Development of a novel virtual coordinate measuring machine

Existing VCMMs (virtual coordinate measuring machine) have been mainly developed to either simulate the measurement process hence enabling the off-line programming, or to perform error analysis and uncertainty evaluation. Their capability and performance could be greatly improved if there is a complete solution to cover the whole process and provide an integrated environment. The aim of this study is to develop such a VCMM that not only supports measurement process simulation, but also performs uncertainty evaluation. It makes use of virtual reality techniques to provide an accurate model of a physical CMM, together with uncertainty evaluation. An interface is also provided to communicate with CMM controller, allowing the measuring programs generated and simulated in the VCMM to be executed or tested on the physical CMM afterwards. This paper discusses the proposal of a novel VCMM design and the preliminary results

Caching and Auditing in the RPPM Model

Crampton and Sellwood recently introduced a variant of relationship-based access control based on the concepts of relationships, paths and principal matching, to which we will refer as the RPPM model. In this paper, we show that the RPPM model can be extended to provide support for caching of authorization decisions and enforcement of separation of duty policies. We show that these extensions are natural and powerful. Indeed, caching provides far greater advantages in RPPM than it does in most other access control models and we are able to support a wide range of separation of duty policies.Comment: Accepted for publication at STM 2014 (without proofs, which are included in this longer version

Optimal Charging of Electric Vehicles in Smart Grid: Characterization and Valley-Filling Algorithms

Electric vehicles (EVs) offer an attractive long-term solution to reduce the dependence on fossil fuel and greenhouse gas emission. However, a fleet of EVs with different EV battery charging rate constraints, that is distributed across a smart power grid network requires a coordinated charging schedule to minimize the power generation and EV charging costs. In this paper, we study a joint optimal power flow (OPF) and EV charging problem that augments the OPF problem with charging EVs over time. While the OPF problem is generally nonconvex and nonsmooth, it is shown recently that the OPF problem can be solved optimally for most practical power networks using its convex dual problem. Building on this zero duality gap result, we study a nested optimization approach to decompose the joint OPF and EV charging problem. We characterize the optimal offline EV charging schedule to be a valley-filling profile, which allows us to develop an optimal offline algorithm with computational complexity that is significantly lower than centralized interior point solvers. Furthermore, we propose a decentralized online algorithm that dynamically tracks the valley-filling profile. Our algorithms are evaluated on the IEEE 14 bus system, and the simulations show that the online algorithm performs almost near optimality ($<1$ relative difference from the offline optimal solution) under different settings.Comment: This paper is temporarily withdrawn in preparation for journal submissio