Scientific requirements for an engineered model of consciousness

The building of a non-natural conscious system requires more than the design of physical or virtual machines with intuitively conceived abilities, philosophically elucidated architecture or hardware homologous to an animal’s brain. Human society might one day treat a type of robot or computing system as an artificial person. Yet that would not answer scientific questions about the machine’s consciousness or otherwise. Indeed, empirical tests for consciousness are impossible because no such entity is denoted within the theoretical structure of the science of mind, i.e. psychology. However, contemporary experimental psychology can identify if a specific mental process is conscious in particular circumstances, by theory-based interpretation of the overt performance of human beings. Thus, if we are to build a conscious machine, the artificial systems must be used as a test-bed for theory developed from the existing science that distinguishes conscious from non-conscious causation in natural systems. Only such a rich and realistic account of hypothetical processes accounting for observed input/output relationships can establish whether or not an engineered system is a model of consciousness. It follows that any research project on machine consciousness needs a programme of psychological experiments on the demonstration systems and that the programme should be designed to deliver a fully detailed scientific theory of the type of artificial mind being developed – a Psychology of that Machine

Aerospace medicine and biology: A continuing bibliography with indexes

This bibliography lists 161 reports, articles, and other documents introduced into the NASA scientific and technical information system in November, 1987

Evaluation of artificial neural network method applied to probe diagnostics in plasma

Plasma diagnostics consist on predicting the plasma parameter by measuring their properties. Models to predict these parameters are based on theoretical equations, which sometimes are complicated and tedious to solve. This thesis aims to evaluate a new method to obtain these plasma parameters using a branch of artificial intelligence, the Artificial Neural Network. To develop this thesis, the software MATLAB was used to create the neural network. Two theories were tested: Planar Langmuir Probe and Orbital Motion Limited. And two problems were solved: forward problem to obtain the properties from the parameters and the inverse problem to obtain the parameters from the properties. The later approach is used to evaluate the parameters obtained by network with experimental data from laboratory measurements. Artificial Neural Network was implemented successfully in both forward and inverse problems with incredible accuracy. The results obtained have created a theoretical framework for plasma diagnostic using neural networks and it has been concluded that they can work with any theory presented, as long as as proper database is obtained. Regarding the simulation with experimental data, the results were not good respect the measurements taken. Although the network performed well, the database used for training and simulation had discrepancies from realistic data since it was obtained from equations. Sources of error of this last problem are found and different approaches and work to be developed in the future are proposed.Ingeniería Aeroespacia

Evaluating performance for procurement: A structured method for assessing the usability of future speech interfaces

Procurement is a process by which organizations acquire equipment to enhance the effectiveness of their operations. Equipment will only enhance effectiveness if it is usable for its purpose in the work environment, i.e. if it enables tasks to be performed to the desired quality with acceptable costs to those who operate it. Procurement presents a requirement, then, for evaluations of the performance of human-machine work systems. This thesis is concerned with the provision of information to support procurers in performing such evaluations. The Ministry of Defence (an equipment procurer) has presented a particular requirement for a means of assessing the usability of speech interfaces in the establishment of the feasibility of computerized battlefield work systems. A structured method was developed to meet this requirement, the scope, notation and process of which sought to be explicit and proceduralized. The scope was specified in terms of a conceptualization of human-computer interaction: the method supported the development of representations of the task, device and user, which could be implemented as simulations and used in empirical evaluations of system performance. Notations for representations were proposed, and procedures enabling the use of the notations. The specification and implementation of the four sub-methods is described, and subsequent enhancement in the context of evaluations of speech interfaces for battlefield observation tasks. The complete method is presented. An evaluation of the method was finally performed with respect to the quality of the assessment output and costs to the assessor. The results suggested that the method facilitated systematic assessment, although some inadequacies were identified in the expression of diagnostic information which was recruited by the procedures, and in some of the procedures themselves. The research offers support for the use of structured human factors evaluation methods in procurement. Qualifications relate to the appropriate expression of knowledge of device-user interaction, and to the conflict between requirements for flexibility and low-level proceduralization

A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas

This paper provides a comprehensive tutorial for Bayesian practitioners in pharmacometrics using Pumas workflows. We start by giving a brief motivation of Bayesian inference for pharmacometrics highlighting limitations in existing software that Pumas addresses. We then follow by a description of all the steps of a standard Bayesian workflow for pharmacometrics using code snippets and examples. This includes: model definition, prior selection, sampling from the posterior, prior and posterior simulations and predictions, counter-factual simulations and predictions, convergence diagnostics, visual predictive checks, and finally model comparison with cross-validation. Finally, the background and intuition behind many advanced concepts in Bayesian statistics are explained in simple language. This includes many important ideas and precautions that users need to keep in mind when performing Bayesian analysis. Many of the algorithms, codes, and ideas presented in this paper are highly applicable to clinical research and statistical learning at large but we chose to focus our discussions on pharmacometrics in this paper to have a narrower scope in mind and given the nature of Pumas as a software primarily for pharmacometricians

Performance based diagnostics of a twin shaft aeroderivative gas turbine: water wash scheduling

Aeroderivative gas turbines are used all over the world for different applications as Combined Heat and Power (CHP), Oil and Gas, ship propulsion and others. They combine flexibility with high efficiencies, low weight and small footprint, making them attractive where power density is paramount as off shore Oil and Gas or ship propulsion. In Western Europe they are widely used in CHP small and medium applications thanks to their maintainability and efficiency. Reliability, Availability and Performance are key parameters when considering plant operation and maintenance. The accurate diagnose of Performance is fundamental for the plant economics and maintenance planning. There has been a lot of work around units like the LM2500® , a gas generator with an aerodynamically coupled gas turbine, but nothing has been found by the author for the LM6000® . Water wash, both on line or off line, is an important maintenance practice impacting Reliability, Availability and Performance. This Thesis aims to select and apply a suitable diagnostic technique to help establishing the schedule for off line water wash on a specific model of this engine type. After a revision of Diagnostic Methods Artificial Neural Network (ANN) has been chosen as diagnostic tool. There was no WebEngine model available of the unit under study so the first step of setting the tool has been creating it. The last step has been testing of ANN as a suitable diagnostic tool. Several have been configured, trained and tested and one has been chosen based on its slightly better response. Finally, conclusions are discussed and recommendations for further work laid out

Artificial Reversible Skin

This project aims to improve the realism of medical simulation mannequins by developing an adaptable system for the skin that is capable of displaying physiological changes in the skin caused by conscious and unconscious perturbations. A design of an artificial skin is developed, which uses organic light emitting diode (OLED) displays implanted underneath the skin of a medical simulation mannequin. After performing fatigue analysis and constructing a proof of concept, it is shown that the use of strategically placed displays can realistically simulate color changes similar to the human physiology

Automated Fault Detection, Diagnostics, Impact Evaluation, and Service Decision-Making for Direct Expansion Air Conditioners

This work describes approaches for automatically detecting, diagnosis, and evaluating the impacts of common faults in unitary rooftop air conditioning equipment. A semi-empirical component-based modeling approach using virtual sensors has been implemented using low-cost microcontrollers and tested on fixed-speed and variable-speed equipment using laboratory psychrometric test chambers. A previously developed virtual refrigerant charge sensor was applied to a fixed-speed rooftop unit with combinations of condenser types and expansion valve types and resulted in average prediction errors less than 10%. In addition, a methodology was developed that can be used to tune the empirical parameters of the model using data collected without psychrometric chambers, greatly reducing the experimental effort and costs required for the model. Virtual sensors previously developed for fixed-speed systems were also implemented for a variable-speed rooftop unit without significant loss of accuracy. Much of this work has been devoted to estimating the performance impacts of faults that grow over time, like heat exchanger fouling or refrigerant charge leakage. To estimate these impacts, semi-empirical models for predicting the normal performance of fixed-speed and variable-speed systems have been developed and evaluated using experimentally collected data. In addition, the virtual sensor approaches for estimating the actual performance of systems using low-cost sensor measurements were evaluated. Together, normal performance models and virtual sensor estimations were used to estimate the overall impacts of several faults on system performance. A methodology for quantifying the performance impacts of simultaneously occurring faults has been developed and tested using a detailed system model and experimental results. While relatively simple, simulated and experimentally collected results showed the fault impact models were accurate within 10% of the actual fault impacts. The fault impact evaluation models could be embedded in an AFDD system and used to determine when performance degradation faults should be serviced from an operating cost perspective. In addition, different service and maintenance strategies are compared in this work using a simulation environment that was developed. A data-driven artificial neural network model of a rooftop unit with faults has been derived for this purpose using a detailed fault impact model for direct expansion cooling equipment. This model was coupled with a building model to simulate operating cost impacts of performance degradations and service over the life of cooling equipment. An optimization problem was formulated with the goal to minimize lifetime energy and service costs and was solved using dynamic programming. Using the optimal solution as a baseline, suboptimal service decision-making strategies were implemented and simulated using the building model. It was found that condition-based maintenance strategies using the outputs of automated fault detection and diagnostics tools can significantly reduce lifetime operating costs over periodic service policies

Implementation of Virtual Reality (VR) simulators in Norwegian maritime pilotage training

With millions of tons of cargo transported to and from Norwegian ports every year, the maritime waterways in Norway are heavily used. The high consequences of accidents and mishaps require well-trained seafarers and safe operating practices. The normal crews of vessels are supported by the Norwegian Coastal Administration (NCA) pilot service when operating vessels not meeting specific regulations. Simulator training is used as part of the toolset designed to educate, train, and advance the knowledge of maritime pilots in order to improve their operability. The NCA is working on an internal project to distribute Virtual Reality (VR) simulators to selected pilot stations along the coast and train and familiarize maritime pilots with the tool. There has been a lack of research on virtual reality simulators and how they are implemented in maritime organizations. The goal of this research is to see if a VR-simulator can be used as a training tool within the Norwegian Coastal Administration's pilot service. Furthermore, the findings of this study contribute to the understanding of VR-simulators in the field of Maritime Education and Training (MET). The thesis is addressing two research questions: 1. Is the Virtual Reality training useful in the competence development process of Norwegian maritime pilots? 2. How can the Virtual Reality simulators improve training outcomes of today’s maritime pilot education? The data gathered from the systematic literature review corresponds to the findings of the interviews. Considering the similarities with previous study findings from sectors such as healthcare, construction, and education, it is concluded that the results of the interviews can be generalized. For maritime pilots, the simulator offers recurrent scenario-based training and a high level of immersion. Pilots can learn at home, onboard a vessel, at the pilot station, and in group settings thanks to the system's mobility and user-friendliness. In terms of motivation and training effectiveness, the study finds that VR-simulators are effective and beneficial. The technology received positive reviews from the pilots. The simulator can be used to teach both novice and experienced maritime pilots about new operations, larger tonnage, and new operational areas, according to the findings of the research. After the NCA has utilized VR-simulators for some time, additional research may analyze the success of VR-simulators using a training evaluation study and investigate the impact of VR-training in the organization