This guide was created to assist Artists and Engineers, to learn the basics of mesh modelling of non deformable objects with 'Blender'. It uses a structured approach to introducing Blenders tools and work-methods. Following the guide should enable you to become familiar with blender and create models from the simplest of parts to complex accurate engineering assemblies and designs. The guide focuses solely on Blenders Mesh Modelling capabilities, it ignores the myriad of animation,
Import 2D outline drawing into Catia Build 3D curves based on the imported drawing Build the upper surfaces of the mouse (by Generative Shape Design) Do the draft analysis to search any undercut portion on the upper surfaces Adjust the curvature of the problem surface manually Build the lower surfaces of the mouse Convert the surfaces into a solid Build the parting surfaces based on the imported drawing Create components from the finished model Re-assemble the components into a product Modify the outlook of the master model and then get all components updated automatically...
–Using the Process Editor to create a modified version of the sink process model.
–Adding a new statistic to compute ETE delay.
1. Create modified sink process model to compute ETE delay.
2. When there is a packet arrival, get the packet, obtain the creation time, write out its ETE
delay as a global statistic and destroy the packet.
3. Incorporate new sink process model into existing node model.
4. Create ETE delay statistic probe.
4. Run simulation for a duration of 2,000 seconds to ensure convergence.
5. Filter the “View Results” graphs to answer questions....
Using GIS to create descriptive models of the world
--representations of reality as it exists.
Using GIS to answer a question or test an hypothesis.
Often involves creating a new conceptual output layer, (or table or chart),
the values of which are some transformation of the values in the
descriptive input layer.
--e.g. buffer or slope or aspect layers
This book focuses on how to create game art properly for a game engine,
as well as how to export that art to the engine and make script
changes so that the art becomes a viable part of the game.
Although many of the processes and techniques will apply to specific
modeling, texturing, animation, and game software solutions, this book
will use 3ds Max release 8 to generate models and animations, and the
Torque Game Engine for the game-side examples.
The first all-inclusive guidebook for designing, building, and implementing a sturdy core valuation/projection model
In today’s no-room-for-error corporate finance market, precise and effective financial modeling is essential for both determining a company’s current value and projecting its future performance. Yet few books have explained how to build models that accurately interpret a company’s financial statement, while none have focused on projection models.
Analyze tabular data using the BI Semantic Model (BISM) in Microsoft® SQL Server® 2012 Analysis Services—and discover a simpler method for creating corporate-level BI solutions. Led by three BI experts, you’ll learn how to build, deploy, and query a BISM tabular model with step-by-step guides, examples, and best practices. This hands-on book shows you how the tabular model’s in-memory database enables you to perform rapid analytics—whether you’re a professional BI developer new to Analysis Services or familiar with its multidimensional model....
Beginning Blender covers the Blender 2.5 release in-depth. The book starts with the creation of simple figures using basic modeling and sculpting. It then teaches you how to bridge from modeling to animation, and from scene setup to texture creation and rendering, lighting, rigging, and ultimately, full animation. You will create and mix your own movie scenes, and you will even learn the basics of games logic and how to deal with games physics.
Every student of finance or applied economics learns the lessons of Franco Modigliani and Merton Miller. Their landmark paper, published in 1958, laid out the basic underpinnings of modern finance and these two distinguished academics were both subsequently awarded the Nobel Prize in Economics. Simply stated, companies create value when they generate returns that exceed their costs. More specifically, the returns of successful companies will exceed the risk-adjusted cost of the capital used to run the business.
Activity Based Costing Model to Cost Academic Programs and Estimate Costs for Support Services in California Community Colleges In either case, however, we
can expect little effect of expansions of Tiebout choice on school efficiency, as in the former
even markets with only a few districts can provide market discipline and in the latter no
plausible amount of governmental fragmentation will create efficiency-enhancing incentives
for school administrators.
[ Team LiB ] 9.3 Conditional Compilation and Execution A portion of Verilog might be suitable for one environment but not for another. The designer does not wish to create two versions of Verilog design for the two environments.
In the method of creating digital terrain model (DTM) by using digital photogrammetry, the picket sampling interval (PSI) plays an important role since it strongly influences on the production effectiveness and on the accuracy of created DTMs. The optimal value of PSI must be balanced between requirements of effectiveness and of accuracy. This research is focused on the influence of PSI on root mean square error (RMSE) of created DTM and on the number of error pickets (caused by limitation of image matching technique) that must be checked and corrected manually.
In this paper, a new language model, the Multi-Class Composite N-gram, is proposed to avoid a data sparseness problem for spoken language in that it is difﬁcult to collect training data. The Multi-Class Composite N-gram maintains an accurate word prediction capability and reliability for sparse data with a compact model size based on multiple word clusters, called MultiClasses. In the Multi-Class, the statistical connectivity at each position of the N-grams is regarded as word attributes, and one word cluster each is created to represent the positional attributes. ...
This paper introduces new methods based on exponential families for modeling the correlations between words in text and speech. While previous work assumed the effects of word co-occurrence statistics to be constant over a window of several hundred words, we show that their influence is nonstationary on a much smaller time scale.
Topic models have been used extensively as a tool for corpus exploration, and a cottage industry has developed to tweak topic models to better encode human intuitions or to better model data. However, creating such extensions requires expertise in machine learning unavailable to potential end-users of topic modeling software. In this work, we develop a framework for allowing users to iteratively reﬁne the topics discovered by models such as latent Dirichlet allocation (LDA) by adding constraints that enforce that sets of words must appear together in the same topic. ...
We present an unsupervised model for joint phrase alignment and extraction using nonparametric Bayesian methods and inversion transduction grammars (ITGs). The key contribution is that phrases of many granularities are included directly in the model through the use of a novel formulation that memorizes phrases generated not only by terminal, but also non-terminal symbols. This allows for a completely probabilistic model that is able to create a phrase table that achieves competitive accuracy on phrase-based machine translation tasks directly from unaligned sentence pairs. ...
Call centers handle customer queries from various domains such as computer sales and support, mobile phones, car rental, etc. Each such domain generally has a domain model which is essential to handle customer complaints. These models contain common problem categories, typical customer issues and their solutions, greeting styles. Currently these models are manually created over time.
This paper describes a dependency structure analysis of Japanese sentences based on the maximum entropy models. Our model is created by learning the weights of some features from a training corpus to predict the dependency between bunsetsus or phrasal units. The dependency accuracy of our system is 87.2% using the Kyoto University corpus. We discuss the contribution of each feature set and the relationship between the number of training data and the accuracy.