Power Train Research Articles

Effects of Physical Training and Associations Between Physical Performance Characteristics and Golf Performance in Female Players: A Systematic Review With Meta-Analysis.

Robinson, L, Murray, A, Ehlert, A, Wells, J, Jarvis, P, Turner, A, Glover, D, Coughlan, D, Hembrough, R, and Bishop, C. Effects of physical training and associations between physical performance characteristics and golf performance in female players: A systematic review with meta-analysis. J Strength Cond Res 37(12): e646-e655, 2023-The aims of this systematic review were to assess the association between physical performance and measures of golf performance, and the effects of physical training on measures of golf performance, in female golfers. A systematic literature search was conducted in PubMed, SPORTDiscus, Medline, and CINAHL. Inclusion criteria required studies to (a) have conducted a physical training intervention of any duration in female players and determine the effects on measures of golf performance, (b) determine the association between physical performance in at least one test and golf performance in female players, and (c) be peer-reviewed and published in English language. Methodological quality was assessed using a modified version of the Downs and Black Quality Index tool, and heterogeneity was examined through the Q statistic and I2 . Pooled effect sizes were calculated using standardized mean differences (SMDs) (with 95% confidence interval [CI]s) within a random-effects model, with Egger's regression test used to assess small study bias (inclusive of publication bias). Of the 2,378 articles screened, only 9 were included in the final review, with 3 of these being associative by design and 6 being training interventions. From an associative standpoint, clubhead speed (CHS) was reported in all 3 studies and was associated with measures of strength ( r = 0.54), lower-body power ( r = 0.60), upper-body power ( r = 0.56-0.57), and flexibility ( r = 0.52-0.71). When assessing the effects of physical training interventions, CHS was again the most commonly reported golf outcome measure ( n = 5). The random-effect model indicated that CHS significantly improves within each training group following training interventions (SMD = 0.73 [95% CIs: 0.32-1.14], Z = 3.50, p < 0.001), with trivial heterogeneity ( I2 = 0.00%, Q = 0.18; p = 0.9963) and no prevalence of small study bias depicted through the Egger's regression test ( z = -0.28, p = 0.78). From the available research, it seems that CHS can be positively affected from strength, power, and flexibility training interventions. From an associative standpoint, only 3 studies have been conducted solely in female players, with one showcasing questionable methodology. Future research should aim to carefully select test measures which better represent the physical capacities needed for the sport when determining the effects of and relationships with golf performance.

PredDRBP-MLP: Prediction of DNA-binding proteins and RNA-binding proteins by multilayer perceptron

Proteins interact with many molecules in order to maintain the vital activities in cells. Proteins that interact with DNA are called DNA-binding proteins (DBP), and proteins that interact with RNA are called RNA-binding proteins (RBP). Since DBPs and RBPs are involved in critical biological processes, their classification is quite important. Although the convolutional neural network and bidirectional long-short-term memory hybrid model (CNN-BiLSTM) is very popular in DBP and RBP classification, it has problems such as requirement of high processing power and long training time. Therefore, a multilayer perceptron (MLP) based predictor, PredDRBP-MLP (Predictor of DNA-Binding Proteins and RNA-Binding Proteins - Multilayer Perceptron) was developed in this study. PredDRBP-MLP is an artificial learning model that performs multi-class classification of DBPs, RBPs and non-nucleic acid-binding proteins (NNABP). PredDRBP-MLP achieved quite successful results on the independent dataset, specifically in the NNABP class, compared to the existing predictors, in addition to requiring lower processing power and being able to train quicker compared to CNN-BiLSTM based predictors. In NNABP class, PredDRBP-MLP predictor achieved 0.578 precision, 0.522 recall and 0.549 F1-score, while other multi-class predictor achieved 0.486 precision, 0.183 recall and 0.266 F1-score. A desktop application was developed for PredDRBP-MLP. The application is freely accessible at https://sourceforge.net/projects/preddrbp-mlp.

Fuel consumption of a wheel loader with power reflux hydraulic transmission system

The world is facing a limited supply of fossil fuel resources and stringent environmental constraints. Therefore, it is very important to develop advanced technologies to improve vehicle fuel economy, especially for construction vehicles, which have large engine displacements and poor emission characteristics. The majority of these vehicles use hydraulic mechanical transmission in the power train in order to improve maneuverability. However, a key issue on the hydraulic mechanical transmission is the low-efficiency torque converter. Focusing on this issue, we proposed the power reflux hydraulic transmission system (PRHTS), which is a new continuously variable transmission system. The PRHTS can improve the overall transmission efficiency of the power train by splitting the engine power into mechanical and hydraulic power. Therefore, the PRHTS is a valid solution to reduce the fuel consumption and subsequently decrease emissions from construction vehicles. In order to quantitatively study the effect of using the PRHTS on improving the fuel economy for construction vehicles, a wheel loader coupled with the PRHTS is modeled, and numerical simulation is conducted under the wheel loader driving condition. The simulation results show that the total fuel consumption of the wheel loader coupled with PRHTS is reduced by 3.39 % compared with that of the original wheel loader.

Home-based power training improves functional parameters and blood pressure after coronary artery bypass graft surgery: preliminary study

This study aimed to investigate the impact of remotely supervised home-muscle power training (RSHMPT) on physical function (PF), aerobic capacity (AC), autonomic cardiac control (ACC), and blood pressure (BP) in patients after coronary artery bypass grafting (CABG). The study sample consisted of 4 patients who underwent CABG. PF (dynamic balance, lower limb functional capacity, elbow flexion strength, handgrip strength), AC, ACC, and BP were evaluated before and after 8 weeks of RSHMPT. Experienced exercise physiologists administered the exercise protocol (8 weeks) virtually by WhatsApp® video calls. Remotely supervised RSHMPT consisted of multiarticular and monoarticular exercises performed by elastic bands. Clinical improvement has been demonstrated in dynamic balance (

Design of the Superconducting AC and DC Distribution for the ASCEND Demonstrator at Airbus

The ground-based Advanced Superconducting and Cryogenic Experimental power train Demonstrator (ASCEND) at Airbus intends to demonstrate the potential and feasibility of a cryogenic and superconducting powertrain as a breakthrough electric propulsion solution on future electric aircraft. A direct current distribution network is used in a propulsion chain to transfer 500 kW of power from the source to an electrical converter, which transforms the power into an alternating voltage/current to drive a superconducting motor. The working point of 1,700 A and 300 V is chosen for safety and installation reasons by operating at relatively low voltage. The direct current (DC) bus of ASCEND will be formed by a pair of high-temperature superconducting CORC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> cables inserted into a 10-meter-long narrow cryostat, resulting in a compact and lightweight solution. The 2-meter-long alternating current (AC) bus between the inverter and the electric motor is formed by a three-phase CORC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> cable. The challenge associated with 500 Hz operation in which a balance between AC loss in the cable and the size and mass of the system needs to be found, will be outlined. The AC and DC buses include several devices that connect the liquid nitrogen cooled power cables with the other system components that, in the case of the room temperature generator, operate at significantly higher temperatures. These devices thus include conduction-cooled current leads that are dimensioned to minimize the heat inleak from the warm to the cold environment. An overview of the design of the AC and DC buses and connecting devices will be provided and some of the design and operational challenges will be outlined.

Reduction of rejects by combining data from the casting process and automatic X-ray inspection

Automatic inspection of castings with X-rays (radiographic and computed tomography) is widespread for parts that are relevant for safety or have high quality requirements. Examples in the automotive sector are aluminum wheels, chassis parts and new parts within the electric power train. Those parts are automatically inspected, which means that both the image acquisition and the evaluation of the images is done fully automatically. Today, in most industrial implementations, the generated data with a size up to several gigabytes per part is summarized to a simple good or bad decision, according to specification. All other data is dismissed, although this information can be valuable to optimize production processes and thus minimize rejects. This contribution gives an overview about the results of the project Cast Control, which is a collaboration of Fraunhofer Development Center for X-ray Technology EZRT, Fraunhofer Center for Applied Research on Supply Chain Services SCS and industry partner RONAL GROUP. RONAL GROUP is a major aluminum wheel manufacturer, mainly for the OEM market. Within the project we combined serial production data from the low pressure die casting process from a foundry of the RONAL GROUP with the data generated in the automatic X-ray inspection. After collecting a large base of sample data, we were able train a neural network for the prediction of error metrics obtained by X-ray inspection. We apply a combination of layer-wise relevance propagation and dimensionality reduction to find correlations between data of the casting machines (process and sensor) and the characteristics of anomalies detected during X-ray inspection. With this information, it is possible to adjust the casting process in an early stage – even before rejects are produced. This enables the foundry to reduce their rejects rate, which saves costs and energy and results in a better competitivenessin a better competitiveness.

Functional mobility and physical fitness are improved through a multicomponent training program in institutionalized older adults

Physical exercise has demonstrated its effectiveness in the management of the deleterious process of aging. However, it is less studied in institutionalized elderly people. This investigation aims to clarify the benefits of a multicomponent training program in institutionalized older adults. A randomized controlled trial was conducted with institutionalized older adults (≥ 70 years old). Intervention group (IG; N = 18) were submitted to a multicomponent training program based on muscle power training and interval endurance exercise, 2 times/week for 12 weeks. Control group (CG; N = 16) continued their usual mobility exercises. Independence was estimated with the Barthel index, and physical fitness and functional mobility were evaluated by the Short Physical Performance Battery (SPPB), the Timed Up and Go (TUG) test, the 6-min Walking Test (6’WT), the 10-Meter Walking Test (10MWT), hand grip strength dynamometry, and lower limb muscle strength and power. The IG improved, compared with the CG, in TUG scores in -7.43 s (95% IC: 3.28, 11.59; p < 0.001); in 10MWT scores in -5.19 s (95% IC: 1.41, 8.97; p = 0.004) and -4.43 s (95% IC: 1.14, 7.73; p = 0.002), 6’WT scores in + 54.54 m (95% IC: 30.24, 78.84; p < 0.001); and SPPB in + 2.74 points (95% IC: 2.10, 3.37; p < 0.001). Maximum muscle power and maximum strength did not show statistically significant differences. The multicomponent training program based on muscle power and interval endurance exercise was shown to be safe, well tolerated and effective for the improvement of functional mobility and physical fitness, but not for independence in institutionalized older adults.

Network Meta-analysis of Combined Strength and Power Training for Countermovement Jump Height.

Combined strength and power training in a training program is considered to improve the vertical jump, which is frequently quantified using the countermovement jump height. It is not yet clear whether one of the different training set structures, such as complex training, contrast training, compound training and traditional training, is superior to another. The aim of this review is to describe and assess the comparative effects of the set structures on countermovement jump height in healthy subjects. A systematic review and network meta-analysis (NMA) was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Network Meta-Analyses. Three databases were systematically searched. Risk of bias was assessed using the Risk of Bias 2 tool. NMAs were performed using a random-effects model. Twenty-four studies were included. All interventions were superior to control (no intervention) with mean differences ranging from 2.87 [95% confidence interval (CI): 1.99 to 3.74] for complex training to 3.43 (95% CI: 2.61 to 4.26) for traditional training. None of the training interventions were superior compared to each other in strength and/or power trained subjects, as well as in non-strength and/or power trained subjects. The findings support the combination of strength and power training to improve countermovement jump height.

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that will be encountered at test time. This assumption is challenged by the threat of poisoning, an attack that manipulates the training data to compromise the model’s performance at test time. Although poisoning has been acknowledged as a relevant threat in industry applications, and a variety of different attacks and defenses have been proposed so far, a complete systematization and critical review of the field is still missing. In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the past 15 years. We start by categorizing the current threat models and attacks and then organize existing defenses accordingly. While we focus mostly on computer-vision applications, we argue that our systematization also encompasses state-of-the-art attacks and defenses for other data modalities. Finally, we discuss existing resources for research in poisoning and shed light on the current limitations and open research questions in this research field.

Simulation as a key training method for inculcating public health leadership skills: a mixed methods study.

Successful management of public health challenges requires developing and nurturing leadership competencies. We aimed to evaluate the effectiveness of training simulations to assess public health leadership and decision-making competencies during emergencies as an effective learning and training method. We examined the effects of two simulation scenarios on public health school students in terms of their experience (compared to face-to-face learning) and new skills acquired for dealing with similar emergent situations in the future. A mixed-methods design included developing a validated and pre-tested questionnaire with open-and closed-ended questions that examined the simulation impact and the degree of student satisfaction with the conditions in which it was conducted. Semi-structured in-depth interviews were conducted with the students after going through the simulations. The questionnaire results were evaluated using descriptive analytics. The interviews were analyzed using thematic analyses. All data were collected during June 2022. The questionnaire results indicate that students strengthened their interpersonal communication skills and learned about the importance of listening to the opinions of others before formulating their positions. Four themes emerged from 16 in-depth interviews, according to Kolb's experimental learning cycle. Students emphasized the effectiveness of experiential learning versus traditional classroom learning. The simulation scenarios were felt to realistically convey critical issues regarding leadership, decision-making, and teamwork challenges. They effectively conveyed the importance of building a culture of conducting substantive and respectful discussions. Simulation is a powerful pedagogical training tool for public health leadership competencies. Simulations were seen to be advantageous over face-to-face learning in imparting a range of leadership skills and hands-on practice. We recommend integrating simulations in all public health leadership training programs.

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

The Current Burden and Future Solutions for Preoperative Cataract-Refractive Evaluation Diagnostic Devices: A Modified Delphi Study.

To obtain consensus on the key areas of burden associated with existing devices and to understand the requirements for a comprehensive next-generation diagnostic device to be able to solve current challenges and provide more accurate prediction of intraocular lens (IOL) power and presbyopia correction IOL success. Thirteen expert refractive cataract surgeons including three steering committee (SC) members constituted the voting panel. Three rounds of voting included a Round 1 structured electronic questionnaire, Round 2 virtual face-to-face meeting, and Round 3 electronic questionnaire to obtain consensus on topics related to current limitations and future solutions for preoperative cataract-refractive diagnostic devices. Forty statements reached consensus including current limitations (n = 17) and potential solutions (n = 23) associated with preoperative diagnostic devices. Consistent with existing evidence, the panel reported unmet needs in measurement accuracy and validation, IOL power prediction, workflow, training, and surgical planning. A device that facilitates more accurate corneal measurement, effective IOL power prediction formulas for atypical eyes, simplified staff training, and improved decision-making process for surgeons regarding IOL selection is expected to help alleviate current burdens. Using a modified Delphi process, consensus was achieved on key unmet needs of existing preoperative diagnostic devices and requirements for a comprehensive next-generation device to provide better objective and subjective outcomes for surgeons, technicians, and patients.

Basic Concepts of Plyometric Training in Soccer Players

Exercises that involve plyometrics are typically broken up into a stretch-shortening cycle with three phases: an eccentric phase (rapid muscle lengthening), an amortization phase (short resting interval), and a concentric phase (explosive muscle shortening). How quickly an athlete transitions from the eccentric to the concentric phase is a measure of his power. Plyometric training (PT) affects the neuromuscular components of the body for maximum contraction of muscle in minimum time. Soccer is a multidimensional sport and requires various strength training, endurance training, power and agility training, and soccer tactical training. Considering all facts, PT has got a strong tendency to develop cardiovascular and neuromuscular fitness. PT improves VO2 max, explosive strength, linear sprint speed, effective kicking, endurance, agility, soccer skills, and vertical jump ability in athletes of any age. PT improves tendon strengthening and increases muscle elasticity, which results in avoiding injuries.

A design of ultra-short-term power prediction algorithm driven by wind turbine operation and maintenance data for LSTM-SA neural network

Due to factors such as meteorology and geography, the generated power of wind turbines fluctuates frequently. In this way, power changes should be predicted in grid connection to take control measures in time. In this paper, an operation and maintenance data-driven LSTM-SA (long short-term memory with self-attention) prediction algorithm is designed to predict the ultra-short-term power of wind turbines. First, the wind turbine operation and maintenance data, including wind speed, blade deflection angle, yaw angle, humidity, and temperature, are subjected to feature selection by using the Pearson correlation coefficient method and the Lasso algorithm, thereby establishing the correlation between wind speed, blade deflection angle, and out power. Then, full-connect neural network is trained to establish a mapping model of wind speed, blade deflection angle, and out power. The power change rate k is calculated by the derivative of output power to wind speed. Finally, based on the historical power data and the power change rate k, the LSTM neural network power prediction model is trained to calculate the output power prediction value. In order to increase the training efficiency and reduce the delay, the self-attention mechanism is used to optimize the hidden layer of the LSTM model. The test results show that, compared with similar prediction algorithms, this algorithm has higher prediction accuracy, faster convergence speed, and better stability, which can solve the problem of accurately predicting ultra-short-term power when wind power training data is inadequate.

The Effect of Limb Muscle Explosive Power Training on the Speed of the Results of Pencak Silat Kicks of PSHT Students

Internal problem study this is there any effect of leg muscle explosive power training on the speed of sickle kick results of Pencak Silat PSHT students at SMA Negeri 4 Sekayu. The purpose of this study was to determine whether or not there was an effect of leg muscle explosive power training on the speed of sickle kick results of Pencak Silat PSHT Students of SMA Negeri 4 Sekayu. The method used in this study uses the method Experiment Design That is pre- Experiment Design Often also called “Almost Experiment” or mock experiments. The type of design used one group pretest-posttest design. Data collection techniques with tests. Data analysis techniques using statistical analysis. Researchers use several method statistics, namely simple linear regression, product moment correlation, and testing the hypothesis used t test The conclusion shows the results of the regression Y = 0.008 + 1.16 X the results of calculating the product moment correlation (rxy) of 0.17 is included in the category of the level of a strong relationship between the independent variables and the variables bound, and the determinant coefficient (r2) = 51% of the speed of sickle kick results and the rest is influenced by other factors. Also, through hypothesis testing, namely t-count&gt; t-table (5.29 &gt; 1.701). clarifies that Ha is accepted and Ho rejected significantly. Thus, there is an effect of muscle explosive power training limbs on the speed of the sickle kick results of Pencak Silat PSHT students at SMA Negeri 4 Sekayu.

The Effects of Power Training Frequency on Muscle Power and Functional Performance in Older Women: A Randomized Controlled Trial.

Katsoulis, K and Amara, CE. The effects of power training frequency on muscle power and functional performance in older women: a randomized controlled trial. J Strength Cond Res 37(11): 2289-2297, 2023-Low-intensity power training (PT) has emerged as an effective method for improving muscle power and functional performance in older adults. However, effects of low training frequencies are less understood and could expand the repertoire of exercise prescription, particularly in older women who experience greater functional disability with age compared with men. This study investigated the impact of frequency of low-intensity (40% of 1 repetition maximum, 1RM) PT on lower-body power and functional performance in healthy older women. Women (74 ± 4 years) were randomized to 12 weeks of PT of 1 (PT1, n = 14), 2 (PT2, n = 17), or 3 (PT3, n = 17) d·wk -1 or wait control (CON, n = 15). Measures included leg press 1RM, knee extension power (KEP), and functional performance (stair climb power, stair climb time, 30-second chair stands, 400-m walk, Short Physical Performance Battery). There were no differences between the frequency of training in changes in leg press 1RM, KEP, or functional performance after 12 weeks. Pre-post data for individual training groups revealed that leg press 1RM improved in all PT groups (20-33%, p < 0.05). Furthermore, KEP improved in PT2 and PT3 by 10 and 12%, respectively, and all PT groups improved in the 30-second chair stands and Short Physical Performance Battery (6-22%), whereas PT1 and PT3 improved in the 400-m walk and PT2 improved in stair climb power and stair climb time after training (4-7%, p < 0.05). One to 3 weekly low-intensity PT sessions can improve functional performance, although improvements in both functional performance and power might require 2 or 3 sessions per week in older healthy women.

Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook

For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In other words, how the physics is encoded into DNNs and how the physics and data components are represented. In this paper, we offer an overview of a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE), a central problem in transportation engineering. When observation data, problem type, and goal vary, we demonstrate potential architectures of PIDL computational graphs and compare these variants using the same real-world dataset.

Residual effects of 12 weeks of power-oriented resistance training plus high-intensity interval training on muscle dysfunction, systemic oxidative damage, and antioxidant capacity after 10 months of training cessation in older people with COPD.

This study aimed to assess the residual effects of a 12-week concurrent training program (power training + high-intensity interval training) in older adults with chronic obstructive pulmonary disease (COPD). A total of 21 older adults with COPD [intervention (INT), n = 8; control (CON), n = 13; 76.9 ± 6.8 years] were assessed at baseline and 10 months after the completion of the intervention by the short physical performance battery (SPPB), health-related quality of life (EQ-5D-5L), vastus lateralis muscle thickness (MT), peak pulmonary oxygen uptake (peak VO2 ) and peak work rate (Wpeak ), early and late isometric rate of force development (RFD), leg and chest press maximum muscle power (LPmax and CPmax ), and systemic oxidative damage and antioxidant capacity. Compared to baseline, after 10 months of detraining, the INT group presented increased SPPB (∆ = 1.0 point), health-related quality of life (∆ = 0.07 points), early RFD (∆ = 834 N∙s-1 ), LPmax (∆ = 62.2 W), and CPmax (∆ = 16.0 W) (all p < 0.05). In addition, a positive effect was noted in INT compared to CON regarding MT and Wpeak (both p < 0.05). No between-group differences were reported in peak VO2 , late RFD, systemic oxidative damage, and antioxidant capacity from baseline to 10 months after the completion of the intervention (all p > 0.05). Twelve weeks of concurrent training were enough to ensure improved physical function, health-related quality of life, early RFD and maximum muscle power and to preserve MT and Wpeak but not peak VO2 , late RFD, systemic oxidative damage and antioxidant capacity in the subsequent 10 months of detraining in older adults with COPD.

Optimization of cost to customer of power train in commercial vehicle using knapsack dynamic programming influenced by vehicle IoT data

Optimization of cost to customer of power train in commercial vehicle using knapsack dynamic programming influenced by vehicle IoT data

Analysis of the effect of compound training for explosive power on improving the psychological resilience of football athletes

Abstract In view of the problem that in the training of football athletes, coaches pay attention to their football skills and physical quality and ignore their psychological quality training, which leads to poor psychological quality of the athletes, this paper studies the effect of compound training on improving the psychological resilience of football athletes in depth. On the one hand, this paper modeled human lower-limb motion chain. First of all, the explosive power of lower limbs of football athletes was evaluated, and the maximum value of the product was obtained in the power-velocity curve, which aims to further increase the velocity and power of movements. Then, the human multi-rigid body model of lower limbs was established, and the motion chain curve planning of zero-point moment and center of gravity was carried out, which transforms the motion modeling problem of human lower-limb motion chain into the basic variable set problem of human lower-limb motion state. Finally, through the basic variables planning of human lower-limb motion state, the accurate modeling of human lower-limb motion chain was obtained. On the other hand, this paper analyzed and summarized the theory that compound training is capable of improving psychological resilience of football athletes, sorted out and integrated the influencing factors of psychological resilience, and on this basis conducted research and put forward a new development direction of psychological resilience. The experimental results show that the PEM value of goal focus and emotion control is 0.82, and the PEM value of positive cognition is 0.73, indicating that athletes’ performance in positive cognition has been significantly improved. Therefore, it is concluded that psychological resilience can reduce athletes’ athletic burnout, stimulate athletes’ athletic motivation, and enable athletes to take a more positive and optimistic attitude toward training and competition, so as to achieve better athletic performance.