皎然《诗式》诗学融承儒释道三教思想研究

皎然周游三教而终归于释,其《诗式》融承儒释道而自铸伟辞。《诗式》尤重儒家诗教,以“体”之角度确立儒家诗教的“诗体”论和儒家德化的“诗德”论。《诗式》亦缘采道家逍遥自然之旨。“高”“逸”两体既融通逍遥也挈领于辩体一十九字。“自然”观突破习惯思维,是精思锻造后的“至丽而自然”。《诗式》之“取境”与“作用”乃得佛教润染。“取境”最尚“高”“逸”,依托佛教之境无所依傍,且与佛教思想中的“绝对真实的精神本体”相通。“作用”则汲取佛教内心缘虑取境之功而极重创作之心理内容。

《河岳英灵集》诗学融承儒释道三教思想研究

盛唐殷瑶《河岳英灵集》融承儒释道三教思想之处极多。从总体来看,殷璠选诗有尊古重《诗》、恶华好朴与禅近旨远三大特征。从范畴而言,风骨与兴象因殷璠吸收三教之旨而增添了庄禅兼融、雅逸备具的诗学内涵。另外,殷璠诗学"三来"之"神来"乃是盛唐释教禅意之悟的极大突显,"气来"是六朝隋唐以来儒家刚健之体的强势复位,"情来"是六朝隋唐以来道家法天贵真之思的大幅回潮。

Fault diagnosis method of AC motor rolling bearing based on heterogeneous data fusion of current and infrared image

In order to improve the accuracy of rolling bearing fault diagnosis when the motor is running under non-stationary conditions,an AC motor rolling bearing fault diagnosis method was proposed based on heterogeneous data fusion of current and infrared images.Firstly,VMD was used to decompose the motor current signal and extract the low-frequency component of the bearing fault signal.On this basis,the current signal was transformed into a two-dimensional graph suitable for convolutional neural network,and the data set was classified by convolutional neural network and softmax classifier.Secondly,the infrared image was segmented and the fault features were extracted,so as to calculate the similarity with the infrared image of the fault bearing in the library,and further the sigmod classifier was used to classify the data.Finally,a decision-level fusion method was introduced to fuse the current signal with the infrared image signal diagnosis result according to the weight,and the motor bearing fault diagnosis result was obtained.Through experimental verification,the proposed fault diagnosis method could be used for the fault diagnosis of motor bearing outer ring under the condition of load variation,and the accuracy of fault diagnosis can reach 98.85%.

Multi-view feature fusion for rolling bearing fault diagnosis using random forest and autoencoder

To improve the accuracy and robustness of rolling bearing fault diagnosis under complex conditions, a novel method based on multi-view feature fusion is proposed. Firstly, multi-view features from perspectives of the time domain, frequency domain and time-frequency domain are extracted through the Fourier transform, Hilbert transform and empirical mode decomposition (EMD).Then, the random forest model (RF) is applied to select features which are highly correlated with the bearing operating state. Subsequently, the selected features are fused via the autoencoder (AE) to further reduce the redundancy. Finally, the effectiveness of the fused features is evaluated by the support vector machine (SVM). The experimental results indicate that the proposed method based on the multi-view feature fusion can effectively reflect the difference in the state of the rolling bearing, and improve the accuracy of fault diagnosis.

Online residual useful life prediction of large-size slewing bearings A data fusion method

To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.

The Legal Dilemma of the Financialization of Agricultural Land and a Way Forward

The strict limits on the financialization of agricultural land in the current legal system impede the development of rural finance. Objective needs have driven local governments＇ exploration of different modes of financialization, providing samples we can analyze in connection with the next steps in institutional development. The financialization of agricultural land arises naturally from its nature as an asset. Whether one acknowledges the land＇s social security function does not affect its financialization. In institutional rebuilding, the particular character of mortgages on agricultural land should be considered. The mortgage-holder must be a financial organization, not a natural person or an ordinary finn. The person mortgaging the right to operate the land under the contract responsibility system need not have a stable non-agricultural occupation or stable source of income, or the consent of the party issuing the contract. Registration constitutes public notification of the setting up of a mortgage and is also the condition whereby it comes into effect. The mortgage cannot be realized by means of discounting, but compulsory management can be used to subcontractor let the relevant agricultural land. The debt can then be liquidated from this income and the land returned to the mortgagor.