127,496 research outputs found

    Task analysis of discrete and continuous skills: a dual methodology approach to human skills capture for automation

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    There is a growing requirement within the field of intelligent automation for a formal methodology to capture and classify explicit and tacit skills deployed by operators during complex task performance. This paper describes the development of a dual methodology approach which recognises the inherent differences between continuous tasks and discrete tasks and which proposes separate methodologies for each. Both methodologies emphasise capturing operators’ physical, perceptual, and cognitive skills, however, they fundamentally differ in their approach. The continuous task analysis recognises the non-arbitrary nature of operation ordering and that identifying suitable cues for subtask is a vital component of the skill. Discrete task analysis is a more traditional, chronologically ordered methodology and is intended to increase the resolution of skill classification and be practical for assessing complex tasks involving multiple unique subtasks through the use of taxonomy of generic actions for physical, perceptual, and cognitive actions

    Identifying how automation can lose its intended benefit along the development process : a research plan

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    Doctoral Consortium Presentation © The Authors 2009Automation is usually considered to improve performance in virtually any domain. However it can fail to deliver the target benefit as intended by those managers and designers advocating the introduction of the tool. In safety critical domains this problem is of significance not only because the unexpected effects of automation might prevent its widespread usage but also because they might turn out to be a contributor to incident and accidents. Research on failures of automation to deliver the intended benefit has focused mainly on human automation interaction. This paper presents a PhD research plan that aims at characterizing decisions for those involved in development process of automation for safety critical domains, taken under productive pressure, to identify where and when the initial intention the automation is supposed to deliver can be lost along the development process. We tentatively call such decisions as drift and the final objective is to develop principles that will allow to identify and compensate for possible sources of drift in the development of new automation. The research is based on case studies and is currently entering Year 2

    The psychology of driving automation: A discussion with Professor Don Norman

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    Introducing automation into automobiles had inevitable consequences for the driver and driving. Systems that automate longitudinal and lateral vehicle control may reduce the workload of the driver. This raises questions of what the driver is able to do with this 'spare' attentional capacity. Research in our laboratory suggests that there is unlikely to be any spare capacity because the attentional resources are not 'fixed'. Rather, the resources are inextricably linked to task demand. This paper presents some of the arguments for considering the psychological aspects of the driver when designing automation into automobiles. The arguments are presented in a conversation format, based on discussions with Professor Don Norman. Extracts from relevant papers to support the arguments are presented

    Electric field emissions of FPGA chip based on gigahertz transverse electromagnetic cell modeling and measurements

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    Modern integrated circuits (ICs) are significant sources of undesired electromagnetic wave. Therefore, characterization of chip-level emission is essential to comply with EMC tests at the product level. A Gigahertz Transverse Electromagnetic (GTEM) cell is a common test instrument used to measure IC radiated emission and the test cost is relatively low. Regular IC radiated emission measurements using GTEM tend to neglect some significant emission sources. Thus, this research proposed an alternative methodology to perform field measurement of the IC inside the GTEM cell in order to optimize the field measurements. This research study also attempted analysis of the overall GTEM cell performance using transmission line theory. An FPGA chip was adopted as the IC under test because of its flexibility in configuration to any digital circuit. The investigations discovered that the impact of the FPGA board supporting components and interconnection cables can be significantly reduced with appropriate shielding and grounding. The electric field predict a far distance from the FPGA chip was carried out based on the dipole moment technique. In particular, the dipole moment model emphasizing the tiny horizontal and vertical radiation elements inside the FPGA chip as Hertzian antenna and small current loop. Equations to predict the horizontal and vertical electric field were developed based on Hertzian antenna and small current loop which relate the tiny radiation sources to electric and magnetic dipole moments. The prediction was validated with 3-meter field measurements in a semi-anechoic chamber. On top of that, a spiral-like pattern was developed to obtain a correction factor for further improvement of the correlation between prediction and SAC measurement. The results revealed that the correction factor effectively reduced the gap between the prediction and measurement fields and boosted the correlation coefficient by 44%. The difference of peak values also has limited to less than 0dB after correction. These results suggest a promising finding for a future EMI test of ICs with a cheaper GTEM cell

    Driver behaviour with adaptive cruise control

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    This paper reports on the evaluation of adaptive cruise control (ACC) from a psychological perspective. It was anticipated that ACC would have an effect upon the psychology of driving, i.e. make the driver feel like they have less control, reduce the level of trust in the vehicle, make drivers less situationally aware, but workload might be reduced and driving might be less stressful. Drivers were asked to drive in a driving simulator under manual and ACC conditions. Analysis of variance techniques were used to determine the effects of workload (i.e. amount of traffic) and feedback (i.e. degree of information from the ACC system) on the psychological variables measured (i.e. locus of control, trust, workload, stress, mental models and situation awareness). The results showed that: locus of control and trust were unaffected by ACC, whereas situation awareness, workload and stress were reduced by ACC. Ways of improving situation awareness could include cues to help the driver predict vehicle trajectory and identify conflicts

    Tracking technical refinement in elite performers: The good, the better, and the ugly

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    This study extends coaching research examining the practical implementation of technical refinement in elite-level golfers. In doing so, we provide an initial check of precepts pertaining to the Five-A Model and, examine the dynamics between coaching, psychomotor, biomechanical and psychological inputs to the process. Three case studies of golfers attempting refinements to their already well-established techniques are reported. Kinematic data were supplemented with intra-individual movement variability and self-perceptions of mental effort as measures of tracking behaviour and motor control. Results showed different levels of success in refining technique and subsequent ability to return to executing under largely subconscious control. In one case, the technique was refined as intended but without consistent reduction of conscious attention, in another, both were successfully apparent, whereas in the third case neither was achieved. Implications of these studies are discussed with reference to the process’ interdisciplinary nature and importance of the initial and final stages

    Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)

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    Universiti Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian Technical University. There is a great development of UTHM since its formation in 1993. Therefore, it is crucial to have accurate future electricity consumption forecasting for its future energy management and saving. Even though there are previous works of electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS), but most of their data are multivariate data. In this study, we have only univariate data of UTHM electricity consumption from January 2009 to December 2018 and wish to forecast 2019 consumption. The univariate data was converted to multivariate and ANFIS was chosen as it carries both advantages of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS). ANFIS yields the MAPE between actual and predicted electricity consumption of 0.4002% which is relatively low if compared to previous works of UTHM electricity forecasting using time series model (11.14%), and first-order fuzzy time series (5.74%), and multiple linear regression (10.62%)
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