ESTIMATION FOR NONNEGATIVE AUTOREGRESSIVE PROCESSES WITH AN UNKNOWN LOCATION PARAMETER

Abstract. Consider an AR(1) process given by X ^t=γ+ø X _t+ Z _t≥ 1. where 0 ≤γ, 0 ≤ø 1 are unknown parameters and the innovations Z _t, ≥ 1, are independently and identically distributed positive random variables. We propose estimates of (γø) which are obtained as the solution to a linear programming problem and establish their strong consistency. When the Z _ts have the exponential distribution. our estimate becomes the conditional maximum likelihood estimate given X ₀. Under the assumption of regular variation of the innovation distribution at its left and right endpoints (assumed to be 0 and ∝ respectively), we establish asymptotic limit laws for the estimates. Consistent estimators for a class of moving-average processes with heavy-tailed innovation distribution are also presented.     

An analytical tuning approach to multivariable model predictive controllers

Multivariable model predictive control is a widely used advanced process control methodology, where handling delays and constraints are its key features. However, successful implementation of model predictive control requires an appropriate tuning of the controller parameters. This paper proposes an analytical tuning approach to multivariable model predictive controllers. The considered multivariable plants are square and consist of first-order plus dead time transfer functions. Most of the existing model predictive control tuning methods are based on trial and error or numerical approaches. In the case of no active constraints, closed loop transfer function matrices are derived and decoupling conditions are addressed. For control horizon of one, analytical tuning equations and achievable performances are obtained. Finally, simulation results are used to verify the effectiveness of the proposed tuning strategy.     

Degradation of p-nitrotoluene by 03/H2 02 process and oxidation mechanism

The degradation of p-nitrotoluene by O2/H2O2 process in a bubble contact column was investigated.Effects of the molar ratio of hydrogen peroxide to ozone, pH value and t-butanol on the oxidation process were discussed. It was found that the proper H2 O2/O3 molar ratio for the degradation of p-nitrotoluene was around O. 6, different pH values and the presence of t-butanol highly influenced the removal efficiency of p-nitrotoluene. 5-metbyl-2-nitrophenol, 2-methy1-5-nitrophenol, (4-nitrophenyl) methanol, 5-(hydroxymethyl) -2-nitro phenol, acetic acid, 2-methylpropanc diacid and 2-(hydroxylmethyl) propane diacid were identified as degradation intermediates and products through GC-MS. Radical reaction mechanism and degradation pathway were proposed based on the results of experiments. It is deduced that the benzene ring of p-nitrotoluene can be only destroyed by hydroxyl radicals through a polyhydrexy intermediate pathway. Then unstable polybydroxy intermediates can be oxidized to different acids with low molecular weight rapidly.     

A Thermo-Mechanical Study of Some Epoxy Adhesives Subjected to Combined Dynamic and Static Stresses

The object of the present study was to investigate the effect of superimposed dynamic and static stresses on mechanical and thermal properties of some epoxy adhesives. It was found that combinations of shear creep and torsional oscillations, applied simultaneously to adhesive joints at temperatures within the glassy range of the adhesive, led to strengthening of the joints in shear and to an increase in the glass transition temperature of the adhesive. Similar loading stresses applied at temperatures close to the T_g of the adhesive, led to opposite effects on the above mentioned properties of the joints. The width of the glassy-rubbery transition of the adhesives increased, in the whole range of temperatures used in this study and for all epoxy compositions, as a result of subjecting the joints to superimposed dynamic and static stresses. The broadening of the glass transition was interpreted as a result of the stiffening of polymer network during the combined stressing experiments. A linear relationship was found between the area of endothermal peaks in the first DSC scan of specimens subjected prior to test to superimposed dynamic and static stresses at temperatures below T_g, and the shear strength of the joints. In agreement with this observation and with literature data, a linear relationship was revealed also between the glass transition temperature of the resins (measured in the first DSC scan) and the shear strength of the joints. Based on experimental observations and on some literature information, it was suggested that the strengthening of the joint, as well as the changes in thermal properties of the adhesives, are mainly due to physical processes, such as short-range orientation of network chains and an increase in intermolecular interaction between highly polar sites of the network. The possibility that superimposed stressing led to changes in chemical crosslinking was discussed but it seems that no such reactions occurred.     

A joint introduction to Gaussian Processes and Relevance Vector Machines with connections to Kalman filtering and other kernel smoothers

The expressive power of Bayesian kernel-based methods has led them to become an important tool across many different facets of artificial intelligence, and useful to a plethora of modern application domains, providing both power and interpretability via uncertainty analysis. This article introduces and discusses two methods which straddle the areas of probabilistic Bayesian schemes and kernel methods for regression: Gaussian Processes and Relevance Vector Machines. Our focus is on developing a common framework with which to view these methods, via intermediate methods a probabilistic version of the well-known kernel ridge regression, and drawing connections among them, via dual formulations, and discussion of their application in the context of major tasks: regression, smoothing, interpolation, and filtering. Overall, we provide understanding of the mathematical concepts behind these models, and we summarize and discuss in depth different interpretations and highlight the relationship to other methods, such as linear kernel smoothers, Kalman filtering and Fourier approximations. Throughout, we provide numerous figures to promote understanding, and we make numerous recommendations to practitioners. Benefits and drawbacks of the different techniques are highlighted. To our knowledge, this is the most in-depth study of its kind to date focused on these two methods, and will be relevant to theoretical understanding and practitioners throughout the domains of data-science, signal processing, machine learning, and artificial intelligence in general.     

供电企业绩效管理对策研究

绩效管理是人力资源管理中的重要举措之一,对现代企业增强对员工的掌控力和核心竞争力具有重要作用。随着我国供电体制改革的不断深入,供需平衡已成为电力能源供应的发展趋势。基于当前成功企业的绩效管理方法和供电企业在实施绩效管理工作中存在的突出问题,从绩效管理的计划、指标、流程和应用等环节,深入研究了供电企业建立符合自身特点的全员绩效管理体系的方法。     

A minimum Hellinger distance estimator for stochastic differential equations: An application to statistical inference for continuous time interest rate models

A minimum disparity estimator minimizes a φ-divergence between the marginal density of a parametric model and its non-parametric estimate. This principle is applied to the estimation of stochastic differential equation models, choosing the Hellinger distance as particular φ-divergence. Under an hypothesis of stationarity, the parametric marginal density is provided by solving the Kolmogorov forward equation. A particular emphasis is put on the non-parametric estimation of the sample marginal density which has to take into account sample dependence and kurtosis. A new window size determination is provided. The classical estimator is presented alternatively as a distance minimizer and as a pseudo-likelihood maximizer. The latter presentation opens the way to Bayesian inference. The method is applied to continuous time models of the interest rate. In particular, various models are tested using alternatively tests and their results are discussed.     

Queueing analysis of a server node in transaction processing middleware systems

Quantitative performance modeling of complex information systems is of immense importance for designing enterprise e-business infrastructures and applications. In this paper, we present a traffic model of a server node in a typical transaction processing middleware system as well as a quantitative framework to model and analyze its performance. A multi-class open queueing network model is presented in which multi-class jobs are admitted to a number of server processes sharing hardware resources including the CPU and the disk. We have developed a viable approximation method, which decomposes the dependent components into their independent counterparts while preserving their relevant characteristics. We have conducted queueing-theoretic delay analyses and verified the approach using simulation. Results demonstrate the strength of our approach in predicting delays, elapsed times and other system performance measures.     

Querying business processes with BP-QL

We present in this paper BP-QL, a novel query language for querying business processes. The BP-QL language is based on an intuitive model of business processes, an abstraction of the emerging BPEL (business process execution language) standard. It allows users to query business processes visually, in a manner very analogous to how such processes are typically specified, and can be employed in a distributed setting, where process components may be provided by distinct providers.