Department of Mechanical Engineering

In cloud manufacturing systems, the multi-granularity of service resource and design task models leads to the complexity of cloud service matching. In order to satisfy the preference of resource requesters for large-granularity service resources, we propose a multistage cloud-service matching strategy to solve the problem of matching tasks and resources with different granularity sizes. First, a multistage cloud-service matching framework is proposed, and the basic strategy of matching tasks with cloud services is planned. Then, the context-aware task-ontology modeling method is studied, and a context-related task-ontology model is established. Thirdly, a process-decomposition method of design tasks is studied, and the product development process with small granularity tasks is established. Fourthly, a matching strategy of ontology tasks and cloud services is studied, and the preliminary matching is accomplished. Finally, intelligent optimization is carried out, and the optimal cloud service composition is found with the optimal design period as the objective function. With the help of the preceding method, the service matching of maximizing the task granularity is realized on the premise of ensuring the matching success rate, which meets the preference of resource requesters for large-granularity service resources.

Nitrogen-vacancy (NV^-) centers in nanodiamonds have emerged as a versatile platform for a wide range of applications, including bioimaging, photonics, and quantum sensing. However, the widespread adoption of nanodiamonds in practical applications has been hindered by the challenges associated with patterning them into high-resolution features with sufficient throughput. In this work, we overcome these limitations by introducing a direct laser-writing bubble printing technique that enables the precise fabrication of two-dimensional nanodiamond patterns. The printed nanodiamonds exhibit a high packing density and strong photoluminescence emission, as well as robust optically detected magnetic resonance (ODMR) signals. We further harness the spatially resolved ODMR of the nanodiamond patterns to demonstrate the mapping of two-dimensional temperature gradients using high frame rate widefield lock-in fluorescence imaging. This capability paves the way for integrating nanodiamond-based quantum sensors into practical devices and systems, opening new possibilities for applications involving high-resolution thermal imaging and biosensing.

System current state estimation (or condition monitoring) and future state prediction (or failure prognostics) constitute the core elements of condition-based maintenance programs. For complex systems whose internal state variables are either inaccessible to sensors or hard to measure under normal operational conditions, inference has to be made from indirect measurements using approaches such as Bayesian learning. In recent years, the auxiliary particle filter (APF) has gained popularity in Bayesian state estimation; the APF technique, however, has some potential limitations in real-world applications. For example, the diversity of the particles may deteriorate when the process noise is small, and the variance of the importance weights could become extremely large when the likelihood varies dramatically over the prior. To tackle these problems, a regularized auxiliary particle filter (RAPF) is developed in this paper for system state estimation and forecasting. This RAPF aims to improve the performance of the APF through two innovative steps: (1)regularize the approximating empirical density and redraw samples from a continuous distribution so as to diversify the particles; and (2)smooth out the rather diffused proposals by a rejection/resampling approach so as to improve the robustness of particle filtering. The effectiveness of the proposed RAPF technique is evaluated through simulations of a nonlinear/non-Gaussian benchmark model for state estimation. It is also implemented for a real application in the remaining useful life (RUL) prediction of lithium-ion batteries. © 2011 IOP Publishing Ltd.