Virtual assembly rapid prototyping of near net shapes

Virtual reality (VR) provides another dimension to many engineering applications. Its immersive and interactive nature allows an intuitive approach to study both cognitive activities and performance evaluation. Market competitiveness means having products meet form, fit and function quickly. Rapid Prototyping and Manufacturing (RP&M) technologies are increasingly being applied to produce functional prototypes and the direct manufacturing of small components. Despite its flexibility, these systems have common drawbacks such as slow build rates, a limited number of build axes (typically one) and the need for post processing. This paper presents a Virtual Assembly Rapid Prototyping (VARP) project which involves evaluating cognitive activities in assembly tasks based on the adoption of immersive virtual reality along with a novel non-layered rapid prototyping for near net shape (NNS) manufacturing of components. It is envisaged that this integrated project will facilitate a better understanding of design for manufacture and assembly by utilising equivalent scale digital and physical prototyping in one rapid prototyping system. The state of the art of the VARP project is also presented in this paper

Virtual bloXing - assembly rapid prototyping for near net shapes

Virtual reality (VR) provides another dimension to many engineering applications. Its immersive and interactive nature allows an intuitive approach to study both cognitive activities and performance evaluation. Market competitiveness means having products meet form, fit and function quickly. Rapid Prototyping and Manufacturing (RP&M) technologies are increasingly being applied to produce functional prototypes and the direct manufacturing of small components. Despite its flexibility, these systems have common drawbacks such as slow build rates, a limited number of build axes (typically one) and the need for post processing. This paper presents a Virtual Assembly Rapid Prototyping (VARP) project which involves evaluating cognitive activities in assembly tasks based on the adoption of immersive virtual reality along with a novel nonlayered rapid prototyping for near net shape (NNS) manufacturing of components. It is envisaged that this integrated project will facilitate a better understanding of design for manufacture and assembly by utilising equivalent scale digital and physical prototyping in one rapid prototyping system. The state of the art of the VARP project is also presented in this paper

Analyzing controllability of dynamical systems modelling brain neural networks

The brain structure can be modelled as a deep recurrent complex neuronal network. Networked systems are expressly interesting systems to control because of the role of the underlying architecture, which predisposes some components to particular control motions. The concept of brain cognitive control is analogous to the mathematical concept of control used in engineering, where the state of a complex system can be adjusted by a particular input. The in-depth study on the controllability character of dynamical systems, despite being very difficult, could help to regulate the brain cognitive function. small advances in the study can favour the study and action against learning difficulties such as dyscalculia or other disturbances like the phenomena of forgetting.Peer ReviewedPostprint (published version

The Link between Cognition and the Complexity of Engineering Systems Design

This paper focuses on the role of human cognition in the design of large complex systems. It contrasts the physical system that is the product of the design with the cognitive model that is used by the designer to “understand” the system. The complexity of the system relevant to the designer is a function not only of the physical system, but also of the cognitive model that the designer holds in his mind. Furthermore, the level of cognitive model available to an experienced designer depends on the state of domain knowledge. To be useful in answering the question, “How complex is this system to design?” the state of the domain knowledge available to the designer must be assessed with respect to the level at which the design problem is posed. The concept of conceptual distance is introduced that depends on the disparity between the present level of integrated knowledge and the conceptual level of the design problem. This “distance” is a measure of the complexity of the design task and is called the cognitive complexity of the design. To investigate the concept of cognitive complexity a model of human knowledge is proposed along with a set of graphical abstractions. It is concluded that the cognitive complexity of the design task is neither wholly intrinsic (a property of the system) nor wholly subjective (a property of the mind) but requires an objective evaluation of the engineering problem with respect to present knowledge. It is noted that the structure of knowledge in a specific domain can be mapped and therefore a research program can be launched to systematically determine the difficulty of various engineering endeavors

Successes and critical failures of neural networks in capturing human-like speech recognition

Natural and artificial audition can in principle evolve different solutions to a given problem. The constraints of the task, however, can nudge the cognitive science and engineering of audition to qualitatively converge, suggesting that a closer mutual examination would improve artificial hearing systems and process models of the mind and brain. Speech recognition - an area ripe for such exploration - is inherently robust in humans to a number transformations at various spectrotemporal granularities. To what extent are these robustness profiles accounted for by high-performing neural network systems? We bring together experiments in speech recognition under a single synthesis framework to evaluate state-of-the-art neural networks as stimulus-computable, optimized observers. In a series of experiments, we (1) clarify how influential speech manipulations in the literature relate to each other and to natural speech, (2) show the granularities at which machines exhibit out-of-distribution robustness, reproducing classical perceptual phenomena in humans, (3) identify the specific conditions where model predictions of human performance differ, and (4) demonstrate a crucial failure of all artificial systems to perceptually recover where humans do, suggesting a key specification for theory and model building. These findings encourage a tighter synergy between the cognitive science and engineering of audition

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing