Artificial intelligence for the early design phases of space missions

Recent introduction of data mining methods has led to a paradigm shift in the way we can analyze space data. This paper demonstrates that Artificial Intelligence (AI), and especially the field of Knowledge Representation and Reasoning (KRR), could also be successfully employed at the start of the space mission life cycle via an Expert System (ES) used as a Design Engineering Assistant (DEA). An ES is an AI-based agent used to solve complex problems in particular fields. There are many examples of ES being successfully implemented in the aeronautical, agricultural, legal or medical fields. Applied to space mission design, and in particular, in the context of concurrent engineering sessions, an ES could serve as a knowledge engine and support the generation of the initial design inputs, provide easy and quick access to previous design decisions or push to explore new design options. Integrated to the User design environment, the DEA could become an active assistant following the design iterations and flagging model inconsistencies. Today, for space missions design, experts apply methods of concurrent engineering and Model-Based System Engineering, relying both on their implicit knowledge (i.e., past experiences, network) and on available explicit knowledge (i.e., past reports, publications, data sheets). The former knowledge type represents still the most significant amount of data, mostly unstructured, non-digital or digital data of various legacy formats. Searching for information through this data is highly time-consuming. A solution is to convert this data into structured data to be stored into a Knowledge Graph (KG) that can be traversed by an inference engine to provide reasoning and deductions on its nodes. Knowledge is extracted from the KG via a User Interface (UI) and a query engine providing reliable and relevant knowledge summaries to the Human experts. The DEA project aims to enhance the productivity of experts by providing them with new insights into a large amount of data accumulated in the field of space mission design. Natural Language Processing (NLP), Machine Learning (ML), Knowledge Management (KM) and Human-Machine Interaction (HMI) methods are leveraged to develop the DEA. Building the knowledge base manually is subjective, timeconsuming, laborious and error bound. This is why the knowledge base generation and population rely on Ontology Learning (OL) methods. This OL approach follows a modified model of the Ontology Layer Cake. This paper describes the approach and the parameters used for the qualitative trade-off for the selection of the software to be adopted in the architecture of the ES. The study also displays the first results of the multiword extraction and highlights the importance of Word Sense Disambiguation for the identification of synonyms in the context. This paper includes the detailed software architecture of both front and back-ends, as well as the tool requirements. Both architectures and requirements were refined after a set of interviews with experts from the European Space Agency. The paper finally presents the preliminary strategy to quantify and mitigate uncertainties within the ES

Towards an artificial intelligence based design engineering assistant for the early design of space missions

This paper describes a solution to enhance Knowledge Management (KM) and Reuse at the early stages of space mission design in the frame of Concurrent Engineering (CE) studies via the implementation of an Expert System (ES). CE is a centralized engineering approach which significantly accelerates and increases the reliability of space mission feasibility assessment by having experts work concurrently, thus enhancing the communication flow. An ES is an AI-based agent capturing Human expertise in a computer program. There are many examples of ES being successfully implemented in the aeronautical, agricultural, legal or medical fields. To assess the feasibility of a mission, experts rely both on their implicit knowledge (i.e., past experiences, network, etc.) and on available explicit knowledge (i.e., past reports, publications, datasheets, books, etc.). This latter type of knowledge represents a substantial amount of unstructured data, continuously increasing over the past decades. The amount of information has become highly time consuming to search through within the limited timeframe of a feasibility study and is therefore often underutilised. A solution is to convert this data into structured data and store them into a Knowledge Graph (KG) that can be traversed through an inference engine to provide reasoning and deductions. Information is extracted from the KG via a querying module from a User Interface (UI) supporting the Human-Machine Interaction (HMI). The Design Engineering Assistant (DEA), the ES for space mission design, aims to enhance the productivity of experts by providing them with new insights on large amount of data accumulated in the field of space mission design. Not only will it act as a Knowledge Engine (KE) but, integrated to the design environment, it could play a much more active part into the design process, advising the Human experts on design iterations. This paper introduces the proposed integration of an Artificial Intelligence (AI) agent into the CE process, the preliminary architecture of the tool and identified challenges. The study will also present the outcomes of a set of experts interviews carried out at the European Space Research and Technology Center (ESTEC) of ESA in July-August 2018, to define the DEA requirements following a User-centred approach

VARDA (VARved sediments DAtabase) – providing and connecting proxy data from annually laminated lake sediments

Varved lake sediments provide long climatic records with high temporal resolution and low associated age uncertainty. Robust and detailed comparison of well-dated and annually laminated sediment records is crucial for reconstructing abrupt and regionally time-transgressive changes as well as validation of spatial and temporal trajectories of past climatic changes. The VARved sediments DAtabase (VARDA) presented here is the first data compilation for varve chronologies and associated palaeoclimatic proxy records. The current version 1.0 allows detailed comparison of published varve records from 95 lakes. VARDA is freely accessible and was created to assess outputs from climate models with high-resolution terrestrial palaeoclimatic proxies. VARDA additionally provides a technical environment that enables to explore the database of varved lake sediments using a connected data-model and can generate a state-of-the-art graphic representation of multi-site comparison. This allows to reassess existing chronologies and tephra events to synchronize and compare even distant varved lake records. Furthermore, the present version of VARDA permits to explore varve thickness data. In this paper, we report in detail on the data mining and compilation strategies for the identification of varved lakes and assimilation of high-resolution chronologies as well as the technical infrastructure of the database. Additional paleoclimate proxy data will be provided in forthcoming updates. The VARDA graph-database and user interface can be accessed online at https://varve.gfz-potsdam.de, all datasets of version 1.0 are available at http://doi.org/10.5880/GFZ.4.3.2019.003 (Ramisch et al., 2019)