07181 Abstracts Collection -- Parallel Universes and Local Patterns

From 1 May 2007 to 4 May 2007 the Dagstuhl Seminar 07181 ``Parallel Universes and Local Patterns\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

Sensor-based Collision Avoidance System for the Walking Machine ALDURO

This work presents a sensor system develop for the robot ALDURO (Antropomorphically Legged and Wheeled Duisburg Robot), in order to allow it to detect and avoid obstacles when moving in unstructured terrains. The robot is a large-scale hydraulically driven 4-legged walking-machine, developed at the Duisburg-Essen University, with 16 degrees of freedom at each leg and will be steered by an operator sitting in a cab on the robot body. The Cartesian operator instructions are processed by a control computer, which converts them into appropriate autonomous leg movements, what makes necessary that the robot automatically recognizes the obstacles (rock, trunks, holes, etc.) on its way, locates and avoids them. A system based on ultra-sound sensors was developed to carry this task on, but there are intrinsic problems with such sensors, concerning to their poor angular precision. To overcome that, a fuzzy model of the used ultra-sound sensor, based on the characteristics of the real one, was developed to include the uncertainties about the measures. A posterior fuzzy inference builds from the measured data a map of the robot’s surroundings, to be used as input to the navigation system. This whole sensor system was implemented at a test stand, where a real size leg of the robot is fully functional. The sensors are assembled in an I2C net, which uses a micro-controller as interface to the main controller (a personal computer). That enables to relieve the main controller of some data processing, which is carried by the microcontroller on. The sensor system was tested together with the fuzzy data inference, and different arrangements to the sensors and settings of the inference system were tried, in order to achieve a satisfactory result

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

Construction management abstracts : cumulative abstracts and indexes of journals in construction management, 1983-2000

The purpose of this document is to provide a single source of reference for every paper published in the journals directly related to research in Construction Management. It is indexed by author and keyword and contains the titles, authors, abstracts and keywords of every article from the following journals: • Building Research and Information (BRI) • Construction Management and Economics (CME) • Engineering, Construction and Architectural Management (ECAM) • Journal of Construction Procurement (JCP) • Journal of Construction Research (JCR) • Journal of Financial Management in Property and Construction (JFM) • RICS Research Papers (RICS) The index entries give short forms of the bibliographical citations, rather than page numbers, to enable annual updates to the abstracts. Each annual update will carry cumulative indexes, so that only one index needs to be consulted

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Exploration and visualization of design spaces with applications to negative stiffness metamaterials

Engineering design problems are commonly hierarchical and multilevel which requires coordination between models at each scale. If the models are computationally expensive or highly nonlinear, such as many materials design applications, identification of an optimal design may be exceptionally difficult. Alternatives to optimization-based methods include set-based methods that classify and track sets or ensembles of high performance designs. By relaxing the requirement for an optimal design, it is often possible to identify promising, high performance regions of the design space efficiently. Bayesian network classifiers (BNCs) are such an approach that can identify these regions of promising designs in the presence of nonlinear relationships and mixed variables. When manufacturing the promising designs identified by the BNC approach, the intended design may not match the physical embodiment due to manufacturing variations. These variations may alter the performance of the design leading to unsatisfactory results and products. To facilitate selection of not only high performance but reliably manufacturable designs, a method for incorporating manufacturing variation, modeled as a joint probability distribution is presented for the BNC approach. The approach utilizes a dual classification strategy that identifies regions of design that are likely to perform well within statistical confidence. These design regions can be high dimensional in which it becomes very difficult to identify and visualize clusters of promising designs. This leads to a lack of understanding of the design space. To enhance the designer’s knowledge of the design space, this work presents a method, based on spectral clustering, that can identify high performance regions in a high dimensional space. Furthermore, a method for visualizing each individual design region is presented that is accomplished by incorporating t-Distributed Stochastic Neighbor Embedding. Through the accomplishment of these three tasks—incorporating manufacturing variation, clustering, and visualizing—a novel design methodology will be developed which will then be applied to identify satisfactory designs for a negative stiffness metamaterials design problem which will be manufactured and tested.Mechanical Engineerin