Enabling Self-aware Smart Buildings by Augmented Reality

Conventional HVAC control systems are usually incognizant of the physical structures and materials of buildings. These systems merely follow pre-set HVAC control logic based on abstract building thermal response models, which are rough approximations to true physical models, ignoring dynamic spatial variations in built environments. To enable more accurate and responsive HVAC control, this paper introduces the notion of "self-aware" smart buildings, such that buildings are able to explicitly construct physical models of themselves (e.g., incorporating building structures and materials, and thermal flow dynamics). The question is how to enable self-aware buildings that automatically acquire dynamic knowledge of themselves. This paper presents a novel approach using "augmented reality". The extensive user-environment interactions in augmented reality not only can provide intuitive user interfaces for building systems, but also can capture the physical structures and possibly materials of buildings accurately to enable real-time building simulation and control. This paper presents a building system prototype incorporating augmented reality, and discusses its applications.Comment: This paper appears in ACM International Conference on Future Energy Systems (e-Energy), 201

Colloquium: Trapped ions as quantum bits -- essential numerical tools

Trapped, laser-cooled atoms and ions are quantum systems which can be experimentally controlled with an as yet unmatched degree of precision. Due to the control of the motion and the internal degrees of freedom, these quantum systems can be adequately described by a well known Hamiltonian. In this colloquium, we present powerful numerical tools for the optimization of the external control of the motional and internal states of trapped neutral atoms, explicitly applied to the case of trapped laser-cooled ions in a segmented ion-trap. We then delve into solving inverse problems, when optimizing trapping potentials for ions. Our presentation is complemented by a quantum mechanical treatment of the wavepacket dynamics of a trapped ion. Efficient numerical solvers for both time-independent and time-dependent problems are provided. Shaping the motional wavefunctions and optimizing a quantum gate is realized by the application of quantum optimal control techniques. The numerical methods presented can also be used to gain an intuitive understanding of quantum experiments with trapped ions by performing virtual simulated experiments on a personal computer. Code and executables are supplied as supplementary online material (http://kilian-singer.de/ent).Comment: accepted for publication in Review of Modern Physics 201

Reference trajectory modification based on spatial iterative learning for contour control of 2-axis NC systems

Contour error is a main factor that affects the quality of products in numerical control (NC) machining. This paper presents a contour control strategy based on digital curves for high-precision control of computer numerical control (CNC) machines. A contour error estimation algorithm is presented for digital curves based on a geometrical method. The dynamic model of the motion control system is transformed from time domain to space domain because the contour error is dependent on space instead of time. Spatial iterative learning control (sILC) is developed to reduce the contour error, by modifying the reference trajectory in the form of G code. This allows system improvement without interference of low-level controllers so it is applicable to many commercial controllers where interpolators and feed-drive controllers cannot be altered. The effectiveness of this method is verified by experiments on a NC machine, which have shown good performance not only for smooth trajectories but also for large curvature trajectories