Generating Knowledge in Maintenance from Experience Feedback

Knowledge is nowadays considered as a significant source of performance improvement, but may be difficult to identify, structure, analyse and reuse properly. A possible source of knowledge is in the data and information stored in various modules of industrial information systems, like CMMS (Computerized Maintenance Management Systems) for maintenance. In that context, the main objective of this paper is to propose a framework allowing to manage and generate knowledge from information on past experiences, for improving the decisions related to the maintenance activity. In that purpose, we suggest an original Experience Feedback process dedicated to maintenance, allowing to capitalize on past interventions by i) formalizing the domain knowledge and experiences using a visual knowledge representation formalism with logical foundation (Conceptual Graphs); ii) extracting new knowledge thanks to association rules mining algorithms, using an innovative interactive approach; iii) interpreting and evaluating this new knowledge thanks to the reasoning operations of Conceptual Graphs. The suggested method is illustrated on a case study based on real data dealing with the maintenance of overhead cranes

Advancing Visual Grounding with Scene Knowledge: Benchmark and Method

Visual grounding (VG) aims to establish fine-grained alignment between vision and language. Ideally, it can be a testbed for vision-and-language models to evaluate their understanding of the images and texts and their reasoning abilities over their joint space. However, most existing VG datasets are constructed using simple description texts, which do not require sufficient reasoning over the images and texts. This has been demonstrated in a recent study~\cite{luo2022goes}, where a simple LSTM-based text encoder without pretraining can achieve state-of-the-art performance on mainstream VG datasets. Therefore, in this paper, we propose a novel benchmark of \underline{S}cene \underline{K}nowledge-guided \underline{V}isual \underline{G}rounding (SK-VG), where the image content and referring expressions are not sufficient to ground the target objects, forcing the models to have a reasoning ability on the long-form scene knowledge. To perform this task, we propose two approaches to accept the triple-type input, where the former embeds knowledge into the image features before the image-query interaction; the latter leverages linguistic structure to assist in computing the image-text matching. We conduct extensive experiments to analyze the above methods and show that the proposed approaches achieve promising results but still leave room for improvement, including performance and interpretability. The dataset and code are available at \url{https://github.com/zhjohnchan/SK-VG}.Comment: Computer Vision and Natural Language Processing. 21 pages, 14 figures. CVPR-202

Discursive design thinking: the role of explicit knowledge in creative architectural design reasoning

The main hypothesis investigated in this paper is based upon the suggestion that the discursive reasoning in architecture supported by an explicit knowledge of spatial configurations can enhance both design productivity and the intelligibility of design solutions. The study consists of an examination of an architect’s performance while solving intuitively a well-defined problem followed by an analysis of the spatial structure of their design solutions. One group of architects will attempt to solve the design problem logically, rationalizing their design decisions by implementing their explicit knowledge of spatial configurations. The other group will use an implicit form of such knowledge arising from their architectural education to reason about their design acts. An integrated model of protocol analysis combining linkography and macroscopic coding is used to analyze the design processes. The resulting design outcomes will be evaluated quantitatively in terms of their spatial configurations. The analysis appears to show that an explicit knowledge of the rules of spatial configurations, as possessed by the first group of architects can partially enhance their function-driven judgment producing permeable and well-structured spaces. These findings are particularly significant as they imply that an explicit rather than an implicit knowledge of the fundamental rules that make a layout possible can lead to a considerable improvement in both the design process and product. This suggests that by externalizing th

Knowledge-based support in Non-Destructive Testing for health monitoring of aircraft structures

Maintenance manuals include general methods and procedures for industrial maintenance and they contain information about principles of maintenance methods. Particularly, Non-Destructive Testing (NDT) methods are important for the detection of aeronautical defects and they can be used for various kinds of material and in different environments. Conventional non-destructive evaluation inspections are done at periodic maintenance checks. Usually, the list of tools used in a maintenance program is simply located in the introduction of manuals, without any precision as regards to their characteristics, except for a short description of the manufacturer and tasks in which they are employed. Improving the identification concepts of the maintenance tools is needed to manage the set of equipments and establish a system of equivalence: it is necessary to have a consistent maintenance conceptualization, flexible enough to fit all current equipment, but also all those likely to be added/used in the future. Our contribution is related to the formal specification of the system of functional equivalences that can facilitate the maintenance activities with means to determine whether a tool can be substituted for another by observing their key parameters in the identified characteristics. Reasoning mechanisms of conceptual graphs constitute the baseline elements to measure the fit or unfit between an equipment model and a maintenance activity model. Graph operations are used for processing answers to a query and this graph-based approach to the search method is in-line with the logical view of information retrieval. The methodology described supports knowledge formalization and capitalization of experienced NDT practitioners. As a result, it enables the selection of a NDT technique and outlines its capabilities with acceptable alternatives

Evidence-Based Dialogue Maps as a research tool to evaluate the quality of school pupils’ scientific argumentation

This pilot study focuses on the potential of Evidence-based Dialogue Mapping as a participatory action research tool to investigate young teenagers’ scientific argumentation. Evidence-based Dialogue Mapping is a technique for representing graphically an argumentative dialogue through Questions, Ideas, Pros, Cons and Data. Our research objective is to better understand the usage of Compendium, a Dialogue Mapping software tool, as both (1) a learning strategy to scaffold school pupils’ argumentation and (2) as a method to investigate the quality of their argumentative essays. The participants were a science teacher-researcher, a knowledge mapping researcher and 20 pupils, 12-13 years old, in a summer science course for “gifted and talented” children in the UK. This study draws on multiple data sources: discussion forum, science teacher-researcher’s and pupils’ Dialogue Maps, pupil essays, and reflective comments about the uses of mapping for writing. Through qualitative analysis of two case studies, we examine the role of Evidence-based Dialogue Maps as a mediating tool in scientific reasoning: as conceptual bridges for linking and making knowledge intelligible; as support for the linearisation task of generating a coherent document outline; as a reflective aid to rethinking reasoning in response to teacher feedback; and as a visual language for making arguments tangible via cartographic conventions

A systematic review of protocol studies on conceptual design cognition: design as search and exploration

This paper reports findings from the first systematic review of protocol studies focusing specifically on conceptual design cognition, aiming to answer the following research question: What is our current understanding of the cognitive processes involved in conceptual design tasks carried out by individual designers? We reviewed 47 studies on architectural design, engineering design and product design engineering. This paper reports 24 cognitive processes investigated in a subset of 33 studies aligning with two viewpoints on the nature of designing: (V1) design as search (10 processes, 41.7%); and (V2) design as exploration (14 processes, 58.3%). Studies on search focused on solution search and problem structuring, involving: long-term memory retrieval; working memory; operators and reasoning processes. Studies on exploration investigated: co-evolutionary design; visual reasoning; cognitive actions; and unexpected discovery and situated requirements invention. Overall, considerable conceptual and terminological differences were observed among the studies. Nonetheless, a common focus on memory, semantic, associative, visual perceptual and mental imagery processes was observed to an extent. We suggest three challenges for future research to advance the field: (i) developing general models/theories; (ii) testing protocol study findings using objective methods conducive to larger samples and (iii) developing a shared ontology of cognitive processes in design

IST Austria Thesis

Deep neural networks have established a new standard for data-dependent feature extraction pipelines in the Computer Vision literature. Despite their remarkable performance in the standard supervised learning scenario, i.e. when models are trained with labeled data and tested on samples that follow a similar distribution, neural networks have been shown to struggle with more advanced generalization abilities, such as transferring knowledge across visually different domains, or generalizing to new unseen combinations of known concepts. In this thesis we argue that, in contrast to the usual black-box behavior of neural networks, leveraging more structured internal representations is a promising direction for tackling such problems. In particular, we focus on two forms of structure. First, we tackle modularity: We show that (i) compositional architectures are a natural tool for modeling reasoning tasks, in that they efficiently capture their combinatorial nature, which is key for generalizing beyond the compositions seen during training. We investigate how to to learn such models, both formally and experimentally, for the task of abstract visual reasoning. Then, we show that (ii) in some settings, modularity allows us to efficiently break down complex tasks into smaller, easier, modules, thereby improving computational efficiency; We study this behavior in the context of generative models for colorization, as well as for small objects detection. Secondly, we investigate the inherently layered structure of representations learned by neural networks, and analyze its role in the context of transfer learning and domain adaptation across visually dissimilar domains