Ontology-based similarity for product information retrieval

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved “by using or adapting solutions to old problems.” In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most non-numeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems.     

Distributed predictive control of continuous-time systems

In the last years focus has been put in the development of distributed Model Predictive Control (MPC) algorithms. With a few exceptions, they have been mostly developed in the discrete-time framework. However, discretization of large-scale systems may destroy the sparsity of the original continuous-time models, making distributed control design and implementation more difficult. Also, more in general, discrete-time control of continuous-time systems does not allow to consider the process inter-sampling behavior. In this paper we present a novel non-cooperative distributed predictive control algorithm for continuous-time systems based on robust MPC concepts. The convergence properties of the proposed control scheme are stated, and its realizability is tested through a simulation case study.     

A distributed solution to the network reconstruction problem

It has been recently shown in Ren et al. (2010) that by collecting noise-contaminated time series generated by a coupled-oscillator system at each node of a network, it is possible to robustly reconstruct its topology, i.e. determine the graph Laplacian. Restricting ourselves to linear consensus dynamics over undirected communication networks, in this paper we introduce a new dynamic average consensus least-squares algorithm to locally estimate these time series at each node, thus making the reconstruction process fully distributed and more easily applicable in the real world. We also propose a novel efficient method for separating the off-diagonal entries of the reconstructed Laplacian, and examine several concepts related to the trace of the dynamic correlation matrix of the coupled single integrators, which is a distinctive element of our network reconstruction method. The theory is illustrated with examples from computer, power and transportation systems.     

Employing early model-based safety evaluation to iteratively derive E/E architecture design

ISO 26262 addresses development of safe in-vehicle functions by specifying methods potentially used in the design and development lifecycle. It does not indicate what is sufficient and leaves room for interpretation. Yet the architects of electric/electronic systems need design boundaries to make decisions during architecture evolutionary design without adding a risk of late changes. Correct selection of safety mechanisms from alternatives at early design stages is vital for time-to-market of critical systems. In this paper we present and discuss an iterative architecture design and refinement process that is centered around ISO 26262 requirements and model-based analysis of safety-related metrics. This process simplifies identification of the most sensitive parts of the architecture, selection of the best suitable safety mechanisms to reduce thereby failure rate on the system level and improve the metrics defined by the standard. To support the defined process we present the metamodels that can be integrated with existing DSL (domain-specific language) frameworks to extend them with information supporting further extraction of fault propagation behavior. We provide a framework for architecture model analysis and selection of safety mechanisms. We provide details on the model-based toolset that has been developed to support the proposed analysis and synthesis methods, and demonstrate its application to analysis of a steer-by-wire system model and selection of safety mechanisms for it.     

Optimal capacity management and planning in services delivery centers

This paper considers human server systems of queues that arise within the information technology services industry. We develop a two-phase stochastic optimization solution approach to effectively and efficiently address the capacity management and planning processes of information technology services delivery centers. A large collection of numerical experiments of real-world human server system environments investigates various issues of both theoretical and practical interest, quantifying the significant benefits of our approach as well as evaluating the financial-performance trade-offs often encountered in practice.     

Sets of Globally Optimal Stream Surfaces for Flow Visualization

Stream surfaces are a well‐studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in time‐consuming trial and error procedures. Only recently automatic selection methods were proposed. Local methods support the selection of a set of stream surfaces, but, contrary to global selection methods, they evaluate only the quality of the seeding lines but not the quality of the whole stream surfaces. Global methods, on the other hand, only support the selection of a single optimal stream surface until now. However, for certain flow fields a single stream surface is not sufficient to represent all flow features. In our work, we overcome this limitation by introducing a global selection technique for a set of stream surfaces. All selected surfaces optimize global stream surface quality measures and are guaranteed to be mutually distant, such that they can convey different flow features. Our approach is an efficient extension of the most recent global selection method for single stream surfaces. We illustrate its effectiveness on a number of analytical and simulated flow fields and analyze the quality of the results in a user study.