Automatic generation of human machine interface screens from component-based reconfigurable virtual manufacturing cell

Increasing complexity and decreasing time-tomarket require changes in the traditional way of building automation systems. The paper describes a novel approach to automatically generate the Human Machine Interface (HMI) screens for component-based manufacturing cells based on their corresponding virtual models. Manufacturing cells are first prototyped and commissioned within a virtual engineering environment to validate and optimise the control behaviour. A framework for reusing the embedded control information in the virtual models to automatically generate the HMI screens is proposed. Finally, for proof of concept, the proposed solution is implemented and tested on a test rig

Mathematical model of hybrid precast gravity frames for smart construction and engineering

© 2014 Seon-Chee Park et al. The structural stability, constructability, economic feasibility, environmental-friendliness, and energy efficiency of hybrid composite frame systems have been demonstrated by practical application and research. A hybrid composite frame system combines the economy of precast concrete structures with the constructability of steel frame structures, including erection speed. Novel composite frames will ultimately maximize the efficiency of structural design and facilitate construction. This paper presents hybrid precast frames, which are precast composite frames based on a simple connection between precast concrete columns and beams. The hybrid precast frames designed to resist gravity loading consist of PC columns, PC beams, and steel inserted in the precast members. Steel sections located between the precast columns were simply connected to steel inserted at each end of the precast beams. Dynamic analysis of a 15-story building designed with the proposed composite frame was performed to determine the dynamic characteristics of a building constructed of hybrid frames, including frequencies and mode shapes

Four Metaphors on Knowledge and Change in Construction

Refurbishment\ua0activities comprise a high proportion of construction industry output in most developed countries. There is no international consensus among statisticians as how to define\ua0refurbishment\ua0or renovation of buildings (Mansfield, 2002). The UK Office for National Statistics publishes data indicating that the volume of ‘repair and maintenance’ corresponded to about 60 per cent of new work during 2014 and 2015. ‘Repair and maintenance’ was roughly equally divided between housing and non-housing, and\ua0it\ua0is probable that much of this was\ua0refurbishment. There are no obvious reasons why ongoing investment in existing buildings should decline and the potential for increasing environmental sustainability by improving energy performance in the building stock remains considerable. The EU Energy Efficiency Directive (2012/27/EU) adopted in 2012 includes a requirement for member states to develop long-term renovation strategies for their national building stocks

The transformation of salinity variance : a new approach to quantifying the influence of straining and mixing on estuarine stratification

Author Posting. © American Meteorological Society, 2018. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 48 (2018): 607-623, doi:10.1175/JPO-D-17-0189.1.The roles of straining and dissipation in controlling stratification are derived analytically using a vertical salinity variance method. Stratification is produced by converting horizontal variance to vertical variance via straining, that is, differential advection of horizontal salinity gradients, and stratification is destroyed by the dissipation of vertical variance through turbulent mixing. A numerical model is applied to the Changjiang estuary in order to demonstrate the salinity variance balance and how it reveals the factors controlling stratification. The variance analysis reveals that dissipation reaches its maximum during spring tide in the Changjiang estuary, leading to the lowest stratification. Stratification increases from spring tide to neap tide because of the increasing excess of straining over dissipation. Throughout the spring–neap tidal cycle, straining is almost always larger than dissipation, indicating a net excess of production of vertical variance relative to dissipation. This excess is balanced on average by advection, which exports vertical variance out of the estuarine region into the plume. During neap tide, tidal straining shows a general tendency of destratification during the flood tide and restratification during ebb, consistent with the one-dimensional theory of tidal straining. During spring tide, however, positive straining occurs during flood because of the strong baroclinicity induced by the intensified horizontal salinity gradient. These results indicate that the salinity variance method provides a valuable approach for examining the spatial and temporal variability of stratification in estuaries and coastal environments.X. Li was supported by the China Scholarship Council. W. R. Geyer was supported by NSF Grants OCE 1736539 and OCE 1634480. J. Zhu was supported by the National Natural Science Foundation of China (41476077 and 41676083). H. Wu was supported by the National Natural Science Foundation of China (41576088 and 41776101).2018-09-0

Perception-aware receding horizon trajectory planning for multicopters with visual-inertial odometry

Visual inertial odometry (VIO) is widely used for the state estimation of multicopters, but it may function poorly in environments with few visual features or in overly aggressive flights. In this work, we propose a perception-aware collision avoidance trajectory planner for multicopters, that may be used with any feature-based VIO algorithm. Our approach is able to fly the vehicle to a goal position at fast speed, avoiding obstacles in an unknown stationary environment while achieving good VIO state estimation accuracy. The proposed planner samples a group of minimum jerk trajectories and finds collision-free trajectories among them, which are then evaluated based on their speed to the goal and perception quality. Both the motion blur of features and their locations are considered for the perception quality. Our novel consideration of the motion blur of features enables automatic adaptation of the trajectory's aggressiveness under environments with different light levels. The best trajectory from the evaluation is tracked by the vehicle and is updated in a receding horizon manner when new images are received from the camera. Only generic assumptions about the VIO are made, so that the planner may be used with various existing systems. The proposed method can run in real-time on a small embedded computer on board. We validated the effectiveness of our proposed approach through experiments in both indoor and outdoor environments. Compared to a perception-agnostic planner, the proposed planner kept more features in the camera's view and made the flight less aggressive, making the VIO more accurate. It also reduced VIO failures, which occurred for the perception-agnostic planner but not for the proposed planner. The ability of the proposed planner to fly through dense obstacles was also validated. The experiment video can be found at https://youtu.be/qO3LZIrpwtQ.Comment: 12 page

Design and control of a collision-resilient aerial vehicle with an icosahedron tensegrity structure

We present the tensegrity aerial vehicle, a design of collision-resilient rotor robots with icosahedron tensegrity structures. The tensegrity aerial vehicles can withstand high-speed impacts and resume operation after collisions. To guide the design process of these aerial vehicles, we propose a model-based methodology that predicts the stresses in the structure with a dynamics simulation and selects components that can withstand the predicted stresses. Meanwhile, an autonomous re-orientation controller is created to help the tensegrity aerial vehicles resume flight after collisions. The re-orientation controller can rotate the vehicles from arbitrary orientations on the ground to ones easy for takeoff. With collision resilience and re-orientation ability, the tensegrity aerial vehicles can operate in cluttered environments without complex collision-avoidance strategies. Moreover, by adopting an inertial navigation strategy of replacing flight with short hops to mitigate the growth of state estimation error, the tensegrity aerial vehicles can conduct short-range operations without external sensors. These capabilities are validated by a test of an experimental tensegrity aerial vehicle operating with only onboard inertial sensors in a previously-unknown forest.Comment: 12 pages, 16 figure

Optimum Tower Crane Selection and Supporting Design Management

To optimize tower crane selection and supporting design, lifting requirements (as well as stability) should be examined, followed by a review of economic feasibility. However, construction engineers establish plans based on data provided by equipment suppliers since there are no tools with which to thoroughly examine a support design’s suitability for various crane types, and such plans lack the necessary supporting data. In such cases it is impossible to optimize a tower crane selection to satisfy lifting requirements in terms of cost, and to perform lateral support and foundation design. Thus, this study is intended to develop an optimum tower crane selection and supporting design management method based on stability. All cases that are capable of generating an optimization of approximately 3,000 ~ 15,000 times are calculated to identify the candidate cranes with minimized cost, which are examined. The optimization method developed in the study is expected to support engineers in determining the optimum lifting equipment management

A Zero-Shot Adaptive Quadcopter Controller

This paper proposes a universal adaptive controller for quadcopters, which can be deployed zero-shot to quadcopters of very different mass, arm lengths and motor constants, and also shows rapid adaptation to unknown disturbances during runtime. The core algorithmic idea is to learn a single policy that can adapt online at test time not only to the disturbances applied to the drone, but also to the robot dynamics and hardware in the same framework. We achieve this by training a neural network to estimate a latent representation of the robot and environment parameters, which is used to condition the behaviour of the controller, also represented as a neural network. We train both networks exclusively in simulation with the goal of flying the quadcopters to goal positions and avoiding crashes to the ground. We directly deploy the same controller trained in the simulation without any modifications on two quadcopters with differences in mass, inertia, and maximum motor speed of up to 4 times. In addition, we show rapid adaptation to sudden and large disturbances (up to 35.7%) in the mass and inertia of the quadcopters. We perform an extensive evaluation in both simulation and the physical world, where we outperform a state-of-the-art learning-based adaptive controller and a traditional PID controller specifically tuned to each platform individually. Video results can be found at https://dz298.github.io/universal-drone-controller/.Comment: Video results can be found on the project webpage https://dz298.github.io/universal-drone-controller