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EFFECTS OF TIME-OF-DAY ON INTERPRETING SIGNS AND ICONS
Published in Paul T. McCabe, Contemporary Ergonomics 2004, 2018
Sine McDougall, Victoria Tyrer, Simon Folkard
In the first experiment of the series, designed to replicate McFadden and Tepas' study, participants searched through displays of icons for a specified target icon and indicated whether or not the target icon was present at 4 times of day (0900, 1200, 1500, 1800). Complex icons consisted of 3 pieces of information: distance (400, 300, 200, 100 yards), route (square, triangle, circle, diamond, star) and direction (straight on, left, right; see Figure 1(b)). Simple icons consisted of two pieces of information: distance and route. In line with previous research, responses were slower for complex icons (M=1675 msecs), which required more visual processing, than for simple icons (M=1321 msecs). Importantly, timeof-day effects were evident in the time it took participants to respond in the icon task. Response times were significantly faster in early/mid-morning (0900/1200) and were slowest at 1500. These findings are summarised in Table 1. They provide support for McFadden & Tepas' original assertion that time-of-day effects are important in determining the ease with which icons can be interpreted.
Flight Management Systems
Published in Cary R. Spitzer, Uma Ferrell, Thomas Ferrell, Digital Avionics Handbook, 2017
The lateral guidance function typically computes dynamic guidance data based on the predicted lateral profile described in Section 24.2.3. The data are comprised of the classic horizontal situation information: Distance to go to the active lateral waypoint (DTG)Desired track (DTRK)Track angle error (TRKERR)Cross-track error (XTRK)Drift angle (DA)Bearing to the go to waypoint (BRG)Lateral track change alert (LNAV alert)
Saving Money with Homegrown Ideas
Published in Kim H. Pries, Jon M. Quigley, Reducing Process Costs with Lean, Six Sigma, and Value Engineering Techniques, 2012
We can summarize this approach with the following comments: We can manage 12 subtopics of one general topic in a networked and self-organizing way.We network the participants together to the maximum degree possible.The information “distance” between the topics is minimized.All the 12 topics are networked not only by members but also by critics and observers.The division of the roles into three (member, critic, and observer) makes possible a clear division of tasks and a clear focus of concentration for the participants.
A novel approach to measuring enterprise procurement decision process: an information distance perspective
Published in Enterprise Information Systems, 2020
Xiong Li, Xiaodong Zhao, Wei Pu
The concept of information distance came from the research field of information theory and theoretical computer science (Bennett et al. 1998; Vyugin 2002; Li 2006; Vitányi 2011; Zhang et al. 2012; Patil et al. 2013; Sahoo et al. 2018; Lavor et al. 2019). Information distance is a parameter-free similarity measure based on compression, used in pattern recognition, data mining, phylogeny, clustering and classification (Vitányi 2011). Based on the concept of information distance, the distance of information-state transition (DIT) theory has been proposed and used in information measurement research of website and network from the qualitative and quantitative aspects (Wang 2004; Chi, Wang, and Chen 2007; Jin and Liu X-D 2010; Zhang 2011; Yuan and Chen 2012). DIT can make the direct quantitative measurement of the convenience of management, control and operation possible. This paper tries to resolve the above problem through quantitative models using the DIT theory. Thus, in our study, we propose a novel DIT-based measurement approach for enterprise procurement decision process.
Prediction of stock movement using phase space reconstruction and extreme learning machines
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2020
Parus Khuwaja, Sunder Ali Khowaja, Imamuddin Khoso, Intizar Ali Lashari
In such a case, the correlation information is extracted from the raw values and projected to high dimensional feature space. In this work, we provide a way to transform the existing low-dimensional feature space to high dimensions to extract the correlation information using PSR. Note that the correlation information in the proposed framework is modelled from the distributions instead of raw values. The information distance computes the relationship between different features of the same category by considering their underlying distributions. Some examples of transformation using PSR are shown in Figure 4.