Soft materials,with the sensitivity to various external stimuli,exhibit high flexibility and stretchability.Accurate prediction of their mechanical behaviors requires advanced hyperelastic constitutive models incorpor...Soft materials,with the sensitivity to various external stimuli,exhibit high flexibility and stretchability.Accurate prediction of their mechanical behaviors requires advanced hyperelastic constitutive models incorporating multiple parameters.However,identifying multiple parameters under complex deformations remains a challenge,especially with limited observed data.In this study,we develop a physics-informed neural network(PINN)framework to identify material parameters and predict mechanical fields,focusing on compressible Neo-Hookean materials and hydrogels.To improve accuracy,we utilize scaling techniques to normalize network outputs and material parameters.This framework effectively solves forward and inverse problems,extrapolating continuous mechanical fields from sparse boundary data and identifying unknown mechanical properties.We explore different approaches for imposing boundary conditions(BCs)to assess their impacts on accuracy.To enhance efficiency and generalization,we propose a transfer learning enhanced PINN(TL-PINN),allowing pre-trained networks to quickly adapt to new scenarios.The TL-PINN significantly reduces computational costs while maintaining accuracy.This work holds promise in addressing practical challenges in soft material science,and provides insights into soft material mechanics with state-of-the-art experimental methods.展开更多
4D printed smart materials is mostly relying on thermal stimulation to actuate,limiting their widely application requiring precise and localized control of the deformations.Most existing strategies for achieving local...4D printed smart materials is mostly relying on thermal stimulation to actuate,limiting their widely application requiring precise and localized control of the deformations.Most existing strategies for achieving localized control rely on hetero-geneous material systems and structural design,thereby increasing design and manufacturing complexity.Here,we endow localized electrothermal,actuation,and sensing properties in electrically-driven soft actuator through parameter-encoded 4D printing.We analyzed the effects of printing parameters on shape memory properties and conductivity,and then explored the multi-directional sensing performance of the 4D printed composites.We demonstrated an integrated actuator-sensor device capable of both shape recovery and perceiving its own position and obstacles simultaneously.Moreover,it can adjust its sensing characteristics through temporary shape programming to adapt to different application scenarios.This study achieves integrated and localized actuation-sensing without the need for multi-material systems and intricate structural designs,offering an efficient solution for the intelligent and lightweight design in the fields of soft robotics,biomedical applications,and aerospace.展开更多
The force model during needle insertion into soft tissue is important for accurate percutaneous intervention.In this paper,a force model for needle insertion into a tissue- equivalent material is presented and a serie...The force model during needle insertion into soft tissue is important for accurate percutaneous intervention.In this paper,a force model for needle insertion into a tissue- equivalent material is presented and a series of experiments are conducted to acquire data from needle soft- tissue interaction process.In order to build a more accurate insertion force model,the interaction force between a surgical needle and soft tissue is divided into three parts:stiffness force,friction force,and cutting force.The stiffness force is modeled on the basis of contact mechanics model.The friction force model is presented using a modified Winkler' s foundation model.The cutting force is viewed as a constant depending on a given tissue.The proposed models in the paper are established on the basis of the mechanical properties and geometric parameters of the needle and soft tissue.The experimental results illustrate that the force models are capable of predicting the needle-tissue interaction force.The force models of needle insertion can provide real-time haptic feedback for robot-assisted procedures,thereby improving the accuracy and safety of surgery.展开更多
Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,whic...Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,which are augmented as state variables.Based on the observability of the singular system,this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters.When the observability is satisfied,the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer.The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation.With the catalyst circulation rate as the only unknown input without model error,one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst circulation rate.However,when uncertain model parameters also exist,additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.展开更多
Soft rocks, such as coal, are afected by sedimentary efects, and the surrounding rock mass of underground coal mines is generally soft and rich in joints and cracks. A clear and deep understanding of the relationship ...Soft rocks, such as coal, are afected by sedimentary efects, and the surrounding rock mass of underground coal mines is generally soft and rich in joints and cracks. A clear and deep understanding of the relationship between crack geometric parameters and rock mechanics properties in cracked rock is greatly important to the design of engineering rock mass struc‑tures. In this study, computed tomography (CT) scanning was used to extract the internal crack network of coal specimens. Based on the crack size and dominant crack number, the parameters of crack area, volume, length, width, and angle were statistically analyzed by diferent sampling thresholds. In addition, the Pearson correlation coefcients between the crack parameters and uniaxial compression rock mechanics properties (uniaxial compressive strength UCS, elasticity modulus E) were calculated to quantitatively analyze the impact of each parameter. Furthermore, a method based on Pearson coefcients was used to grade the correlation between crack geometric parameters and rock mechanical properties to determine threshold values. The results indicated that the UCS and E of the specimens changed with the varied internal crack structures of the specimens, the crack parameters of area, volume, length and width all showed negative correlations with UCS and E, and the dominant crack played an important role both in weakening strength and stifness. The crack parameters of the angle are all positively correlated with the UCS and E. More crack statistics can signifcantly improve the correlation between the parameters of the crack angle and the rock mechanics properties, and the statistics of the geometric parameters of at least 16 cracks or the area larger than 5 mm2 are suggested for the analysis of complex cracked rock masses or physical reproduction using 3D printing. The results are validated and further analyzed with triaxial tests. The fndings of this study have important reference value for future research regarding the accurate and efcient selection of a few cracks with a signifcant infuence on the rock mechanical properties of surrounding rock mass structures in coal engineering.展开更多
To design a power source system and mold for electromagnetic soft-contact continuous casting process and to theoretically estimate the heat losses from the charges and the system power, the effect of structure paramet...To design a power source system and mold for electromagnetic soft-contact continuous casting process and to theoretically estimate the heat losses from the charges and the system power, the effect of structure parameters on system power and magnetic flux density distribution was calculated using finite element method. The results show that as for electromagnetic soft-contact continuous casting system with partial-segment type mold, the power consumption is much more than that with a full-segment type mold; about 62% of electric power is dissipated in the mold, and the effective acting range of magnetic field is relatively narrow. Optimizing mold structure is a crucial measure of remarkably reducing mold power consumption and saving electric energy. Increasing slit number, width, and length can remarkably increase the magnetic flux density in the mold and can reduce the electric energy consumption. Among structure parameters, slit number and slit width are relatively more effective to reduce energy consumption. For a round billet electromagnetic continuous casting system with diameter of 178 ram, the reasonable slit number, width, and length are about 24--32, 0. 5--1.0 mm, and 160 mm, respectively.展开更多
Based on analysis of the supporting object of bulking soft rock and comparing the supporting difficulty of the repairing projects with that of the newly excavated projects, this paper proposes a method to determine re...Based on analysis of the supporting object of bulking soft rock and comparing the supporting difficulty of the repairing projects with that of the newly excavated projects, this paper proposes a method to determine reasonable supporting parameters for soft rock project repairing. This method has been verified to be reasonable and economical in an industrial test.展开更多
In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a u...In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a uniform linear array(ULA).We exploit the inherent incomplete data processing capability of atomic norm soft thresholding(AST)to analyze the space-time matrix and complete the accurate estimation of the hopping time and frequency of the received FH signals.The hopping time is obtained by the sudden changes of the spatial information,which is implemented as the boundary to divide the time domain signal so that each segment of the signal is a superposition of time-invariant multiple components.Then,the frequency of multiple signal components can be estimated precisely by AST within each segment.After obtaining the above two parameters of the hopping time and the frequency of signals,the direction of arrival(DOA)can be directly calculated by them,and the network sorting can be realized.Results of simulation show that the proposed method is superior to the existing technology.Even when a large portion of data observations is missing,as the number of array elements increases,the proposed method still achieves acceptable accuracy of multi-FH signal parameters estimation.展开更多
Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE...Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.展开更多
The soft fault induced by parameter variation is one of the most challenging problems in the domain of fault diagnosis for analog circuits.A new fault location and parameter prediction approach for soft-faults diagnos...The soft fault induced by parameter variation is one of the most challenging problems in the domain of fault diagnosis for analog circuits.A new fault location and parameter prediction approach for soft-faults diagnosis in analog circuits is presented in this paper.The proposed method extracts the original signals from the output terminals of the circuits under test（CUT） by a data acquisition board.Firstly,the phase deviation value between fault-free and faulty conditions is obtained by fitting the sampling sequence with a sine curve.Secondly,the sampling sequence is organized into a square matrix and the spectral radius of this matrix is obtained.Thirdly,the smallest error of the spectral radius and the corresponding component value are obtained through comparing the spectral radius and phase deviation value with the trend curves of them,respectively,which are calculated from the simulation data.Finally,the fault location is completed by using the smallest error,and the corresponding component value is the parameter identification result.Both simulated and experimental results show the effectiveness of the proposed approach.It is particularly suitable for the fault location and parameter identification for analog integrated circuits.展开更多
A comparison between deep learning and standalone models in predicting the compaction parameters of soil is presented in this research.One hundred and ninety and fifty-three soil samples were randomly picked up from t...A comparison between deep learning and standalone models in predicting the compaction parameters of soil is presented in this research.One hundred and ninety and fifty-three soil samples were randomly picked up from two hundred and forty-three soil samples to create training and validation datasets,respectively.The performance and accuracy of the models were measured by root mean square error(RMSE),coefficient of determination(R2),Pearson product-moment correlation coefficient(r),mean absolute error(MAE),variance accounted for(VAF),mean absolute percentage error(MAPE),weighted mean absolute percentage error(WMAPE),a20-index,index of scatter(IOS),and index of agreement(IOA).Comparisons between standalone models demonstrate that the model MD 29 in Gaussian process regression(GPR)and model MD 101 in support vector machine(SVM)can achieve over 96%of accuracy in predicting the optimum moisture content(OMC)and maximum dry density(MDD)of soil,and outperformed other standalone models.The comparison between deep learning models shows that the models MD 46 and MD 146 in long short-term memory(LSTM)predict OMC and MDD with higher accuracy than ANN models.However,the LSTM models outperformed the GPR models in predicting the compaction parameters.The sensitivity analysis illustrates that fine content(FC),specific gravity(SG),and liquid limit(LL)highly influence the prediction of compaction parameters.展开更多
Managers of cement plants are gradually becoming aware of the need for soft sensors in product quality assessment. Cement clinker quality parameters are mostly measured by offline laboratory analysis or by the use of ...Managers of cement plants are gradually becoming aware of the need for soft sensors in product quality assessment. Cement clinker quality parameters are mostly measured by offline laboratory analysis or by the use of online analyzers. The measurement delay and cost, associated with these methods, are a concern in the cement industry. In this study, a regression-based model was developed to predict the clinker quality parameters as a function of the raw meal quality and the kiln operating variables. This model has mean squared error, coefficient of determination, worst case relative error and variance account for (in external data) given as 8.96 × 10<sup>–7</sup>, 0.9999, 2.17% and above 97%, respectively. Thus, it is concluded that the developed model can provide real time estimates of the clinker quality parameters and capture wider ranges of real plant operating conditions from first principle-based cement rotary kiln models. Also, the model developed can be utilized online as soft sensor since they contain only variables that are easily measured online.展开更多
The Tianjin coastal area is a typical soft soil region,where the soil is a marine deposit of the late Quaternary.The soil dynamic parameters from seismic risk assessment reports are collected,and drilling of 15 holes ...The Tianjin coastal area is a typical soft soil region,where the soil is a marine deposit of the late Quaternary.The soil dynamic parameters from seismic risk assessment reports are collected,and drilling of 15 holes was carried out to sample the soils and measure their dynamic characteristics.The data was divided into 7 types based on lithology,namely,muddy clay,muddy silty clay,silt,silty clay,clay,silty sand and fine sand.Statistics of the dynamic parameters of these soils are collected to obtain the mean values of dynamic shear modulus ratio and damping ratio at different depths.Then,two typical drill holes are selected to establish the soil dynamic models to investigate the seismic response in different cases.The dynamic seismic responses of soil are calculated using the statistical values of this paper,and the values of Code(1994) and those recommended by Yuan Xiaoming et al.(2000),respectively.The applicability and pertinence of the statistical value obtained in this paper are demonstrated by the response spectrum shape,peak ground acceleration and response spectral characteristics.The results can be taken as a reference of the soil dynamic value in this area and can be used in the seismic risk assessment of engineering projects.展开更多
基金supported by the National Natural Science Foundation of China(Nos.12172273 and 11820101001)。
文摘Soft materials,with the sensitivity to various external stimuli,exhibit high flexibility and stretchability.Accurate prediction of their mechanical behaviors requires advanced hyperelastic constitutive models incorporating multiple parameters.However,identifying multiple parameters under complex deformations remains a challenge,especially with limited observed data.In this study,we develop a physics-informed neural network(PINN)framework to identify material parameters and predict mechanical fields,focusing on compressible Neo-Hookean materials and hydrogels.To improve accuracy,we utilize scaling techniques to normalize network outputs and material parameters.This framework effectively solves forward and inverse problems,extrapolating continuous mechanical fields from sparse boundary data and identifying unknown mechanical properties.We explore different approaches for imposing boundary conditions(BCs)to assess their impacts on accuracy.To enhance efficiency and generalization,we propose a transfer learning enhanced PINN(TL-PINN),allowing pre-trained networks to quickly adapt to new scenarios.The TL-PINN significantly reduces computational costs while maintaining accuracy.This work holds promise in addressing practical challenges in soft material science,and provides insights into soft material mechanics with state-of-the-art experimental methods.
基金supported in part by National Natural Science Foundation of China under Grant 52305304Jilin Youth Growth Technology Project under Grant 20230508147RC+2 种基金the Science and Technology Research Project of Jilin Provincial Education Department(No.JJKH20231193KJ)supported in part by the National Natural Science Foundation of China under Grant 52021003in part by the Natural Science Foundation of Jilin Province under Grant 20210101053JC.
文摘4D printed smart materials is mostly relying on thermal stimulation to actuate,limiting their widely application requiring precise and localized control of the deformations.Most existing strategies for achieving localized control rely on hetero-geneous material systems and structural design,thereby increasing design and manufacturing complexity.Here,we endow localized electrothermal,actuation,and sensing properties in electrically-driven soft actuator through parameter-encoded 4D printing.We analyzed the effects of printing parameters on shape memory properties and conductivity,and then explored the multi-directional sensing performance of the 4D printed composites.We demonstrated an integrated actuator-sensor device capable of both shape recovery and perceiving its own position and obstacles simultaneously.Moreover,it can adjust its sensing characteristics through temporary shape programming to adapt to different application scenarios.This study achieves integrated and localized actuation-sensing without the need for multi-material systems and intricate structural designs,offering an efficient solution for the intelligent and lightweight design in the fields of soft robotics,biomedical applications,and aerospace.
基金Supported by the National Natural Science Foundation of China(No.51175373)New Century Educational Talents Plan of Chinese Education Ministry(No.NCET-10-0625)+1 种基金Key Technology and Development Program of Tianjin Municipal Science and Technology Commission(No.12ZCDZSY10600)Tianjin Key Laboratory of High Speed Cutting&Precision Machining(TUTE)(2013120024001167)
文摘The force model during needle insertion into soft tissue is important for accurate percutaneous intervention.In this paper,a force model for needle insertion into a tissue- equivalent material is presented and a series of experiments are conducted to acquire data from needle soft- tissue interaction process.In order to build a more accurate insertion force model,the interaction force between a surgical needle and soft tissue is divided into three parts:stiffness force,friction force,and cutting force.The stiffness force is modeled on the basis of contact mechanics model.The friction force model is presented using a modified Winkler' s foundation model.The cutting force is viewed as a constant depending on a given tissue.The proposed models in the paper are established on the basis of the mechanical properties and geometric parameters of the needle and soft tissue.The experimental results illustrate that the force models are capable of predicting the needle-tissue interaction force.The force models of needle insertion can provide real-time haptic feedback for robot-assisted procedures,thereby improving the accuracy and safety of surgery.
基金Supported by the National Natural Science Foundation of China （21006127）, the National Basic Research Program of China （2012CB720500） and the Science Foundation of China University of Petroleum, Beijing （KYJJ2012-05-28）.
文摘Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,which are augmented as state variables.Based on the observability of the singular system,this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters.When the observability is satisfied,the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer.The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation.With the catalyst circulation rate as the only unknown input without model error,one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst circulation rate.However,when uncertain model parameters also exist,additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.
基金supported by the Young Scientist Project of National Key Research and Development Program of China(2021YFC2900600)National Natural Science Foundation of China(52074166)Shandong Province(ZR2021YQ38).
文摘Soft rocks, such as coal, are afected by sedimentary efects, and the surrounding rock mass of underground coal mines is generally soft and rich in joints and cracks. A clear and deep understanding of the relationship between crack geometric parameters and rock mechanics properties in cracked rock is greatly important to the design of engineering rock mass struc‑tures. In this study, computed tomography (CT) scanning was used to extract the internal crack network of coal specimens. Based on the crack size and dominant crack number, the parameters of crack area, volume, length, width, and angle were statistically analyzed by diferent sampling thresholds. In addition, the Pearson correlation coefcients between the crack parameters and uniaxial compression rock mechanics properties (uniaxial compressive strength UCS, elasticity modulus E) were calculated to quantitatively analyze the impact of each parameter. Furthermore, a method based on Pearson coefcients was used to grade the correlation between crack geometric parameters and rock mechanical properties to determine threshold values. The results indicated that the UCS and E of the specimens changed with the varied internal crack structures of the specimens, the crack parameters of area, volume, length and width all showed negative correlations with UCS and E, and the dominant crack played an important role both in weakening strength and stifness. The crack parameters of the angle are all positively correlated with the UCS and E. More crack statistics can signifcantly improve the correlation between the parameters of the crack angle and the rock mechanics properties, and the statistics of the geometric parameters of at least 16 cracks or the area larger than 5 mm2 are suggested for the analysis of complex cracked rock masses or physical reproduction using 3D printing. The results are validated and further analyzed with triaxial tests. The fndings of this study have important reference value for future research regarding the accurate and efcient selection of a few cracks with a signifcant infuence on the rock mechanical properties of surrounding rock mass structures in coal engineering.
基金Item Sponsored by National Natural Science Foundation of China(50274203)National High Technology Research and Development Program of China(2001AA337040)
文摘To design a power source system and mold for electromagnetic soft-contact continuous casting process and to theoretically estimate the heat losses from the charges and the system power, the effect of structure parameters on system power and magnetic flux density distribution was calculated using finite element method. The results show that as for electromagnetic soft-contact continuous casting system with partial-segment type mold, the power consumption is much more than that with a full-segment type mold; about 62% of electric power is dissipated in the mold, and the effective acting range of magnetic field is relatively narrow. Optimizing mold structure is a crucial measure of remarkably reducing mold power consumption and saving electric energy. Increasing slit number, width, and length can remarkably increase the magnetic flux density in the mold and can reduce the electric energy consumption. Among structure parameters, slit number and slit width are relatively more effective to reduce energy consumption. For a round billet electromagnetic continuous casting system with diameter of 178 ram, the reasonable slit number, width, and length are about 24--32, 0. 5--1.0 mm, and 160 mm, respectively.
文摘Based on analysis of the supporting object of bulking soft rock and comparing the supporting difficulty of the repairing projects with that of the newly excavated projects, this paper proposes a method to determine reasonable supporting parameters for soft rock project repairing. This method has been verified to be reasonable and economical in an industrial test.
文摘In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a uniform linear array(ULA).We exploit the inherent incomplete data processing capability of atomic norm soft thresholding(AST)to analyze the space-time matrix and complete the accurate estimation of the hopping time and frequency of the received FH signals.The hopping time is obtained by the sudden changes of the spatial information,which is implemented as the boundary to divide the time domain signal so that each segment of the signal is a superposition of time-invariant multiple components.Then,the frequency of multiple signal components can be estimated precisely by AST within each segment.After obtaining the above two parameters of the hopping time and the frequency of signals,the direction of arrival(DOA)can be directly calculated by them,and the network sorting can be realized.Results of simulation show that the proposed method is superior to the existing technology.Even when a large portion of data observations is missing,as the number of array elements increases,the proposed method still achieves acceptable accuracy of multi-FH signal parameters estimation.
基金supported by the National Key Research and Development Program of China(2023YFB3307800)National Natural Science Foundation of China(62394343,62373155)+2 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024A26)Fundamental Research Funds for the Central Universities.
文摘Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty.Due to the outstanding ability for high-level feature extraction,stacked autoencoder(SAE)has been widely used to improve the model accuracy of soft sensors.However,with the increase of network layers,SAE may encounter serious information loss issues,which affect the modeling performance of soft sensors.Besides,there are typically very few labeled samples in the data set,which brings challenges to traditional neural networks to solve.In this paper,a multi-scale feature fused stacked autoencoder(MFF-SAE)is suggested for feature representation related to hierarchical output,where stacked autoencoder,mutual information(MI)and multi-scale feature fusion(MFF)strategies are integrated.Based on correlation analysis between output and input variables,critical hidden variables are extracted from the original variables in each autoencoder's input layer,which are correspondingly given varying weights.Besides,an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers.Then,the MFF-SAE method is designed and stacked to form deep networks.Two practical industrial processes are utilized to evaluate the performance of MFF-SAE.Results from simulations indicate that in comparison to other cutting-edge techniques,the proposed method may considerably enhance the accuracy of soft sensor modeling,where the suggested method reduces the root mean square error(RMSE)by 71.8%,17.1%and 64.7%,15.1%,respectively.
基金supported by the National Natural Science Foundation of China under Grant No.61371049
文摘The soft fault induced by parameter variation is one of the most challenging problems in the domain of fault diagnosis for analog circuits.A new fault location and parameter prediction approach for soft-faults diagnosis in analog circuits is presented in this paper.The proposed method extracts the original signals from the output terminals of the circuits under test（CUT） by a data acquisition board.Firstly,the phase deviation value between fault-free and faulty conditions is obtained by fitting the sampling sequence with a sine curve.Secondly,the sampling sequence is organized into a square matrix and the spectral radius of this matrix is obtained.Thirdly,the smallest error of the spectral radius and the corresponding component value are obtained through comparing the spectral radius and phase deviation value with the trend curves of them,respectively,which are calculated from the simulation data.Finally,the fault location is completed by using the smallest error,and the corresponding component value is the parameter identification result.Both simulated and experimental results show the effectiveness of the proposed approach.It is particularly suitable for the fault location and parameter identification for analog integrated circuits.
文摘A comparison between deep learning and standalone models in predicting the compaction parameters of soil is presented in this research.One hundred and ninety and fifty-three soil samples were randomly picked up from two hundred and forty-three soil samples to create training and validation datasets,respectively.The performance and accuracy of the models were measured by root mean square error(RMSE),coefficient of determination(R2),Pearson product-moment correlation coefficient(r),mean absolute error(MAE),variance accounted for(VAF),mean absolute percentage error(MAPE),weighted mean absolute percentage error(WMAPE),a20-index,index of scatter(IOS),and index of agreement(IOA).Comparisons between standalone models demonstrate that the model MD 29 in Gaussian process regression(GPR)and model MD 101 in support vector machine(SVM)can achieve over 96%of accuracy in predicting the optimum moisture content(OMC)and maximum dry density(MDD)of soil,and outperformed other standalone models.The comparison between deep learning models shows that the models MD 46 and MD 146 in long short-term memory(LSTM)predict OMC and MDD with higher accuracy than ANN models.However,the LSTM models outperformed the GPR models in predicting the compaction parameters.The sensitivity analysis illustrates that fine content(FC),specific gravity(SG),and liquid limit(LL)highly influence the prediction of compaction parameters.
文摘Managers of cement plants are gradually becoming aware of the need for soft sensors in product quality assessment. Cement clinker quality parameters are mostly measured by offline laboratory analysis or by the use of online analyzers. The measurement delay and cost, associated with these methods, are a concern in the cement industry. In this study, a regression-based model was developed to predict the clinker quality parameters as a function of the raw meal quality and the kiln operating variables. This model has mean squared error, coefficient of determination, worst case relative error and variance account for (in external data) given as 8.96 × 10<sup>–7</sup>, 0.9999, 2.17% and above 97%, respectively. Thus, it is concluded that the developed model can provide real time estimates of the clinker quality parameters and capture wider ranges of real plant operating conditions from first principle-based cement rotary kiln models. Also, the model developed can be utilized online as soft sensor since they contain only variables that are easily measured online.
基金sponsored by the State-level Public Welfare Scientific Research Courtyard Basic Scientific Research ProgramInstitute of Crustal Dynamics+1 种基金CEA (ZDJ2009-07ZDJ2009-23)
文摘The Tianjin coastal area is a typical soft soil region,where the soil is a marine deposit of the late Quaternary.The soil dynamic parameters from seismic risk assessment reports are collected,and drilling of 15 holes was carried out to sample the soils and measure their dynamic characteristics.The data was divided into 7 types based on lithology,namely,muddy clay,muddy silty clay,silt,silty clay,clay,silty sand and fine sand.Statistics of the dynamic parameters of these soils are collected to obtain the mean values of dynamic shear modulus ratio and damping ratio at different depths.Then,two typical drill holes are selected to establish the soil dynamic models to investigate the seismic response in different cases.The dynamic seismic responses of soil are calculated using the statistical values of this paper,and the values of Code(1994) and those recommended by Yuan Xiaoming et al.(2000),respectively.The applicability and pertinence of the statistical value obtained in this paper are demonstrated by the response spectrum shape,peak ground acceleration and response spectral characteristics.The results can be taken as a reference of the soil dynamic value in this area and can be used in the seismic risk assessment of engineering projects.
