R/S analysis of reaction time in Neuron Type Test for human activity in civil aviation

Zhang, Hongyan; Kang, Ming-Cui; Li, Jingqiang; Liu, Haitao

doi:10.1016/j.physa.2016.11.054

Cited by 7 publications

(14 citation statements)

References 26 publications

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“…In [ 31 ], the R/S analysis is used to analyze the Hurst exponent [ 46 – 48 ] of the reaction time data collected from the NTT system. For a discrete reaction time sequence with length ℓ = d × n , it can be segmented into d subsequences in which the m -th subsequence is where m ∈ {0, ⋯, d − 1} and …”

Section: Mathematical Preliminariesmentioning

confidence: 99%

“…Usually, H ∈ (0, 1). For the reaction time sequences in NTT experiment, [ 31 ] shows that H > 0.5 means the process is persistent, H = 0.5 means that the process is near random, and H < 0.5 implies that the process is anti-persistent.…”

Section: Mathematical Preliminariesmentioning

confidence: 99%

“…It is feasible to analyze the VAP quantitatively with the help of our Neuron Type Test (NTT) system [ 31 ] and data collected (see S1 Appendix ). Once the response sequences of ATC students are collected with NTT, we can define, compute and analyze the VAP by modeling the IF with mathematical methods.…”

Section: Introductionmentioning

confidence: 99%

“…The history of aviation safety shows that it is vital to pay more attention on operators instead of equipments [ 44 ]. In this paper, we propose systematic assessment on individual’s IF via the computational IQ by analyzing the response sequence with R/S analysis [ 31 ], K -means clustering algorithm [ 45 ] and statistic inference so as to identify the relation of human error and information processing level specified by the IF. We deem that the predictive quantification assessment for human errors caused by inadequacy of attention qualities could be used for selecting controller candidates and risk management.…”

Section: Introductionmentioning

confidence: 99%

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Systematic assessment of intrinsic factors influencing visual attention performances in air traffic control via clustering algorithm and statistical inference

Zhang

et al. 2018

PLoS ONE

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The intrinsic factors (IF) influencing visual attention performance (VAP) might cause potential human errors, such as “error/mistake”, “forgetting” and “omission”. It is a key issue to develop a systematic assessment of IF in order to distinguish the levels of VAP. Motivated by the Stimulus-Response (S-R) model, we take an interactive cancellation test—Neuron Type Test (NTT)—to explore the IF and present the corresponding systematic assessment. The main contributions of this work include three elements: a) modeling the IF on account of attention span, attention stability, distribution-shift of attention with measurable parameters by combining the psychological and statistical concepts; b) proposing quantitative analysis methods for assessing the IF via its computational representation—intrinsic qualities (IQ)—in the sense of computational model; and c) clustering the IQ of air traffic control (ATC) students in the feature space of interest. The response sequences of participants collected with the NTT system are characterized by three parameters: Hurst exponent, normalized number of decisions (NNoD) and error rate of decisions (ERD). The K-means clustering is applied to partition the feature space constructed from practical data of VAP. For the distinguishable clusters, the statistical inference is utilized to refine the assessment of IF. Our comprehensive analysis shows that the IQ can be classified into four levels, i.e., excellent, good, moderate and unqualified, which has a potential application in selecting air traffic controllers subject to reducing the risk of the inadequacy of attention performances in aviation safety management.

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Section: Mathematical Preliminariesmentioning

confidence: 99%

Section: Mathematical Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Systematic assessment of intrinsic factors influencing visual attention performances in air traffic control via clustering algorithm and statistical inference

Zhang

et al. 2018

PLoS ONE

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“…Nepusz et al [25] modeled the overlapping community detection as a nonlinear constrained optimization problem which can be solved by simulated annealing methods. Zhang et al [26] proposed an algorithm based on the spectral clustering framework. Due to the probabilistic nature, mixture models provide an appropriate framework for overlapping community detection [27,28,29,30].…”

Section: Related Workmentioning

confidence: 99%

Leveraging disjoint communities for detecting overlapping community structure

Chakraborty¹

2015

J. Stat. Mech.

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Network communities represent mesoscopic structure for understanding the organization of real-world networks, where nodes often belong to multiple communities and form overlapping community structure in the network. Due to non-triviality in finding the exact boundary of such overlapping communities, this problem has become challenging, and therefore huge effort has been devoted to detect overlapping communities from the network.In this paper, we present PVOC (Permanence based Vertex-replication algorithm for Overlapping Community detection), a two-stage framework to detect overlapping community structure.We build on a novel observation that non-overlapping community structure detected by a standard disjoint community detection algorithm from a network has high resemblance with its actual overlapping community structure, except the overlapping part. Based on this observation, we posit that there is perhaps no need of building yet another overlapping community finding algorithm; but one can efficiently manipulate the output of any existing disjoint community finding algorithm to obtain the required overlapping structure. We propose a new post-processing technique that by combining with any existing disjoint community detection algorithm, can suitably process each vertex using a new vertex-based metric, called permanence, and thereby finds out overlapping candidates with their community memberships. Experimental results on both synthetic and large real-world networks show that PVOC significantly outperforms six state-of-the-art overlapping community detection algorithms in terms of high similarity of the output with the ground-truth structure. Thus our framework not only finds meaningful overlapping communities from the network, but also allows us to put an end to the constant effort of building yet another overlapping community detection algorithm.

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An Automatic Method for Generating Symbol Sequence with Markov Chain Coding for Test of Cognitive Information Processing Abilities

Zhang,

Chen,

Lin

2023

Lecture Notes in Electrical Engineering

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R/S analysis of reaction time in Neuron Type Test for human activity in civil aviation

Cited by 7 publications

References 26 publications

Systematic assessment of intrinsic factors influencing visual attention performances in air traffic control via clustering algorithm and statistical inference

Systematic assessment of intrinsic factors influencing visual attention performances in air traffic control via clustering algorithm and statistical inference

Leveraging disjoint communities for detecting overlapping community structure

An Automatic Method for Generating Symbol Sequence with Markov Chain Coding for Test of Cognitive Information Processing Abilities

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