Multiple Degrees-Of-Freedom Input Devices for Interactive Command and Control within Virtual Reality in Industrial Visualizations

ABSTRACT The aim of this research is to present a new multimodal interaction mapping framework for 3D object manipulation within the virtual reality (VR) realm, by leveraging the advantages of having multiple DoF (Degree of Freedom). In this new software engineering designed framework, interaction devices such as the keyboard, mouse, joystick, and specialist devices for 3D interactions; the Wing [5] [4] and the 3D connexion spacenavigator, can all be combined to provide a more intuitive and natural interaction command system. This can be applied to many different specific systems including industrial applications within the petroleum, geology and materials sciences

Applicative architecture for embedded distributed technical diagnosis

This article presents an applicative architecture based on a solving method for embedded technical diagnosis of complex systems. This architecture is defined in order to provide services enabling the evaluation of the health status of complex systems. Diagnostic services provide information to the maintenance decision support system that leads to reduce the periods of unavailability and determine if their future mission can be carried out. The architecture presented in this paper implements a distributed diagnostic function using multi-agent techniques. A consistency model-based diagnosis is proposed that leads to the identification of the faulty LRUs and the failed functions of complex systems

An embedded distributed tool for transportation systems health assessment

International audienceThis article presents an embedded distributed tool for health assessment of complex systems. The presented architecture is based on a solving method for embedded technical diagnostics and prognostics. This tool provides services enabling the evaluation of the health status of complex systems. Diagnostic services provide information for the maintenance decision support system that leads to reduce the periods of unavailability and determine if their future mission can be carried out. The diagnostic and prognostic functions are detailed and the exchanged data are specified. An example shows the feasibility of the proposed architecture and demonstrates the correctness of the developed algorithms

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Modelling domain knowledge using explicit conceptualization

Applications are characterized by the tasks and domains involved. Knowledge modeling can be divided into two conceptual subactivities: modeling the task and modeling the domain knowledge. An explicit conceptualization of the domain knowledge at the heart of its organization is discussed. A conceptualization is the objects presumed to exist and the relationships and functions among them. The annotations and the conceptualization guide the construction of applications and support flexible reasoning during problem solving. It also lets domain knowledge be reused

Integrating post-manufacturing issues into design and manufacturing decisions

An investigation is conducted on research into some of the fundamental issues underlying the design for manufacturing, service and recycling that affect engineering decisions early in the conceptual design phase of mechanical systems. The investigation focuses on a system-based approach to material selection, manufacturing methods and assembly processes related to overall product requirements, performance and life-cycle costs. Particular emphasis is placed on concurrent engineering decision support for post-manufacturing issues such as serviceability, recyclability, and product retirement