perspective has limitations that are difficult to eliminate. In the actual detection process, there are problems such as cows blocking each other and difficulty in deployment. The cow lameness detection method from the top view will not be difficult to use on the farm due to occlusion problems. The aim is to solve the occlusion problem under the side view.MethodsIn order to fully explore the movement undulations of the trunk of the cow and the movement information in the time dimension during the walking process of the cow, a cow lameness detection method was proposed from a top view based on fused spatiotemporal flow features. By analyzing the height changes of the lame cow in the depth video stream during movement, a spatial stream feature image sequence was constructed. By analyzing the instantaneous speed of the lame cow's body moving forward and swaying left and right when walking, optical flow was used to capture the instantaneous speed of the cow's movement, and a time flow characteristic image sequence was constructed. The spatial flow and time flow features were combined to construct a fused spatiotemporal flow feature image sequence. Different from traditional image classification tasks, the image sequence of cows walking includes features in both time and space dimensions. There would be a certain distinction between lame cows and non-lame cows due to their related postures and walking speeds when walking, so using video information analysis was feasible to characterize lameness as a behavior. The video action classification network could effectively model the spatiotemporal information in the input image sequence and output the corresponding category in the predicted result. The attention module Convolutional Block Attention Module (CBAM) was used to improve the PP-TSMv2 video action classification network and build the Cow-TSM cow lameness detection model. The CBAM module could perform channel weighting on different modes of cows, while paying attention to the weights between pixels to improve the model's feature extraction capabilities. Finally, cow lameness experiments were conducted on different modalities, different attention mechanisms, different video action classification networks and comparison of existing methods. The data was used for cow lameness included a total of 180 video streams of cows walking. Each video was decomposed into 100‒400 frames. The ratio of the number of video segments of lame cows and normal cows was 1:1. For the feature extraction of cow lameness from the top view, RGB images had less extractable information, so this work mainly used depth video streams.Results and DiscussionsIn this study, a total of 180 segments of cow image sequence data were acquired and processed, including 90 lame cows and 90 non-lame cows with a 1:1 ratio of video segments, and the prediction accuracy of automatic detection method for dairy cow lameness based on fusion of spatiotemporal stream features reaches 88.7%, the model size was 22 M, and the offline inference time was 0.046 s. The prediction accuracy of the common mainstream video action classification models TSM, PP-TSM, SlowFast and TimesFormer models on the data set of automatic detection method for dairy cow lameness based on fusion of spatiotemporal stream features reached 66.7%, 84.8%, 87.1% and 85.7%, respectively. The comprehensive performance of the improved Cow-TSM model in this paper was the most. At the same time, the recognition accuracy of the fused spatiotemporal flow feature image was improved by 12% and 4.1%, respectively, compared with the temporal mode and spatial mode, which proved the effectiveness of spatiotemporal flow fusion in this method. By conducting ablation experiments on different attention mechanisms of SE, SK, CA and CBAM, it was proved that the CBAM attention mechanism used has the best effect on the data of automatic detection method for dairy cow lameness based on fusion of spatiotemporal stream features. The channel attention in CBAM had a better effect on fused spatiotemporal flow data, and the spatial attention could also focus on the key spatial information in cow images. Finally, comparisons were made with existing lameness detection methods, including different methods from side view and top view. Compared with existing methods in the side-view perspective, the prediction accuracy of automatic detection method for dairy cow lameness based on fusion of spatiotemporal stream features was slightly lower, because the side-view perspective had more effective cow lameness characteristics. Compared with the method from the top view, a novel fused spatiotemporal flow feature detection method with better performance and practicability was proposed.ConclusionsThis method can avoid the occlusion problem of detecting lame cows from the side view, and at the same time improves the prediction accuracy of the detection method from the top view. It is of great significance for reducing the incidence of lameness in cows and improving the economic benefits of the pasture, and meets the needs of large-scale construction of the pasture.

Terahertz (THz) waves have gained considerable attention in the rising 6G communication due to their large bandwidth. However, the cost and power consumption become the major constraints for the commercialization of 6G THz systems as the frequency increases. Reconfigurable intelligent surface (RIS) comprising active metasurfaces and digital controllers has been proposed for beamforming in the 6G multiple-input-multiple-output systems, showing good potential to suppress the system size, weight, and power consumption (SWaP). Currently, their controlling diodes can hardly work up to THz frequencies. Therefore, several active stimuli have been investigated as alternatives. Among them, chalcogenide phase-change material Ge2Sb2Te5 (GST) addresses large modulation depth, picosecond switching speed, and non-volatile properties. Notably, the non-volatile GST may enable RIS systems with memory and low control power. This work briefly reviews the advances of GST-tuned THz metamaterials (MTMs), discusses the current obstacles to overcome, and gives a perspective of GST applications in the rising 6G communications.

In the new media era, the creation and development of Chinese pipa music faces new challenges and opportunities. This paper analyses the characteristics of pipa music and explores its innovative path and development trend in the contemporary environment through technical means. Music signal preprocessing, feature extraction and other methods are adopted to provide an in-depth characterization of pipa music. In the study, the pipa music was classified concerning pitch, loudness, timbre and rhythm, and the musical features were extracted using techniques such as MFCC and LPC. The experimental data contained 300 pieces of pipa music of different styles, and a series of specific numerical results were derived from the Characterization of this music. For example, in the MFCC feature retrieval experiment, the retrieval accuracy of a 30-second music clip was improved by 0.024 compared to a 10-second clip. In addition, by analyzing the timbre features of 12 pipas, and using MATLAB to process the data, a quantitative judgement of the goodness of the timbre was derived. The possibility of combining pipa music with electronic music was explored with new media technology. The artistic expression of pipa music can be effectively analyzed and enhanced through scientific methods, providing fresh ideas for the innovative development of pipa music.

In order to improve the motion performance of compliant robots, a rotary series elastic actuator (SEA) based on arc helical springs is designed. The proposed SEA can be used to provide a wide range of flexible torsion by the combination of multi-stage rotary structures. The inner and outer double layers of springs with different stiffness are connected in series. The dynamic model of the flexible joint with the proposed SEA is established. The control law for the flexible joint is derived by the backstepping algorithm. To validate the effectiveness of the proposed SEA and the control law, the dynamic performance of the flexible joint with interference torques is simulated by Matlab and Adams. The results show that the controller can overcome the interference force and restore the balance state of the flexible joint within 0.59 s，which verifies the effectiveness of the designed elastic actuator structure and control law.

BackgroundThe volume of aviation gamma spectrum data is immense. If only a central processing unit (CPU) is used for data post-processing, it would be constrained by computational efficiency.PurposeThis study aims to propose a CUDA-based graphics processing unit (GPU) parallel solution that optimally accelerates the denoising of airborne gamma-ray spectral data using wavelet transformation.MethodsFirst, the impact of different block sizes on computational time was tested to determine the optimal block size for processing airborne gamma-ray spectral data. Subsequently, a GPU, instead of a CPU, was used to calculate the acceleration ratio for handling airborne gamma-ray spectral data of different volumes, and wavelet basis functions were used for those with the same data volume. Finally, by introducing white noise to the experimentally measured airborne gamma-ray spectral data, the signal-to-noise ratio of denoised data was calculated to optimize the threshold denoising method suitable for parallel acceleration of the GPU.ResultsThe optimal two-dimensional block sizes for denoising airborne gamma-ray spectral data are 64×64 and 128×128. Among the wavelet basis functions, those that achieved a total time acceleration ratio exceeding 100 compared to CPU processing account for 80%, while those that reached an acceleration ratio exceeding 90 constitute 91%. The coif5 function achieves an acceleration ratio of 353 times whilst the acceleration ratio of the threshold denoising function approaches 570.ConclusionsAll wavelet functions exhibit insufficient denoising effects at low signal-to-noise ratios and excessive denoising effects at high signal-to-noise ratios. Significant denoising can be achieved using hard thresholding of coif5, soft thresholding of coif1, and improved thresholding of bior3.7.

Swine wastewater poses chemical and biological risks because it contains high concentrations of ammonia and diverse species of pathogens. Herein, a vetiver-Dictyosphaerium sp. co-culture for the rapid removal of ammonia and the effective inactivation of pathogens was developed. Plants and microalgae benefited mutually and co-utilized the nutrients in the wastewater in the co-culture. The pathogens were inactivated by reactive oxygen species that were released by the microalgae as well as the supersaturated concentrations of dissolved oxygen in the enclosed bioreactor. In a greenhouse experiment, the time required for wastewater NH4-N to decrease from 102 mg L−1 to 5 mg L−1 was 65.5 days, 34.2 days, and 13.3 days in the plant culture, the algal culture, and the plant-algal co-culture, respectively. Among the 35 detected genera of bacteria, the operational taxonomic units for 31 tended to decrease with culture time in the plant-algal co-culture. Additionally, certain bacteria (e.g., Escherichia spp.) were completely removed by day 9 or 15, and the aerobic phototrophic bacterium Erythromicrobium spp. became most abundant on day 15 in the plant-algal co-culture. Important positive interactions that were observed between plants and microalgae included co-utilization of the nutrients, wastewater acidification through plant root respiration and algal growth with reduced ammonia toxicity, algal depletion of bicarbonate and alleviation of bicarbonate toxicity to plants, and release of oxygen from algal photosynthesis and plant growth with reduced hypoxic stress. Keywords: Swine wastewater, Nutrients, Pathogen, Vetiver, Dictyosphaerium sp., Co-culture

For coordinating the body change and leg movement to promote jump and over-obstacle ability of a bionic robot,a new type of quadruped bionic robot configurations with spine is introduced. On this basis,the forward kinematics problem of this kind of the robot is studied. Firstly,according to the structure of the robot,the motion equations are established which contain the all driving angles. The motion equations contain many redundant drive angles,which is very difficult to solve the redundant drive angles,a dimensionality-reduction method is adopted. By using the dimensionality-reduction method,one of the formulas is translated to the constraint condition,the iteration procedure is used for the rest of equation sets to seek solutions of the redundant drive angles which satisfy the constraint condition. On this basis,the pose of mass center for the quadruped bionic robot with spine is solved and analyzed. An example is given to illustrate the process,this research is helpful to accelerate the use of this kind of robot.

A kind of auxiliary device for ankle rehabilitation based on rigid-flexible combination drive is introduced. The device has two degrees of freedom,while three-dimensional movement of the ankle can be achieved approximately. To analyze the rehabilitation effect of the device,the working spaces are studied. First,the device working conditions are given,and the constraints of the working space are analyzed. Then,the digital solution model for working space is established. Finally,the working spaces of the device are calculated by using Matlab. The analysis results can be used to optimize the device structure and determine its characteristic parameter. The process of this research can be as a reference for research of working spaces of mechanism with multiple movement mode.

A bionic device for the ankle,which has shaft non-perpendicular is designed in according to the structure characteristics of human physiological ankle. The device is consist of three degrees of freedoms that the rotating joint of lower leg,ankle joint and subtalar joint. To reduce the quality of ankle,the motors driving the ankle and the subtalar joint are installed on the thigh and transmission through cables,which cross the knee,fixed on the sheave to limit the rotation angle of each joint. The D-H coordinate method is used in coordinate system selection. Through simulation,the motion attitude space of the mechanism is analyzed and calculated,and compared with the related attitude parameters of human foot. It shows that this mechanism can achieve motion features that similar to human physiological ankle joint. This mechanism can be used in the field of humanoid robot,and can also be a reference for the research of the movement characteristic of the human ankle joint.

In order to realize the ankle recovery,a kind of auxiliary device for ankle rehabilitation based on a ball pin vice is developed. The device has two degrees of freedom,while three- dimensional movement of the ankle can be achieved approximately. Based on introducing the structure of the auxiliary device for ankle rehabilitation,the motion modeling and analysis of the device are performed. Aiming at multiple kind of drive patterns of the structure,the modeling analysis of rigid- soft combination drive and rigid drive pattern are conducted respectively. The related calculation formulas are deduced and calculated by Matlab. A process of this research can be as a reference for mechanism analysis of more motion models.

According to the problem that the wheeled robots have poor ability to adapt terrain,and the legged robot moving speed is limited,a wheel-leg hybrid robot with four-legs is designed,which can both crawl and roll.And the robot is driven by steel wire rope with torsional spring scheme to realize switching function of working manner.In the overall design of the robot,the legs are detailed designed.The kinematic equation of standing leg is established by the D-H method,and the correctness of the kinematic equations is verified by Geometric method.The swinging leg motion space is obtained by MATLAB software.And the ability of the robot obstacle is analyzed.

According to the problem of the loading capacity is limited and the accidental injury is easy happened,a self- lock pole- climbing robot is designed based on self- lock principle. The structure scheme and working principle of the robot are introduced. The self- locking conditions and motion stability are analyzed. The carrying capacity of robot is calculated. The control method for the robot climbing on variable diameter pole is analyzed. The results show that the robot can climb variable diameter pole stably,which meets the requirements for telegraph pole aerial work.

A multiple linear model was developed for individual tree crown width of Cunninghamia lanceolata (Lamb.) Hook in Fujian province, southeast China. Data were obtained from 55 sample plots of pure China-fir plantation stands. An Ordinary Linear Least Squares (OLS) regression was used to establish the crown width model. To adjust for correlations between observations from the same sample plots, we developed one level linear mixed-effects (LME) models based on the multiple linear model, which take into account the random effects of plots. The best random effects combinations for the LME models were determined by the Akaike's information criterion, the Bayesian information criterion and the -2logarithm likelihood. Heteroscedasticity was reduced by three residual variance functions: the power function, the exponential function and the constant plus power function. The spatial correlation was modeled by three correlation structures: the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)], and the compound symmetry structure (CS). Then, the LME model was compared to the multiple linear model using the absolute mean residual (AMR), the root mean square error (RMSE), and the adjusted coefficient of determination (adj-R2). For individual tree crown width models, the one level LME model showed the best performance. An independent dataset was used to test the performance of the models and to demonstrate the advantage of calibrating LME models.