Key variable identification for classifications is related to many trouble-shooting problems in process indus-tries. Recursive feature elimination based on support vector machine (SVM-RFE) has been proposed recently i...Key variable identification for classifications is related to many trouble-shooting problems in process indus-tries. Recursive feature elimination based on support vector machine (SVM-RFE) has been proposed recently in applica-tion for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diag-nosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee East-man process (TEP) simulator is used to evaluate the effectiveness of the key variable selection using accelerated SVM-RFE (A-SVM-RFE). A-SVM-RFE integrates computational rate and algorithm effectiveness into a consistent framework. It not only can correctly identify the key variables, but also has very good computational rate. In comparison with contribution charts combined with principal component aralysis (PCA) and other two SVM-RFE algorithms, A-SVM-RFE performs better. It is more fitting for industrial application.展开更多
To improve the precision of deep fertilization of paddy fields,a six-row centralized pneumatic deep precision fertilization device for a rice transplanter was designed.This device included a spiral fertilizer distribu...To improve the precision of deep fertilization of paddy fields,a six-row centralized pneumatic deep precision fertilization device for a rice transplanter was designed.This device included a spiral fertilizer distribution system,centralized pneumatic fertilizer delivery system,an opener system,and a fertilization control system.The centralized airflow distribution method was used in the fertilizer delivery system to ensure that the airflow in each fertilizer pipe was evenly distributed.The rotational speeds of the power take-off(PTO)and fertilizer shaft were measured synchronously using photoelectric sensors and matched proportionately in real-time using PID closed-loop control algorithms to achieve precise fertilization rates at each working speed of the rice transplanter.There were two key considerations in the design of the control system to ensure precise fertilization.Firstly,a photoelectric sensor was used to measure the speed of the PTO;the high rotational speed of the PTO could provide a high signal frequency and improve the precision of the measurement of the transplanter’s working speed.Secondly,the fertilizer shaft speed measurement subprogram was set to sleep for a short period to reduce the vibration caused by the engine.During the tests of pneumatic fertilizer delivery system,single-factor tests on airflow distribution methods were conducted.The results showed that the coefficient of variation of the airflow speed for the centralized airflow distribution method was 1.67%,which was the least among the coefficients of the three distribution methods.In the bench tests,the rotational speeds of the fertilizer shaft were set at 10 r/min,20 r/min,30 r/min,and 40 r/min.The maximum coefficient of variation of the fertilization consistency in different rows was 1.49%at the rotational speed of 20 r/min.The maximum coefficient of variation of the fertilization stability was 2.86%at the rotational speed of 40 r/min,while the average fertilizer amount per lap for each distributor was 26.25 g/r.The results of th展开更多
分析实际程序时往往需要分析程序中函数的调用,一般使用过程间分析来实现全程序分析.函数内联是一种最为精确、易于实现的过程间分析方法.通过函数内联,可以使得已有过程内分析方法和工具支持包含函数调用的程序的分析.但是函数内联后...分析实际程序时往往需要分析程序中函数的调用,一般使用过程间分析来实现全程序分析.函数内联是一种最为精确、易于实现的过程间分析方法.通过函数内联,可以使得已有过程内分析方法和工具支持包含函数调用的程序的分析.但是函数内联后代码的规模急剧增加,同时将产生大量中间变量,增加程序分析的变量维度,导致程序分析过程时空开销大大增加.考虑基于抽象解释框架下函数内联过程间分析的一些不足,并提出了相应的优化方法.基于抽象解释的程序分析关注自动推导程序变量之间的不变式约束关系,因此程序变量构成的程序环境大小(即各程序点处须考虑的相关变量集合)对分析的时空开销具有重要影响.为了减少函数内联后程序分析的开销,提出了面向内联函数块的程序环境降维优化方法.该方法针对内联函数后的程序代码,分析确定不同程序点处需维护的程序环境(即相关变量集合),而不是所有程序点共享同一全局程序环境,从而实现程序状态的降维.详细描述了基于该方法所实现的工具DRIP(dimension reduction for analyzing function inlined program)的架构、模块及算法细节.并在WCET Benchmarks测试集开展了分析实验.实验结果表明:DRIP在变量消除上取得的效果良好,甚至在某些测试集上能减少一半以上的变量,并在一定程度上降低了分析过程的时空开销.展开更多
Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen...Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination(UVE). Vegetation indices(VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares(PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy.UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models(UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index(NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients(r > 0.88). PLS regression models showed the lowest root mean square error(RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models.UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.展开更多
The known Fourier-Chernikov algorithm of linear inequality system convolution is complemented with an original procedure of all dependent (redundant) inequalities deletion. The concept of “almost dependent” inequali...The known Fourier-Chernikov algorithm of linear inequality system convolution is complemented with an original procedure of all dependent (redundant) inequalities deletion. The concept of “almost dependent” inequalities is defined and an algorithm for further reducing the system by deletion of these is considered. The concluding algorithm makes it possible to hold actual-time convolution of a general inequality system containing up to 50 variables with the rigorous method of dependent inequalities deletion and up to 100 variables with the approximate method of one. The main application of such an approach consists in solving linear inequality system in an explicit form. These results are illustrated with a series of computer experiments.展开更多
Using Cholesky factorization, the dual face algorithm was described forsolving standard Linear programming (LP) problems, as it would not be very suitablefor sparse computations. To dodge this drawback, this paper pre...Using Cholesky factorization, the dual face algorithm was described forsolving standard Linear programming (LP) problems, as it would not be very suitablefor sparse computations. To dodge this drawback, this paper presents a variant usingGauss-Jordan elimination for solving bounded-variable LP problems.展开更多
基金Supported by China 973 Program (No.2002CB312200), the National Natural Science Foundation of China (No.60574019 and No.60474045), the Key Technologies R&D Program of Zhejiang Province (No.2005C21087) and the Academician Foundation of Zhejiang Province (No.2005A1001-13).
文摘Key variable identification for classifications is related to many trouble-shooting problems in process indus-tries. Recursive feature elimination based on support vector machine (SVM-RFE) has been proposed recently in applica-tion for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diag-nosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee East-man process (TEP) simulator is used to evaluate the effectiveness of the key variable selection using accelerated SVM-RFE (A-SVM-RFE). A-SVM-RFE integrates computational rate and algorithm effectiveness into a consistent framework. It not only can correctly identify the key variables, but also has very good computational rate. In comparison with contribution charts combined with principal component aralysis (PCA) and other two SVM-RFE algorithms, A-SVM-RFE performs better. It is more fitting for industrial application.
基金This research was supported by the National Key Research and Development Project of China(2017YFD0301404-05)and the Fundamental Research Funds for the Central Universities(2662018PY038).
文摘To improve the precision of deep fertilization of paddy fields,a six-row centralized pneumatic deep precision fertilization device for a rice transplanter was designed.This device included a spiral fertilizer distribution system,centralized pneumatic fertilizer delivery system,an opener system,and a fertilization control system.The centralized airflow distribution method was used in the fertilizer delivery system to ensure that the airflow in each fertilizer pipe was evenly distributed.The rotational speeds of the power take-off(PTO)and fertilizer shaft were measured synchronously using photoelectric sensors and matched proportionately in real-time using PID closed-loop control algorithms to achieve precise fertilization rates at each working speed of the rice transplanter.There were two key considerations in the design of the control system to ensure precise fertilization.Firstly,a photoelectric sensor was used to measure the speed of the PTO;the high rotational speed of the PTO could provide a high signal frequency and improve the precision of the measurement of the transplanter’s working speed.Secondly,the fertilizer shaft speed measurement subprogram was set to sleep for a short period to reduce the vibration caused by the engine.During the tests of pneumatic fertilizer delivery system,single-factor tests on airflow distribution methods were conducted.The results showed that the coefficient of variation of the airflow speed for the centralized airflow distribution method was 1.67%,which was the least among the coefficients of the three distribution methods.In the bench tests,the rotational speeds of the fertilizer shaft were set at 10 r/min,20 r/min,30 r/min,and 40 r/min.The maximum coefficient of variation of the fertilization consistency in different rows was 1.49%at the rotational speed of 20 r/min.The maximum coefficient of variation of the fertilization stability was 2.86%at the rotational speed of 40 r/min,while the average fertilizer amount per lap for each distributor was 26.25 g/r.The results of th
文摘分析实际程序时往往需要分析程序中函数的调用,一般使用过程间分析来实现全程序分析.函数内联是一种最为精确、易于实现的过程间分析方法.通过函数内联,可以使得已有过程内分析方法和工具支持包含函数调用的程序的分析.但是函数内联后代码的规模急剧增加,同时将产生大量中间变量,增加程序分析的变量维度,导致程序分析过程时空开销大大增加.考虑基于抽象解释框架下函数内联过程间分析的一些不足,并提出了相应的优化方法.基于抽象解释的程序分析关注自动推导程序变量之间的不变式约束关系,因此程序变量构成的程序环境大小(即各程序点处须考虑的相关变量集合)对分析的时空开销具有重要影响.为了减少函数内联后程序分析的开销,提出了面向内联函数块的程序环境降维优化方法.该方法针对内联函数后的程序代码,分析确定不同程序点处需维护的程序环境(即相关变量集合),而不是所有程序点共享同一全局程序环境,从而实现程序状态的降维.详细描述了基于该方法所实现的工具DRIP(dimension reduction for analyzing function inlined program)的架构、模块及算法细节.并在WCET Benchmarks测试集开展了分析实验.实验结果表明:DRIP在变量消除上取得的效果良好,甚至在某些测试集上能减少一半以上的变量,并在一定程度上降低了分析过程的时空开销.
基金supported by the National Key Research and Development Program of China (2016YFD0300602)China Agricultural Research System (CARS-04-PS19)Chengdu Science and Technology Project (2020-YF09-00033-SN)。
文摘Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination(UVE). Vegetation indices(VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares(PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy.UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models(UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index(NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients(r > 0.88). PLS regression models showed the lowest root mean square error(RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models.UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.
文摘The known Fourier-Chernikov algorithm of linear inequality system convolution is complemented with an original procedure of all dependent (redundant) inequalities deletion. The concept of “almost dependent” inequalities is defined and an algorithm for further reducing the system by deletion of these is considered. The concluding algorithm makes it possible to hold actual-time convolution of a general inequality system containing up to 50 variables with the rigorous method of dependent inequalities deletion and up to 100 variables with the approximate method of one. The main application of such an approach consists in solving linear inequality system in an explicit form. These results are illustrated with a series of computer experiments.
基金the National Natural Science Foundation of China(Nos.10871043 and 70971136).
文摘Using Cholesky factorization, the dual face algorithm was described forsolving standard Linear programming (LP) problems, as it would not be very suitablefor sparse computations. To dodge this drawback, this paper presents a variant usingGauss-Jordan elimination for solving bounded-variable LP problems.
