Abstract

Abstract
In this work, we study a mathematical model for a smart irrigation system, formulated as an optimal control problem and discretized and transcribed into a nonlinear programming problem using a fine mesh. In order to solve the resulting optimization problem, one needs to use Optimization solvers. Hence, we implemented the proposed mathematical model in AMPL and solved it using the IPOPT solver on the NEOS server (https://neos-server.org/neos/index.html). We also tested the model creating several scenarios. The numerical results shows that the mathematical model produces qualitatively good responses. Moreover the execution times are made in few seconds. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract

Abstract
Origin-destination matrices help understand the movement of people within cities. This work is built upon the premise that stakeholders, e.g. decision makers, need to analyze mobility flows from spatio-temporal perspectives that are appropriate to their context of analysis. The data retrieved from sensors and Intelligent Transportation Systems are useful for this purpose due to their lower acquisition costs and fine granularity, although it is complex to use such data in an integrated way, as they might have heterogeneous representations of spatio-temporal attributes and granularities. Most of the related works on the analysis of OD flows consider matrices with a fixed spatio-temporal aggregation level, and do not explore the intrinsic issue of data heterogeneity. Herein we report our findings on building the semantic foundation of knowledge-assisted visualization tools for analyzing OD matrices from multiple stakeholder levels. We propose a set of ontology design patterns for modeling the semantics of OD data, and the relations between the spatio-temporal constructs that stakeholders ought to choose when visualizing urban mobility flows. Our approach aims to be reusable by researchers and practitioners. We describe a practical implementation using estimated flows from smart card data from Porto, Portugal.

Abstract
Augmented and virtual reality have been experiencing rapid growth in recent years, but there is still no deep knowledge regarding their capabilities and in what fields they could be explored. In that sense, this paper presents a study on the accuracy and repeatability of Microsoft's HoloLens 2 (augmented reality device) and HTC Vive (virtual reality device) using an OptiTrack system as ground truth. For the HoloLens 2, the method used was hand tracking, whereas, in HTC Vive, the object tracked was the system's hand controller. A series of tests in different scenarios and situations were performed to explore what could influence the measures. The HTC Vive obtained results in the millimeter range, while the HoloLens 2 revealed not very accurate measurements (around 2 cm). Although the difference can seem to be considerable, the fact that HoloLens 2 was tracking the user's hand and not the system's controller made a huge impact. The results are considered a significant step for the ongoing project of developing a human-robot interface by demonstrating an industrial robot using extended reality, which shows great potential to succeed based on our data.