A process monitoring method for autoregressive-dynamic inner total latent structure projection

As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.

Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor

In soft sensor field, just-in-time learning(JITL) is an effective approach to model nonlinear and time varying processes. However, most similarity criterions in JITL are computed in the input space only while ignoring important output information, which may lead to inaccurate construction of relevant sample set. To solve this problem, we propose a novel supervised feature extraction method suitable for the regression problem called supervised local and non-local structure preserving projections(SLNSPP), in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection,which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently, we present a SLNSPP-JITL framework for developing adaptive soft sensor, including a sparse learning strategy to limit the scale and update the frequency of database. Finally, two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.

MULTIPARAMETER MEASUREMENT FOR RACEWAY GROOVE OF BEARING BASED ON 3D RECONSTRUCTION WITH DIGITAL STRUCTURED LIGHT

A fast 3D reconstruction method based on structured light to measure various parameters of the raceway groove is presented. Digital parallel grating stripes distributed with sine density are projected onto the raceway groove by a DLP projector, and distorting of stripes is happened on the raceway. Simultaneously, aided by three-step phase-shifting approach, three images covered by different stripes are obtained by a high-resolution CCD camera at the same location, thus a more accuracy local topography can be obtained. And then the bearing is rotated on a high precision computer controlled rotational stage. Three images are also obtained as the former step at next planned location triggered by the motor. After one cycle, all images information is combined through the mosaics. As a result, the 3D information of raceway groove can be gained. Not only geometric properties but also surface flaws can be extracted by software. A preliminary hardware system has been built, with which some geometric parameters have been extracted from reconstructed local topography.

BIM Supporting the Development of Multitasks Related with the Structural Project

Building Information Modelling (BIM) is a methodology focused on the centralization and sharing of the project information among all professionals involved, supported on the generation and manipulation of a three-dimensional (3D) digital BIM model. This methodology allows a close collaboration between the architect and the structural engineer and an adequate manipulation of the structural BIM model database, on the definition of multitasks. The collaboration allowed between all disciplines, avoid the detection of conflicts and data omission after in the construction place. Two BIM structural design cases were developed, using Revit as the modelling system and Robot as the structural software. Concerning the structural project the interoperability capacity between the software is still a limitation that engineers must be warned of. In the present study, the benefits and limitations identified within the communication and integration of distinct disciplines and on the development of most frequent multitasks normally related with a structural project, were considered.

GGA+U study of the electronic energy bands and state density of the wurtzite In_(1-x)Ga_xN

The electronic band structures, densities of states （DOSs）, and projected densities of states （PDOSs） of the wurtzite In1-xGaxN with x=0, 0.0625, 0.125 are studied using the generalized-gradient approximation （GGA） and GGA＋U in density functional theory. Our calculations suggest that in the case of wurtzite InN it is important to apply an on-site Hubbard correction to both the d states of indium and the p states of nitrogen in order to recover the correct energy level symmetry and obtain a reliable description of the InN band structure. The method is used to study the electronic properties of the wurtzite In1-xGaxN. The conduction band minimum （CBM） energy increases, while the valence band maximum （VBM） energy decreases with the increase of the gallium concentration. The effect leads to broadening the band gap （BG） and the valence band width （VBW）. Furthermore, the compressive strain in the crystal can cause the BG and the VBW to increase with the increase of gallium concentrations.

Finite element and experimental analysis of pinion bracket-assembly of three gorges project ship lift

The pinion bracket-assembly(PBA) is a major part of three gorges project(TGP) ship lift drive system. The static strength,fatigue strength and stress distribution of hinge pin of PBA were analyzed by ANSYS, and the structure of PBA was optimized. The results show that after the optimization, the maximum comprehensive stress is 259.59 MPa, the maximum fatigue cumulative damage of weld joints is 0.94 and the maximum vertical deformation of hinge pin is 0.14 mm. The elastic deformation, hydropneumatic spring cylinder(HSC) load response and the vibration characteristics of PBA were studied by the bearing test when PBA bore the load caused by different water level errors. The results indicate that when the water level of ship chamber ranges from 3.4 m to 3.6 m,the vertical elastic deformation of the pinion shaft is between-8.58 and 10.50 mm. When upward outage-load(1580 k N) is imposed by the test-rack, the vertical elastic deformation of the pinion shaft is 13.42 and 14.07 mm and HSC load response is 795.80-800.80 k N. In the process of imposing load on the pinion by the test-rack, the maximum vibration amplitude and acceleration of PBA internal components are 0.37° and 2.67 rad/s2, respectively; the maximum impact on the pin caused by vibration is 19.89 k N; the pinion shaft vertical displacement and HSC load response do not fluctuate. There is a great difference between the frequency of meshing force of the pinion and the rack(1.06 Hz) and first-order natural frequency of PBA(8.41 Hz), thus PBA will not resonate.From all above, PBA meets the static strength and fatigue strength requirements. The vibration of PBA internal components has no effect on the vertical displacement of the pinion shaft, HSC load response and smooth operation of PBA. There is a liner relationship in the ratio of 2:1 between the thrust imposed by the test-rack and HSC load, thus HSC can limit the load imposed on the pinion.

Fast Image Correspondence with Global Structure Projection

This paper presents a method for correspondence. This technique works by two recognizing images with steps： reference keypoint flat objects based on global keypoint structure selection and structure projection. The using of global keypoint structure is an extension of an orderless bag-of-features image representation, which is utilized by the proposed matching technique for computation efficiency. Specifically, our proposed method excels in the dataset of images containing ＂flat objects＂ such as CD covers, books, newspaper. The efficiency and accuracy of our proposed method has been tested on a database of nature pictures with flat objects and other kind of objects. The result shows our method works well in both occasions.

Application in soft sensing modeling of chemical process based on K-OPLS method

Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is proposed.Orthogonal projections to latent structures(O-PLS)is a general linear multi-variable data modeling method.It can eliminate systematic variations from descriptive variables(input)that are orthogonal to response variables(output).In the framework of O-PLS model,K-OPLS method maps descriptive variables to high-dimensional feature space by using“kernel technique”to calculate predictive components and response-orthogonal components in the model.Therefore,the K-OPLS method gives the non-linear relationship between the descriptor and the response variables,which improves the performance of the model and enhances the interpretability of the model to a certain extent.To verify the validity of K-OPLS method,it was applied to soft sensing modeling of component content of debutane tower base butane(C4),the quality index of the key product output for industrial fluidized catalytic cracking unit(FCCU)and H 2S and SO 2 concentration in sulfur recovery unit(SRU).Compared with support vector machines(SVM),least-squares support-vector machine(LS-SVM),support vector machine with principal component analysis(PCA-SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)and kernel based extreme learning machine with principal component analysis(PCA-KELM)methods under the same conditions,the experimental results show that the K-OPLS method has superior modeling accuracy and good model generalization ability.

On Complemented Subsystems of Commutative Jordan Quadruple Systems

We extend the notions of commutativity,ideals,anisotropy,and complemented subtriples of Jordan triple systems to those of Jordan quadruple systems.We show that if S is a complemented subsystem of an anisotropic commutative Jordan quadruple system U,then S and its annihilator S^(⊥)are orthogonal ideals and U=S⊕S^(⊥).We also prove that the range of a structural projection on an anisotropic commutative Jordan quadruple system is a complemented ideal and,conversely,a complemented subsystem of an anisotropic commutative Jordan quadruple system is the range of a unique structural projection.

3D scanning modelling of auto parts for intelligent driving

In order to improve the accuracy of auto parts production and processing,and reduce the error of parts manufacturing,a 3D scanning modelling of auto parts for intelligent driving is designed in this paper.With the help of the structured light projection method,the auto parts image is obtained,and its horizontal axis grey value and stripe pixel value are analysed.The contour parameters are determined by k-frame strategy,and the width,length and height parameters of the external contour of vehicle parts are obtained.The basic principle of 3D scanning technology is analysed,its scanning process is designed,and the modelling of auto parts is realised with the help of 3D scanning technology.The experimental results show that the modelling method can effectively improve the machining accuracy and reduce the manufacturing error of the auto parts.

Climate change effects fruiting of the prize matsutake mushroom in China

Climate change affects various facets of life but there is little data on its effects on wild mushroom fruiting.Yunnan Province in China is a rich source of wild mushrooms and has experienced a temperature rise over recent decades.This has resulted in warmer temperatures but the impacts of these changes on mushroom production lack documentation.We collected data on the fruiting of the highly prized matsutake mushroom(Tricholoma matsutake)in West Yunnan,China over an 11 year period from 2000 to 2010.Fruiting phenology and productivity were compared against the driving meteorological variables using Projection to Latent Structure regression.The mushrooms appeared later in the season during the observation period,which is most likely explained by rising temperatures and reduced rain during May and June.High temperature and abundant rain in August resulted in good productivity.The climate response of matsutake production results from a sequence of processes that are possibly linked with regulatory signals and resource availability.To advance the knowledge of this complex system,a holistic research approach integrating biology,ecology,genetics,physiology,and phytochemistry is needed.Our results contribute to a general model of fungal ecology,which can be used to predict the responses of fungi to global climate change.

Data Filtering and Statistical Process Modeling

A new filtering method is presented which extends the SureShrink algorithm by eliminating the peak noise in the wavelet transformed signal to improve the overall filtering properties. Data from industrial plants always contain some peak noise, but ‘denoise’ algorithms such as ‘SureShrink’ can have difficulty in handling sudden large excursions in the corrupting noise. In the new algorithm the peak noise is reduced prior to filtering using the SureShrink algorithm. The pre screened data can be used to build a number of projections to latent structures regression models. Data from an industrial fluidized bed reactor is used to evaluate the new algorithm, which demonstrates improved performance in terms of improved modeling capability through use of the new data pre filtering algorithm.