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
Diagnosing intermittent fault is an important approach to reduce built-in test（BIT） false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit ...Diagnosing intermittent fault is an important approach to reduce built-in test（BIT） false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit of support vector machines （ SVM） which can be trained with a small-sample, an SVM-based diagnostic model of 3 states that include OK state, intermittent state and faulty state is presented. With the features based on the reflection coefficients of an alarm rate （ AR ） model extracted from small vibration samples, these models are trained to diagnose intermittent faults. The experimental results show that this method can diagnose multiple intermittent faults accurately with small training samples and BIT false alarms are reduced.展开更多
This paper presents modified version of a realistic test tool suitable to Design For Testability （DFT） and Built-ln Self Test （BIST） environments. A comprehensive tool is developed in the form of a test simulator....This paper presents modified version of a realistic test tool suitable to Design For Testability （DFT） and Built-ln Self Test （BIST） environments. A comprehensive tool is developed in the form of a test simulator. The simulator is capable of providing a required goal of test for the Circuit Under Test （CUT）. The simulator uses the approach of fault diagnostics with fault grading procedures to provide the optimum tests. The current version of the simulator embeds features of exhaustive and pseudo-random test generation schemes along with the search solutions of cost effective test goals. The simulator provides facilities of realizing all possible pseudo-random sequence generators with all possible combinations of seeds. The tool is developed on a common Personal Computer （PC） platform and hence no special software is required. Thereby, it is a low cost tool hence economical. The tool is very much suitable for determining realistic test sequences for a targeted goal of testing for any CUT. The developed tool incorporates flexible Graphical User Interface （GUI） procedures and can be operated without any special programming skill. The tool is debugged and tested with the results of many bench mark circuits. Further, this developed tool can be utilized for educational purposes for many courses such as fault-tolerant computing, fault diagnosis, digital electronics, and safe-reliable-testable digital logic designs.展开更多
随着智能网联车辆技术的迅速发展,汽车自动化程度越来越高,当总线网络需要更多数量的电子控制单元(ECU,electronic control unit),车载总线的布局越来越复杂时,传统搭建车载总线测试台架效率较低、通用性差,不能满足主机厂测试需求.通...随着智能网联车辆技术的迅速发展,汽车自动化程度越来越高,当总线网络需要更多数量的电子控制单元(ECU,electronic control unit),车载总线的布局越来越复杂时,传统搭建车载总线测试台架效率较低、通用性差,不能满足主机厂测试需求.通过深入研究车载控制器局域网(CAN,controller area network)总线的测试,分析了传统测试台架的缺点,提出了一种基于Vector工具链车载CAN总线自动化测试系统.该系统通过Vector工具链之间的功能联系,采用能被CAPL语言调用的C++语言开发的底层驱动,以实现车载CAN总线、LIN(local interconnect network)总线自动化测试.本文设计了与人工测试的对比实验,验证了该系统不仅能解决车载总线测试通用性差的缺点,而且还提高了测试效率.展开更多
Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine （RVM） to determine the liquefaction potential of soil by using actua...Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine （RVM） to determine the liquefaction potential of soil by using actual cone penetration test （CPT） data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network （ANN） model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction.展开更多
Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft.A novel automatic defect identification system is presented.Wavelet packet analysis(WPA)was appli...Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft.A novel automatic defect identification system is presented.Wavelet packet analysis(WPA)was applied to feature extraction of ultrasonic signal,and optimal Support vector machine(SVM)was used to perform the identification task.Meanwhile,comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models.To validate the method,some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.展开更多
In this study, th e least sq u are su p p o rt v ecto r m achine （LSSVM） alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d c...In this study, th e least sq u are su p p o rt v ecto r m achine （LSSVM） alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d cone p e n e tra tio nte s t （CPT） resu lts w ere used as in p u t variables for pred ictio n o f pile bearin g capacity. The d ata u se d w erecollected from th e existing litera tu re an d consisted o f 50 case records. The application o f LSSVM w ascarried o u t by dividing th e d ata into th re e se ts： a train in g se t for learning th e pro b lem an d obtain in g arelationship b e tw e e n in p u t variables an d pile bearin g capacity, and testin g an d validation sets forevaluation o f th e predictive an d g en eralization ability o f th e o b tain ed relationship. The predictions o f pilebearing capacity by LSSVM w ere evaluated by com paring w ith ex p erim en tal d ata an d w ith th o se bytrad itio n al CPT-based m eth o d s and th e gene ex pression pro g ram m in g （GEP） m odel. It w as found th a t th eLSSVM perform s w ell w ith coefficient o f d eterm in atio n , m ean, an d sta n d ard dev iatio n equivalent to 0.99,1.03, an d 0.08, respectively, for th e testin g set, an d 1, 1.04, an d 0.11, respectively, for th e v alidation set. Thelow values o f th e calculated m ean squared e rro r an d m ean ab so lu te e rro r indicated th a t th e LSSVM w asaccurate in p redicting th e pile bearing capacity. The results o f com parison also show ed th a t th e p roposedalg o rith m p red icted th e pile bearin g capacity m ore accurately th a n th e trad itio n al m eth o d s including th eGEP m odel.展开更多
Abstract Accurate aerodynamic models are the basis of flight simulation and control law design. Mathematically modeling unsteady aerodynamics at high angles of attack bears great difficulties in model structure determ...Abstract Accurate aerodynamic models are the basis of flight simulation and control law design. Mathematically modeling unsteady aerodynamics at high angles of attack bears great difficulties in model structure determination and parameter estimation due to little understanding of the flow mechanism. Support vector machines （SVMs） based on statistical learning theory provide a novel tool for nonlinear system modeling. The work presented here examines the feasibility of applying SVMs to high angle.-of-attack unsteady aerodynamic modeling field. Mainly, after a review of SVMs, several issues associated with unsteady aerodynamic modeling by use of SVMs are discussed in detail, such as sele, ction of input variables, selection of output variables and determination of SVM parameters. The least squares SVM （LS-SVM） models are set up from certain dynamic wind tunnel test data of a delta wing and an aircraft configuration, and then used to predict the aerodynamic responses in other tests. The predictions are in good agreement with the test data, which indicates the satisfving learning and generalization performance of LS-SVMs.展开更多
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.
文摘Diagnosing intermittent fault is an important approach to reduce built-in test（BIT） false alarms. Aiming at solving the shortcoming of the present diagnostic method of intermittent fault, and according to the merit of support vector machines （ SVM） which can be trained with a small-sample, an SVM-based diagnostic model of 3 states that include OK state, intermittent state and faulty state is presented. With the features based on the reflection coefficients of an alarm rate （ AR ） model extracted from small vibration samples, these models are trained to diagnose intermittent faults. The experimental results show that this method can diagnose multiple intermittent faults accurately with small training samples and BIT false alarms are reduced.
文摘This paper presents modified version of a realistic test tool suitable to Design For Testability （DFT） and Built-ln Self Test （BIST） environments. A comprehensive tool is developed in the form of a test simulator. The simulator is capable of providing a required goal of test for the Circuit Under Test （CUT）. The simulator uses the approach of fault diagnostics with fault grading procedures to provide the optimum tests. The current version of the simulator embeds features of exhaustive and pseudo-random test generation schemes along with the search solutions of cost effective test goals. The simulator provides facilities of realizing all possible pseudo-random sequence generators with all possible combinations of seeds. The tool is developed on a common Personal Computer （PC） platform and hence no special software is required. Thereby, it is a low cost tool hence economical. The tool is very much suitable for determining realistic test sequences for a targeted goal of testing for any CUT. The developed tool incorporates flexible Graphical User Interface （GUI） procedures and can be operated without any special programming skill. The tool is debugged and tested with the results of many bench mark circuits. Further, this developed tool can be utilized for educational purposes for many courses such as fault-tolerant computing, fault diagnosis, digital electronics, and safe-reliable-testable digital logic designs.
文摘随着智能网联车辆技术的迅速发展,汽车自动化程度越来越高,当总线网络需要更多数量的电子控制单元(ECU,electronic control unit),车载总线的布局越来越复杂时,传统搭建车载总线测试台架效率较低、通用性差,不能满足主机厂测试需求.通过深入研究车载控制器局域网(CAN,controller area network)总线的测试,分析了传统测试台架的缺点,提出了一种基于Vector工具链车载CAN总线自动化测试系统.该系统通过Vector工具链之间的功能联系,采用能被CAPL语言调用的C++语言开发的底层驱动,以实现车载CAN总线、LIN(local interconnect network)总线自动化测试.本文设计了与人工测试的对比实验,验证了该系统不仅能解决车载总线测试通用性差的缺点,而且还提高了测试效率.
文摘Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine （RVM） to determine the liquefaction potential of soil by using actual cone penetration test （CPT） data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network （ANN） model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction.
基金Supported by the Research Program of International Technology Collaboration and Communication of Sichuan(2007H12-017)
文摘Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of large shaft.A novel automatic defect identification system is presented.Wavelet packet analysis(WPA)was applied to feature extraction of ultrasonic signal,and optimal Support vector machine(SVM)was used to perform the identification task.Meanwhile,comparative study on convergent velocity and classified effect was done among SVM and several improved BP network models.To validate the method,some experiments were performed and the results show that the proposed system has very high identification performance for large shafts and the optimal SVM processes better classification performance and spreading potential than BP manual neural network under small study sample condition.
文摘In this study, th e least sq u are su p p o rt v ecto r m achine （LSSVM） alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d cone p e n e tra tio nte s t （CPT） resu lts w ere used as in p u t variables for pred ictio n o f pile bearin g capacity. The d ata u se d w erecollected from th e existing litera tu re an d consisted o f 50 case records. The application o f LSSVM w ascarried o u t by dividing th e d ata into th re e se ts： a train in g se t for learning th e pro b lem an d obtain in g arelationship b e tw e e n in p u t variables an d pile bearin g capacity, and testin g an d validation sets forevaluation o f th e predictive an d g en eralization ability o f th e o b tain ed relationship. The predictions o f pilebearing capacity by LSSVM w ere evaluated by com paring w ith ex p erim en tal d ata an d w ith th o se bytrad itio n al CPT-based m eth o d s and th e gene ex pression pro g ram m in g （GEP） m odel. It w as found th a t th eLSSVM perform s w ell w ith coefficient o f d eterm in atio n , m ean, an d sta n d ard dev iatio n equivalent to 0.99,1.03, an d 0.08, respectively, for th e testin g set, an d 1, 1.04, an d 0.11, respectively, for th e v alidation set. Thelow values o f th e calculated m ean squared e rro r an d m ean ab so lu te e rro r indicated th a t th e LSSVM w asaccurate in p redicting th e pile bearing capacity. The results o f com parison also show ed th a t th e p roposedalg o rith m p red icted th e pile bearin g capacity m ore accurately th a n th e trad itio n al m eth o d s including th eGEP m odel.
文摘Abstract Accurate aerodynamic models are the basis of flight simulation and control law design. Mathematically modeling unsteady aerodynamics at high angles of attack bears great difficulties in model structure determination and parameter estimation due to little understanding of the flow mechanism. Support vector machines （SVMs） based on statistical learning theory provide a novel tool for nonlinear system modeling. The work presented here examines the feasibility of applying SVMs to high angle.-of-attack unsteady aerodynamic modeling field. Mainly, after a review of SVMs, several issues associated with unsteady aerodynamic modeling by use of SVMs are discussed in detail, such as sele, ction of input variables, selection of output variables and determination of SVM parameters. The least squares SVM （LS-SVM） models are set up from certain dynamic wind tunnel test data of a delta wing and an aircraft configuration, and then used to predict the aerodynamic responses in other tests. The predictions are in good agreement with the test data, which indicates the satisfving learning and generalization performance of LS-SVMs.
