Gaussian mixture model for robust design optimization of planar steel frames

A new method is presented for an application of the Gaussian mixture model (GMM) to a multi-objective robust design optimization (RDO) of planar steel frame structures under aleatory (stochastic) uncertainty in material properties, external loads, and discrete design variables. Uncertainty in the discrete design variables is modeled in the wide range between the smallest and largest values in the catalog of the cross-sectional areas. A weighted sum of Gaussians is statistically trained based on the sampled training data to capture an underlying joint probability distribution function (PDF) of random input variables and the corresponding structural response. A simple regression function for predicting the structural response can be found by extracting the information from a conditional PDF, which is directly derived from the captured joint PDF. A multi-objective RDO problem is formulated with three objective functions, namely, the total mass of the structure, and the mean and variance values of the maximum inter-story drift under some constraints on design strength and serviceability requirements. The optimization problem is solved using a multi-objective genetic algorithm utilizing the trained GMM for calculating the statistical values of objective and constraint functions to obtain Pareto-optimal solutions. Since the three objective functions are highly conflicting, the best trade-off solution is desired and found from the obtained Pareto-optimal solutions by performing fuzzy-based compromise programming. The robustness and feasibility of the proposed method for finding the RDO of planar steel frame structures with discrete variables are demonstrated through two design examples

Design concept development in transportation design

The paper presents results of a study about design concept development in transportation design. The main question of this study concerns mainly the existence and development of design concepts and its status in the design process furthermore it partially describes its content, manifestation and function. From the view of industrial psychology, the design concept is one of the most important stages in the design process, because its availability determines the success, regarding the design object. A design concept can be understood as the first solid and focused unit of knowledge in design processes with ill-defined problems. In the Design Process Planning, based on Action Regulation Theory, design concepts act as a compact guiding principle, including the anticipation of the artefact. Using this as a scientific basis a long term study with 25 students including cross section and longitudinal aspects were held from 2005 to 2008. Three concept types derived from the literature preceded the investigation, whereby the holistic experience-oriented one after Uhlmann (2006a) forms the beginning. This focus was confirmed within the investigation for the majority of the projects, yet one must assume that, functional (construction-oriented) or formal concepts successfully finds to application. Holistic concepts enable a more comprehensive and more balanced treatment within the design process. Within the work two general methods of generating design concepts: extracting and compiling were defined. Following the typical processes they can be assigned to different fields: transportation design (extracting) and industrial design (compiling). Furthermore three designer types and an open category could be identified. The three types “automobile”, “design” and “story” can be clearly and consistently assigned by the students. The research closes with a recommendation of a hybrid design concept processing using aspects of the two generating methods as well as instruments of different designer types. Keywords: Design Concept, Transportation Design, Field study, Early stages</p

Planets: Integrated Services for Digital Preservation

The Planets Project is developing services and technology to address core challenges in digital preservation. This article introduces the motivation for this work, describes the extensible technical architecture and places the Planets approach into the context of the Open Archival Information System (OAIS) Reference Model. It also provides a scenario demonstrating Planets’ usefulness in solving real-life digital preservation problems and an overview of the project’s progress to date

Automating Metadata Extraction: Genre Classification

A problem that frequently arises in the management and integration of scientific data is the lack of context and semantics that would link data encoded in disparate ways. To bridge the discrepancy, it often helps to mine scientific texts to aid the understanding of the database. Mining relevant text can be significantly aided by the availability of descriptive and semantic metadata. The Digital Curation Centre (DCC) has undertaken research to automate the extraction of metadata from documents in PDF([22]). Documents may include scientific journal papers, lab notes or even emails. We suggest genre classification as a first step toward automating metadata extraction. The classification method will be built on looking at the documents from five directions; as an object of specific visual format, a layout of strings with characteristic grammar, an object with stylo-metric signatures, an object with meaning and purpose, and an object linked to previously classified objects and external sources. Some results of experiments in relation to the first two directions are described here; they are meant to be indicative of the promise underlying this multi-faceted approach.

Intelligent Management and Efficient Operation of Big Data

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources, the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services, and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201