Real-Time Identification of Artifacts: Synthetic Data for AI Model

The collections represent the constitutive element and the raison d'être of each museum. Their management , care and dissemination are therefore a task of primary importance for every museum. Applying new Artificial Intelligence technologies in this area could lead to new initiatives. However, the development of certain tools requires structured and labeled datasets for the training phases which are not always easily available. The proposed contribution is within the domain of the construction of specific datasets with low budget tools and explores the results of a first step in this direction by testing algorithms for the recognition and labeling of heritage objects. The developed workflow is part of a first prototype that could be used both in heritage dissemination or gamification applications, and for use in heritage research tools

Coping with power outages in mobile networks

In a foreseen "post-peak" era, when oil production declines making energy price increase and leading to important economical implications descending from oil being the foundation of our industrial system, the development and maintenance of large infrastructures will become the more and more difficult due to possible power distribution outages and instabilities. The situation for communication infrastructures will probably be particularly critical due to the expected growth of service demand which will require the expansion of the networks and, corresponding, a growth of the power needs that could become difficult to continuously guarantee. Hence, critical infrastructures like communication networks will have to be equipped with power backup systems. In this paper, we focus on the effect of possible power outages on heterogeneous mobile networks. Starting from the characterisation of today outages, we assess the impact of outages on Quality of Service (QoS) of Radio Access Networks (RANs) and we discuss power backup system sizing

Tools development to optimize the use of micro-drones for architectural cultural heritage survey

In view of the increasingly widespread use of inoffensive UAS for photogrammetric acquisitions in the architectural and infrastructural spheres, there is a need to be able to program flight missions suited to the operator’s needs. This contribution presents the results of two experiments conducted by the research group. The first proposed procedure, based on low-cost instrumentation and algorithms in a VPL environment, fills the gap of proprietary applications and allows the coding and customisation of flight missions for photogrammetry. Obtaining this information is not always easy; immovable or unforeseen obstacles lead to lengthy post-production of the photogrammetric cloud to remove them. The second procedure, by constructing an object segmentation framework, fills this gap by automatically processing photogrammetric images by recreating masks that remove unwanted objects from the dense cloud calculation. Despite some shortcomings, the results are promising and manage to make up for these shortcomings, at least in part

Photogrammetric Survey for a Fast Construction of Synthetic Dataset

In this work we show how Physically Based Rendering (PBR) tools can be used to extend the training image datasets of Machine Learning (ML) algorithms for the recognition of built heritage. In the field of heritage valorization, the combination of Artificial Intelligence (AI) and Augmented Reality (AR) has allowed to recognize built heritage elements with mobile devices, anchoring digital products to the physical environment in real time, thus making the access to information related to real space more intuitive and effective. However, the availability of training data required for these systems is extremely limited and a large–scale image dataset is required to achieve accurate results in image recognition. Manually collecting and annotating images can be very resource and time–consuming. In this contribution we explore the use of PBR tools as a viable alternative to supplement an otherwise inadequate dataset

IoT and BIM Interoperability: Digital Twins in Museum Collections

According to the 2017 International Council of Museums (ICOM) guidelines, data on museum collections must be stored in a secure environment, supported by backup systems that allow access by all legitimate users, complete and unique identification, and description (associations, provenance, condition, treatment and current location) of each object are required. Concerning these indications, it is therefore, a priority to establish precise protocols for the preventive conservation and analysis of data concerning not only the identity of the asset or the information collected during its study, but also how it is preserved. This paper proposes a digital framework for the management of museum structures and collections, integrating Building Information Modelling (BIM) methodologies for the preservation and visualization of data with Internet of Things (IoT) methodologies for its collection and analysis

DEcay Classification using Artificial Intelligence

The paper presents DECAI - DEcay Classification using Artificial Intelligence, a novel study using machine learning algorithms to identify materials, degradations or surface gaps of an architectural artefact in a semi-automatic way. A customised software has been developed to allow the operator to choose which categories of materials to classify, and selecting sample data from an orthophoto of the artefact to train the machine learning algorithms. Thanks to Visual Programming Language algorithms, the classification results are directly imported into the H-BIM environment and used to enrich the H-BIM model of the artefact. To date, the developed tool is dedicated to research use only; future developments will improve the graphical interface to make this tool accessible to a wider public

AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems

3D Outputs for an Archeological Site: The Priene Theater

This paper critically analyzes some of the results of the International Summer School Priene—Architecture and Archaeology. Survey, Documentation and Design at Priene. The archaeological site of Priene was included in 2018, by Turkey’s delegation to UNESCO, within the provisional list of properties considered cultural and/or natural heritage of outstanding universal value; thus eligible for inclusion in the World Heritage List. The subject of the study concerns the theater, one of the most relevant areas of the archaeological site. A photogrammetric survey of the theater’s area was done during the educational experience. The paper presents a workflow from the initial input phase, of digital acquisition, to the output phase, of data restitution by creating a three-dimensional model. The resulting model obtained, for research and dissemination purposes, was used to experiment with Virtual Reality (VR) and Augmented Reality (AR) applications to show the theater’s original splendor, now in a good state of preservation but still an archaeological ruin

Parametric Optimization of Window-to-Wall Ratio for Passive Buildings Adopting A Scripting Methodology to Dynamic-Energy Simulation

Counterbalancing climate change is one of the biggest challenges for engineers around the world. One of the areas in which optimization techniques can be used to reduce energy needs, and with that the pollution derived from its production, is building design. With this study of a generic office located both in a northern country and in a temperate/Mediterranean site, we want to introduce a coding approach to dynamic energy simulation, able to suggest, from the early-design phases when the main building forms are defined, optimal configurations considering the energy needs for heating, cooling and lighting. Generally, early-design considerations of energy need reduction focus on the winter season only, in line with the current regulations; nevertheless a more holistic approach is needed to include other high consumption voices, e.g., for space cooling and lighting. The main considered design parameter is the WWR (window-to-wall ratio), even if further variables are considered in a set of parallel analyses (level of insulation, orientation, activation of low-cooling strategies including shading devices and ventilative cooling). Finally, the effect of different levels of occupancy was included in the analysis to regress results and compare the WWR with corresponding heating and cooling needs. This approach is adapted to Passivhaus design optimization, working on energy need minimisation acting on envelope design choices. The results demonstrate that it is essential to include, from the early-design configurations, a larger set of variables in order to optimize the expected energy needs on the basis of different aspects (cooling, heating, lighting, design choices). Coding is performed using Python scripting, while dynamic energy simulations are based on EnergyPlus

De Marchi, Massimo

Con il rapido sviluppo delle tecnologie digitali per la raccolta, gestione diffusione di dati spaziali, la ricerca geografica ha beneficiato in anni recenti di numerosi contributi basati sulla partecipazione o collaborazione di cittadini, singolarmente o in gruppo, facilitata da tecnologie dell’informazione geografica, geotool, geoapp. Processi di questo genere possono essere catalogati sotto diverse terminologie: Citizen Science, Volunteered Geographic Information (VGI), Participatory Mapping, Public Participation Geographic Information Systems (PPGIS) e altre ancora. Uno dei campi di applicazione più diffusi delle ricerche basate sull’interazione tra ricercatori e cittadini è la realtà urbana, in special modo le azioni di mappatura di tematismi rilevanti dal punto di vista sociale o ambientale. Nel Laboratorio GIScience e Drones for Good attivo presso il Dipartimento di Ingegneria Civile Edile e Ambientale dell’Università di Padova, e con la collaborazione di altri dipartimenti, il gruppo di ricerca collegato al Master di II livello in GIScience e Sistemi a Pilotaggio Remoto per la gestione integrata del territorio e delle risorse naturali sviluppa da alcuni anni una linea di ricerca fondata su metodologie di mappatura partecipata a supporto di analisi sulla sostenibilità urbana. In questo contributo vengono illustrati tre dei progetti afferenti a tale linea di ricerca: “Il Valore del Suolo”, per mappare la permeabilità delle superfici in un quartiere campione di Padova; “Piste riCiclabili”, per individuare le criticità dei percorsi ciclabili padovani; “MUES – Mapping Urban Empty Spaces”, per la mappatura di spazi abbandonati nel comune di Padova. Per ogni progetto si descrivono obiettivi, metodologie, tecnologie dell’informazione geografica utilizzate, attori coinvolti e risultati ottenuti, allo scopo di trovare connessioni tra questi elementi e ragionare su pregi e limiti di tali operazioni.The rapid development of digital technologies to collect, manage and spread spatial data has led the geospatial research field to be involved in a great number of projects based on participation by citizens, both individually and in groups, facilitated by geographic information technologies, geotools, geoapps. Such processes may be classified under different terms and definitions: Citizen Science, Volunteered Geographic Information (VGI), Participatory Mapping, Public Participation Geographic Information Systems (PPGIS) and more. One of the most common fields of application for interaction-based geographical researches is the urban context, especially mapping features and themes which are relevant from a social or environmental point of view. Within the GIScience e Drones for Good Lab, part of the Department of Civil, Environmental and Architectural Engineering of University of Padova, and with the collaboration of other departments, the group linked to the post-graduate Master in GIScience and UAV has been leading for several years a line of research based on participatory mapping methodologies in support of urban sustainability analysis. Here, three of the performed projects are presented: “Il Valore del Suolo”, to map perviousness of surfaces in a sample neighbourhood in Padova; “Piste Riciclabili”, to detect critical issues in Padova cycle paths; “MUES – Mapping Urban Empty Spaces”, to map abandoned sites in Padova. For each of these projects, objectives, methodologies, involved geographic information technologies and actors, results are described, with the aim to find connections between such elements and think about pros and cons of these kinds of processes