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Simulation of cave hydrology using a conventional computer spreadsheet
Published in A. Kranjc, Tracer Hydrology 97, 2020
If the spreadsheet is to be used to simulate rainfall, a stream and associated water storages and transports, the data model is represented as a graph, i.e. a series of nodes connected by edges. Both nodes and edges are objects, i.e. entities which have both data and intelligence. In the hydrological simulation the nodes are reservoirs which store water, and these are indicated by circles. The edges are processes which distribute water in the percentage proportions (fixed or variable) given on the output edges, with some variable time delay. It is possible to have recursive edges, applicable for the reservoir nodes, where the contents of the reservoir in the next time period is partially a function of the contents of the same reservoir in the previous time period, and partially a function of other inputs. There will be outputs from the reservoirs even if there are no inputs, which explains why a stream continues to flow even if there has been no rainfall, getting most of its low-level flow from the storage reservoirs. The recursive edges are indicated by circular arcs with arrows, and each reservoir effectively has a memory of its own previous state with a delay of one time period in the current time granularity. A spreadsheet is normally used as a combinational machine, where cells are evaluated in a dendritic fashion, the outputs (cell evaluations) are a direct function of the inputs at a point in time (i.e. of other cell values), and no circular linkages are allowed (these produce the error message ‘Cannot resolve circular references’). But in the simulation described the spreadsheet has been used in a novel fashion as a sequential machine, with some time relationships. Since there may still be no circular linkages, successive rows of the spreadsheet are used for successive time periods in the current time granularity. Furthermore, the time relationships are not restricted to invariant formulae for the functions of previous variables, but may embody some logical tests which are evaluated continuously. This is just what is required for a simulation where the soil storage may become saturated, for example, new cave streams may be activated as flood levels increase, or the transmission or feedback parameters change with time.
Design of a polishing tool for collaborative robotics using minimum viable product approach
Published in International Journal of Computer Integrated Manufacturing, 2019
Carlos Perez-Vidal, Luis Gracia, Samuel Sanchez-Caballero, J. Ernesto Solanes, Alessandro Saccon, Josep Tornero
In particular, Figure. 8 shows the circular reference trajectory (thick line) and the real robot trajectory (thin line), where it can be seen that the robot tracks the circular reference trajectory while performing the polishing task except during the manoeuvring process of the human operator. In this sense, the bottom plot of Figure. 9 shows signal , which is related to the force exerted by the operator in the plane perpendicular to the robot end-effector, where it can be seen that the operator manoeuvring process starts at around 20s and ends at around 35s. Hence, when the operator starts pushing the robot handles at 20s the robot abandons the tracking of the circular reference trajectory and resumes it when the operator releases the handles at 35s.
Energy consumption in mine haulage due to road pavement performance
Published in Mining Technology, 2019
Jarrad Coffey, Hamid Nikraz, Colin Leek
Maintenance blading in both models is considered to return the pavement to a minimum theoretical roughness, which is represented by RGα in Equation (2) and RDSMIN in Equation (7). The minimum roughness is assumed to be 4.6 m/km in both models, based on advice in Paige-Green (1990), Paterson (1991), Archondo-Callao (1999), Tan et al (2011), Hore-Lacy et al (2015) and the results of measured haul road IRI in Coffey et al (2018). This assumption is necessary as the equations that calculate minimum roughness in the TRH-20 model need to be calibrated against test data, which is not available for this investigation. A routine to estimate the minimum roughness is included in the TRH-20 model, but utilization of this function creates a circular reference with Equation (2).
Predictor-based control design for UAVs: robust stability analysis and experimental results
Published in International Journal of Control, 2021
Next we consider a tracking problem with horizontal circular reference trajectory. The circle is centred about the origin at a height of 1 m and has a radius of 0.3 m. The desired speed is . The value of is the same as in the hover experiment. The results with and without with the predictor are shown in Figures 14 and 15. The trajectories are compared in 3D in Figure 16 where the proposed controller has a solid curve, the desired trajectory has a dashed curve, and the nominal control has a dotted curve. The proposed control is less oscillatory in behaviour, especially for motion in the vertical axis.