In this paper,a comparative study for kernel-PCA based linear parameter varying(LPV)model approximation of sufficiently nonlinear and reasonably practical systems is carried out.Linear matrix inequalities(LMIs)to be s...In this paper,a comparative study for kernel-PCA based linear parameter varying(LPV)model approximation of sufficiently nonlinear and reasonably practical systems is carried out.Linear matrix inequalities(LMIs)to be solved in LPV controller design process increase exponentially with the increase in a number of scheduling variables.Fifteen kernel functions are used to obtain the approximate LPV model of highly coupled nonlinear systems.An error to norm ratio of original and approximate LPV models is introduced as a measure of accuracy of the approximate LPV model.Simulation examples conclude the effectiveness of kernel-PCA for LPV model approximation as with the identification of accurate approximate LPV model,computation complexity involved in LPV controller design is decreased exponentially.展开更多
Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model ...Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.展开更多
Linux Test Projec(t简称LTP)是一个以改善日益庞大的Linux内核为目标的组织机构,它通过引入自动化测试来完成Linux内核的测试。为了实现自动化测试这一目标,LTP开发出了可运行在多种Linux操作系统上的测试工具组件。实验结果表明,LTP...Linux Test Projec(t简称LTP)是一个以改善日益庞大的Linux内核为目标的组织机构,它通过引入自动化测试来完成Linux内核的测试。为了实现自动化测试这一目标,LTP开发出了可运行在多种Linux操作系统上的测试工具组件。实验结果表明,LTP测试工具组件不仅可以充分用于验证Linux内核的可靠性、健壮性和稳定性,而且它也是改善Linux内核测试最有效的方法之一。展开更多
Methyl or ethyl esters of vegetable oils are the reliable alternative fuels for the petroleum diesel, because their properties are very nearer to the petroleum diesel. But the flash point and auto-ignition temperature...Methyl or ethyl esters of vegetable oils are the reliable alternative fuels for the petroleum diesel, because their properties are very nearer to the petroleum diesel. But the flash point and auto-ignition temperatures are very high for these esters. CR （compression ratio） is one of the parameter which influences the atomization and vaporization of fuel. It is also caused for improvement in the turbulence which leads to better combustion. In this work the single cylinder diesel engine was tested at different compression ratios i.e. 16.5：1, 17.5：1, 18.5：1, 19：1 with palm kernel methyl ester without modifications. On increasing compression ratio closeness of molecules of air increases and fuel is injected into that air caused for better combustion. The inbuilt oxygen of methyl or ethyl ester will participate in the combustion and causes for reduction of HC and CO. Better compression ratio for an engine with particular fuel provides satisfactory thermal efficiency and less environmental pollution. In the investigations, for palm kernel methyl ester, 18.5：1 compression ratio is preferable on single cylinder Dl-diesel engine.展开更多
作为钢铁冶金制造的核心工序,高炉炼铁是典型的高能耗过程,其运行能耗约占钢铁总能耗的50%以上,其中,80%的能耗是焦炭和煤粉等燃料消耗.因此,对表征高炉燃料消耗的燃料比参数进行监测,并尽可能早地识别影响燃料比异常波动的关键因素,对...作为钢铁冶金制造的核心工序,高炉炼铁是典型的高能耗过程,其运行能耗约占钢铁总能耗的50%以上,其中,80%的能耗是焦炭和煤粉等燃料消耗.因此,对表征高炉燃料消耗的燃料比参数进行监测,并尽可能早地识别影响燃料比异常波动的关键因素,对于高炉炼铁过程的节能降耗具有重要意义.本文针对先验故障知识少的高炉燃料比监测与异常识别难题,提出一种基于核偏最小二乘(Kernel partial least squares,KPLS)鲁棒重构误差的故障识别方法.该方法首先建立过程变量与监测变量的KPLS监测模型,然后根据非线性映射空间的协方差矩阵和核空间Gram矩阵之间的关系,反向估计原始空间变量的正常估值.为了增强算法的鲁棒性,采用迭代去噪算法减少异常数据对原始空间正常估值的影响.通过利用原始空间正常估值和真实值来构造故障识别指标,并给出故障识别指标的控制限.基于实际工业数据的高炉数据实验表明所提方法不仅可以监测出正常工况下影响燃料比异常变化的潜在因素,还可识别出异常工况下影响燃料比异常变化的关键因素,具有很好的工程应用前景.展开更多
文摘In this paper,a comparative study for kernel-PCA based linear parameter varying(LPV)model approximation of sufficiently nonlinear and reasonably practical systems is carried out.Linear matrix inequalities(LMIs)to be solved in LPV controller design process increase exponentially with the increase in a number of scheduling variables.Fifteen kernel functions are used to obtain the approximate LPV model of highly coupled nonlinear systems.An error to norm ratio of original and approximate LPV models is introduced as a measure of accuracy of the approximate LPV model.Simulation examples conclude the effectiveness of kernel-PCA for LPV model approximation as with the identification of accurate approximate LPV model,computation complexity involved in LPV controller design is decreased exponentially.
基金Supported partially by the Post Doctoral Natural Science Foundation of China(2013M532118,2015T81082)the National Natural Science Foundation of China(61573364,61273177,61503066)+2 种基金the State Key Laboratory of Synthetical Automation for Process Industriesthe National High Technology Research and Development Program of China(2015AA043802)the Scientific Research Fund of Liaoning Provincial Education Department(L2013272)
文摘Strong mechanical vibration and acoustical signals of grinding process contain useful information related to load parameters in ball mills. It is a challenge to extract latent features and construct soft sensor model with high dimensional frequency spectra of these signals. This paper aims to develop a selective ensemble modeling approach based on nonlinear latent frequency spectral feature extraction for accurate measurement of material to ball volume ratio. Latent features are first extracted from different vibrations and acoustic spectral segments by kernel partial least squares. Algorithms of bootstrap and least squares support vector machines are employed to produce candidate sub-models using these latent features as inputs. Ensemble sub-models are selected based on genetic algorithm optimization toolbox. Partial least squares regression is used to combine these sub-models to eliminate collinearity among their prediction outputs. Results indicate that the proposed modeling approach has better prediction performance than previous ones.
文摘Linux Test Projec(t简称LTP)是一个以改善日益庞大的Linux内核为目标的组织机构,它通过引入自动化测试来完成Linux内核的测试。为了实现自动化测试这一目标,LTP开发出了可运行在多种Linux操作系统上的测试工具组件。实验结果表明,LTP测试工具组件不仅可以充分用于验证Linux内核的可靠性、健壮性和稳定性,而且它也是改善Linux内核测试最有效的方法之一。
文摘Methyl or ethyl esters of vegetable oils are the reliable alternative fuels for the petroleum diesel, because their properties are very nearer to the petroleum diesel. But the flash point and auto-ignition temperatures are very high for these esters. CR （compression ratio） is one of the parameter which influences the atomization and vaporization of fuel. It is also caused for improvement in the turbulence which leads to better combustion. In this work the single cylinder diesel engine was tested at different compression ratios i.e. 16.5：1, 17.5：1, 18.5：1, 19：1 with palm kernel methyl ester without modifications. On increasing compression ratio closeness of molecules of air increases and fuel is injected into that air caused for better combustion. The inbuilt oxygen of methyl or ethyl ester will participate in the combustion and causes for reduction of HC and CO. Better compression ratio for an engine with particular fuel provides satisfactory thermal efficiency and less environmental pollution. In the investigations, for palm kernel methyl ester, 18.5：1 compression ratio is preferable on single cylinder Dl-diesel engine.
文摘作为钢铁冶金制造的核心工序,高炉炼铁是典型的高能耗过程,其运行能耗约占钢铁总能耗的50%以上,其中,80%的能耗是焦炭和煤粉等燃料消耗.因此,对表征高炉燃料消耗的燃料比参数进行监测,并尽可能早地识别影响燃料比异常波动的关键因素,对于高炉炼铁过程的节能降耗具有重要意义.本文针对先验故障知识少的高炉燃料比监测与异常识别难题,提出一种基于核偏最小二乘(Kernel partial least squares,KPLS)鲁棒重构误差的故障识别方法.该方法首先建立过程变量与监测变量的KPLS监测模型,然后根据非线性映射空间的协方差矩阵和核空间Gram矩阵之间的关系,反向估计原始空间变量的正常估值.为了增强算法的鲁棒性,采用迭代去噪算法减少异常数据对原始空间正常估值的影响.通过利用原始空间正常估值和真实值来构造故障识别指标,并给出故障识别指标的控制限.基于实际工业数据的高炉数据实验表明所提方法不仅可以监测出正常工况下影响燃料比异常变化的潜在因素,还可识别出异常工况下影响燃料比异常变化的关键因素,具有很好的工程应用前景.
