Improving communication in design: Recommendations from the literature

Communication permeates every aspect of an engineer's work-from clarifying product specifications to shaping social ties. This paper offers an overview of recommendations from literature to improve communication within and among engineering teams. We assume communication problems are often the outcome of underlying factors and that it is fruitful to study and improve these influences. Having been empirically elicited in prior research, 24 factors considered in this paper include, e.g., availability of information about product specifications, roles and responsibilities, and overview of sequence of tasks. To improve these factors in order to enable effective communication, this paper collates more than hundred recommendations from journal articles and textbooks published in the fields of engineering design, management science, sociology, and psychology. Recommendations include, for example, identify priorities through risk and bottleneck analysis, give clear descriptions and role expectations, and employ effective process modeling tools. Contributions of this paper are a list of recommendations for industry practitioners and an effort-benefit evaluation of individual recommendations. Copyright © 2002-2012 The Design Society. All rights reserved.

Information models used to manage engineering change: A review of the literature 2005-2010

Engineering change is a significant part of any product development programme. Changes can arise at many points throughout the product life-cycle, resulting in rework which can ripple through different stages of the design process. Managing change processes is thus a critical aspect of any design project, especially in complex design. Through a literature review, this paper shows the diversity of information models used by different change management methods proposed in the literature. A classification framework for organising these change management approaches is presented. The review shows an increase in the number of cross-domain models proposed to help manage changes.

State estimation schemes for independent component coupled hidden Markov models

Conventional Hidden Markov models generally consist of a Markov chain observed through a linear map corrupted by additive noise. This general class of model has enjoyed a huge and diverse range of applications, for example, speech processing, biomedical signal processing and more recently quantitative finance. However, a lesser known extension of this general class of model is the so-called Factorial Hidden Markov Model (FHMM). FHMMs also have diverse applications, notably in machine learning, artificial intelligence and speech recognition [13, 17]. FHMMs extend the usual class of HMMs, by supposing the partially observed state process is a finite collection of distinct Markov chains, either statistically independent or dependent. There is also considerable current activity in applying collections of partially observed Markov chains to complex action recognition problems, see, for example, [6]. In this article we consider the Maximum Likelihood (ML) parameter estimation problem for FHMMs. Much of the extant literature concerning this problem presents parameter estimation schemes based on full data log-likelihood EM algorithms. This approach can be slow to converge and often imposes heavy demands on computer memory. The latter point is particularly relevant for the class of FHMMs where state space dimensions are relatively large. The contribution in this article is to develop new recursive formulae for a filter-based EM algorithm that can be implemented online. Our new formulae are equivalent ML estimators, however, these formulae are purely recursive and so, significantly reduce numerical complexity and memory requirements. A computer simulation is included to demonstrate the performance of our results. © Taylor & Francis Group, LLC.