Fast Voronoi modeling

The common way to construct Voronoi tessellations is to compare the distances between given reference points using a given distance function. To generalize this distance-function concept we expand an existing approach which defines distance functions by their ``unit circles'. Our new approach allows modeling the ``unit circles' by a closed Spline curve. Changing the control polygon directly affects the tessellation's appearance. Typically generalized Voronoi diagrams are represented by Voronoi vertices and curves separating the individual tiles. To obtain interactive modeling we extended an existing hardware accelerated rendering approach computing a bitmap-representation using different colors for individual tiles. With our extension, we are able to use our Spline distance representations as input for a growing process. This growing process easily takes into account weighting approaches like multiplicative, additive, and even free functional weighting.     

High accuracy NC milling simulation using composite adaptively sampled distance fields

We describe a new approach to shape representation called a composite adaptively sampled distance field (composite ADF) and describe its application to NC milling simulation. In a composite ADF each shape is represented by an analytic or procedural signed Euclidean distance field and the milled workpiece is given as the Boolean difference between distance fields representing the original workpiece volume and distance fields representing the volumes of the milling tool swept along the prescribed milling path. The computation of distance field of the swept volume of a milling tool is handled by an inverted trajectory approach where the problem is solved in tool coordinate frame instead of a world coordinate frame. An octree bounding volume hierarchy is used to sample the distance functions and provides spatial localization of geometric operations thereby dramatically increasing the speed of the system. The new method enables very fast simulation, especially of free-form surfaces, with accuracy better than 1 micron, and low memory requirements. We describe an implementation of 3 and 5-axis milling simulation.     

User-oriented problem abstractions in scheduling

In this paper we describe a modeling framework aimed at facilitating the customization and deployment of artificial intelligence (AI) scheduling technology in real-world contexts. Specifically, we describe an architecture aimed at facilitating software product line development in the context of scheduling systems. The framework is based on two layers of abstraction: a first layer providing an interface with the scheduling technology, on top of which we define a formalism to abstract domain-specific concepts. We show how this two-layer modeling framework provides a versatile formalism for defining user-oriented problem abstractions, which is pivotal for facilitating interaction between domain experts and technologists. Moreover, we describe a graphical user interface (GUI)-enhanced tool which allows the domain expert to interact with the underlying core scheduling technology in domain-specific terms. This is achieved by automatically instantiating an abstract GUI template on top of the second modeling layer.     

Representing complex intuitionistic fuzzy set by quaternion numbers and applications to decision making

Intuitionistic fuzzy sets are useful for modeling uncertain data of realistic problems. In this paper, we generalize and expand the utility of complex intuitionistic fuzzy sets using the space of quaternion numbers. The proposed representation can capture composite features and convey multi-dimensional fuzzy information via the functions of real membership, imaginary membership, real non-membership, and imaginary non-membership. We analyze the order relations and logic operations of the complex intuitionistic fuzzy set theory and introduce new operations based on quaternion numbers. We also present two quaternion distance measures in algebraic and polar forms and analyze their properties. We apply the quaternion representations and measures to decision-making models. The proposed model is experimentally validated in medical diagnosis, which is an emerging application for tackling patient’s symptoms and attributes of diseases.     

Case-base maintenance by conceptual clustering of graphs

Case-base maintenance typically involves the addition, removal or revision of cases, but can also include changes to the retrieval knowledge. In this paper, we consider the learning of the retrieval knowledge (organization) as well as the prototypes and the cases as case-based maintenance. We address this problem based on cases that have a structural case representation. Such representations are common in computer vision and image interpretation, building design, timetabling or gene-nets. In this paper we propose a similarity measure for an attributed structural representation and an algorithm that incrementally learns the organizational structure of a case base. This organization schema is based on a hierarchy and can be updated incrementally as soon as new cases are available. The tentative underlying conceptual structure of the case base is visually presented to the user. We describe two approaches for organizing the case base. Both are based on approximate graph subsumption. The first approach is based on a divide-and-conquer strategy whereas the second one is based on a split-and-merge strategy which better allows to fit the hierarchy to the actual structure of the application but takes more complex operations.     

Modeling context-aware and intention-aware in-car infotainment systems

It is fundamental to understand users’ intentions to support them when operating a computer system with a dynamically varying set of functions, e.g., within an in-car infotainment system. The system needs to have sufficient information about its own and the user’s context to predict those intentions. Although the development of current in-car infotainment systems is already model-based, explicitly gathering and modeling contextual information and user intentions is currently not supported. However, manually creating software that understands the current context and predicts user intentions is complex, error-prone and expensive. Model-based development can help in overcoming these issues. In this paper, we present an approach for modeling a user’s intention based on Bayesian networks. We support developers of in-car infotainment systems by providing means to model possible user intentions according to the current context. We further allow modeling of user preferences and show how the modeled intentions may change during run-time as a result of the user’s behavior. We demonstrate feasibility of our approach using an industrial case study of an intention-aware in-car infotainment system. Finally, we show how modeling of contextual information and modeling user intentions can be combined by using model transformation.     

Predicting user’s preferences using neural networks and psychology models

In this paper we describe a new model suitable for optimization problems with explicitly unknown optimization functions using user’s preferences. The model addresses an ability to learn not known optimization functions thus perform also a learning of user’s preferences. The model consists of neural networks using fuzzy membership functions and interactive evolutionary algorithms in the process of learning. Fuzzy membership functions of basic human values and their priorities were prepared by utilizing Schwartz’s model of basic human values (achievement, benevolence, conformity, hedonism, power, security, self-direction, stimulation, tradition and universalism). The quality of the model was tested on “the most attractive font face problem” and it was evaluated using the following criteria: a speed of optimal parameters computation, a precision of achieved results, Wilcoxon signed rank test and a similarity of letter images. The results qualify the developed model as very usable in user’s preference modeling.     

QUESTO: Interactive Construction of Objective Functions for Classification Tasks

Building effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta-information through the design of the objective functions that they solve. However, such objective functions are hard for users to specify as they are a specific mathematical formulation of their intents. In this paper, we present an approach that allows users to generate objective functions for classification problems through an interactive visual interface. Our approach adopts a semantic interaction design in that user interactions over data elements in the visualization are translated into objective function terms. The generated objective functions are solved by a machine learning solver that provides candidate models, which can be inspected by the user, and used to suggest refinements to the specifications. We demonstrate a visual analytics system QUESTO for users to manipulate objective functions to define domain-specific constraints. Through a user study we show that QUESTO helps users create various objective functions that satisfy their goals.     

A two-level approach to implicit surface modeling with compactly supported radial basis functions

We describe a two-level method for computing a function whose zero-level set is the surface reconstructed from given points scattered over the surface and associated with surface normal vectors. The function is defined as a linear combination of compactly supported radial basis functions (CSRBFs). The method preserves the simplicity and efficiency of implicit surface interpolation with CSRBFs and the reconstructed implicit surface owns the attributes, which are previously only associated with globally supported or globally regularized radial basis functions, such as exhibiting less extra zero-level sets, suitable for inside and outside tests. First, in the coarse scale approximation, we choose basis function centers on a grid that covers the enlarged bounding box of the given point set and compute their signed distances to the underlying surface using local quadratic approximations of the nearest surface points. Then a fitting to the residual errors on the surface points and additional off-surface points is performed with fine scale basis functions. The final function is the sum of the two intermediate functions and is a good approximation of the signed distance field to the surface in the bounding box. Examples of surface reconstruction and set operations between shapes are provided.     

A Unified Approach for Physical and Geometric Modeling for Graphics and Animation

We present a unified approach for geometric and physical modeling using implicit functions, for application to graphics and animation. This method extends previously proposed techniques, and allows the standard finite element method to be directly combined with geometric modeling, resulting in quick calculation of an object's mass and stiffness matrices, and its vibration modes and frequencies. Because the approach is based on an implicitfunction representation, it allows very fast collision detection and characterization. Examples of complex physical and geometric modeling are presented.     

Modeling a Murex cabritii sea shell with a structured implicit surface modeler

BlobTree , and its application to the generation of a complex and visually accurate biological model of the sea shell Murex cabritii. Since the model is purely procedurally defined and does not rely on polygon mesh operations, it is resolution independent and can be rendered directly using ray tracing. An interface has been built for the BlobTree using an interpreted programming language (Python). The language interface readily allows a user to procedurally describe the shell based on numeric data taken from the actual object. Published online: 15 March 2002     

Signed algebraic level sets on NURBS surfaces and implicit Boolean compositions for isogeometric CAD–CAE integration

Boundary-representation (B-rep) geometrical models, often mathematically represented using Non-Uniform Rational B-Spline (NURBS) surfaces, are the starting point for complex downstream product life-cycle evaluations including Computer-Aided Engineering (CAE). Boolean operations during B-rep model generation require surface intersection computations to describe the composed entity. However, for parametric NURBS surfaces, intersection operations are non-trivial and typically carried out numerically. The numerical intersection computations introduce challenges relating to the accuracy of the resulting representation, efficiency with which the computation is carried out, and robustness of the result to small variations in geometry. Often, for downstream CAE evaluations, an implicit, procedural knowledge of the Boolean operations between the composed objects that can resolve point containment queries (exact to the original NURBS bounding surfaces) maybe sufficient during quadrature. However, common point containment queries on B-rep models are numerical, iterative and relatively expensive. Thus, the first goal of the present paper is to describe a purely algebraic, and therefore non-iterative, approach to carrying out point containment queries on complex B-rep models built using low-degree NURBS surfaces. For CAE operations, the boundary representation of B-rep solids is, in general, not convenient and as a result, the B-rep model is converted to a meshed volumetric approximation. The major challenges to such a conversion include capturing the geometric features accurately when constructing the secondary (meshed) representation, apart from the efficiency of carrying out such a mesh generation step repeatedly as the geometric shape evolves. Thus, an ideal analysis procedure would operate directly on B-rep CAD models, without needing a secondary mesh, and would procedurally unify the geometric operations during CAD as well as CAE stages. Therefore, the second and broader goal of the present paper is to demonstrate CAD–CAE integration using signed algebraic level set operations directly on B-rep models by embedding or immersing the bounding surfaces within a discretized domain while preserving the geometric accuracy of the surfaces exact to the original NURBS representation during analysis.     

Edit distance-based kernel functions for structural pattern classification

A common approach in structural pattern classification is to define a dissimilarity measure on patterns and apply a distance-based nearest-neighbor classifier. In this paper, we introduce an alternative method for classification using kernel functions based on edit distance. The proposed approach is applicable to both string and graph representations of patterns. By means of the kernel functions introduced in this paper, string and graph classification can be performed in an implicit vector space using powerful statistical algorithms. The validity of the kernel method cannot be established for edit distance in general. However, by evaluating theoretical criteria we show that the kernel functions are nevertheless suitable for classification, and experiments on various string and graph datasets clearly demonstrate that nearest-neighbor classifiers can be outperformed by support vector machines using the proposed kernel functions.     

Neural Representation of Open Surfaces

Neural implicit surfaces have emerged as an effective, learnable representation for shapes of arbitrary topology. However, representing open surfaces remains a challenge. Different methods, such as unsigned distance fields (UDF), have been proposed to tackle this issue, but a general solution remains elusive. The generalized winding number (GWN), which is often used to distinguish interior points from exterior points of 3D shapes, is arguably the most promising approach. The GWN changes smoothly in regions where there is a hole in the surface, but it is discontinuous at points on the surface. Effectively, this means that it can be used in lieu of an implicit surface representation while providing information about holes, but, unfortunately, it does not provide information about the distance to the surface necessary for e.g. ray tracing, and special care must be taken when implementing surface reconstruction. Therefore, we introduce the semi-signed distance field (SSDF) representation which comprises both the GWN and the surface distance. We compare the GWN and SSDF representations for the applications of surface reconstruction, interpolation, reconstruction from partial data, and latent vector analysis using two very different data sets. We find that both the GWN and SSDF are well suited for neural representation of open surfaces.     

A prototype-based method for classification with time constraints: a case study on automated planning

The main goal of Nearest Prototype Classification is to reduce storage space and retrieval time of classical Instance-Based Learning (IBL) algorithms. This motivation is higher in relational data since relational distance metrics are much more expensive to compute than classical distances like Euclidean distance. In this paper, we present an algorithm to build Relational Nearest Prototype Classifiers (RNPCs). When compared with Relational Instance-Based Learning (Relational IBL or RIBL) approaches, the algorithm is able to dramatically reduce the number of instances by selecting the most relevant prototypes, maintaining similar accuracy. The number of prototypes is obtained automatically by the algorithm, although it can also be bound by the user. In this work, we also show an application of RNPC for automated planning. Specifically, we describe a modeling task where a relational policy is built following an IBL approach. This approach uses the decisions taken by a planning system as learning examples. We show that when the number of learning examples is reduced with RNPC, the resulting policy is able to scale up better than the original planning system.     

以网络为中心的协同特征造型

在网络化同步协同设计环境中,如何实现CAD系统之间的实时数据交换和模型同步,成为协同几何造型的关键问题。文中提出了一种新的复制式协同特征造型方法,该方法将现有的特征造型技术与分布计算、网络通信技术相结合,通过简单的造型消息交换来实现增量式协同造型,可以较好地满足几何模型实时同步的需要,而且也部分地解决了异构CAD软件之间的数据交换问题;并详细地介绍了协同造型通信协议、对象引用机制、以及分布式协同造型系统。     

Integrity constraint integration in heterogeneous databases: An enhanced methodology for schema integration

In today's technologically diverse corporate environment, it is common to find several different databases being used to accomplish the organization's operational data management functions. Providing interoperability among these databases is important to the successful operation of the organization. One approach to providing interoperability among heterogeneous database systems, is to define one or more schemas which represent a coherent view of the underlying databases. In the past, most approaches have used schematic knowledge about the underlying databases to generate integrated representations of the databases. In this paper we present a seven step methodology for utilizing integrity constraint knowledge from heterogeneous databases. Specifically, we describe how we can generate a set of integrity constraints applicable at the integrated level from constraints specified on local databases. We introduce the concept of constraint-based relationships between objects in heterogeneous databases and describe the role that these relationships play in integrity constraint integration. Finally, we describe how the integrated set of constraints generated using our methodology can be used to facilitate semantic query processing in a heterogeneous database environment.     

Using shared representations to improve coordination and intent inference

In groupware, users must communicate about their intentions and aintain common knowledge via communication channels that are explicitly designed into the system. Depending upon the task, generic communication tools like chat or a shared whiteboard may not be sufficient to support effective coordination. We have previously reported on a methodology that helps the designer develop task specific communication tools, called coordinating representations, for groupware systems. Coordinating representations lend structure and persistence to coordinating information. We have shown that coordinating representations are readily adopted by a user population, reduce coordination errors, and improve performance in a domain task. As we show in this article, coordinating representations present a unique opportunity to acquire user information in collaborative, user-adapted systems. Because coordinating representations support the exchange of coordinating information, they offer a window onto task and coordination-specific knowledge that is shared by users. Because they add structure to communication, the information that passes through them can be easily exploited by adaptive technology. This approach provides a simple technique for acquiring user knowledge in collaborative, user-adapted systems. We document our application of this approach to an existing groupware system. Several empirical results are provided. First, we show how information that is made available by a coordinating representation can be used to infer user intentions. We also show how this information can be used to mine free text chat for intent information, and show that this information further enhances intent inference. Empirical data shows that an automatic plan generation component, which is driven by information from a coordinating representation, reduces coordination errors and cognitive effort for its users. Finally, our methodology is summarized, and we present a framework for comparing our approach to other strategies for user knowledge acquisition in adaptive systems.     

Multi-Level Partition of Unity Algebraic Point Set Surfaces

We present a multi-level partition of unity algebraic set surfaces (MPU-APSS) for surface reconstruction which can be represented by either a projection or in an implicit form. An algebraic point set surface (APSS) defines a smooth surface from a set of unorganized points using local moving least-squares (MLS) fitting of algebraic spheres. However, due to the local nature, APSS does not work well for geometry editing and modeling. Instead, our method builds an implicit approximation function for the scattered point set based on the partition of unity approach. By using an octree subdivision strategy, we first adaptively construct local algebraic spheres for the point set, and then apply weighting functions to blend together these local shape functions. Finally, we compute an error-controlled approximation of the signed distance function from the surface. In addition, we present an efficient projection operator which makes our representation suitable for point set filtering and dynamic point resampling. We demonstrate the effectiveness of our unified approach for both surface reconstruction and geometry modeling such as surface completion.