Creativity is required in engineering design. It is required in the aspects of problem-solving - conceptualizing a new solution to a problem, and problem-exploring - conceptualizing a new problem. Studies show that, in both aspects, creativity is a difficult task in practice. The aim of this study is to support the engineering design community by easing the difficulty in the problem-exploring practice. To achieve this, a computational problem-exploring (CPE) model is developed to mimic how design engineers identify a valid design problem. Consequently, a CPE tool - Pro-Explora V1 is developed based on the CPE model. The CPE model consists of a synergy of emergent computational technologies including data retrieval and machine learning. A Markovian model is employed in the CPE model to enable a data-driven random process for exploring design problems. In pilot test, Pro-Explora V1 generated some engineering design-related problems which are meaningful, unique, and could not be distinguished from naturally generated ones. It provides support to design engineers in problem-exploring at the early stage in engineering design. This study contributes to the global effort towards data-driven processes in the fourth industrial revolution.

A work cell with multiple robots increases manufacturing flexibility and productivity. Robots in the work cell equipped in a sparse area usually share a motion path, resources and workspace. In this paper, an embedded Markov chain model for a multi-robot system that has a common workspace is constructed on the basis of the concept of a multi-processor system. With the presented model, we measure the performance of error recovery schemes under different workloads and analyze the sensitivity of the execution time with respect to the robot speed. We verify the presented model with an experimental multi-robot work cell. This study is useful in evaluating the performance of a robotic work cell and presents a guide for designing a complex work cell.

We introduce a continuous-time Markov chain model for the evolution of microsatellites, simple sequence repeats in DNA. We prove the existence of a unique stationary distribution for our model, and fit the model to data from approximately 106 base pairs of DNA from fruit flies, mice, and humans. The slippage rates from the best fit for our model are consistent with experimental findings.

We consider a fixed size Markov chain model which suffers losses and admits controlled recruitment. The family of n-step maintainable structures is described geometrically and examined.