The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing...The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adap-tiveness and fast responding features.The Model Predictive Control(MPC)tech-nique is a widely used,safe and reliable control method based on constraints.On the other hand,the Eddy Current dynamometers are highly nonlinear braking sys-tems whose performance parameters are related to many processes related vari-ables.This study is based on an adaptive model predictive control that utilizes selected AI methods.The presented approach presents an updated the mathema-tical model of an Eddy Current Dynamometer based on experimentally obtained system operational data.Finally,the comparison of AI methods and related learn-ing performances based on the assessment technique of mean absolute percentage error(MAPE)issues are discussed.The results indicate that Single Hidden Layer Neural Network(SHLNN),General Regression Neural Network(GRNN),Radial Basis Network(RBNN),Neuro Fuzzy Network(ANFIS)coupled MPC have quite satisfying performances.The presented results indicate that,amongst them,GRNN appears to provide the best performance.展开更多
Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigat...Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigates the muscle strength performance(MSP)of 289 adult and teenage CA,exercisers,and physically inactive individuals(PI),and proposes predictive models of MSP for adults.Methods:Muscle maximal,speed,and explosive strength(MMS/MSS/MES)at unilateral maximal concentric flexion and extension contraction(FC/EC)were evaluated using Biodex System 3 PRO^(TM)at 60°/s,180°/s,and 300°/s,while additional performance markers were assessed through field ergometric testing.Participants were interviewed about their lifestyle,dietary habits,physical activity,injury,and medical history.Body composition was assessed via bioelectrical impedance.gDNA was extracted from biochemical samples and then genotyped.Statistical analysis was conducted using IBM SPSS Statistics v21.0 and R.Results:Age,fitness,and sex impacted correlations of MSP with body composition and anthropometric measurements(p<0.05).Among CA,females outperformed males in accuracy(p<0.001)while,males outperformed females in anaerobic power,MSP,speed,and endurance(p<0.001).Adult CA outperformed exercisers and PI in MMS,MSS,and MES(p<0.05).Multiple linear regression models,with predictors age,FFM,body extremity,training load explained the majority of variation in MMS(R^(2)_(adj):71.4%–88.9%),MSS(R^(2)_(adj):64.8%–78.4%),and MES(R^(2)_(adj):52.7%–68.4%)at EC,FC,and their mean(p<0.001).Conclusions:Muscle-strengthening strategies should be customized according to individual fitness levels,body composition,and anthropometric measurements.The innovative sex-specific regression models assessing MMS,MSS,and MES at EC and FC provide a framework for personalizing rehabilitation and skill-specific training strategies.展开更多
Electric vehicle(EV) drive trains are constantly subjected to an imbalance between demanded torque and generated electromagnetic torque due to unpredictable terrain, traffic, and other external factors. This imbalance...Electric vehicle(EV) drive trains are constantly subjected to an imbalance between demanded torque and generated electromagnetic torque due to unpredictable terrain, traffic, and other external factors. This imbalance leads to significant torsional vibrations and speed fluctuations, which not only compromise passenger comfort but also exert additional mechanical stress on the EVs. Conventional sensorless methods offer speed estimation and control;however, they provide suboptimal performance with sudden load torque disturbances and operational uncertainties, especially at low speeds and across diverse real-world driving cycles. To address these challenges and improve system robustness, this work proposes an advanced sensorless integral sliding mode control(ASISMC) that enhances performance under diverse operating conditions. The proposed ASISMC methodology shows robust performance across a wide speed range, effectively mitigating abrupt load torque disturbances while minimizing the effect of uncertainties within the system dynamics. The approach is experimentally validated for a wide range of speeds and periodic/non-periodic load torque disturbances. Additional validation through the new European driving cycle(NEDC) and urban dynamometer driving schedule(UDDS) demonstrates the method's effectiveness and reliability in real-world driving conditions.展开更多
A method for a vehicle durability emission test using a robot driver insteadof human drivers on the chassis dynamometer is presented. The system architecture of vehicledurability emission test cell, the road load simu...A method for a vehicle durability emission test using a robot driver insteadof human drivers on the chassis dynamometer is presented. The system architecture of vehicledurability emission test cell, the road load simulation strategy and the tele-monitoring systembased on Browser/Client structure are described. Furthermore, the construction of the robot driver,vehicle performance self-learning algorithm, multi-mode vehicle control model and vehicle speedtracking strategy based on fuzzy logic arealso discussed. Besides, the capability of controlparameters self-compensation on-line makes it possible to compensate the wear of vehicle componentsand the variety of clutch true bite point during the long term test. Experimental results show thattherobot driver can be applicable to a wide variety of vehicles and the obtained results stay withina tolerance band of ± 2 km/h. Moreover the robot driver is able to control tested vehicles withgood repeatability and consistency; therefore, this methodpresents a solution to eliminate theuncertainty of emission test results by human drivers and to ensure the accuracy and reliability ofemission test results.展开更多
Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification a...Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification are two key steps. The dynamometer card is firstly divided into four parts which include different production information according to the ＂four point method＂ used in actual oilfield production, and then the moment invariants for pattern recognition are extracted. An improved support vector machine （SVM） method is used for pattern classification whose error penalty parameter C and kernel function parameter g are optimally chosen by the particle swarm optimization （PSO） algorithm. The simulation results show the method proposed in this paper has good classification results.展开更多
It is important to achieve continuous, stable and efficient pumping well operation in actual oilfield operation. Down-hole pumping well working conditions can be monitored in real-time and a reasonable production sche...It is important to achieve continuous, stable and efficient pumping well operation in actual oilfield operation. Down-hole pumping well working conditions can be monitored in real-time and a reasonable production scheme can be designed when computer diagnosis is used. However, it is difficult to make a comprehensive analysis to supply efficient technical guidance for operation of the pumping well with multiple faults of down-hole conditions, which cannot be effectively dealt with by the common methods. To solve this problem, a method based on designated component analysis （DCA） is used in this paper. Freeman chain code is used to represent the down-hole dynamometer card whose important characteristics are extracted to construct a designated mode set. A control chart is used as a basis for fault detection. The upper and lower control lines on the control chart are determined from standard samples in normal working conditions. In an incompletely orthogonal mode, the designated mode set could be divided into some subsets in which the modes are completely orthogonal. The observed data is projected into each designated mode to realize fault detection according to the upper and lower control lines. The examples show that the proposed method can effectively diagnose multiple faults of down-hole conditions.展开更多
Traffic vehicles, many of which are powered by port fuel injection(PFI) engines, are major sources of particulate matter in the urban atmosphere. We studied particles from the emission of a commercial PFI-engine vehic...Traffic vehicles, many of which are powered by port fuel injection(PFI) engines, are major sources of particulate matter in the urban atmosphere. We studied particles from the emission of a commercial PFI-engine vehicle when it was running under the states of cold start, hot start, hot stabilized running, idle and acceleration, using a transmission electron microscope and an energy-dispersive X-ray detector. Results showed that the particles were mainly composed of organic, soot, and Ca-rich particles, with a small amount of S-rich and metal-containing particles, and displayed a unimodal size distribution with the peak at 600 nm. The emissions were highest under the cold start running state, followed by the hot start, hot stabilized, acceleration, and idle running states. Organic particles under the hot start and hot stabilized running states were higher than those of other running states. Soot particles were highest under the cold start running state. Under the idle running state, the relative number fraction of Ca-rich particles was high although their absolute number was low. These results indicate that PFI-engine vehicles emit substantial primary particles,which favor the formation of secondary aerosols via providing reaction sites and reaction catalysts, as well as supplying soot, organic, mineral and metal particles in the size range of the accumulation mode. In addition, the contents of Ca, P, and Zn in organic particles may serve as fingerprints for source apportionment of particles from PFI-engine vehicles.展开更多
The existing design of the pumping systems mainly focuses on the approximate computational formulae and procedures,which are developed based on the analytic approaches of conventional oil/gas fields.The calculation of...The existing design of the pumping systems mainly focuses on the approximate computational formulae and procedures,which are developed based on the analytic approaches of conventional oil/gas fields.The calculation of polished rod loads usually just concerns about the static and inertial loads.And the computation of gearbox torque generally uses empirical formulae and correction factors.The above modeling procedures,if applied to the coalbed methane(CBM)wells,can not give the desired accuracy of the system design and its pertinent analysis.In this paper,based on the kinematic and dynamic analysis of the pumping system,the kinematic relation of polished rod is analyzed,and the variation of the total loads of polished rod is developed with the limits of CBM well conditions along the string.The gearbox torque calculation model is established by combining the counterbalance effect with the calculated dynamometer cards and torque factors.The application characteristics of this model are demonstrated by the example of ZH002-4 well in Qinshui basin.The interpretations of results show that the cranks of beam units should rotate in a counter clockwise direction viewed with the wellhead to the right.Compared with oil?gas fields,the dynamic and friction to polished rod load ratios are relatively high and the computation of polished rod loads should involve the static and inertial loads,as well as vibration and friction loads.And the dynamic load ratio decreases rapidly during the production.Besides,the total deformation of the string is small in CBM wells.As for balanced operation,the gearbox torque load usually has two approximately equal peaks and the magnitudes of instantaneous torque are just within 50%of unbalanced gearbox loadings.The proposed research improves efficiency of the pumping system,loads the pumping unit more uniformly,and provides the reasonable basis for selecting the units.展开更多
A highly precise and timely diagnosis technology can help effectively monitor and adjust the sucker rod production system(SRPS)used in oil wells to ensure a safe and efficient production.The current diagnosis method i...A highly precise and timely diagnosis technology can help effectively monitor and adjust the sucker rod production system(SRPS)used in oil wells to ensure a safe and efficient production.The current diagnosis method is pattern recognition of a dynamometer card(DC)based on feature extraction and perceptron.The premise of this method is that the training and target data have the same distribution.However,the training data are collected from a field SRPS with different system parameters designed to adapt to production conditions,which may significantly affect the diagnostic accuracy.To address this issue,in this study,an improved model of the sucker rod string(SRS)is derived by adding faultparameter dimensions,with which DCs under 16 working conditions could be generated.Subsequently an adaptive diagnosis method is proposed by taking simulated DCs generated near the working point of the target SRPS as training data.Meanwhile,to further improve the accuracy of the proposed method,the DC features are improved by relative normalization and using additional features of the DC position to increase the distance between different types of samples.The parameters of the perceptron are optimized to promote its discriminability.Finally,the accuracy and real-time performance of the proposed adaptive diagnosis method are validated using field data.展开更多
Fifteen heavy-duty diesel vehicles were tested on chassis dynamometer by using typical heavy duty driving cycle and fuel economy cycle. The air from the exhaust was sampled by 2,4- dinitrophenyhydrazine cartridge and ...Fifteen heavy-duty diesel vehicles were tested on chassis dynamometer by using typical heavy duty driving cycle and fuel economy cycle. The air from the exhaust was sampled by 2,4- dinitrophenyhydrazine cartridge and 23 carbonyl compounds were analyzed by high performance liquid chromatography. The average emission factor of carbonyls was 97.2 mg/km, higher than that of light-duty diesel vehicles and gasoline-powered vehicles. Formaldehyde, acetaldehyde, acetone and propionaidehyde were the species with the highest emission factors. Main influencing factors for carbonyl emissions were vehicle type, average speed and regulated emission standard, and the impact of vehicle loading was not evident in this study. National emission of carbonyls from diesel vehicles exhaust was calculated for China, 2011, based on both vehicle miles traveled and fuel consumption. Carbonyl emission of diesel vehicle was estimated to be 45.8 Gg, and was comparable to gasolinepowered vehicles （58.4 Gg）. The emissions of formaldehyde, acetaldehyde and acetone were 12.6, 6.9, 3.8 Gg, respectively. The ozone formation potential of carbonyls from diesel vehicles exhaust was 537 mg O3/km, higher than 497 mg O3/km of none-methane hydrocarbons emitted from diesel vehicles.展开更多
High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff...High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.展开更多
Increasing operating speed of modern passenger railway vehicles leads to higher thermal load onthe braking system. Organic composite brake pads are poor thermal conductors, hence frictionalheat is absorbed mainly by t...Increasing operating speed of modern passenger railway vehicles leads to higher thermal load onthe braking system. Organic composite brake pads are poor thermal conductors, hence frictionalheat is absorbed mainly by the disc. In this study three brake pad types were tested on thedynamometer. Metallic fibres, steel and copper, were introduced to the formulation of twomaterials. The third was a non-metallic material - a reference case. Dynamometer test comprisedemergency brake applications to determine the frictional characteristics of the materials andconstant-power drag braking to analyse the effect of metal fibres on temperature evolution,measured by six thermocouples embedded in the brake disc. Mean friction coefficient is analysedand discussed. It is concluded that conductive fibre in the friction material formulation mayinfluence its tribological characteristics. Despite high thermal conductivity, metal fibres in theconcentration tested in this study, did not reduce temperature of the brake disc.展开更多
In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump...In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.展开更多
AC motors, especially the squirrel cage induction motors have the advantages of simple structure, good reliability and low cost. They are more suitable to be used as electrical dynamometers to provide dynamic load for...AC motors, especially the squirrel cage induction motors have the advantages of simple structure, good reliability and low cost. They are more suitable to be used as electrical dynamometers to provide dynamic load for bench test systems. But, the speed and torque of induction motors are not easy to be controlled accurately. In this work, an electrical dynamometer based on the induction motor is proposed. In order to get better control performance of torque and speed of induction motor, an improved direct torque control method(DTC) is also developed based on the space vector modulation(SVM) technique. The performance of the proposed dynamometer system is validated in the Matlab/Simulink platform. The simulation results show that the new dynamometer has good torque and stator flux response. And the torque and stator current ripples of it are reduced significantly compared with using the conventional DTC method.展开更多
Background： The majority of injuries reported in female basketball players are ankle sprains and mechanisms leading to injury have been debated. Investigations into muscular imbalances in barefoot versus shod conditi...Background： The majority of injuries reported in female basketball players are ankle sprains and mechanisms leading to injury have been debated. Investigations into muscular imbalances in barefoot versus shod conditions and their relationship with injury severity have not been performed. The purpose of this study was to investigate the effects of wearing athletic shoes on muscular strength and its relationship to lower extremity injuries, specifically female basketball players due to the high incidence of ankle injuries in this population. Methods： During pre-season, 11 female collegiate basketball players underwent inversion and eversion muscle strength testing using an iso- kinetic dynamometer in both a barefoot and shod conditions. The difference between conditions was calculated for inversion and eversion peak torque, time to peak torque as well as eversion-to-inversion peak torque percent strength ratio for both conditions. Lower extremity injuries were documented and ranked in severity. The ranked difference between barefoot and shod conditions for peak torque and time to peak torque as well as percent strength ratio was correlated with injury ranking using a Spearman rho correlation （p） with an a level of 0.05. Results： The ranked differences in barefoot and shod for peak eversion and inversion torque at 120°/s were correlated with their injury ranking. Ranking of the athletes based on the severity of injuries that were sustained during the season was found to have a strong, positive relationship with the difference in peak eversion torque between barefoot and shod （p = 0.78; p = 0.02）. Conclusion： It is possible that a large discrepancy between strength in barefoot and shod conditions can predispose an athlete to injury. Nar- rowing the difference in peak eversion torque between barefoot and shod could decrease propensity to injury. Future work should investigate the effect of restoration of muscular strength during barefoot and shod exercise on injury rates.展开更多
A dynamometer was designed and manufactured to measure the joining force in the linear friction welding process.The error percentages of the dynamometer in the upset(x)and vibration(y)directions were 0.9% and 0.75%,re...A dynamometer was designed and manufactured to measure the joining force in the linear friction welding process.The error percentages of the dynamometer in the upset(x)and vibration(y)directions were 0.9% and 0.75%,respectively.The cross-sensitivity range of the dynamometer was 1.2%—3.3% in the two directions.The precision level satisfies the requirements of the dynamometer test.The joining force of the TC17 and TC11 heterogeneous titanium alloys in the linear friction welding process was used to test the manufactured dynamometer.The test results showed that the upset force was large,but the vibration force showed a smaller change in TC11 during the linear friction welding process.In addition,the upset and vibration forces of the linear friction welding were greater with a short welding time than those with a long welding time.展开更多
Alternating current electrical dynamometer is a common device to measure the torque of engines, such as the gasoline engine. In order to solve the problems such as high cost, high energy consumption and complicated me...Alternating current electrical dynamometer is a common device to measure the torque of engines, such as the gasoline engine. In order to solve the problems such as high cost, high energy consumption and complicated measurement system which exists in the direct measurement on the torque of alternating current electrical dynamometer, copper loss and iron loss are taken as two key factors and a soft-sensing model on the torque of alternating current electrical dynamometer is established using the fuzzy least square support vector machine (FLS-SVM). Then, the FLS-SVM parameters such as penalty factor and kernel parameter are optimized by adaptive genetic algorithm, torque soft-sensing is investigated in the alternating current electrical dynamometer, as well as the energy feedback efficiency and energy consumption during the measurement phase of a gasoline engine loading continual test is obtained. The results show that the minimum soft-sensing error of torque is about 0.0018, and it fluctuates within a range from -0.3 to 0.3 N·m. FLS-SVM soft-sensing method can increase by 1.6% power generation feedback compared with direct measurement, and it can save 500 kJ fuel consumption in the gasoline engine loading continual test. Therefore, the estimation accuracy of the soft measurement model on the torque of alternating current electrical dynamometer including copper loss and iron loss is high and this indirect measurement method can be feasible to reduce production cost of the alternating current electrical dynamometer and energy consumption during the torque measurement phase of a gasoline engine, replacing the direct method of torque measurement.展开更多
In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the ...In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.展开更多
Human drivers seem to have different characteristics,so different drivers often yield different results from the same driving mode tests with identical vehicles and same chassis dynamometer.However,drivers with differ...Human drivers seem to have different characteristics,so different drivers often yield different results from the same driving mode tests with identical vehicles and same chassis dynamometer.However,drivers with different experiences often yield similar results under the same driving conditions.If the features of human drivers are known,the control inputs to each driver,including warnings,will be customized to optimize each man–machine vehicle system.Therefore,it is crucial to determine how to characterize human drivers quantitatively.This study proposes a method to estimate the parameters of a theoretical model of human drivers.The method uses an artificial neural network(ANN)model and a numerical procedure to interpret the identified ANN models theoretically.Our approach involves the following process.First,we specify each ANN driver model through chassis dynamometer tests performed by each human driver and vehicle.Subsequently,we obtain the parameters of a theoretical driver model using the ANN model for the corresponding driver.Specifically,we simulate the driver’s behaviors using the identified ANN models with controlled inputs.Finally,we estimate the theoretical driver model parameters using the numerical simulation results.A proportional-integral-differential(PID)control model is used as the theoretical model.The results of the parameter estimation indicate that the PID driver model parameter combination can characterize human drivers.Moreover,the results suggest that vehicular factors influence the parameter combinations of human drivers.展开更多
A new measurement and analysis method was proposed to investigate the changes in elbow joint moments that occur with the use of a front-wheeled walker. A strain gauge-based walker instrumentation system was developed ...A new measurement and analysis method was proposed to investigate the changes in elbow joint moments that occur with the use of a front-wheeled walker. A strain gauge-based walker instrumentation system was developed to monitor the hand loads during walker-assisted walking and integrated with an upper extremity biomechanical model, Preliminary system data were collected for 12 subjects following informed consent. Bilateral upper extremity kinematic data were acquired with a six-camera motion analysis system. Internal joint moments at the elbow were determined in the three clinical planes using the inverse dynamics method. Results showed that during a walker-assisted gait elbow joint moments mainly distributed in the walker stance period. There was a noted demand on the elbow extensor in the sagittal plane with the greatest record as 0.381 N.m/（kg.m）, An interesting “bare phase” of mean elbow joint moments was also found in phase angle-240°-340° of gait cycle. Complete description of elbow joint moments of walkerassisted gait may provide insight into walker use parameters and rehabilitative strategies.展开更多
文摘The recent studies on Artificial Intelligence(AI)accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adap-tiveness and fast responding features.The Model Predictive Control(MPC)tech-nique is a widely used,safe and reliable control method based on constraints.On the other hand,the Eddy Current dynamometers are highly nonlinear braking sys-tems whose performance parameters are related to many processes related vari-ables.This study is based on an adaptive model predictive control that utilizes selected AI methods.The presented approach presents an updated the mathema-tical model of an Eddy Current Dynamometer based on experimentally obtained system operational data.Finally,the comparison of AI methods and related learn-ing performances based on the assessment technique of mean absolute percentage error(MAPE)issues are discussed.The results indicate that Single Hidden Layer Neural Network(SHLNN),General Regression Neural Network(GRNN),Radial Basis Network(RBNN),Neuro Fuzzy Network(ANFIS)coupled MPC have quite satisfying performances.The presented results indicate that,amongst them,GRNN appears to provide the best performance.
文摘Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigates the muscle strength performance(MSP)of 289 adult and teenage CA,exercisers,and physically inactive individuals(PI),and proposes predictive models of MSP for adults.Methods:Muscle maximal,speed,and explosive strength(MMS/MSS/MES)at unilateral maximal concentric flexion and extension contraction(FC/EC)were evaluated using Biodex System 3 PRO^(TM)at 60°/s,180°/s,and 300°/s,while additional performance markers were assessed through field ergometric testing.Participants were interviewed about their lifestyle,dietary habits,physical activity,injury,and medical history.Body composition was assessed via bioelectrical impedance.gDNA was extracted from biochemical samples and then genotyped.Statistical analysis was conducted using IBM SPSS Statistics v21.0 and R.Results:Age,fitness,and sex impacted correlations of MSP with body composition and anthropometric measurements(p<0.05).Among CA,females outperformed males in accuracy(p<0.001)while,males outperformed females in anaerobic power,MSP,speed,and endurance(p<0.001).Adult CA outperformed exercisers and PI in MMS,MSS,and MES(p<0.05).Multiple linear regression models,with predictors age,FFM,body extremity,training load explained the majority of variation in MMS(R^(2)_(adj):71.4%–88.9%),MSS(R^(2)_(adj):64.8%–78.4%),and MES(R^(2)_(adj):52.7%–68.4%)at EC,FC,and their mean(p<0.001).Conclusions:Muscle-strengthening strategies should be customized according to individual fitness levels,body composition,and anthropometric measurements.The innovative sex-specific regression models assessing MMS,MSS,and MES at EC and FC provide a framework for personalizing rehabilitation and skill-specific training strategies.
文摘Electric vehicle(EV) drive trains are constantly subjected to an imbalance between demanded torque and generated electromagnetic torque due to unpredictable terrain, traffic, and other external factors. This imbalance leads to significant torsional vibrations and speed fluctuations, which not only compromise passenger comfort but also exert additional mechanical stress on the EVs. Conventional sensorless methods offer speed estimation and control;however, they provide suboptimal performance with sudden load torque disturbances and operational uncertainties, especially at low speeds and across diverse real-world driving cycles. To address these challenges and improve system robustness, this work proposes an advanced sensorless integral sliding mode control(ASISMC) that enhances performance under diverse operating conditions. The proposed ASISMC methodology shows robust performance across a wide speed range, effectively mitigating abrupt load torque disturbances while minimizing the effect of uncertainties within the system dynamics. The approach is experimentally validated for a wide range of speeds and periodic/non-periodic load torque disturbances. Additional validation through the new European driving cycle(NEDC) and urban dynamometer driving schedule(UDDS) demonstrates the method's effectiveness and reliability in real-world driving conditions.
文摘A method for a vehicle durability emission test using a robot driver insteadof human drivers on the chassis dynamometer is presented. The system architecture of vehicledurability emission test cell, the road load simulation strategy and the tele-monitoring systembased on Browser/Client structure are described. Furthermore, the construction of the robot driver,vehicle performance self-learning algorithm, multi-mode vehicle control model and vehicle speedtracking strategy based on fuzzy logic arealso discussed. Besides, the capability of controlparameters self-compensation on-line makes it possible to compensate the wear of vehicle componentsand the variety of clutch true bite point during the long term test. Experimental results show thattherobot driver can be applicable to a wide variety of vehicles and the obtained results stay withina tolerance band of ± 2 km/h. Moreover the robot driver is able to control tested vehicles withgood repeatability and consistency; therefore, this methodpresents a solution to eliminate theuncertainty of emission test results by human drivers and to ensure the accuracy and reliability ofemission test results.
基金support from the Key Project of the National Natural Science Foundation of China (61034005)Postgraduate Scientific Research and Innovation Projects of Basic Scientific Research Operating Expenses of Ministry of Education (N100604001)
文摘Downhole working conditions of sucker rod pumping wells are automatically identified on a computer from the analysis of dynamometer cards. In this process, extraction of feature parameters and pattern classification are two key steps. The dynamometer card is firstly divided into four parts which include different production information according to the ＂four point method＂ used in actual oilfield production, and then the moment invariants for pattern recognition are extracted. An improved support vector machine （SVM） method is used for pattern classification whose error penalty parameter C and kernel function parameter g are optimally chosen by the particle swarm optimization （PSO） algorithm. The simulation results show the method proposed in this paper has good classification results.
基金supported by the Key Program of National Natural Science Foundation of China (61034005)Postgraduate Scientific Research and Innovation Projects of Basic Scientific Research Operating Expensesof Ministry of Education (N100604001)Excellent Doctoral Dissertations Cultivation Project of Northeastern University
文摘It is important to achieve continuous, stable and efficient pumping well operation in actual oilfield operation. Down-hole pumping well working conditions can be monitored in real-time and a reasonable production scheme can be designed when computer diagnosis is used. However, it is difficult to make a comprehensive analysis to supply efficient technical guidance for operation of the pumping well with multiple faults of down-hole conditions, which cannot be effectively dealt with by the common methods. To solve this problem, a method based on designated component analysis （DCA） is used in this paper. Freeman chain code is used to represent the down-hole dynamometer card whose important characteristics are extracted to construct a designated mode set. A control chart is used as a basis for fault detection. The upper and lower control lines on the control chart are determined from standard samples in normal working conditions. In an incompletely orthogonal mode, the designated mode set could be divided into some subsets in which the modes are completely orthogonal. The observed data is projected into each designated mode to realize fault detection according to the upper and lower control lines. The examples show that the proposed method can effectively diagnose multiple faults of down-hole conditions.
基金supported by the Projects of International Cooperation and Exchanges of National Science Foundation of China (No.41571130031)the National Basic Research Program of China (No.2013CB228503)partly supported by a Grant-in-Aid for Scientific Research (B) (No.16H02942) from the JSPS
文摘Traffic vehicles, many of which are powered by port fuel injection(PFI) engines, are major sources of particulate matter in the urban atmosphere. We studied particles from the emission of a commercial PFI-engine vehicle when it was running under the states of cold start, hot start, hot stabilized running, idle and acceleration, using a transmission electron microscope and an energy-dispersive X-ray detector. Results showed that the particles were mainly composed of organic, soot, and Ca-rich particles, with a small amount of S-rich and metal-containing particles, and displayed a unimodal size distribution with the peak at 600 nm. The emissions were highest under the cold start running state, followed by the hot start, hot stabilized, acceleration, and idle running states. Organic particles under the hot start and hot stabilized running states were higher than those of other running states. Soot particles were highest under the cold start running state. Under the idle running state, the relative number fraction of Ca-rich particles was high although their absolute number was low. These results indicate that PFI-engine vehicles emit substantial primary particles,which favor the formation of secondary aerosols via providing reaction sites and reaction catalysts, as well as supplying soot, organic, mineral and metal particles in the size range of the accumulation mode. In addition, the contents of Ca, P, and Zn in organic particles may serve as fingerprints for source apportionment of particles from PFI-engine vehicles.
基金supported by National Key Sci-tech Major Special Item of China(Grant No.2009ZX05038004)Shandong Provincial Science and Technology Development Project of China(Grant No.2009GG10007008)Graduate Innovation Fund of China University of Petroleum(Grant No.CXZD11-09)
文摘The existing design of the pumping systems mainly focuses on the approximate computational formulae and procedures,which are developed based on the analytic approaches of conventional oil/gas fields.The calculation of polished rod loads usually just concerns about the static and inertial loads.And the computation of gearbox torque generally uses empirical formulae and correction factors.The above modeling procedures,if applied to the coalbed methane(CBM)wells,can not give the desired accuracy of the system design and its pertinent analysis.In this paper,based on the kinematic and dynamic analysis of the pumping system,the kinematic relation of polished rod is analyzed,and the variation of the total loads of polished rod is developed with the limits of CBM well conditions along the string.The gearbox torque calculation model is established by combining the counterbalance effect with the calculated dynamometer cards and torque factors.The application characteristics of this model are demonstrated by the example of ZH002-4 well in Qinshui basin.The interpretations of results show that the cranks of beam units should rotate in a counter clockwise direction viewed with the wellhead to the right.Compared with oil?gas fields,the dynamic and friction to polished rod load ratios are relatively high and the computation of polished rod loads should involve the static and inertial loads,as well as vibration and friction loads.And the dynamic load ratio decreases rapidly during the production.Besides,the total deformation of the string is small in CBM wells.As for balanced operation,the gearbox torque load usually has two approximately equal peaks and the magnitudes of instantaneous torque are just within 50%of unbalanced gearbox loadings.The proposed research improves efficiency of the pumping system,loads the pumping unit more uniformly,and provides the reasonable basis for selecting the units.
基金support by the Major Scientific and Technological Projects of CNPC under Grant no.ZD2019-184-004the National Research Council of Science and Technology Major Project under Grant no.2016ZX05042004+1 种基金the Fundamental Research Funds for the Central University under Grant no.20CX02307Athe Opening Fund of National Engineering Laboratory of Offshore Geophysical and Exploration Equipment under Grant no.20CX02307A
文摘A highly precise and timely diagnosis technology can help effectively monitor and adjust the sucker rod production system(SRPS)used in oil wells to ensure a safe and efficient production.The current diagnosis method is pattern recognition of a dynamometer card(DC)based on feature extraction and perceptron.The premise of this method is that the training and target data have the same distribution.However,the training data are collected from a field SRPS with different system parameters designed to adapt to production conditions,which may significantly affect the diagnostic accuracy.To address this issue,in this study,an improved model of the sucker rod string(SRS)is derived by adding faultparameter dimensions,with which DCs under 16 working conditions could be generated.Subsequently an adaptive diagnosis method is proposed by taking simulated DCs generated near the working point of the target SRPS as training data.Meanwhile,to further improve the accuracy of the proposed method,the DC features are improved by relative normalization and using additional features of the DC position to increase the distance between different types of samples.The parameters of the perceptron are optimized to promote its discriminability.Finally,the accuracy and real-time performance of the proposed adaptive diagnosis method are validated using field data.
基金supported by the Natural Science Foundation for Outstanding Young Scholars(No.41125018)the National Commonweal Project of the Ministry of Environmental Protection(No.201009057)
文摘Fifteen heavy-duty diesel vehicles were tested on chassis dynamometer by using typical heavy duty driving cycle and fuel economy cycle. The air from the exhaust was sampled by 2,4- dinitrophenyhydrazine cartridge and 23 carbonyl compounds were analyzed by high performance liquid chromatography. The average emission factor of carbonyls was 97.2 mg/km, higher than that of light-duty diesel vehicles and gasoline-powered vehicles. Formaldehyde, acetaldehyde, acetone and propionaidehyde were the species with the highest emission factors. Main influencing factors for carbonyl emissions were vehicle type, average speed and regulated emission standard, and the impact of vehicle loading was not evident in this study. National emission of carbonyls from diesel vehicles exhaust was calculated for China, 2011, based on both vehicle miles traveled and fuel consumption. Carbonyl emission of diesel vehicle was estimated to be 45.8 Gg, and was comparable to gasolinepowered vehicles （58.4 Gg）. The emissions of formaldehyde, acetaldehyde and acetone were 12.6, 6.9, 3.8 Gg, respectively. The ozone formation potential of carbonyls from diesel vehicles exhaust was 537 mg O3/km, higher than 497 mg O3/km of none-methane hydrocarbons emitted from diesel vehicles.
基金We would like to thank the associate editor and the reviewers for their constructive comments.This work was supported in part by the National Natural Science Foundation of China under Grant 62203234in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03+1 种基金in part by the Natural Science Foundation of Liaoning Province under Grant 2023-BS-025in part by the Research Program of Liaoning Liaohe Laboratory under Grant LLL23ZZ-02-02.
文摘High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.
基金supported by the National Science Centre of Poland (Research project No. 2017/27/B/ST8/01249)
文摘Increasing operating speed of modern passenger railway vehicles leads to higher thermal load onthe braking system. Organic composite brake pads are poor thermal conductors, hence frictionalheat is absorbed mainly by the disc. In this study three brake pad types were tested on thedynamometer. Metallic fibres, steel and copper, were introduced to the formulation of twomaterials. The third was a non-metallic material - a reference case. Dynamometer test comprisedemergency brake applications to determine the frictional characteristics of the materials andconstant-power drag braking to analyse the effect of metal fibres on temperature evolution,measured by six thermocouples embedded in the brake disc. Mean friction coefficient is analysedand discussed. It is concluded that conductive fibre in the friction material formulation mayinfluence its tribological characteristics. Despite high thermal conductivity, metal fibres in theconcentration tested in this study, did not reduce temperature of the brake disc.
文摘In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.
基金Project(SS2012AA04104)supported by High-tech Research and Development Program of China
文摘AC motors, especially the squirrel cage induction motors have the advantages of simple structure, good reliability and low cost. They are more suitable to be used as electrical dynamometers to provide dynamic load for bench test systems. But, the speed and torque of induction motors are not easy to be controlled accurately. In this work, an electrical dynamometer based on the induction motor is proposed. In order to get better control performance of torque and speed of induction motor, an improved direct torque control method(DTC) is also developed based on the space vector modulation(SVM) technique. The performance of the proposed dynamometer system is validated in the Matlab/Simulink platform. The simulation results show that the new dynamometer has good torque and stator flux response. And the torque and stator current ripples of it are reduced significantly compared with using the conventional DTC method.
文摘Background： The majority of injuries reported in female basketball players are ankle sprains and mechanisms leading to injury have been debated. Investigations into muscular imbalances in barefoot versus shod conditions and their relationship with injury severity have not been performed. The purpose of this study was to investigate the effects of wearing athletic shoes on muscular strength and its relationship to lower extremity injuries, specifically female basketball players due to the high incidence of ankle injuries in this population. Methods： During pre-season, 11 female collegiate basketball players underwent inversion and eversion muscle strength testing using an iso- kinetic dynamometer in both a barefoot and shod conditions. The difference between conditions was calculated for inversion and eversion peak torque, time to peak torque as well as eversion-to-inversion peak torque percent strength ratio for both conditions. Lower extremity injuries were documented and ranked in severity. The ranked difference between barefoot and shod conditions for peak torque and time to peak torque as well as percent strength ratio was correlated with injury ranking using a Spearman rho correlation （p） with an a level of 0.05. Results： The ranked differences in barefoot and shod for peak eversion and inversion torque at 120°/s were correlated with their injury ranking. Ranking of the athletes based on the severity of injuries that were sustained during the season was found to have a strong, positive relationship with the difference in peak eversion torque between barefoot and shod （p = 0.78; p = 0.02）. Conclusion： It is possible that a large discrepancy between strength in barefoot and shod conditions can predispose an athlete to injury. Nar- rowing the difference in peak eversion torque between barefoot and shod could decrease propensity to injury. Future work should investigate the effect of restoration of muscular strength during barefoot and shod exercise on injury rates.
基金supported by the National Natural Science Foundation of China (Nos.51175255 , 51305199)the Excellent Young Talents Fund Key Project of the Anhui Higher Education Institutions of China (No. 2013SQRL089ZD)the Starting Foundation for Talents from HuangShan University of China (No.2013xkjq003)
文摘A dynamometer was designed and manufactured to measure the joining force in the linear friction welding process.The error percentages of the dynamometer in the upset(x)and vibration(y)directions were 0.9% and 0.75%,respectively.The cross-sensitivity range of the dynamometer was 1.2%—3.3% in the two directions.The precision level satisfies the requirements of the dynamometer test.The joining force of the TC17 and TC11 heterogeneous titanium alloys in the linear friction welding process was used to test the manufactured dynamometer.The test results showed that the upset force was large,but the vibration force showed a smaller change in TC11 during the linear friction welding process.In addition,the upset and vibration forces of the linear friction welding were greater with a short welding time than those with a long welding time.
基金Project(11772126) supported by the National Natural Science Foundation of China
文摘Alternating current electrical dynamometer is a common device to measure the torque of engines, such as the gasoline engine. In order to solve the problems such as high cost, high energy consumption and complicated measurement system which exists in the direct measurement on the torque of alternating current electrical dynamometer, copper loss and iron loss are taken as two key factors and a soft-sensing model on the torque of alternating current electrical dynamometer is established using the fuzzy least square support vector machine (FLS-SVM). Then, the FLS-SVM parameters such as penalty factor and kernel parameter are optimized by adaptive genetic algorithm, torque soft-sensing is investigated in the alternating current electrical dynamometer, as well as the energy feedback efficiency and energy consumption during the measurement phase of a gasoline engine loading continual test is obtained. The results show that the minimum soft-sensing error of torque is about 0.0018, and it fluctuates within a range from -0.3 to 0.3 N·m. FLS-SVM soft-sensing method can increase by 1.6% power generation feedback compared with direct measurement, and it can save 500 kJ fuel consumption in the gasoline engine loading continual test. Therefore, the estimation accuracy of the soft measurement model on the torque of alternating current electrical dynamometer including copper loss and iron loss is high and this indirect measurement method can be feasible to reduce production cost of the alternating current electrical dynamometer and energy consumption during the torque measurement phase of a gasoline engine, replacing the direct method of torque measurement.
基金supported by the National Natural Science Foundation of China under Grant 52325402, 52274057, 52074340 and 51874335the National Key R&D Program of China under Grant 2023YFB4104200+1 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028。
文摘In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.
文摘Human drivers seem to have different characteristics,so different drivers often yield different results from the same driving mode tests with identical vehicles and same chassis dynamometer.However,drivers with different experiences often yield similar results under the same driving conditions.If the features of human drivers are known,the control inputs to each driver,including warnings,will be customized to optimize each man–machine vehicle system.Therefore,it is crucial to determine how to characterize human drivers quantitatively.This study proposes a method to estimate the parameters of a theoretical model of human drivers.The method uses an artificial neural network(ANN)model and a numerical procedure to interpret the identified ANN models theoretically.Our approach involves the following process.First,we specify each ANN driver model through chassis dynamometer tests performed by each human driver and vehicle.Subsequently,we obtain the parameters of a theoretical driver model using the ANN model for the corresponding driver.Specifically,we simulate the driver’s behaviors using the identified ANN models with controlled inputs.Finally,we estimate the theoretical driver model parameters using the numerical simulation results.A proportional-integral-differential(PID)control model is used as the theoretical model.The results of the parameter estimation indicate that the PID driver model parameter combination can characterize human drivers.Moreover,the results suggest that vehicular factors influence the parameter combinations of human drivers.
基金National Natural Science Foundation of China (No. 60501005)National High Technology Research and Development Program of China(No. 2007AA04Z236)Key Program of Tianjin Science and Technology Support Plan (No. 07ZCKFSF01300)
文摘A new measurement and analysis method was proposed to investigate the changes in elbow joint moments that occur with the use of a front-wheeled walker. A strain gauge-based walker instrumentation system was developed to monitor the hand loads during walker-assisted walking and integrated with an upper extremity biomechanical model, Preliminary system data were collected for 12 subjects following informed consent. Bilateral upper extremity kinematic data were acquired with a six-camera motion analysis system. Internal joint moments at the elbow were determined in the three clinical planes using the inverse dynamics method. Results showed that during a walker-assisted gait elbow joint moments mainly distributed in the walker stance period. There was a noted demand on the elbow extensor in the sagittal plane with the greatest record as 0.381 N.m/（kg.m）, An interesting “bare phase” of mean elbow joint moments was also found in phase angle-240°-340° of gait cycle. Complete description of elbow joint moments of walkerassisted gait may provide insight into walker use parameters and rehabilitative strategies.
