Multidimensional Design Space Research Articles

Iteratively constructive sequential design of experiments and surveys with nonlinear parameter‐data relationships

In experimental design, the main aim is to minimize postexperimental uncertainty on parameters by maximizing relevant information collected in a data set. Using an entropy‐based method constructed on a Bayesian framework, it is possible to design experiments for highly nonlinear problems. However, the method is computationally infeasible for design spaces with even a few dimensions. We introduce an iteratively constructive method that reduces the computational demand by introducing one new datum at a time for the design. The method reduces the multidimensional design space to a single‐dimensional space at each iteration by fixing the experimental setup of the previous iteration. Both a synthetic experiment using a highly nonlinear parameter‐data relationship and a seismic amplitude versus offset (AVO) experiment are used to illustrate that the results produced by the iteratively constructive method closely match the results of a global design method at a fraction of the computational cost. This work thus extends the class of iterative design methods to nonlinear problems and makes fully nonlinear design methods applicable to higher dimensional real‐world problems.

Optimization of a pipe end upsetting process

This paper discusses the application of Design of Experiments, Response Surface Methodology and Robust Optimization techniques for efficient optimization of pipe end upsetting with a non-uniform and transient temperature field. First, target pipe end shape and objective functions are defined, and constraints on the final shape, forging pressures, temperatures and strain rates are specified. Second, parametric initial work piece shape and initial temperature distributions are defined. Material data are obtained from independent compression tests. Third, a Box-Behnken experimental design (BBD) is defined for a set of shape and temperature variables. A set of ABAQUS simulations is run according to the BBD. The results from the simulation runs are processed by a MATLAB-script. The script calculates regression relations for all essential responses and estimates the value of the object functions and constraints in the entire design space. Further, it performs both standard and robust constrained optimization. The robust optimization considers the effects of variability in design variables. Finally, MATLAB plots several 2D cross-sections of objective and constraints functions in the multi-dimensional design space. Such process windows make prediction of the capability of the upsetting process possible and are very valuable input to the design and quality assurance work.

Basic design of a series propeller with vibration consideration by genetic algorithm

Genetic algorithms (GAs) can powerfully search for parameters in a large multidimensional design space. Thus, the principle can be applied to preliminary series propeller design problems with multiple considerations. In the present study, B-series propeller design was conducted using a GA for both hydrodynamic efficiency and vibration consideration. The objective function was set by users who could freely weight the relative importance of efficiency and vibration. GAs were successfully shown to be able to obtain an optimal set of parameters leading to efficient performance and low vibration.

Multi-parametric optimisation of the modular computer architecture

The paper describes an approach that allows optimisation of computing architecture selection in case of multi-parametric requirements. The proposed method is oriented to systems combined from modular components. The approach is based on decomposition of a multi-dimensional design space into multiple two-dimensional subdesign spaces. The paper presents the procedure that drastically reduces the number of architectural variants to be analysed. This has become possible by the presentation of each subdesign space in a form of a tree, hierarchical arrangement, and further pruning of subdesign spaces. The proposed approach is applicable to any system architecture based on modules with known performance parameters. As a result, the proposed method has allowed selection of the architecture, which satisfies multiple performance constrains (e.g., performance, cost, power consumption, reliability, etc.), in a small time range. The implementation of the method for the architectural optimisation of the reconfigurable data-stream processors, which are based on the Field Programmable Gate Array (FPGA) devices, is also discussed.

Parasitic-Aware RF Circuit Design and Optimization

RF circuit synthesis techniques based on particle swarm optimization and adaptive simulated annealing with tunneling are described, and comparisons of parasitic-aware designs of an RF distributed amplifier and a nonlinear power amplifier are presented. Synthesized in 0.35-/spl mu/m digital CMOS using a single 3.3-V power supply, the designs provide an 8-dB gain and 8-GHz bandwidth for a four-stage distributed amplifier, and 1.2-W output power with 55% drain efficiency at 900 MHz for a three-stage power amplifier. A standard circuit simulator, HSPICE or SPECTRE, embedded in an optimization loop is used to evaluate cost functions. The proposed design and optimization methodology is computationally efficient and robust in searching complex multidimensional design spaces.

The early development of the steam engine: an evolutionary interpretation using complexity theory

In this paper we analyse the early development of the steam engine as a search process in a multidimensional design space. This perspective allows us to make use of recent insights coming from complex systems theory, in particular, of a generalized version of Kauffman's NK-model. We analyse yearly distributions of steam engine designs and their sectors of application for the period 1760–1800. We interpret the patterns of variety and differentiation characterizing the behaviour of these distributions as emerging properties of underlying search processes unfolding in the design space. We conclude that the early development of steam power technology can be understood as a process of ‘technological speciation’ of various engine designs in distinct users' niches.

Towards a tool for predicting speech functionality

In these days of multimodal systems and interfaces, many research teams are investigating the purposes for which novel combinations of modalities can be used. It is easy to forget that we still lack solid foundations for evaluating the functionality of individual families of input–output modalities, such as the speech modalities. The reason why these foundations are missing is the complexity of the problem. Based on the study of particular applications, empirical investigations of speech functionality address points in a vast multi-dimensional design space. At best, solid findings yield low-level generalisations which can be used by designers developing almost identical applications. Furthermore, the conceptual and theoretical apparatus needed to describe these findings in a principled way is largely missing. This paper argues that a shift in perspective can help address issues of modality choice both scientifically and in design practice. Instead of empirically focusing on fragments of the virtually infinite combinatorics of tasks, environments, performance parameters, user groups, cognitive properties, etc., the problem of modality functionality is addressed as a problem of choosing between modalities which have very different properties with respect to the representation and exchange of information between user and system. Based on a study of 120 claims on speech functionality from the literature, it is shown that a small set of modality properties are surprisingly powerful in justifying, supporting and correcting the claims set. The paper analyses why modality properties can be used for these purposes and argues that their power could be made available to systems and interface designers who have to make modality choices during early design of speech-related systems and interfaces. Using hypertext, it is illustrated how this power may be harnessed for the purpose of predictively supporting speech modality choice during early systems and interface design.

A comparison of analog and digital circuit implementations of low power matched filters for use in portable wireless communication terminals

In this paper, analog and digital circuit realizations of a parallel programmable matched filter are examined. Through wide variations of the design space parameters, the general trend that is observed is that short, fast circuits tend to favor an analog implementation, while longer, slower circuits make a digital implementation more appropriate. A methodology is provided for choosing the preferable circuit-implementing technology when power consumption-as a function of data precision, filter length, operating frequency, technology scaling, and the maturity of the fabrication process-is used as the primary metric of comparison. It is shown that neither the analog nor the digital matched filter implementation is universally more power efficient than the other. Rather, a surface is mapped in the multidimensional design space where, on one side of this surface, a digital solution is preferable, while on the other side of the surface, an analog circuit is appropriate. Equations are given which delineate the position of this transitional surface in terms of the design space parameters, and example calculations and plots depicting the regions of dominance for the digital and analog matched filters for specific process and system parameters are presented.

Mixed analog/digital hardware synthesis of artificial neural networks

A methodology for automated synthesis of mixed analog/digital hardware architectures for Artificial Neural Network (ANN) applications is presented, The ANN system is input in the form of a Data Flow Graph (DFG) of operators. The output is a high-level interconnection description of mixed analog/digital building block circuits that is generated by searching a multidimensional design space to satisfy the throughput requirements and minimize an area-time cost function. A combination of quantitative analysis and behavioral modeling techniques tackles the problems of hardware nonidealities by screening suitable analog circuits. Digital hardware is employed when no analog circuit is found that satisfies the performance requirements of any operation. A heuristic partitioning scheme reduces the interfaces between blocks of circuits operating in analog and digital modes. The parallelism in ANN systems is, handled by a new scheduling and allocating procedure that selectively sequentializes groups of operations with the goal of iteratively improving an area-time cost function. The synthesizer is also capable of embedding hardware units that are intended to operate asynchronously. The synthesis methodology is illustrated with two design examples.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Optimized configurations for actively shielded magnetic resonance imaging magnets

A unique design approach based on the multistage Monte Carlo method is suggested. It allows for a thorough search for optimized designs within the bounds of the multidimensional design space created by the competing requirements and imposed spatial restrictions. This design space is shown to contain a multiplicity of different coil configurations, each processing a sharply defined minimum in the resulting stray field level, field inhomogeneity, or amount of conductor. Optimization techniques that depend on local gradients would tend, in such a case, to be trapped in the many local minima and fail to locate a global minimum. Several examples are discussed to highlight the advantages of the proposed design approach.