Automatic Modeling Techniques Research Articles

Basement depth estimation from gravity anomalies: two 2.5D approaches coupled with the exponential density contrast model

We develop two automatic techniques in the spatial domain using the exponential density contrast model (EDCM) to trace the bottom surface of a 2.5D sedimentary basin from the observed gravity anomalies. The interface between the sediments and basement is described with a finite strike polygonal source, whose depth ordinates become the unknown parameters to be estimated. The proposed automatic modeling technique makes use of the forward difference approximation and the inversion solves a system of normal equations using the ridge regression to estimate the unknown parameters. Furthermore, the proposed inversion technique simultaneously estimates the regional gravity background that is associated with the residual gravity anomaly. In either case, forward modeling is realized in the spatial domain through a method that combines both analytical and numerical approaches. The utility of each algorithm was successfully tested on a theoretically produced noisy residual gravity dataset. The validity of the inversion technique is also exemplified with the noisy gravity anomalies attributable to a synthetic structure in the presence of regional gravity background. We demonstrate that the magnitude of gravity anomaly is offset dependent and that it would influence the modeling result. Additionally, some applications with real gravity datasets from the Gediz and Buyuk Menderes grabens in western Turkey using the derived EDCMs have produced geologically reasonable results which are in close agreement with those reported previously.

Automatic modeling technique for tightly correlative structure in aircraft manufacturing tooling

Automatic modeling technique for tightly correlative structure in aircraft manufacturing tooling

Kinematics Analysis of the Driving Cylinder for Dual-Rotor Piston Engine

The engine is the most common power source in our life. For a long time, people never stopped research it. Kinematics analysis study the relationship among the position, velocity and acceleration. In recent years, with computer technology, graphics technology and automatic modeling techniques together, much kinematics analysis software came into the world, which makes the engine kinematics analysis more quickly and easily. The article base on the idea of a novel dual-rotor piston engine, describe the structure and movement principle of the driving cylinder, build the kinetic theory model of the key components, and then expand the simulation study, discuss their kinematics properties.

Bayesian Synthesis: Combining subjective analyses, with an application to ozone data

Bayesian model averaging enables one to combine the disparate predictions of a number of models in a coherent fashion, leading to superior predictive performance. The improvement in performance arises from averaging models that make different predictions. In this work, we tap into perhaps the biggest driver of different predictions---different analysts---in order to gain the full benefits of model averaging. In a standard implementation of our method, several data analysts work independently on portions of a data set, eliciting separate models which are eventually updated and combined through a specific weighting method. We call this modeling procedure Bayesian Synthesis. The methodology helps to alleviate concerns about the sizable gap between the foundational underpinnings of the Bayesian paradigm and the practice of Bayesian statistics. In experimental work we show that human modeling has predictive performance superior to that of many automatic modeling techniques, including AIC, BIC, Smoothing Splines, CART, Bagged CART, Bayes CART, BMA and LARS, and only slightly inferior to that of BART. We also show that Bayesian Synthesis further improves predictive performance. Additionally, we examine the predictive performance of a simple average across analysts, which we dub Convex Synthesis, and find that it also produces an improvement.

Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol

Common benchmark data sets, standardized performance metrics, and baseline algorithms have demonstrated considerable impact on research and development in a variety of application domains. These resources provide both consumers and developers of technology with a common framework to objectively compare the performance of different algorithms and algorithmic improvements. In this paper, we present such a framework for evaluating object detection and tracking in video: specifically for face, text, and vehicle objects. This framework includes the source video data, ground-truth annotations (along with guidelines for annotation), performance metrics, evaluation protocols, and tools including scoring software and baseline algorithms. For each detection and tracking task and supported domain, we developed a 50-clip training set and a 50-clip test set. Each data clip is approximately 2.5 minutes long and has been completely spatially/temporally annotated at the I-frame level. Each task/domain, therefore, has an associated annotated corpus of approximately 450,000 frames. The scope of such annotation is unprecedented and was designed to begin to support the necessary quantities of data for robust machine learning approaches, as well as a statistically significant comparison of the performance of algorithms. The goal of this work was to systematically address the challenges of object detection and tracking through a common evaluation framework that permits a meaningful objective comparison of techniques, provides the research community with sufficient data for the exploration of automatic modeling techniques, encourages the incorporation of objective evaluation into the development process, and contributes useful lasting resources of a scale and magnitude that will prove to be extremely useful to the computer vision research community for years to come.

An intelligent multi-blackboard CAD system

In this paper, automatic modeling techniques in intelligent CAD systems are discussed and a multi-blackboard based modeling method is presented. It supports modeling with multilevel accuracy and can convert abstract symbolbased description of a design object into the desired 3D model. These characteristics meet the modeling requirements for supporting the overall design process and are suitable for intelligent CAD systems. We implement them in an intelligent chair design system, AUTOCHAIR.