Load Monitoring Research Articles

Identification of Moving Train Axle Loads for Simply Supported Composite Beam Bridges in Urban Rail Transit

With the rapid urbanization and expansion of rail transit systems, the axle loads of trains, which are a critical aspect of their configuration, have significantly increased. This increase poses substantial potential threats to the safety and service life of existing bridges within urban rail transit networks. Therefore, it is imperative to develop methods for monitoring and identifying train axle loads. In this study, a strain field measurement scheme was devised and implemented for an operational simply supported composite beam bridge in urban rail transit. This involved numerical modeling and validation of the bridge’s structural response, followed by the calculation of strain influence lines at specific measurement points. Subsequently, a method for identifying train axle loads, considering the dynamic amplification effect, was established. This method integrates principles from strain influence lines with mathematical optimization techniques. Specifically, the axle loads of locomotives on the forward AC03 type trains of Shanghai Metro Line 3 were found to conform to a logarithmic normal distribution model, while those of middle carriages followed a normal distribution model. Their respective mean axle loads were determined as 9.64 t and 10.77 t, with a shared variance of 0.8. Similarly, the axle loads of locomotives and middle carriages on reverse AC03-type trains also followed normal distribution models, with identical mean values around 10.5 t. The variances for axle loads of locomotives and middle carriages of reverse trains were found to be 1.36 and 0.8, respectively. The developed method effectively enables the monitoring of train axle loads and assessment of their impact on bridge structures, therefore enhancing safety and operational reliability within urban rail transit systems.

Adherence of healthcare providers to Enhanced Adherence Counseling (EAC) intervention protocol in West Amhara Public Health Facilities, Northwest Ethiopia, 2023: mixed method evaluation

BackgroundIn Ethiopia, there were an estimated 670,906 people living with the Human Immune Virus (HIV). Implementing an HIV test and treat strategy and rapid scale-up of anti-retroviral treatment (ART) provided health facilities increased the number of the number of people living with HIV/AIDS. In the same way, the expansion of viral load monitoring in these health facilities and poor adherence to ART increase the number of high-viral load (HVL) patients. To alleviate this problem, the World Health Organization (WHO) recommended EAC intervention for HVL patients. Therefore, the aim of this research was to determine the level of healthcare providers’ adherence to the EAC intervention protocol and explore barriers and facilitators of the intervention in West Amhara, Northwest Ethiopia.MethodDescriptive cross-sectional study design with concurrent mixed-method evaluation was employed. The adherence dimension, with its sub-dimensions of content, coverage, frequency, and duration of the EAC intervention, was used with sixteen indicators. A total of 20 high-case-load public health facilities and 173 HVL patients were included in our study. Quantitative data was entered into Epi Info and exported to SPSS version 25 for analysis. Descriptive statistics are analyzed in terms of frequencies, percentages, variances, and means and presented as narrations, frequency tables, graphs, and charts. Qualitative data were transcribed, translated, coded, and analyzed thematically using Open Code version 4.0 software. The qualitative findings were used to triangulate the quantitative findings.ResultThe average adherence level of health care providers (HCPs) to the EAC intervention protocol was 55.3%, from which content, coverage, frequency, and duration of the intervention contributed 70.3%, 86.3%, 36.9%, and 27.7%, respectively. Most of the intervention contents were delivered during the session, but none of the providers developed a patient adherence plan at the end of the session. All HVL patients were linked and enrolled in the EAC intervention. But only 6% of them were tested for repeat VL.ConclusionThe average adherence level of HCPs to the EAC intervention protocol was very inadequate. The main gap identified was difficulties in completing the EAC intervention sessions based on schedules. Implementing adherence improvement strategies, assigning an adequate number of EAC providers in ART and Prevention of Mother-to-Child Transmission (PMTCT) clinics, and allowing sufficient time during EAC sessions are important.

Optimizing smart home appliance energy monitoring using Factorial Hidden Markov Models for Internet of Behavior

The energy demand which is increasing globally strongly underlines the necessity of solutions that will improve power efficiency. This study addresses the research problem of enhancing energy management in smart homes through Non-Intrusive Load Monitoring (NILM), a method that enables consumers and companies to optimize their energy usage by disaggregating total household energy consumption into specific appliance-level data. The research focuses on the creation and establishment of an Internet of Behavior (IoB) model that encourages NILM to develop more energy-efficient and clever residential buildings as its main goal. The Factorial Hidden Markov Model (FHMM) is employed as the core methodology in this NILM process, facilitating the disaggregation of overall household energy usage into individual appliance consumption patterns. This method was used over the known Reference Energy Disaggregation Dataset (REDD) for comparing the actual energy consumption of the appliances with the projected estimates. The results are evidence that the FHMM method is an energy disaggregation technique that can provide important information about the customer usage of energy and, hence will help in the strategic shifting of high-energy appliances to non-peak hours for the user. This study's main value lies in the new application of IoB-based NILM methods to increase energy efficiency, which is the real solution for energy costs and the grid peak load problem. The results of the proposed method are compared with other methods and real data. The comparisons show the acceptable response of the proposed method when it is compared with the real data and is better than another method.

An Ensemble Method for Non-Intrusive Load Monitoring (NILM) Applied to Deep Learning Approaches

An Ensemble Method for Non-Intrusive Load Monitoring (NILM) Applied to Deep Learning Approaches

Wavelet-based convolutional neural network for non-intrusive load monitoring of next generation shipboard Power Systems

In this study, a non-intrusive load monitoring (NILM) framework is developed for next generation shipboard power systems (SPS) based on a discrete wavelet transform signal processing and a convolutional neural network (CNN). We have applied the developed NILM method to a four-zone medium voltage direct current (MVDC) SPS to evaluate the effectiveness of the proposed method. Each zone of the MVDC SPS consists of multiple components, such as propulsion load, pulsed load, high ramp rate load, cooling load, and hotel load. The current signals from the main generators are the main inputs to the NILM model. The current signals are first processed through a discrete wavelet transform to create a coefficient vector that reflects the status of all the components in each zone. Then, a multi-class classification problem is formulated and solved using a CNN architecture model to monitor the load statuses in real time. The results of case studies show that the developed NILM model in comparison with benchmark methods can (i) accurately monitor the status of all components with a total accuracy of over 98%, (ii) identify unique pulsed loads with a total accuracy of over 99%, and (iii) sustain the functionality of load monitoring under extreme events such as cyber/physical attacks, load uncertainty, and noisy inputs.

Development of Material Sensors Made of Metastable Austenitic Stainless Steel for Load Monitoring

AbstractMetastable stainless steels can be used as a load-sensitive sensor. In combination with an eddy current testing system, mechanical overloads of a component can be detected directly during operation. Material sensors were prepared by shot peening fatigue specimen of metastable austenitic steel to obtain a martensitic surface layer and a local heating by a laser beam to obtain an austenitic area in the layer. In order to investigate the response of the material sensor to overload and achieve different trigger thresholds, the thermal energy applied to create the sensor material and the geometry of the material sensors were varied. It is shown that the austenitized volume and the martensite fraction in the material sensor correlate with the phase of the eddy current signals. Starting from the martensitic surface layer, the phase decreases as the austenitized volume increases. If martensite formation takes place due to an overload, the phase increases as a result. To determine the threshold stress needed to trigger the material sensor, cyclic rotating bending tests were carried out on austenitic stainless steel 1.4301 (AISI 304). In step tests, the bending stress was gradually increased and subsequently ex-situ eddy current testing was carried out. The potential for predicting and classifying an overload is significantly greater with a higher applied thermal energy. Three different sensor geometries (rhombus, cross and ring) were employed in tests. In comparison, the rhombus-shaped material sensor provided the greatest potential for load history interpretation due to the significant phase change.

Load Recognition With Few-Shot Transfer Learning Based on Meta-Learning and Relational Network in Non-Intrusive Load Monitoring

Load Recognition With Few-Shot Transfer Learning Based on Meta-Learning and Relational Network in Non-Intrusive Load Monitoring

Scientific preparation for JRT: Wind pressure prediction model for large radio telescope based on real data from multi-sensors

Jingdong 120-meter radio telescope (JRT) is poised to become the world's largest single-aperture fully steerable medium-low frequency radio telescope. However, like other large-aperture radio telescopes, the JRT is vulnerable to wind loads, which can cause structural deformation and pointing errors. Addressing this challenge requires the ability to predict dynamic winds in real-time. This study developed a wind pressure preprocessing and prediction model using sensor data collected from the Kunming 40-meter radio telescope (KRT), enabling real-time prediction of wind pressure on the telescope. The model employs adaptive noise and Variational Mode Decomposition (VMD) techniques to eliminate random noise from the original wind pressure data. Subsequently, wind pressure predictions are made using a Bidirectional Long Short-term Memory (BiLSTM) model. By conducting predictions under various stabilization conditions and conducting a thorough analysis of measurement data from five sensors, the study has achieved impressive results in predicting wind pressure on the KRT reflector surface. The proposed model demonstrates the lowest MAE, RMSE, and MAPE, while achieving the highest R2 across various data sets. Where the average R2 of the proposed model is 0.9392 at 45° pitch angle attitude and the RMSE, MAE and MAPE values are 1.4923, 1.2377 and 1.82% respectively. This model helps wind load monitoring of real-time wind pressure monitoring of the telescope surface, to study the effects of wind load on pointing accuracy. By adjusting the control parameters to reduce wind load interference, to ensure the high-precision work of a large radio telescope, such as JRT.

Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security

Deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data Processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, Injection, Masquerade, Normal, and Replay. Random Forest produced AUC values of 1.00 for all classes and an overall F1-score of 0.99 for all classes. The Decision Tree model also shows robust performance for detecting Fault and Injection intrusions (AUC = 0.98), with an overall F1-score of 0.94. However, the LDA and SVM models do not perform well in detecting Injection intrusions with overall F1-scores of 0.83 and 0.86. Advances in machine learning can be used to improve smart grid security, reliability, and efficiency, according to this study. These findings highlight the potential of advanced machine learning techniques to enhance smart grid reliability and efficiency. Doi: 10.28991/HIJ-2024-05-03-018 Full Text: PDF

Degradation assessment of wind turbine based on additional load measurements

In recent years, there has been an increasing focus on the degradation assessment of wind turbines using supervisory control and data acquisition (SCADA) data, establishing condition monitoring as the preferred approach. However, SCADA data primarily focuses on general condition variables, such as temperature, while overlooking critical metrics like load factors for assessing the mechanical properties of wind turbines. The load parameters can be utilized to assess the mechanical stresses and strains experienced by wind turbine components, thereby offering more precise information on degradation. To address this gap, we explore the feasibility of implementing additional load monitoring for the degradation assessment of wind turbines. Specifically, we design a cost-effective load sensor system for collecting load data from wind turbines. On this basis, a novel degradation assessment method that incorporates additional load data and a nonlinear dynamic state-space neural network model is proposed to extract degradation information from wind turbines. Then, we introduce a dynamic degradation index, derived through trend analysis, which effectively captures and quantifies the degradation levels of wind turbines over time. Finally, a case study using a dataset collected from a real wind farm is provided to validate the proposed method. The results demonstrate a significant enhancement in degradation assessment. By incorporating additional load data, the proposed method exhibits heightened responsiveness to degradation levels and more effectively captures the progression of degradation trends.

Load Changes on a Short-Segment Posterior Instrumentation After Transosseous Disruption of L3 Vertebra - A Biomechanical Human Cadaveric Study.

Biomechanical Cadaveric Study. Following the successful use of a novel implantable sensor (Monitor) in evaluating the progression of fracture healing in long bones and posterolateral fusion of the spine based on implant load monitoring, the aim of this study was to investigate its potential to assess healing of transosseous fractures of a lumbar vertebra stabilized with a pedicle-screw-rod construct. Six human cadaveric spines were instrumented with pedicle screws and rods spanning L3 vertebra. The spine was loaded in Flexion-Extension (FE), Lateral-Bending (LB) and Axial-Rotation (AR) with an intact L3 vertebra and after its transosseous disruption, creating an AO B1 type fracture. The implant load was measured on the one rod using the Monitor and on the contralateral rod by strain gauges to validate the Monitor's measurements. In parallel, the range of motion (ROM) was assessed. ROM increased significantly in all directions in the fractured model (P ≤ 0.049). The Monitor measured a significant increase in implant load in FE (P = 0.002) and LB (P = 0.045), however, not in AR. The strain gauge - aligned with the rod axis and glued onto its posterior side - detected an increased implant load not only in FE (P = 0.001) and LB (P = 0.016) but also in AR (P = 0.047). After a complete transosseous disruption of L3 vertebra, the implant load on the rods was considerably higher vs the state with an intact vertebral body. Innovative implantable sensors could monitor those changes, allowing assessment of the healing progression based on quantifiable data.

Fusion of computer vision and piezoelectric tactility approach to measuring moving vehicle loads

Vehicle overloading seriously threatens the safety and long-term operation of global traffic infrastructure. The emergence of computer vision-based non-contact weigh-in-motion (NC-WIM) technique has made it possible to expand the coverage of vehicle load monitoring, but misjudgment of vehicle weight due to abnormal tire pressure is still an insurmountable drawback of this approach. In this study, we propose a novel approach to measure moving vehicle loads by fusing computer vision and piezoelectric tactility. Meanwhile, a visual-tactile sensing system based on the proposed method is built on a real road. Field tests show that the proposed method can effectively avoid the misjudgment caused by abnormal tire pressure, and the errors of vehicle weight estimation are within the range of ± 10 % in both underinflated and overinflated cases. The proposed solution not only ensures the accuracy of overload warnings, but also promises to provide support for the design, operation, and maintenance of transport infrastructure.

Non-Intrusive Load Monitoring Based on Dimensionality Reduction and Adapted Spatial Clustering

Non-invasive load monitoring (NILM) deduces changes in energy consumption patterns and operational statuses of electrical equipment from power signals in the feed line. With the emergence of fine-grained power load distribution, the importance of utilizing this technology for implementing demand-side energy management in smart grid development has become increasingly prominent. To address the issue of low load identification accuracy stemming from complex and diverse load types, this paper introduces a NILM method based on uniform manifold approximation and projection (UMAP) reduction and enhanced density-based spatial clustering of applications with noise (DBSCAN). Firstly, this paper combines the characteristics of user load under transient and steady-state conditions and selects data with significant differences to construct a load-characteristic database. Additionally, UMAP is employed to reduce the dimensionality of high-dimensional load features and rebuild a load feature database. Subsequently, DBSCAN is utilized to categorize typical user loads, followed by a correlation analysis with the load-characteristic database to determine the types or classes of loads that involve switching actions. Finally, this paper simulates and analyzes the proposed method using the electricity consumption data of industrial users from the CER–Electricity–Data dataset. It identifies the electricity load data commonly utilized by users in a specific area of Zhejiang Province in China. The experimental results indicate that the accuracy of the proposed non-invasive load identification method reaches 95%. Compared to the wavelet transform, decision tree, and backpropagation network methods, the improvement is approximately 5%.

A training-free non-intrusive air conditioning load monitoring method based on fuzzy comprehensive evaluation

Air conditioner (AC) is a grid friendly load, monitoring its operating status plays a key role in evaluating user side potential for participation in demand response. With continuous innovations in AC technology, the AC operation characteristics have shown increasing complexity, which poses significant challenges for non-intrusive AC load monitoring. Furthermore, obtaining labeled training data for target scenarios is often challenging, which hinders deployment of many Non-Intrusive Load Monitoring (NILM) methods in practice. In this regard, this article proposes a training-free non-intrusive AC load monitoring method based on fuzzy comprehensive evaluation (FCE), which is very easy to deploy and has good robustness. Firstly, considering various influence factors, a FCE model is constructed based on common knowledge about AC. This model simulates the manual labeling process to locate AC operation periods. Secondly, an AC power estimation method based on weighted filtering is established, in which weighted filter is used to eliminate the non-AC load components that may overlap during AC operation periods. Specifically, an optimization model is constructed to make the cumulative power of the AC events in its working cycle approach zero, with the filter weight vector as the objective variable, in which a heuristic greedy algorithm is used to find the optimal filter weight vector. When multiple feasible solutions with similar objective function values are derived in the solving process, the probability of load events appearing in AC and non-AC operation periods are compared. The final solution is selected, taking into account the likelihood of each feasible solution corresponding to a load event sequence belonging to AC load. The testing results on the PecanStreet dataset and multiple real-world measured datasets prove the effectiveness of the proposed method in monitoring complex ACs with poor waveform consistency.

Experimental study on evaluating fracture processes of different rocks using multiple physical parameters

To improve the accuracy of rock fracture evaluation, three types of rocks with different lithologies, namely, granite, coal, and sandstone, were processed into cuboid samples with unilateral notches for three-point bending tests. Real-time data were obtained via load, resistance, and acoustic emission (AE) monitoring. Various fracture patterns of the fractured rocks were collected, and the correlations between multiple physical parameters were determined. The results indicated that granite had the highest fracture toughness and initial fracture energy required for macroscopic fracturing, followed by sandstone, which had the lowest fracture toughness and initial fracture energy. The primary crack participated in the fracture process of coal and produced the most complex crack morphology, exhibiting the characteristics of “Progressive fracture” different from the “Instantaneous fracture” of granite and sandstone. The crack length reached 90.5 mm, which was much longer than that of sandstone. The temporal characteristics obtained from multiple physical quantities were synchronized with the fracture behavior. The resistivity increased gradually with the fracture, and the resistivity fluctuation caused by crack propagation increased. The resistivity change rate of granite was the highest when macrofractures occurred, whereas that of coal after complete fracture was the highest. Rock fracture produced a large number of AE events with small amplitude and low peak frequency, concentrated in the 100 ± 50 kHz band. The AE count corresponded to the crack propagation process, and the AE location indicated the crack complexity. The peak change rate of the resistivity was used to evaluate the fracture performance and exhibited a significant linear relationship with the peak load, fracture toughness, and initial fracture energy. The relationship between the accumulated AE energy and the fracture length was a quadratic function, which was used to evaluate the fracture complexity. The multiple physical quantity monitoring method is promising for predicting the fracture behavior of rocks.

A bridge dynamic response analysis and load recognition method using traffic imaging

As the primary variable load of bridges, vehicle load is an important parameter for bridge health monitoring. However, traditional Weigh-in-Motion (WIM) systems and the commonly used method of placing sensors on the bridge are challenging to apply in load monitoring for many small and medium-sized bridges. Therefore, this paper proposes a bridge vehicle load identification method based on traffic surveillance video data. Leveraging the surveillance video data on the bridge, without introducing additional hardware devices, the displacement of target points is detected through sub-pixel level image detection algorithms, enabling non-contact measurement of bridge structural response through imaging. A spatiotemporal relationship model of structural displacement, vehicle load, and load distribution is established to solve for vehicle load. Finally, model bridge tests under various loading conditions and engineering practice experiments are conducted to validate the feasibility of the method. The results of the model bridge tests show that the structural displacement measured using traffic video measurement has a deviation of less than 10% compared to the measurements obtained using contact displacement sensors (LVDT), and it can accurately reflect the displacement characteristics of the structure. The results of the field tests demonstrate that the average estimation deviation for heavy vehicle loads ranging from 12 to 18 tons is approximately 18%, meeting the engineering requirements. The proposed method can provide load statistical information for the extensive health monitoring of small and medium-sized bridges and offer a new technical pathway for obtaining bridge load information.

Quantifying internal and external training loads in collegiate male volleyball players during a competitive season

BackgroundThe long-term monitoring of internal and external training load is crucial for the training effectiveness of athletes. This study aims to quantify the internal and external training loads of collegiate male volleyball players during the competitive season. The internal and external training load variables were analyzed across mesocycles and playing positions.MethodsFourteen participants with age of 20.2 ± 1.3 years, height of 1.81 ± 0.05 m, and body weight of 70.8 ± 5.9 kg were recruited. The data were collected over a 29-week period that was divided into four mesocycles: preparation 1 (P1, weeks 1–7), competition 1 (C1, weeks 8–14, including a 5-day tournament in week 14), preparation 2 (P2, weeks 15–23), and competition 2 (C2, weeks 24–29, including a 6-day tournament in week 29). Each participant wore an inertial measurement unit and reported the rating of perceived exertion in each training session. The internal training load variables included weekly session rating of perceived exertion, acute: chronic workload ratio, and training monotony and strain. The external training load variables included jump count and height and the percentage of jumps exceeding 80% of maximal height.ResultsC2 had the highest average weekly internal training load (3022 ± 849 AU), whereas P2 had the highest average weekly acute: chronic workload ratio (1.46 ± 0.13 AU). The number of weekly jumps in C1 (466.0 ± 176.8) was significantly higher than in other mesocycles. Weekly jump height was significantly higher in C1, P2, and C2. Internal training load was positively correlated with jump count (ρ = 0.477, p < 0.001). Jump count was negatively correlated with jump height (ρ = −0.089, p = 0.006) and the percentage of jumps exceeding 80% of maximal height (ρ = −0.388, p < 0.001). The internal and external training load variables were similar among different playing positions.ConclusionThe participants exhibited significantly higher internal training load in C2 and higher jump height after P1. A high jump count was associated with higher internal training load and lower jump height. Excessive jumps may result in fatigue and reduce height.

Challenges and Opportunities with at-Home Blood Collection for HIV-1 Viral Load Monitoring among Sexual Minoritized Men who use Stimulants.

Sexually minoritized men (SMM) with HIV who use stimulants experience difficulties achieving and maintaining an undetectable viral load (VL). Home-based VL monitoring could augment HIV care by supporting interim, early identification of detectable VL. We describe implementation challenges associated with a home-collection device for laboratory-based VL testing among SMM with HIV who use stimulants. From March-May 2022, cisgender SMM with HIV reporting moderate-to-severe stimulant use disorder and suboptimal (< 90%) past-month antiretroviral therapy (ART) adherence were recruited via a consent-to-contact participant registry. Eligible men completed teleconference-based informed consent and were mailed a HemaSpot-HD blood collection device (volume capacity 160 µL; lower limit of detection 839 copies/mL) with detailed instructions for home blood self-collection and return shipment. Implementation process measures included estimated blood volume and VL quantification. Among 24 participants, 21 (88%) returned specimens with a median duration of 23 days (range: 10-71 days) between sending devices to participants and receiving specimens. Of these, 13/21 (62%) included enough blood (≥ 40 µL) for confidence in detectable/undetectable results; 10/13 (77%) had detectable VL, with 4/10 (40%) were quantifiable at ≥ 839 copies/mL. The remaining 8/21 had low blood volume (< 40 µL), but 3/8 (38%) still had detectable VL, with 1/3 (33%) quantifiable at ≥ 839 copies/mL. Home blood collection of ≥ 40 µL using HemaSpot-HD was feasible among this high-priority population, with > 50% having a VL detected. However, interim VL monitoring using HemaSpot-HD among those experiencing difficulties with ART adherence may be strengthened by building rapport via teleconferencing and providing detailed instructions to achieve adequate sample volume.

DYNAMIC VIRAL LOAD MONITORING AND METAGENOMIC SEQUENCING IN ACUTE RETINAL NECROSIS CAUSED BY VARICELLA-ZOSTER VIRUS.

To analyze the trend of intraocular viral load after antiviral treatment in patients with varicella-zoster virus-induced acute retinal necrosis and to explore the effect of viral genotypes on clinical manifestations. In this case series, viral load was detected using polymerase chain reaction from aqueous humor during treatment; viral load curves were fitted, and the time required to reach the inflection point between plateau phase and logarithmic reduction phase was estimated. Variations in viral genomes were detected by metagenomic sequencing. Twenty eyes of 20 patients were included. The median (interquartile range) initial viral load was 5.9 × 10 7 (1.1 × 10 7 -1.1 × 10 8 ) copies/mL. The average duration of retinitis was 5 ± 3 weeks. The average time required to reach the inflection point was 4.2 ± 1.6 days. Time required to reach the inflection point was correlated with the duration of retinitis ( P = 0.025). Patients with varicella-zoster virus carrying the p.S715* variation in ribonucleotide reductase ( RNR ) subunit 1 gene had lower initial viral loads (median 1.3 × 10 7 copies/mL) than those without (median 1.1 × 10 8 copies/mL; adjusted P = 0.030). The inflection of viral load curve is helpful to estimate the length of plateau phase and the duration of retinitis during antiviral treatment in patients with acute retinal necrosis. Loss-of-function variation in RNR gene might be correlated with lower virulence of varicella-zoster virus.

Scientific and Systematic Research on Technical and Tactical Training of Basketball Players

This article examines the subdivision of basketball skill improvement and individual differences, pointing out the enhancing effect of biomechanics on training rationality, and also examining the importance of athletes' psychological regulation on skill release. Among them, the team tactical system is constantly constructed based on the characteristics of confrontation, and exercises and on-site operations should be combined to ensure the effectiveness and flexibility of the overall tactical layout. The monitoring and evaluation methods of exercise load monitoring, technical and tactical effect evaluation, and physical function testing provide continuous optimization power for basketball training.