Parametric modelling

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  • In this chapter, students will be able to understand: Create simple extruded solid models, understand the basic parametric modeling procedure, create 2-D sketches, understand the “shape before size” design approach, use the dynamic viewing commands, create and edit parametric dimensions.

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  • It is well known that occurrence counts of words in documents are often modeled poorly by standard distributions like the binomial or Poisson. Observed counts vary more than simple models predict, prompting the use of overdispersed models like Gamma-Poisson or Beta-binomial mixtures as robust alternatives. Another deficiency of standard models is due to the fact that most words never occur in a given document, resulting in large amounts of zero counts. We propose using zeroinflated models for dealing with this, and evaluate competing models on a Naive Bayes text classification task.

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  • [ Team LiB ] 7.8 Generate Blocks Generate statements allow Verilog code to be generated dynamically at elaboration time before the simulation begins. This facilitates the creation of parametrized models

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Image Resolution Enhancement via Data-Driven Parametric Models in the Wavelet Space

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  • (BQ) Part 1 book "Mathematical statistics" has contents: Probability theory, fundamentals of statistics, unbiased estimation, estimation in parametric models. Please refer to the content.

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  • Sketching is an important tool for quickly and efficiently communicating design ideas. It is a particularly useful tool early in the design process when several ideas are being explored. A fundamental technique within most parametric modeling programs is to sketch 2-D features then use a 3-D construction operation, such as extrude, revolve, or sweep, to create a 3-D model.

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  • This lecture Autodesk inventor provides knowledge of part documentation. The main contents of the chapter consist of the following: Parametric modeling files/modes, concurrent engineering, model data associativity, documentation drawings, drawing views, drawing annotation.

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  • Introduction to Creo Parametric 3.0 includes introduction to Creo Parametric 3.0; introduction to the Creo Parametric Basic Modeling Process; understanding Creo Parametric Concepts; using the Creo Parametric Interface; selecting Geometry, Features, and Models; editing Geometry, Features, and Models; creating Sketcher Geometry; using Sketcher Tools; creating Sketches for Features and some things else.

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  • The goal of this course is to teach you how to use the SolidWorks mechanical design automation software to build parametric models of parts and assemblies and how to make simple drawings of those parts and assemblies.

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  • Sheetmetal Design using Creo Parametric 2.0 presents Sheetmetal Design using Creo Parametric 2.0; Sheetmetal Model Fundamentals; Creating Primary Sheetmetal Wall Features; Creating Secondary Sheetmetal Wall Features; Bending and Unbending Sheetmetal Models; Modifying Sheetmetal Models; Sheetmetal Setup and Tools; Detailing Sheetmetal Designs.


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  • Linear parametric models of stationary random processes, whether signal or noise, have been found to be useful in a wide variety of signal processing tasks such as signal detection, estimation, filtering,

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  • We propose a non-parametric Bayesian model for unsupervised semantic parsing. Following Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) decompose the syntactic dependency tree of a sentence into fragments, (2) assign each of these fragments to a cluster of semantically equivalent syntactic structures, and (3) predict predicate-argument relations between the fragments.

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  • This document includes: Measurement of efficiency, Mathematical programming models: the non-parametric approach, Stochastic frontier models: the parametric approach.

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  • In this paper we propose a method for the automatic decipherment of lost languages. Given a non-parallel corpus in a known related language, our model produces both alphabetic mappings and translations of words into their corresponding cognates. We employ a non-parametric Bayesian framework to simultaneously capture both low-level character mappings and highlevel morphemic correspondences.

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  • In this paper* I will argue for a model of grammatical processing that is based on uniform processing and knowledge sources. The main feature of this model is to view parsing and generation as two strongly interleaved tasks performed by a single parametrized deduction process. It will be shown that this view supports flexible and efficient natural language processing.

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  • The paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies. The model assigns probability to every joint sequence of words-binary-parse-structure with headword annotation. The model, its probabilistic parametrization, and a set of experiments meant to evaluate its predictive power are presented.

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  • The paper describes an experimental model of syntactic structure generation starting from the limited fragment of semantics that deals with the quantitative values of object parameters. To present the input information the basic semantic units of four types are proposed:"object", "parameter", "function" and "constant".

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  • (BQ) This paper reports the development of a thermo-physical model for die-sinking electric discharge machining (EDM) process using finite element method (FEM). Numerical analysis of the single spark operation of EDM process has been carried out considering the two-dimensional axi-symmetric process continuum

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  • In this chapter you learn how to use more advanced features for modeling in the assembly environment, learn how to minimize the impact of file size for large assemblies, learn how to create weldments and assembly-level-only features, learn how to work more effectively in the assembly environment.

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  • Since the publication of my book Mathematical Statistics (Shao, 2003), I have been asked many times for a solution manual to the exercises in my book. Without doubt, exercises form an important part of a textbook on mathematical statistics, not only in training students for their research ability in mathematical statistics but also in presenting many additional results as complementary material to the main text.

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