CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

On the use of Machine Learning and Deep Learning for Text Similarity and Categorization and its Application to Troubleshooting Automation

Troubleshooting is a labor-intensive task that includes repetitive solutions to similar problems. This task can be partially or fully automated using text-similarity matching to find previous solutions, lowering the workload of technicians. We develop a systematic literature review to identify the best approaches to solve the problem of troubleshooting automation and classify incidents effectively. We identify promising approaches and point in the direction of a comprehensive set of solutions that could be employed in solving the troubleshooting automation problem

Let’s augment the future together!:Augmented reality troubleshooting support for IT/OT rolling stock failures

The railway industry is moving to a socio-technological system that relies on computer-controlled and human-machine interfaces. Opportunities arise for creating new services and commercial business cases by using technological innovations and traffic management systems. The convergence of Information Technology (IT) with Operational Technology (OT) is critical for cost-effective and reliable railway operations. However, this convergence introduces complexities, leading to more intricate rolling stock system failures. Hence, operators necessitate assistance in their troubleshooting and maintenance strategy to simplify the decision-making and action-taking processes. Augmented Reality (AR) emerges as a pivotal tool for troubleshooting within this context. AR enhances the operator’s ability to visualize, contextualize, and understand complex data by overlaying real-time and virtual information onto physical objects. AR supports the identification of IT/OT rolling stock system failures, offers troubleshooting directions, and streamlines maintenance procedures, ultimately enhancing decision-making and action-taking processes. This thesis investigates how AR can support operators in navigating troubleshooting and maintenance challenges posed by IT/OT rolling stock system failures in the railway industry

Adaptive fault diagnosis in interactive electronic technical manuals (IETMs)

An Interactive Electronic Technical Manual (IETM) is a technical manual that is prepared in digital format to provide information about the diagnostics and maintenance of complex systems.;In this thesis we present a methodology for adaptive fault diagnosis in IETMs, a methodology that constantly adapts the fault diagnosis procedure, according to the experience of the user performing the diagnosis. We develop a framework of adaptation that constantly monitors user behavior, and learns about the fault and its possible causes as the system is used, hence making it easier to perform such procedures, which increases efficiency of usage of such a manual, an essential factor in performing fault diagnosis. We will also extend S1000D to incorporate all information necessary for our adaptation methodology.;The outcome of our methodology will be an IETM which contains adaptable fault diagnosis procedures that adapt to users according to their expertise levels making these procedures less cumbersome for users to accomplish, hence increasing their productivity and efficiency. (Abstract shortened by UMI.)

Image-Based Search Engine For Art Exhibition Gallery (ImBa SEA Exhibition/Gallery)

The image-based search engine for art exhibition/gallery (ImBa SEA) is like any search engine but will provide a better functionality. The ImBa SEA is specially design for art gallery where lies thousands of arts displayed on the exhibition. With the help of the ImBa SEA, the user can upload image that they have snapped into the system to retrieve the information about the art itself. Some features are very difficult to describe with text, some special textures and complex shapes cannot be clearly represented by alphanumeric inputs. The arts need to be digitalized and stored in the gallery’s database. Based on the existing way of accessing information for art gallery, instead of directly using the image as a ‘keyword’ to retrieve information, each art have its own name. The name is stored in the art gallery database, with the relevant information. However, this method takes some time because some arts have the similar name or a long name. This decreases the accuracy of the search engine to retrieve information of the art wish to be accessed. With ImBa SEA, the user directly uses the image to retrieve the information on the given art. Each art will have its own unique id to ease the process of retrieving information. The image is uploaded into the search engine, and then it will process the image to look for similarity of the image with the images stored in the database. Once the similar image is found in the database, the unique id of the art is used to retrieve the information of the image. Basically,ImBa SEA compares the two images pixel by pixel to look for similarities. This technique will surely provide better efficiency and accuracy on information retrieval. This paper will discuss everything from the problem faced, and scope concentrated to achieve the objectives of the project which are discussed in Chapter 1. The system or technologies related or similar with the ImBa SEA are discussed in Chapter 2: Literature Review. Also, the research methodology of the project is discussed later in Chapter 3 together with the development methodology. The basic structure is also discussed in this paper to give a clear view on how the system works. The future plans on the ImBa SEA are discussed at the end of this paper

Complex low volume electronics simulation tool to improve yield and reliability

Assembly of Printed Circuit Boards (PCB) in low volumes and a high-mix requires a level of manual intervention during product manufacture, which leads to poor first time yield and increased production costs. Failures at the component-level and failures that stem from non-component causes (i.e. system-level), such as defects in design and manufacturing, can account for this poor yield. These factors have not been incorporated in prediction models due to the fact that systemfailure causes are not driven by well-characterised deterministic processes. A simulation and analysis support tool being developed that is based on a suite of interacting modular components with well defined functionalities and interfaces is presented in this paper. The CLOVES (Complex Low Volume Electronics Simulation) tool enables the characterisation and dynamic simulation of complete design; manufacturing and business processes (throughout the entire product life cycle) in terms of their propensity to create defects that could cause product failure. Details of this system and how it is being developed to fulfill changing business needs is presented in this paper. Using historical data and knowledge of previous printed circuit assemblies (PCA) design specifications and manufacturing experiences, defect and yield results can be effectively stored and re-applied for future problem solving. For example, past PCA design specifications can be used at design stage to amend designs or define process options to optimise the product yield and service reliability

A case-based reasoning approach for low volume, high added value electronics

This paper will report on the application of the Case-Based Reasoning (CBR) approach [1] to develop a defect prediction system to support the development of new printed circuit assembly (PCA) products. Using a CBR system, past PCA design specifications and manufacturing experiences including defect and yield results can be effectively stored and reapplied for future problem solving. For example, the CBR can then be used at design stage to amend designs or define process options to optimise the product yield and service reliability. A case study using a case-base provided by a PCA manufacturer is presented