A Modular Incremental Model for English Full Parsing

In this paper, we present a modular incremental statistical model for English full parsing. Unlike other full parsing approaches in which the analysis of the sentence is a uniform process, our model separates the full parsing into shallow parsing and sentence skeleton parsing. In shallow parsing, we finish POS tagging, Base NP identification, prepositional phrase attachment and subordinate clause identification. In skeleton parsing, we use a layered feature-oriented statistical method. Modularity possesses the advantage of solving different problems in parsing with corresponding mechanisms. Feature-oriented rule is able to express the complex lingual phenomena at the key point if needed. Evaluated on Penn Treebank corpus, we obtained 89.2% precision and 89.8% recall.

Intuitionistic Logic as the Implement of Incremental Model Construction for Natural Language

The process of understanding natural language can be viewed as the process of model construction. This paper? employing Kripke frame for intuitionistic logic semantics as the implement of model construction for natural language, introduces a method of incremental model construction.

INCREMENTAL MICRO-MECHANICAL MODEL OF PLAIN WOVEN FABRIC

Warp yarns and weft yarns of plain woven fabric are the principal axes of mate- rial of fabric. They are orthogonal in their original con?guration, but are obliquely crisscross in deformed con?guration in general. In this paper the expressions of incremental components of strain tensor are derived, the non-linear model of woven fabric is linearized physically and its geometric non-linearity survives. The convenience of determining the total deformation is shown by the choice of the coordinate system of the principal axes of the material, with the convergence of the incremental methods illustrated by examples. This incremental model furnishes a basis for numerical simulations of fabric draping and wrinkling based on the micro-mechanical model of fabric.

Species-specific,pan-European diameter increment models based on data of 2.3 million trees

Background: Over the last decades, many forest simulators have been developed for the forests of individual European countries. The underlying growth models are usually based on national datasets of varying size, obtained from National Forest Inventories or from long-term research plots. Many of these models include country-and location-specific predictors, such as site quality indices that may aggregate climate, soil properties and topography effects. Consequently, it is not sensible to compare such models among countries, and it is often impossible to apply models outside the region or country they were developed for. However, there is a clear need for more generically applicable but still locally accurate and climate sensitive simulators at the European scale, which requires the development of models that are applicable across the European continent. The purpose of this study is to develop tree diameter increment models that are applicable at the European scale, but still locally accurate. We compiled and used a dataset of diameter increment observations of over 2.3 million trees from 10 National Forest Inventories in Europe and a set of 99 potential explanatory variables covering forest structure, weather, climate, soil and nutrient deposition.Results: Diameter increment models are presented for 20 species/species groups. Selection of explanatory variables was done using a combination of forward and backward selection methods. The explained variance ranged from10% to 53% depending on the species. Variables related to forest structure(basal area of the stand and relative size of the tree) contributed most to the explained variance, but environmental variables were important to account for spatial patterns. The type of environmental variables included differed greatly among species.Conclusions: The presented diameter increment models are the first of their kind that are applicable at the European scale. This is an important step towards the development of a new generation of forest development simulators that can be applied at the European scale, but that are sensitive to variations in growing conditions and applicable to a wider range of management systems than before. This allows European scale but detailed analyses concerning topics like CO2 sequestration, wood mobilisation, long term impact of management, etc.

Nonlinear adaptive switching control for a class of non-affine nonlinear systems

An improved nonlinear adaptive switching control method is presented to relax the assumption on the higher order nonlinear terms of a class of discrete-time non-affine nonlinear systems. The proposed control strategy is composed of a linear adaptive controller, a neural network(NN) based nonlinear adaptive controller and a switching mechanism. An incremental model is derived to represent the considered system and an improved robust adaptive law is chosen to update the parameters of the linear adaptive controller. A new performance criterion of the switching mechanism is designed to select the proper controller. Using this control scheme, all the signals in the system are proved to be bounded. Numerical examples verify the effectiveness of the proposed algorithm.

Improved MPPT Control Strategy for PV Connected to Grid Using IncCond-PSO-MPC Approach

This paper proposes a new hybrid maximum power point tracking(MPPT)control strategy for grid-connected solar systems based on Incremental conductance—Particle Swarm Optimization and Model Predictive Controller(IncCond-PSOMPC).The purpose of the suggested method is to create as much power as feasible from a PV system during environmental changes,then transfer it to the power grid.To accomplish this,a hybrid combination of incremental conductance(IncCond)and particle swarm optimization(PSO)is proposed to locate maximum power,followed by model predictive control(MPC)to track maximum power and control the boost converter to achieve high performance regardless of parameter variations.A two-level inverter,likewise,controlled by Model Predictive Control,is employed to inject the PV power generated.In this application,the MPC is based on minimizing the difference between the reference and prediction powers,which is computed to select the switching state of the inverter.The proposed system is simulated and evaluated in a variety of dynamic conditions using Matlab/Simulink.Results reveal that the proposed control mechanism is effective at tracking the maximum power point(MPP)with fewer power oscillations.

Nonlinear Finite Element Method Considering Martensite Plasticity for Shape Memory Alloy Structure

This work presents a nonlinear finite element method to simulate the macroscopic mechanical responses and the effects of martensite plasticity in a shape memory alloy(SMA)structure.A linear relationship formulation is adopted to express the influence of martensite plasticity on the inverse martensitic phase transition of SMA material.Incorporating with a trigonometric-type phase transition evolution law and an exponential-type plastic flow evolution law,an incremental mechanical model with two internal variables is supposed based on the macroscopic experimental phenomena.A nonlinear finite element equation is formulated and solved by the principle of virtual displacement and Newton-Raphson method respectively.By employing the proposed nonlinear finite element method,the uniform tensile bar and three-point bending beam are simulated and analyzed.Results illustrate that the presented nonlinear finite element method is suitable to act as an effective computational tool for the wide applications based on the SMA material considering the effects of martensite plasticity because all material constants related to the method can be obtained from macroscopic experiments.