The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded sys- tem, and the effective software testing is an important way to ensure software quality and reliab...The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded sys- tem, and the effective software testing is an important way to ensure software quality and reliability. Based on the analysis of the characteristics of real-time embedded software, the formal method is introduced into the real-time embedded software testing field and the real-time extended finite state machine （RT-EFSM） model is studied firstly. Then, the time zone division method of real-time embedded system is presented and the definition and description methods of time-constrained transition equivalence class （timeCTEC） are presented. Furthermore, the approaches of the testing sequence and test case generation are put forward. Finally, the proposed method is applied to a typical avionics real- time embedded software testing practice and the examples of the timeCTEC, testing sequences and test cases are given. With the analysis of the testing result, the application verification shows that the proposed method can effectively describe the real-time embedded software state transition characteristics and real-time requirements and play the advantages of the formal methods in accuracy, effectiveness and the automation supporting. Combined with the testing platform, the real-time, closed loop and automated simulation testing for real-time embedded software can be realized effectively.展开更多
For qualifying the anti-shock performance of shipboard equipments and simulating actual underwater explosion environments, a novel dual-wave shock test machine is proposed to increase testing capability of shock test ...For qualifying the anti-shock performance of shipboard equipments and simulating actual underwater explosion environments, a novel dual-wave shock test machine is proposed to increase testing capability of shock test machines as well as to meet certain shock testing specification. The machine can generate a double-pulse acceleration shock for test articles according to specification defined in BV043/85. On the basis of the impact theory, a nonlinear dynamic model of the hydraulically-actuated test machine is established with thorough analysis on its mechanism which involves conversion of gas potential energy and dissipation of kinetic energy. Simulation results have demonstrated that the machine can produce a double-pulse acceleration shock in the time domain or a desired shock response spectrum in the frequency domain, which sets a theoretical base for the construction of the proposed machine.展开更多
Compared with the traditional non-cutting measurement,machining tests can more accurately reflect the kinematic errors of five-axis machine tools in the actual machining process for the users.However,measurement and c...Compared with the traditional non-cutting measurement,machining tests can more accurately reflect the kinematic errors of five-axis machine tools in the actual machining process for the users.However,measurement and calculation of the machining tests in the literature are quite difficult and time-consuming.A new method of the machining tests for the trunnion axis of five-axis machine tool is proposed.Firstly,a simple mathematical model of the cradle-type five-axis machine tool was established by optimizing the coordinate system settings based on robot kinematics.Then,the machining tests based on error-sensitive directions were proposed to identify the kinematic errors of the trunnion axis of cradle-type five-axis machine tool.By adopting the error-sensitive vectors in the matrix calculation,the functional relationship equations between the machining errors of the test piece in the error-sensitive directions and the kinematic errors of C-axis and A-axis of five-axis machine tool rotary table was established based on the model of the kinematic errors.According to our previous work,the kinematic errors of C-axis can be treated as the known quantities,and the kinematic errors of A-axis can be obtained from the equations.This method was tested in Mikron UCP600 vertical machining center.The machining errors in the error-sensitive directions can be obtained by CMM inspection from the finished test piece to identify the kinematic errors of five-axis machine tool trunnion axis.Experimental results demonstrated that the proposed method can reduce the complexity,cost,and the time consumed substantially,and has a wider applicability.This paper proposes a new method of the machining tests for the trunnion axis of five-axis machine tool.展开更多
The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels. To overcome some of the defects of the inflatable wing paramet...The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels. To overcome some of the defects of the inflatable wing parameter design method, this paper proposes an optimization design scheme based on orthogonal testing and support vector machines （SVMs）. Orthogonal testing design is used to estimate the appropriate initial value and variation domain of each variable to decrease the number of iterations and improve the identification accuracy and efficiency. Orthogonal tests consisting of three factors and three levels are designed to analyze the parameters of pressure, uniform applied load and the number of chambers that affect the bending response of inflatable wings. An SVM intelligent model is established and limited orthogonal test swatches are studied. Thus, the precise relationships between each parameter and product quality features, as well the signal-to-noise ratio （SNR）, can be obtained. This can guide general technological design optimization.展开更多
BACKGROUND Ischemic heart disease(IHD)impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.AIM To compare variations in the parameters of the single-lead electrocardiogram...BACKGROUND Ischemic heart disease(IHD)impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.AIM To compare variations in the parameters of the single-lead electrocardiogram(ECG)during resting conditions and physical exertion in individuals diagnosed with IHD and those without the condition using vasodilator-induced stress computed tomography(CT)myocardial perfusion imaging as the diagnostic reference standard.METHODS This single center observational study included 80 participants.The participants were aged≥40 years and given an informed written consent to participate in the study.Both groups,G1(n=31)with and G2(n=49)without post stress induced myocardial perfusion defect,passed cardiologist consultation,anthropometric measurements,blood pressure and pulse rate measurement,echocardiography,cardio-ankle vascular index,bicycle ergometry,recording 3-min single-lead ECG(Cardio-Qvark)before and just after bicycle ergometry followed by performing CT myocardial perfusion.The LASSO regression with nested cross-validation was used to find the association between Cardio-Qvark parameters and the existence of the perfusion defect.Statistical processing was performed with the R programming language v4.2,Python v.3.10[^R],and Statistica 12 program.RESULTS Bicycle ergometry yielded an area under the receiver operating characteristic curve of 50.7%[95%confidence interval(CI):0.388-0.625],specificity of 53.1%(95%CI:0.392-0.673),and sensitivity of 48.4%(95%CI:0.306-0.657).In contrast,the Cardio-Qvark test performed notably better with an area under the receiver operating characteristic curve of 67%(95%CI:0.530-0.801),specificity of 75.5%(95%CI:0.628-0.88),and sensitivity of 51.6%(95%CI:0.333-0.695).CONCLUSION The single-lead ECG has a relatively higher diagnostic accuracy compared with bicycle ergometry by using machine learning models,but the difference was not statistically significant.However,further investigations are required to uncover the hidden capabilities of single-lead ECG in IHD diagnosis.展开更多
Classical machine learning algorithms seem to be totally incapable of processing tremendous amounts of data,while quantum machine learning algorithms could deal with big data with ease and provide exponential accelera...Classical machine learning algorithms seem to be totally incapable of processing tremendous amounts of data,while quantum machine learning algorithms could deal with big data with ease and provide exponential acceleration over classical counterparts.Meanwhile,variational quantum algorithms are widely proposed to solve relevant computational problems on noisy,intermediate-scale quantum devices.In this paper,we apply variational quantum algorithms to quantum support vector machines and demonstrate a proof-of-principle numerical experiment of this algorithm.In addition,in the classification stage,fewer qubits,shorter circuit depth,and simpler measurement requirements show its superiority over the former algorithms.展开更多
Based on three kinds of dynamic test of MEMS, a dynamic system for the vibration test of micro machined gyroscope based on high speed photography is introduced. Firstly, the architecture of the system hardware is intr...Based on three kinds of dynamic test of MEMS, a dynamic system for the vibration test of micro machined gyroscope based on high speed photography is introduced. Firstly, the architecture of the system hardware is introduced. Secondly, the image tracking performance is compared by the test using the template matching algorithm, the mean shift algorithm and the SURF algorithm. The vibration curve shows that high speed photograph combined with SURF algorithm is faster, more ac- curate, and more suitable for the vibration test of micro machined gyroscope. After the frequency a- nalysis and related interpolation, more characteristics of micro gyroscope can be obtained.展开更多
In order to qualify shock resistance performance of shipboard equipments and simulate real underwater explosion environment,a novel dual-pulse shock test machine is proposed.The new machine will increase testing capab...In order to qualify shock resistance performance of shipboard equipments and simulate real underwater explosion environment,a novel dual-pulse shock test machine is proposed.The new machine will increase testing capability and meet special shock testing requirement.Two key parts of the machine,the velocity generator and the shock pulse regulator,play an important role in producing the positive acceleration pulse and the succeeding negative acceleration pulse,respectively.The generated dual-pulse shock for test articles is in conformity with an anti-shock test specification.Based on the impact theory,a nonlinear dynamic model of the hydraulically-actuated test machine is established with thorough analysis on its mechanism that involves conversion of gas potential energy and dissipation of kinetic energy.Simulation results have demonstrated that the proposed machine is able to produce a double-pulse acceleration shock in the time domain or a desired shock response spectrum in the frequency domain,which sets up a base for the construction of the machine.展开更多
Permeability is one of the main oil reservoir characteristics.It affects potential oil production,well-completion technologies,the choice of enhanced oil recovery methods,and more.The methods used to determine and pre...Permeability is one of the main oil reservoir characteristics.It affects potential oil production,well-completion technologies,the choice of enhanced oil recovery methods,and more.The methods used to determine and predict reservoir permeability have serious shortcomings.This article aims to refine and adapt machine learning techniques using historical data from hydrocarbon field development to evaluate and predict parameters such as the skin factor and permeability of the remote reservoir zone.The article analyzes data from 4045 wells tests in oil fields in the Perm Krai(Russia).An evaluation of the performance of different Machine Learning(ML)al-gorithms in the prediction of the well permeability is performed.Three different real datasets are used to train more than 20 machine learning regressors,whose hyperparameters are optimized using Bayesian Optimization(BO).The resulting models demonstrate significantly better predictive performance compared to traditional methods and the best ML model found is one that never was applied before to this problem.The permeability prediction model is characterized by a high R^(2) adjusted value of 0.799.A promising approach is the integration of machine learning methods and the use of pressure recovery curves to estimate permeability in real-time.The work is unique for its approach to predicting pressure recovery curves during well operation without stopping wells,providing primary data for interpretation.These innovations are exclusive and can improve the accuracy of permeability forecasts.It also reduces well downtime associated with traditional well-testing procedures.The proposed methods pave the way for more efficient and cost-effective reservoir development,ultimately sup-porting better decision-making and resource optimization in oil production.展开更多
The fracture energy of fiber-reinforced concrete(FRC)affects the durability and structural performance of concrete elements.Advancements in experimental studies have yet to overcome the challenges of estimating fractu...The fracture energy of fiber-reinforced concrete(FRC)affects the durability and structural performance of concrete elements.Advancements in experimental studies have yet to overcome the challenges of estimating fracture energy,as the process remains time-intensive and costly.Therefore,machine learning techniques have emerged as powerful alternatives.This study aims to investigate the performance of machine learning techniques to predict the fracture energy of FRC.For this purpose,500 data points,including 8 input parameters that affect the fracture energy of FRC,are collected fromthree-point bending tests and employed to train and evaluate themachine learning techniques.The findings showed that Gaussian process regression(GPR)outperforms all other models in terms of predictive accuracy,achieving the highest R2 of 0.93 and the lowest RMSE of 13.91 during holdout cross-validation.It is then followed by support vector regression(SVR)and extreme gradient boosting regression(XGBR),whereas K-nearest neighbours(KNN)and random forest regression(RFR)show the weakest predictions.The superiority of GPR is further reinforced in a 5-fold cross-validation,where it consistently delivers an average R2 above 0.96 and ranks highest in overall predictive performance.Empirical testing with additional sample sets validates GPR’s model on the key mix parameter’s impact on fracture energy,cementing its claim.The Fly-Ash cement exhibits the greatest fracture energy due to superior fiber-matrix interaction,whereas the glass fiber dominates energy absorption amongst the other types of fibers.In addition,increasing the water-to-cement(W/C)ratio from 0.30 to 0.50 yields a significant improvement in fracture energy,which aligns well with the machine learning predictions.Similarly,loading rate positively correlates with fracture energy,highlighting the strain-rate sensitivity of FRC.This work is the missing link to integrate experimental fracture mechanics and computational intelligence,optimally and reasonably predicting and refining the fracture energy of FRC.展开更多
Sinkhole formation poses a significant geohazard in karst regions,where unpredictable subsurface erosion often necessitates costly grouting for stabilization.Accurate estimation of grout volume remains a persistent ch...Sinkhole formation poses a significant geohazard in karst regions,where unpredictable subsurface erosion often necessitates costly grouting for stabilization.Accurate estimation of grout volume remains a persistent challenge due to spatial variability,site-specific conditions,and the limitations of traditional empirical methods.This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test(CPT)-derived Sinkhole Resistance Ratio(SRR)values,spatial correlations between CPT and grouting points(GPs),and field-recorded grout volumes from six sinkhole sites in Florida.Three data trans-formation methods,the Proximal Allocation Method(PAM),the Equitable Distribution Method(EDM),and the Threshold-based Equitable Distribution Method(TEDM),were applied to distribute grout influence across CPTs,with TEDM demonstrating superior predictive performance.Synthetic data augmentation using spline method-ology further improved model robustness.A high-degree polynomial regression model,optimized with ridge regularization,achieved high accuracy(R^(2)=0.95;PEV=0.94)and significantly outperformed existing linear and logarithmic models.Results confirm that lower SRR values correlate with higher grout demand,and the proposed model reliably captures these nonlinear relationships.This research advances sinkhole remediation practice by providing a data-driven,accurate,and generalizable framework for grout volume estimation,enabling more efficient resource allocation and improved project outcomes.展开更多
Software has been developed for digital control of WDW series testing machine and the measurement of fracture toughness by modularized design. Development of the software makes use of multi-thread and serial communica...Software has been developed for digital control of WDW series testing machine and the measurement of fracture toughness by modularized design. Development of the software makes use of multi-thread and serial communication techniques, which can accurately control the testing machine and measure the fracture toughness in real-time. Three-point bending specimens were used in the measurement. The software operates stably and reliably, expanding the function of WDW series testing machine.展开更多
Ultrasonic testing(UT)is increasingly combined with machine learning(ML)techniques for intelligently identifying damage.Extracting signifcant features from UT data is essential for efcient defect characterization.More...Ultrasonic testing(UT)is increasingly combined with machine learning(ML)techniques for intelligently identifying damage.Extracting signifcant features from UT data is essential for efcient defect characterization.Moreover,the hidden physics behind ML is unexplained,reducing the generalization capability and versatility of ML methods in UT.In this paper,a generally applicable ML framework based on the model interpretation strategy is proposed to improve the detection accuracy and computational efciency of UT.Firstly,multi-domain features are extracted from the UT signals with signal processing techniques to construct an initial feature space.Subsequently,a feature selection method based on model interpretable strategy(FS-MIS)is innovatively developed by integrating Shapley additive explanation(SHAP),flter method,embedded method and wrapper method.The most efective ML model and the optimal feature subset with better correlation to the target defects are determined self-adaptively.The proposed framework is validated by identifying and locating side-drilled holes(SDHs)with 0.5λcentral distance and different depths.An ultrasonic array probe is adopted to acquire FMC datasets from several aluminum alloy specimens containing two SDHs by experiments.The optimal feature subset selected by FS-MIS is set as the input of the chosen ML model to train and predict the times of arrival(ToAs)of the scattered waves emitted by adjacent SDHs.The experimental results demonstrate that the relative errors of the predicted ToAs are all below 3.67%with an average error of 0.25%,signifcantly improving the time resolution of UT signals.On this basis,the predicted ToAs are assigned to the corresponding original signals for decoupling overlapped pulse-echoes and reconstructing high-resolution FMC datasets.The imaging resolution is enhanced to 0.5λby implementing the total focusing method(TFM).The relative errors of hole depths and central distance are no more than 0.51%and 3.57%,respectively.Finally,the superior performance of the proposed FS-MIS is validated by comparing it with initial feature space and conventional dimensionality reduction techniques.展开更多
In order to compare the compensation effect of expansive materials with different mineral sources on the temperature stress of concrete,we investigated the temperature stress of concrete when adding calcium sulfoalumi...In order to compare the compensation effect of expansive materials with different mineral sources on the temperature stress of concrete,we investigated the temperature stress of concrete when adding calcium sulfoaluminate type expansive materials(CSA)or CaO and calcium sulfoaluminate mixed type expansive materials(HCSA)at different temperatures by temperature-stress testing machine(TSTM)considering the influence of temperature history on the expansion.The experimental results show that the expansion characteristics of the two kinds of expansive materials with different mineral sources significantly vary.When adding expansive materials,the growth rate of compressive stress during the heating stage increases obviously,the maximum compressive stress is higher,while the decline rate of tensile stress in the late cooling stage becomes slow,and finally cracking temperature decreases.It is proved that concrete with HCSA has lower cracking temperatures and better temperature shrinkage compensation effect.Therefore,it is rational to choose HCSA when preparing concrete with high expansion energy to reduce thermal cracking.展开更多
National Quality Supervision ＆ Inspection Center for Refractories Business scope： Selective examination for national quality supervision; Identification of production license; Arbitration inspection and technical ac...National Quality Supervision ＆ Inspection Center for Refractories Business scope： Selective examination for national quality supervision; Identification of production license; Arbitration inspection and technical achievements evaluation; Commodities inspection and otherquality inspections .展开更多
In the present study, an aero pneumatic fatigue testing machine for complete dentures was designed, fabricated, and tested for the evaluation of the fatigue life of reinforced complete upper denture (CUD). On completi...In the present study, an aero pneumatic fatigue testing machine for complete dentures was designed, fabricated, and tested for the evaluation of the fatigue life of reinforced complete upper denture (CUD). On completion and testing, it was observed that the machine has the potential of generating reliable number of cyclic data. The machine’s performance was evaluated using test specimens of identical CUDs that were machined in conformity with standard procedures. The fatigue machine compressed the lower dental arch over the upper denture-specimen in centric occlusion, in the same way that the two masticatory muscles pull the lower jaw over the upper jaw during chewing. The incorporation of glass fibres into the CUD using a sandwich technique quadruples the lifespan of the denture (<em>P</em> = 0.004). The low standard deviation, along with the low coefficient of variation (CV) of the group of unreinforced dentures shows the repeatability of the results and the reliability of the machine. The high standard deviation and coefficient of variation of reinforced dentures was expected, since a high variation of results is usually recorded in fibre reinforcement cases. This research confirmed the view that the crack during denture fracture initiates in the anterior palatal area and propagates to the posterior.展开更多
Building high confidence regression test suites to validate new system versions is a challenging problem. A modelbased approach to build a regression test suite from a given test suite is described. The generated test...Building high confidence regression test suites to validate new system versions is a challenging problem. A modelbased approach to build a regression test suite from a given test suite is described. The generated test suite includes every test that will traverse a change performed to produce the new version, and consists of only such tests to reduce the testing costs. Finite state machines extended with typed variables (EFSMs) are used to model systems and system changes are mapped to EFSM transition changes adding/deleting/replacing EFSM transitions and states. Tests are a sequence of input and expected output messages with concrete parameter values over the supported data types. An invariant is formulated to characterize tests whose runtime behavior can be accurately predicted by analyzing their descriptions along with the model. Incremental procedures to efficiently evaluate the invariant and to select tests for regression are developed. Overlaps among the test descriptions are exploited to extend the approach to simultaneously select multiple tests to reduce the test selection costs. Effectiveness of the approach is demonstrated by applying it to several protocols, Web services, and model programs extracted from a popular testing benchmark. Our experimental results show that the proposed approach is economical for regression test selection in all these examples. For all these examples, the proposed approach is able to identify all tests exercising changes more efficiently than brute-force symbolic evaluation.展开更多
To ensure the quality of Web applications, Web testing is one of the effective methods. The testing is a process of revealing errors that is used to give confidence that the implementation of a Web application meets i...To ensure the quality of Web applications, Web testing is one of the effective methods. The testing is a process of revealing errors that is used to give confidence that the implementation of a Web application meets its original specification. This work proposes a Web testing framework based on Stream X-Machines (SXMs), which provides a way to derive test cases for a Web application. It starts from constructing the SXM model, from which a test translator is employed to extract the test paths and then translates them into an XML-style test specification, which is the input of test engine. The test engine generates test cases and then executes them, and finally produces test report. This testing method is a significant contribution to informed research.展开更多
A high fidelity dynamic model of a high-energy hydraulically-actuated shock test machine for heavy weight devices is presented to satisfy the newly-built shock resistance standard and simulate the actual underwater ex...A high fidelity dynamic model of a high-energy hydraulically-actuated shock test machine for heavy weight devices is presented to satisfy the newly-built shock resistance standard and simulate the actual underwater explosion environments in laboratory as well as increase the testing capability of shock test machine. In order to produce the required negative shock pulse in the given time duration, four hydraulic actuators are utilized. The model is then used to formulate an advanced feedforward controller for the system to produce the required negative waveform and to address the motion synchronization of the four cylinders. The model provides a safe and easily controllable way to perform a ＂virtual testing＂ before starting potentially destructive tests on specimen and to predict performance of the system. Simulation results have demonstrated the effectiveness of the controller.展开更多
Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst.The bursting liability is important to evaluate coal burst in mining.In this paper,the needle penetration test was ...Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst.The bursting liability is important to evaluate coal burst in mining.In this paper,the needle penetration test was carried out to determinate the coal bursting liability,and the empirical criterion of coal bursting liability was proposed.Moreover,the machine learning method was applied to coal bursting liability determination.Through analyzing the elastic strain energy release and failure time,the residual elastic strain energy release rate index K_(RE)was proposed to evaluate the coal bursting liability.According to the relationship between needle penetration index(NPI),K_(RE)and the critical value of K_(RE),the Needle Penetration Test-based Empirical Classification Criterion(NPT-ECC)was obtained.In addition,four machine learning classification models were constructed.After training and testing of the models,Needle Penetration Test-based Machine Learning Classification Model(NPT-MLCM)was proposed.The research results show that the accuracy of NPT-ECC is 6.66%higher than that of China National Standard Comprehensive Evaluation(CNSCE)according to verification of the coal fragment ejection ratio F.Gridsearch cross validation-extreme gradient boosting(GSCV-XGBoost)has the best prediction performance among all the models,and accuracy,Macro-Precision,Macro-Recall and Macro-F1-score of which were 86.67%,88.97%,87.50%and 87.37%.Based on this,the Needle Penetration Test-based GSCV-XGBoost(NPT-GSCV-XGBoost)was proposed.After comparative analysis and discussion,NPT-GSCV-XGBoost is superior to NPT-ECC and CNSCE in the comprehensive prediction ability.展开更多
基金supported by the Aviation Science Foundation of China
文摘The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded sys- tem, and the effective software testing is an important way to ensure software quality and reliability. Based on the analysis of the characteristics of real-time embedded software, the formal method is introduced into the real-time embedded software testing field and the real-time extended finite state machine （RT-EFSM） model is studied firstly. Then, the time zone division method of real-time embedded system is presented and the definition and description methods of time-constrained transition equivalence class （timeCTEC） are presented. Furthermore, the approaches of the testing sequence and test case generation are put forward. Finally, the proposed method is applied to a typical avionics real- time embedded software testing practice and the examples of the timeCTEC, testing sequences and test cases are given. With the analysis of the testing result, the application verification shows that the proposed method can effectively describe the real-time embedded software state transition characteristics and real-time requirements and play the advantages of the formal methods in accuracy, effectiveness and the automation supporting. Combined with the testing platform, the real-time, closed loop and automated simulation testing for real-time embedded software can be realized effectively.
基金supported by China Naval Armament Department (No. 05131/1046).
文摘For qualifying the anti-shock performance of shipboard equipments and simulating actual underwater explosion environments, a novel dual-wave shock test machine is proposed to increase testing capability of shock test machines as well as to meet certain shock testing specification. The machine can generate a double-pulse acceleration shock for test articles according to specification defined in BV043/85. On the basis of the impact theory, a nonlinear dynamic model of the hydraulically-actuated test machine is established with thorough analysis on its mechanism which involves conversion of gas potential energy and dissipation of kinetic energy. Simulation results have demonstrated that the machine can produce a double-pulse acceleration shock in the time domain or a desired shock response spectrum in the frequency domain, which sets a theoretical base for the construction of the proposed machine.
基金Supported by National Nature Science Foundation of China(Grant No.51175461)Science Fund for Creative Research Groups of National Natural Science Foundation of China(Grant No.51221004)Program for Zhejiang Leading Team of S&T Innovation of China(Grant No.2009R50008)
文摘Compared with the traditional non-cutting measurement,machining tests can more accurately reflect the kinematic errors of five-axis machine tools in the actual machining process for the users.However,measurement and calculation of the machining tests in the literature are quite difficult and time-consuming.A new method of the machining tests for the trunnion axis of five-axis machine tool is proposed.Firstly,a simple mathematical model of the cradle-type five-axis machine tool was established by optimizing the coordinate system settings based on robot kinematics.Then,the machining tests based on error-sensitive directions were proposed to identify the kinematic errors of the trunnion axis of cradle-type five-axis machine tool.By adopting the error-sensitive vectors in the matrix calculation,the functional relationship equations between the machining errors of the test piece in the error-sensitive directions and the kinematic errors of C-axis and A-axis of five-axis machine tool rotary table was established based on the model of the kinematic errors.According to our previous work,the kinematic errors of C-axis can be treated as the known quantities,and the kinematic errors of A-axis can be obtained from the equations.This method was tested in Mikron UCP600 vertical machining center.The machining errors in the error-sensitive directions can be obtained by CMM inspection from the finished test piece to identify the kinematic errors of five-axis machine tool trunnion axis.Experimental results demonstrated that the proposed method can reduce the complexity,cost,and the time consumed substantially,and has a wider applicability.This paper proposes a new method of the machining tests for the trunnion axis of five-axis machine tool.
文摘The robust parameter design method is a traditional approach to robust experimental design that seeks to obtain the optimal combination of factors/levels. To overcome some of the defects of the inflatable wing parameter design method, this paper proposes an optimization design scheme based on orthogonal testing and support vector machines （SVMs）. Orthogonal testing design is used to estimate the appropriate initial value and variation domain of each variable to decrease the number of iterations and improve the identification accuracy and efficiency. Orthogonal tests consisting of three factors and three levels are designed to analyze the parameters of pressure, uniform applied load and the number of chambers that affect the bending response of inflatable wings. An SVM intelligent model is established and limited orthogonal test swatches are studied. Thus, the precise relationships between each parameter and product quality features, as well the signal-to-noise ratio （SNR）, can be obtained. This can guide general technological design optimization.
基金Supported by Government Assignment,No.1023022600020-6RSF Grant,No.24-15-00549Ministry of Science and Higher Education of the Russian Federation within the Framework of State Support for the Creation and Development of World-Class Research Center,No.075-15-2022-304.
文摘BACKGROUND Ischemic heart disease(IHD)impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.AIM To compare variations in the parameters of the single-lead electrocardiogram(ECG)during resting conditions and physical exertion in individuals diagnosed with IHD and those without the condition using vasodilator-induced stress computed tomography(CT)myocardial perfusion imaging as the diagnostic reference standard.METHODS This single center observational study included 80 participants.The participants were aged≥40 years and given an informed written consent to participate in the study.Both groups,G1(n=31)with and G2(n=49)without post stress induced myocardial perfusion defect,passed cardiologist consultation,anthropometric measurements,blood pressure and pulse rate measurement,echocardiography,cardio-ankle vascular index,bicycle ergometry,recording 3-min single-lead ECG(Cardio-Qvark)before and just after bicycle ergometry followed by performing CT myocardial perfusion.The LASSO regression with nested cross-validation was used to find the association between Cardio-Qvark parameters and the existence of the perfusion defect.Statistical processing was performed with the R programming language v4.2,Python v.3.10[^R],and Statistica 12 program.RESULTS Bicycle ergometry yielded an area under the receiver operating characteristic curve of 50.7%[95%confidence interval(CI):0.388-0.625],specificity of 53.1%(95%CI:0.392-0.673),and sensitivity of 48.4%(95%CI:0.306-0.657).In contrast,the Cardio-Qvark test performed notably better with an area under the receiver operating characteristic curve of 67%(95%CI:0.530-0.801),specificity of 75.5%(95%CI:0.628-0.88),and sensitivity of 51.6%(95%CI:0.333-0.695).CONCLUSION The single-lead ECG has a relatively higher diagnostic accuracy compared with bicycle ergometry by using machine learning models,but the difference was not statistically significant.However,further investigations are required to uncover the hidden capabilities of single-lead ECG in IHD diagnosis.
基金supported by the Shandong Provincial Natural Science Foundation for Quantum Science No.ZR2020LLZ003,ZR2021LLZ002。
文摘Classical machine learning algorithms seem to be totally incapable of processing tremendous amounts of data,while quantum machine learning algorithms could deal with big data with ease and provide exponential acceleration over classical counterparts.Meanwhile,variational quantum algorithms are widely proposed to solve relevant computational problems on noisy,intermediate-scale quantum devices.In this paper,we apply variational quantum algorithms to quantum support vector machines and demonstrate a proof-of-principle numerical experiment of this algorithm.In addition,in the classification stage,fewer qubits,shorter circuit depth,and simpler measurement requirements show its superiority over the former algorithms.
文摘Based on three kinds of dynamic test of MEMS, a dynamic system for the vibration test of micro machined gyroscope based on high speed photography is introduced. Firstly, the architecture of the system hardware is introduced. Secondly, the image tracking performance is compared by the test using the template matching algorithm, the mean shift algorithm and the SURF algorithm. The vibration curve shows that high speed photograph combined with SURF algorithm is faster, more ac- curate, and more suitable for the vibration test of micro machined gyroscope. After the frequency a- nalysis and related interpolation, more characteristics of micro gyroscope can be obtained.
文摘In order to qualify shock resistance performance of shipboard equipments and simulate real underwater explosion environment,a novel dual-pulse shock test machine is proposed.The new machine will increase testing capability and meet special shock testing requirement.Two key parts of the machine,the velocity generator and the shock pulse regulator,play an important role in producing the positive acceleration pulse and the succeeding negative acceleration pulse,respectively.The generated dual-pulse shock for test articles is in conformity with an anti-shock test specification.Based on the impact theory,a nonlinear dynamic model of the hydraulically-actuated test machine is established with thorough analysis on its mechanism that involves conversion of gas potential energy and dissipation of kinetic energy.Simulation results have demonstrated that the proposed machine is able to produce a double-pulse acceleration shock in the time domain or a desired shock response spectrum in the frequency domain,which sets up a base for the construction of the machine.
基金funded by the Ministry of Science and Higher Education of the Russian Federation(Project No.FSNM-2024-0005).
文摘Permeability is one of the main oil reservoir characteristics.It affects potential oil production,well-completion technologies,the choice of enhanced oil recovery methods,and more.The methods used to determine and predict reservoir permeability have serious shortcomings.This article aims to refine and adapt machine learning techniques using historical data from hydrocarbon field development to evaluate and predict parameters such as the skin factor and permeability of the remote reservoir zone.The article analyzes data from 4045 wells tests in oil fields in the Perm Krai(Russia).An evaluation of the performance of different Machine Learning(ML)al-gorithms in the prediction of the well permeability is performed.Three different real datasets are used to train more than 20 machine learning regressors,whose hyperparameters are optimized using Bayesian Optimization(BO).The resulting models demonstrate significantly better predictive performance compared to traditional methods and the best ML model found is one that never was applied before to this problem.The permeability prediction model is characterized by a high R^(2) adjusted value of 0.799.A promising approach is the integration of machine learning methods and the use of pressure recovery curves to estimate permeability in real-time.The work is unique for its approach to predicting pressure recovery curves during well operation without stopping wells,providing primary data for interpretation.These innovations are exclusive and can improve the accuracy of permeability forecasts.It also reduces well downtime associated with traditional well-testing procedures.The proposed methods pave the way for more efficient and cost-effective reservoir development,ultimately sup-porting better decision-making and resource optimization in oil production.
基金Prince Sultan University for their supportPrincess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R300)supported via funding from Prince Sattam Bin Abdulaziz University project number(PSAU/2025/R/1447).
文摘The fracture energy of fiber-reinforced concrete(FRC)affects the durability and structural performance of concrete elements.Advancements in experimental studies have yet to overcome the challenges of estimating fracture energy,as the process remains time-intensive and costly.Therefore,machine learning techniques have emerged as powerful alternatives.This study aims to investigate the performance of machine learning techniques to predict the fracture energy of FRC.For this purpose,500 data points,including 8 input parameters that affect the fracture energy of FRC,are collected fromthree-point bending tests and employed to train and evaluate themachine learning techniques.The findings showed that Gaussian process regression(GPR)outperforms all other models in terms of predictive accuracy,achieving the highest R2 of 0.93 and the lowest RMSE of 13.91 during holdout cross-validation.It is then followed by support vector regression(SVR)and extreme gradient boosting regression(XGBR),whereas K-nearest neighbours(KNN)and random forest regression(RFR)show the weakest predictions.The superiority of GPR is further reinforced in a 5-fold cross-validation,where it consistently delivers an average R2 above 0.96 and ranks highest in overall predictive performance.Empirical testing with additional sample sets validates GPR’s model on the key mix parameter’s impact on fracture energy,cementing its claim.The Fly-Ash cement exhibits the greatest fracture energy due to superior fiber-matrix interaction,whereas the glass fiber dominates energy absorption amongst the other types of fibers.In addition,increasing the water-to-cement(W/C)ratio from 0.30 to 0.50 yields a significant improvement in fracture energy,which aligns well with the machine learning predictions.Similarly,loading rate positively correlates with fracture energy,highlighting the strain-rate sensitivity of FRC.This work is the missing link to integrate experimental fracture mechanics and computational intelligence,optimally and reasonably predicting and refining the fracture energy of FRC.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2022R1C1C1005409)supported by the Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure and Trans-port(Grant RS-2023-00251002)+2 种基金the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(Grant No.RS-2025-00516147)support provided by the NSF PREM program(DMR-2122178)the Institute of Advanced Manufacturing(IAM)at the University of Texas at Rio Grande Valley(UTRGV).
文摘Sinkhole formation poses a significant geohazard in karst regions,where unpredictable subsurface erosion often necessitates costly grouting for stabilization.Accurate estimation of grout volume remains a persistent challenge due to spatial variability,site-specific conditions,and the limitations of traditional empirical methods.This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test(CPT)-derived Sinkhole Resistance Ratio(SRR)values,spatial correlations between CPT and grouting points(GPs),and field-recorded grout volumes from six sinkhole sites in Florida.Three data trans-formation methods,the Proximal Allocation Method(PAM),the Equitable Distribution Method(EDM),and the Threshold-based Equitable Distribution Method(TEDM),were applied to distribute grout influence across CPTs,with TEDM demonstrating superior predictive performance.Synthetic data augmentation using spline method-ology further improved model robustness.A high-degree polynomial regression model,optimized with ridge regularization,achieved high accuracy(R^(2)=0.95;PEV=0.94)and significantly outperformed existing linear and logarithmic models.Results confirm that lower SRR values correlate with higher grout demand,and the proposed model reliably captures these nonlinear relationships.This research advances sinkhole remediation practice by providing a data-driven,accurate,and generalizable framework for grout volume estimation,enabling more efficient resource allocation and improved project outcomes.
文摘Software has been developed for digital control of WDW series testing machine and the measurement of fracture toughness by modularized design. Development of the software makes use of multi-thread and serial communication techniques, which can accurately control the testing machine and measure the fracture toughness in real-time. Three-point bending specimens were used in the measurement. The software operates stably and reliably, expanding the function of WDW series testing machine.
基金Supported by National Natural Science Foundation of China(Grant Nos.U22B2068,52275520,52075078)National Key Research and Development Program of China(Grant No.2019YFA0709003).
文摘Ultrasonic testing(UT)is increasingly combined with machine learning(ML)techniques for intelligently identifying damage.Extracting signifcant features from UT data is essential for efcient defect characterization.Moreover,the hidden physics behind ML is unexplained,reducing the generalization capability and versatility of ML methods in UT.In this paper,a generally applicable ML framework based on the model interpretation strategy is proposed to improve the detection accuracy and computational efciency of UT.Firstly,multi-domain features are extracted from the UT signals with signal processing techniques to construct an initial feature space.Subsequently,a feature selection method based on model interpretable strategy(FS-MIS)is innovatively developed by integrating Shapley additive explanation(SHAP),flter method,embedded method and wrapper method.The most efective ML model and the optimal feature subset with better correlation to the target defects are determined self-adaptively.The proposed framework is validated by identifying and locating side-drilled holes(SDHs)with 0.5λcentral distance and different depths.An ultrasonic array probe is adopted to acquire FMC datasets from several aluminum alloy specimens containing two SDHs by experiments.The optimal feature subset selected by FS-MIS is set as the input of the chosen ML model to train and predict the times of arrival(ToAs)of the scattered waves emitted by adjacent SDHs.The experimental results demonstrate that the relative errors of the predicted ToAs are all below 3.67%with an average error of 0.25%,signifcantly improving the time resolution of UT signals.On this basis,the predicted ToAs are assigned to the corresponding original signals for decoupling overlapped pulse-echoes and reconstructing high-resolution FMC datasets.The imaging resolution is enhanced to 0.5λby implementing the total focusing method(TFM).The relative errors of hole depths and central distance are no more than 0.51%and 3.57%,respectively.Finally,the superior performance of the proposed FS-MIS is validated by comparing it with initial feature space and conventional dimensionality reduction techniques.
基金Funded by the National Key R&D Program of China(2017YFB0310102)。
文摘In order to compare the compensation effect of expansive materials with different mineral sources on the temperature stress of concrete,we investigated the temperature stress of concrete when adding calcium sulfoaluminate type expansive materials(CSA)or CaO and calcium sulfoaluminate mixed type expansive materials(HCSA)at different temperatures by temperature-stress testing machine(TSTM)considering the influence of temperature history on the expansion.The experimental results show that the expansion characteristics of the two kinds of expansive materials with different mineral sources significantly vary.When adding expansive materials,the growth rate of compressive stress during the heating stage increases obviously,the maximum compressive stress is higher,while the decline rate of tensile stress in the late cooling stage becomes slow,and finally cracking temperature decreases.It is proved that concrete with HCSA has lower cracking temperatures and better temperature shrinkage compensation effect.Therefore,it is rational to choose HCSA when preparing concrete with high expansion energy to reduce thermal cracking.
文摘National Quality Supervision ＆ Inspection Center for Refractories Business scope： Selective examination for national quality supervision; Identification of production license; Arbitration inspection and technical achievements evaluation; Commodities inspection and otherquality inspections .
文摘In the present study, an aero pneumatic fatigue testing machine for complete dentures was designed, fabricated, and tested for the evaluation of the fatigue life of reinforced complete upper denture (CUD). On completion and testing, it was observed that the machine has the potential of generating reliable number of cyclic data. The machine’s performance was evaluated using test specimens of identical CUDs that were machined in conformity with standard procedures. The fatigue machine compressed the lower dental arch over the upper denture-specimen in centric occlusion, in the same way that the two masticatory muscles pull the lower jaw over the upper jaw during chewing. The incorporation of glass fibres into the CUD using a sandwich technique quadruples the lifespan of the denture (<em>P</em> = 0.004). The low standard deviation, along with the low coefficient of variation (CV) of the group of unreinforced dentures shows the repeatability of the results and the reliability of the machine. The high standard deviation and coefficient of variation of reinforced dentures was expected, since a high variation of results is usually recorded in fibre reinforcement cases. This research confirmed the view that the crack during denture fracture initiates in the anterior palatal area and propagates to the posterior.
文摘Building high confidence regression test suites to validate new system versions is a challenging problem. A modelbased approach to build a regression test suite from a given test suite is described. The generated test suite includes every test that will traverse a change performed to produce the new version, and consists of only such tests to reduce the testing costs. Finite state machines extended with typed variables (EFSMs) are used to model systems and system changes are mapped to EFSM transition changes adding/deleting/replacing EFSM transitions and states. Tests are a sequence of input and expected output messages with concrete parameter values over the supported data types. An invariant is formulated to characterize tests whose runtime behavior can be accurately predicted by analyzing their descriptions along with the model. Incremental procedures to efficiently evaluate the invariant and to select tests for regression are developed. Overlaps among the test descriptions are exploited to extend the approach to simultaneously select multiple tests to reduce the test selection costs. Effectiveness of the approach is demonstrated by applying it to several protocols, Web services, and model programs extracted from a popular testing benchmark. Our experimental results show that the proposed approach is economical for regression test selection in all these examples. For all these examples, the proposed approach is able to identify all tests exercising changes more efficiently than brute-force symbolic evaluation.
文摘To ensure the quality of Web applications, Web testing is one of the effective methods. The testing is a process of revealing errors that is used to give confidence that the implementation of a Web application meets its original specification. This work proposes a Web testing framework based on Stream X-Machines (SXMs), which provides a way to derive test cases for a Web application. It starts from constructing the SXM model, from which a test translator is employed to extract the test paths and then translates them into an XML-style test specification, which is the input of test engine. The test engine generates test cases and then executes them, and finally produces test report. This testing method is a significant contribution to informed research.
文摘A high fidelity dynamic model of a high-energy hydraulically-actuated shock test machine for heavy weight devices is presented to satisfy the newly-built shock resistance standard and simulate the actual underwater explosion environments in laboratory as well as increase the testing capability of shock test machine. In order to produce the required negative shock pulse in the given time duration, four hydraulic actuators are utilized. The model is then used to formulate an advanced feedforward controller for the system to produce the required negative waveform and to address the motion synchronization of the four cylinders. The model provides a safe and easily controllable way to perform a ＂virtual testing＂ before starting potentially destructive tests on specimen and to predict performance of the system. Simulation results have demonstrated the effectiveness of the controller.
基金supported by the National Natural Science Foundation of China(52225402 and U1910206).
文摘Coal bursting liability refers to the mechanical property of the degree and possibility of coal burst.The bursting liability is important to evaluate coal burst in mining.In this paper,the needle penetration test was carried out to determinate the coal bursting liability,and the empirical criterion of coal bursting liability was proposed.Moreover,the machine learning method was applied to coal bursting liability determination.Through analyzing the elastic strain energy release and failure time,the residual elastic strain energy release rate index K_(RE)was proposed to evaluate the coal bursting liability.According to the relationship between needle penetration index(NPI),K_(RE)and the critical value of K_(RE),the Needle Penetration Test-based Empirical Classification Criterion(NPT-ECC)was obtained.In addition,four machine learning classification models were constructed.After training and testing of the models,Needle Penetration Test-based Machine Learning Classification Model(NPT-MLCM)was proposed.The research results show that the accuracy of NPT-ECC is 6.66%higher than that of China National Standard Comprehensive Evaluation(CNSCE)according to verification of the coal fragment ejection ratio F.Gridsearch cross validation-extreme gradient boosting(GSCV-XGBoost)has the best prediction performance among all the models,and accuracy,Macro-Precision,Macro-Recall and Macro-F1-score of which were 86.67%,88.97%,87.50%and 87.37%.Based on this,the Needle Penetration Test-based GSCV-XGBoost(NPT-GSCV-XGBoost)was proposed.After comparative analysis and discussion,NPT-GSCV-XGBoost is superior to NPT-ECC and CNSCE in the comprehensive prediction ability.
