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.

Latent Structure Linear Regression

A short review is given of standard regression analysis. It is shown that the results presented by program packages are not always reliable. Here is presented a general framework for linear regression that includes most linear regression methods based on linear algebra. The H-principle of mathematical modelling is presented. It uses the analogy between the modelling task and measurement situation in quantum mechanics. The principle states that the modelling task should be carried out in steps where at each step an optimal balance should be determined between the value of the objective function, the fit, and the associated precision. H-methods are different methods to carry out the modelling task based on recommendations of the H-principle. They have been applied to different types of data. In general, they provide better predictions than linear regression methods in the literature.

Statistical Identification of Syndromes Feature and Structure of Disease of Western Medicine Based on General Latent Structure Model

Syndrome differentiation is the character of Chinese medicine （CM）. Disease differentiation is the principle of Western medicine （WM）. Identifying basic syndromes feature and structure of disease of WM is an important avenue for prevention and treatment of integrated Chinese and Western medicine. The idea here is first to divide all patients suffering from a disease of WM into several groups in the light of the stage of the disease, and secondly to identify basic syndromes feature in a distinct stage, and finally to achieve the purpose of syndrome differentiation. Syndrome differentiation is simply taken as a classifier that classifies patients into distinct classes primarily based on overall observation of their symptoms. Previous clustering methods are unable to cope with the complexity of CM. We therefore show a new multi-dimensional clustering method in the form of general latent structure （GLS） model, which is a suitable statistical learning technique of latent class analysis. In this paper, we learn an optimal GLS model which reflects much better model quality compared with other latent class models from the osteoporosis patient of community women （OPCW） real data including 40 65 year old women whose bone mineral density （BMD） is less than mean2.0 standard deviation （M 2.0SD）. Further, we illustrate a case analysis of statistical identification of CM syndromes feature and structure of OPCW from qualitative and quantitative contents through the GLS model. Our analysis has discovered natural clusters and structures that correspond well to CM basic syndrome and factors of osteoporosis patients （OP）. The GLS model suggests the possibility of establishing objective and quantitative diagnosis standards for syndrome differentiation on OPCW. Hence, for the future it can provide a reference for the similar study from the perspective of a combination of disease differentiation and syndrome differentiation.

DISCOVERY OF LATENT STRUCTURES: EXPERIENCE WITH THE COIL CHALLENGE 2000 DATA SET

The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （2002）, but that study involved only small applications with 4 or 5 observed variables and no more than 2 latent variables due to the lack of efficient learning algorithms. Significant progress has been made since then on algorithmic research, and it is now possible to learn HLC models with dozens of observed variables. This allows us to demonstrate the benefits of HLC models more convincingly than before. The authors have successfully analyzed the CoIL Challenge 2000 data set using HLC models. The model obtained consists of 22 latent variables, and its structure is intuitively appealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data.

Network autoregression model with grouped factor structures

Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network.An iterative algorithm is employed to minimize a least-squares objective function,allowing for simultaneous estimation of both the parameters and the group structure.To determine the unknown number of groups and factors,a PIC criterion is introduced.Additionally,statistical inference of the estimated parameters is presented.To assess the validity of the proposed estimation and inference procedures,we conduct extensive numerical studies.We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market.

A numerical simulation of latent heating within Typhoon Molave

The weather research and forecasting（WRF） model is a new generation mesoscale numerical model with a fine grid resolution（2 km）, making it ideal to simulate the macro-and micro-physical processes and latent heating within Typhoon Molave（2009）. Simulations based on a single-moment, six-class microphysical scheme are shown to be reasonable, following verification of results for the typhoon track, wind intensity, precipitation pattern, as well as inner-core thermodynamic and dynamic structures. After calculating latent heating rate, it is concluded that the total latent heat is mainly derived from condensation below the zero degree isotherm, and from deposition above this isotherm. It is revealed that cloud microphysical processes related to graupel are the most important contributors to the total latent heat. Other important latent heat contributors in the simulated Typhoon Molave are condensation of cloud water, deposition of cloud ice, deposition of snow, initiation of cloud ice crystals, deposition of graupel, accretion of cloud water by graupel, evaporation of cloud water and rainwater,sublimation of snow, sublimation of graupel, melting of graupel, and sublimation of cloud ice. In essence, the simulated latent heat profile is similar to ones recorded by the Tropical Rainfall Measuring Mission, although specific values differ slightly.

《金瓶梅词话》校读拾零(三)

As far as Chinese art,YIN YANG viewpoint and TAI JI thought derived from ZHOU YI have been embedded deeply into Chinese artistic creating for thousand years,the typical examples are traditional Chinese painting and mountains-and -waters painting.If people do not find the basis to explain the methodology of "Qi"and"Yun"from the origin,the know of traditional culture is all in vain."Qi"expresses an invisible space concept,it has raised to a cultural philosophical concept in Chinese eyes,the explanation of this problem will be of benefit to the search for the latent structure in Chinese national artistic form,and the modern signification of latent structure at present would be found.

Multi-label local discriminative embedding

Multi-label classification problems arise frequently in text categorization, and many other related applications. Like conventional categorization problems, multi-label categorization tasks suffer from the curse of high dimensionality. Existing multi-label dimensionality reduction methods mainly suffer from two limitations. First, latent nonlinear structures are not utilized in the input space. Second, the label information is not fully exploited. This paper proposes a new method, multi-label local discriminative embedding (MLDE), which exploits latent structures to minimize intraclass distances and maximize interclass distances on the basis of label correlations. The latent structures are extracted by constructing two sets of adjacency graphs to make use of nonlinear information. Non-symmetric label correlations, which are the case in real applications, are adopted. The problem is formulated into a global objective function and a linear mapping is achieved to solve out-of-sample problems. Empirical studies across 11 Yahoo sub-tasks, Enron and Bibtex are conducted to validate the superiority of MLDE to state-of-art multi-label dimensionality reduction methods.

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.

The effect of differential temperatures on the latent heat in the nucleation of CdSe quantum dots

This work studied the effect of differential temperatures on the latent heat in the nucleation of CdSe quantum dots（QDs）.The result showed that,by the formula of phase change,with increasing the reaction temperature,the latent heat in the nucleation of QDs reduced.CdSe QDs with the size-dispersion from 2.7 to 3.6 nm were synthesized via oleic acid-paraffin liquid system by controlling the reaction temperature from 180 to 220℃.Synthesized QDs were characterized by UV-vis absorption spectra and X-ray diffraction（XRD）.The result of UV-vis absorption spectra showed that with increasing of reaction temperature,the first absorption peak was red-shifted and the size of QD increased.The result of XRD showed that the synthesized QDs were zinc-blende structure.

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.