Dynamic characteristics and optimal design of the manipulator for automatic tool changer

In order to improve the reliability of changing tool for ATC (automatic tool changer), a horizontal tool changer of machining center is chosen as the example to study the dynamic characteristics in the condition of changing a heavy tool. This paper analyzes the structure and properties of the tool changer by simulation and experiment, and the space trajectory equations of the manipulator and tool are derived. The maximum force is calculated in the processing of changing tool. A virtual platform for the automatic tool changer is built to simulate and verify the dynamic performance of the tool changer; the simulation results show an obvious vibration in the process of changing tool, which increases the probability of failure for changing tool. Moreover, in order to find out the device's vibration reasons, a professional experiment platform is built to test the dynamic characteristics. Based on the testing results for a horizontal tool changer, it is known that the unstable vibration is mainly caused by the collision of the tool. Finally, an optimization method for the manipulator is proposed to reduce this vibration and improve the reliability of the tool changer. The final simulation and experiment results show that the optimized manipulator can grasp the heavy tool stably, and the vibration amplitude is significantly reduced in the process of changing tool

An improved robust function correction-adaptive extended Kalman filtering algorithm for SOC estimation of lithium-ion batteries.

State of Charge (SOC) is one of the key indicators for evaluating the state of electric vehicles. In order to cope with the uncertainty of random noise in nonlinear systems, an improved robust function correction-adaptive extended Kalman filtering (RFC-AEKF) algorithm is proposed for SOC prediction. Using FFRLS method to verify the Dual Polarization model established in this paper. The robust function is an abstract method that describes system state noise and observation noise, and performs real-time correction, combined with adaptive methods to estimate SOC. The experimental results show that the proposed RFC-AEKF algorithm has the smallest mean absolute error (MAE) and root mean square error (RMSE) compared to other algorithms. Under the Beijing bus dynamic stress test (BJDST) conditions, the MAE and RMSE of the RFC-AEKF are 0.354% and 0.658%, respectively, indicating that the RFC-AEKF algorithm can improve SOC estimation accuracy and enhance robustness

GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework

Many gait recognition methods first partition the human gait into N-parts and then combine them to establish part-based feature representations. Their gait recognition performance is often affected by partitioning strategies, which are empirically chosen in different datasets. However, we observe that strips as the basic component of parts are agnostic against different partitioning strategies. Motivated by this observation, we present a strip-based multi-level gait recognition network, named GaitStrip, to extract comprehensive gait information at different levels. To be specific, our high-level branch explores the context of gait sequences and our low-level one focuses on detailed posture changes. We introduce a novel StriP-Based feature extractor (SPB) to learn the strip-based feature representations by directly taking each strip of the human body as the basic unit. Moreover, we propose a novel multi-branch structure, called Enhanced Convolution Module (ECM), to extract different representations of gaits. ECM consists of the Spatial-Temporal feature extractor (ST), the Frame-Level feature extractor (FL) and SPB, and has two obvious advantages: First, each branch focuses on a specific representation, which can be used to improve the robustness of the network. Specifically, ST aims to extract spatial-temporal features of gait sequences, while FL is used to generate the feature representation of each frame. Second, the parameters of the ECM can be reduced in test by introducing a structural re-parameterization technique. Extensive experimental results demonstrate that our GaitStrip achieves state-of-the-art performance in both normal walking and complex conditions.Comment: Accepted to ACCV202

A cohort study of factors influencing the physical fitness of preschool children: a decision tree analysis

ObjectiveBased on the decision tree model, to explore the key influencing factors of children’s physical fitness, rank the key influencing factors, and explain the complex interaction between the influencing factors.MethodsA cohort study design was adopted. 1,276 children (ages 3–6) from 23 kindergartens in Nanchang, China, were chosen for the study to measure the children’s physical fitness at baseline and a year later and to compare the physical fitness scores at the two stages. The study was conducted following the Chinese National Physical Fitness Testing Standard (Children Part); To identify the primary influencing factors of changes in physical fitness, a decision tree model was developed, and a questionnaire survey on birth information, feeding patterns, SB, PA, dietary nutrition, sleep, parental factors, and other relevant information was conducted.ResultsThe levels of physical fitness indicators among preschool children showed a significant increase after 1 year. The accuracy of the CHAID model is 84.17%. It showed that 7 variables were strongly correlated with the physical changes of children’s fitness, the order of importance of each variable was weekend PA, weekend MVPA, mother’s BMI, mother’s sports frequency, father’s education, mother’s education, and school day PA. Three factors are related to PA. Four factors are related to parental circumstances. In addition to the seven important variables mentioned, variables such as breakfast frequency on school day, puffed food, frequency of outing, school day MVPA, parental feeling of sports, father’s occupation, and weekend breakfast frequency are all statistically significant leaf node variables.ConclusionPA, especially weekend PA, is the most critical factor in children’s physical fitness improvement and the weekend MVPA should be increased to more than 30 min/d based on the improvement of weekend PA. In addition, parental factors and school day PA are also important in making decisions about changes in fitness for children. The mother’s efforts to maintain a healthy BMI and engage in regular physical activity are crucial for enhancing the physical fitness of children. Additionally, other parental factors, such as the parents’ educational levels and the father’s occupation, can indirectly impact the level of physical fitness in children

The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming

Pork accounts for an important proportion of livestock products. For pig farming, a lot of manpower, material resources and time are required to monitor pig health and welfare. As the number of pigs in farming increases, the continued use of traditional monitoring methods may cause stress and harm to pigs and farmers and affect pig health and welfare as well as farming economic output. In addition, the application of artificial intelligence has become a core part of smart pig farming. The precision pig farming system uses sensors such as cameras and radio frequency identification to monitor biometric information such as pig sound and pig behavior in real-time and convert them into key indicators of pig health and welfare. By analyzing the key indicators, problems in pig health and welfare can be detected early, and timely intervention and treatment can be provided, which helps to improve the production and economic efficiency of pig farming. This paper studies more than 150 papers on precision pig farming and summarizes and evaluates the application of artificial intelligence technologies to pig detection, tracking, behavior recognition and sound recognition. Finally, we summarize and discuss the opportunities and challenges of precision pig farming

Self-assembly of virus-like particles of porcine circovirus type 2 capsid protein expressed from <it>Escherichia coli</it>

<p>Abstract</p> <p>Background</p> <p>Porcine circovirus 2 (PCV2) is a serious problem to the swine industry and can lead to significant negative impacts on profitability of pork production. Syndrome associated with PCV2 is known as porcine circovirus closely associated with post-weaning multisystemic wasting syndrome (PMWS). The capsid (Cap) protein of PCV2 is a major candidate antigen for development of recombinant vaccine and serological diagnostic method. The recombinant Cap protein has the ability to self-assemble into virus-like particles (VLPs) <it>in vitro</it>, it is particularly opportunity to develop the PV2 VLPs vaccine in <it>Escherichia coli</it>,(<it>E.coli </it>), because where the cost of the vaccine must be weighed against the value of the vaccinated pig, when it was to extend use the VLPs vaccine of PCV2.</p> <p>Results</p> <p>In this report, a highly soluble Cap-tag protein expressed in <it>E.coli </it>was constructed with a p-SMK expression vector with a fusion tag of small ubiquitin-like modifiers (SUMO). The recombinant Cap was purified using Ni²⁺affinity resins, whereas the tag was used to remove the SUMO protease. Simultaneously, the whole native Cap protein was able to self-assemble into VLPs <it>in vitro </it>when viewed under an electron microscope. The Cap-like particles had a size and shape that resembled the authentic Cap. The result could also be applied in the large-scale production of VLPs of PCV2 and could be used as a diagnostic antigen or a potential VLP vaccine against PCV2 infection in pigs.</p> <p>Conclusion</p> <p>we have, for the first time, utilized the SUMO fusion motif to successfully express the entire authentic Cap protein of PCV2 in <it>E. coli</it>. After the cleavage of the fusion motif, the nCap protein has the ability to self-assemble into VLPs, which can be used as as a potential vaccine to protect pigs from PCV2-infection.</p

Load Spectrum Compilation Method of Hybrid Electric Vehicle Reducers Based on Multi-Criteria Decision Making

This article proposes a method for compiling the load spectra of reducers for hybrid electric vehicles. Selecting typical working conditions for real vehicle data collection, the load data under each typical working condition were divided into five categories according to the state of the power source and the data were preprocessed. The optimal sample loads for compiling load spectra were obtained based on a multi-criteria decision-making method, rainflow counting for optimal sample loads was performed according to different power source output patterns, non-parametric extrapolation was performed to obtain the full-life two-dimensional load spectrum after dimensionality reduction, and a full-life eight-level programmed load spectrum that could be used for bench tests was obtained. Using the programmed load spectrum and the extracted sample load as the load input, a fatigue life prediction simulation of the reducer gear of a hybrid electric vehicle was carried out. The reducer gear fatigue life from the programmed load spectrum was compared to the gear fatigue life under actual load. The fatigue life of the reducer gear when the programmed load spectrum was used as the input was 1.412 × 103. When the actual load was used as the input load, the fatigue life of the reducer gear was 1.933 × 103. The relative error between the two is only 26%, which is in the normal range. The results show that the programmed load spectrum is effective and reliable and that the load spectrum compilation method provides a basis for accurately evaluating the reliability of the hybrid electric vehicle reducer