
Â
Â
Â
3D and spectral digital recording of cultural heritage monuments is a common activity for their documentation, preservation, conservation management, and reconstruction. Recent developments in 3D and spectral technologies have provided enough flexibility in selecting one technology over another, depending on the data content and quality demands of the data application. Each technology has its own pros/cons, suited perfectly to some situations and not to others. They are mostly unknown to humanities experts, besides having a limited understanding of the data requirements demanded by the research question. These are often left to technical experts who again have a limited understanding of cultural heritage requirements. A common point of view has to be achieved through interdisciplinary discussions. Such agreements need to be documented for their future references and re-uses. We present a method based on semantic concepts that not only documents the semantic essence of such discussions, but also uses it to infer a guidance mechanism that recommends technologies/technical process to generate the required data based on individual needs. Experts' knowledge is represented explicitly through a knowledge representation that allows machines to manage and infer recommendations. First, descriptive semantics guide end users to select the optimal technology/technologies for recording data. Second, structured knowledge controls the processing chain extracting and classifying objects contained in the acquired data. Circumstantial situations during object recording and the behaviour of the technologies in that situation are taken into account. We will explain the approach as such and give results from tests at a CH object.
Disaster management requires both individual and collaborative preparedness among the various stakeholders.
Collaborative exercises aim to train stakeholders to apply the plans prepared and to identify potential problems and areas for improvement. As these exercises are costly, computer simulation is an interesting tool to evaluate preparation through a wider variety of contexts.
However, research on simulation and disaster management focuses on a particular problem rather than on the overall assessment of the plans prepared. This limitation is explained by the challenge of creating a simulation model that can represent and adapt to a wide variety of plans from various disciplines.
The work presented in this paper addresses this challenge by adapting the simulation model based on disaster management information and plans integrated into a knowledge base. The simulation model created is then automatically programmed to perform simulation experiments to improve action plans.
The results of the experiments are analyzed in order to generate new knowledge and know-how to enrich disaster management plans in a virtuous cycle.
This paper presents a proof of concept on the French national Novi plan, for which simulation experiments have made it possible to know the impact of the distribution of doctors on the application of the plan as well as to identify their distribution.
Experts’ knowledge about optical technologies for spatial and spectral recording is logically structured and stored in an ontology-based knowledge representation with the aim to provide objective recommendations for recording strategies. Besides operational functionalities and technical parameters such as measurement principles, instruments, and setups further factors such as the targeted application, data, physical characteristics of the object, and external influences are considered creating a holistic view on spectral and spatial recording strategies. Through this approach impacting factors on the technologies and generated data are identified. Semantic technologies allow to flexibly store this knowledge in a hierarchical class structure with dependencies, interrelations and description logic statements. Through an inference system the knowledge can be retrieved adapted to individual needs.
This paper addresses the factors that conditioned the choices in lithic resource procurement for tool making at the Late Aurignacian site of Stratzing-Galgenberg (Austria), based on the lithic assemblage from the main area of the site. The raw materials used in the analysed assemblage are varied and partly relate to various local and non-local proveniences. The importance of non-local flint in the assemblage contradicts the distance decay model according to which the amount of a given raw material decreases with the increasing distance from its source. Drawing on the approach developed recently by Lucy Wilson, we examine the predictive ability of “source attractiveness” with respect to terrain difficulty and energy expenditure to understand why some sources were used more than others, using a Geographic Information System (GIS). Our results indicate that terrain difficulty and mobility costs matter and have a better predictive ability than Euclidean distance alone to explain assemblage variability in the Aurignacian of the Middle Danube region.
Gerne bieten wir Ihnen ein unverbindliches Beratungsgespräch zu Themen aus unseren Forschungsbereichen an.Â