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Distributed situation awareness in dynamic systems: theoretical development and application of an ergonomics methodology
Published in Eduardo Salas, Aaron S Dietz, Situational Awareness, 2017
N.A. Stanton, R. Stewart, D. Harris, R.J. Houghton, C. Baber, R. Mcmaster, P. Salmon, G. Hoyle, G. Walker, M.S. Young, M. Linsell, R. Dymott, D. Green
Propositional networks are like semantic networks in that they contain nodes (with words) and links between nodes, but differ in two ways. First, the words are not necessarily randomly added to the network but involve the definition of propositions. A proposition is a basic statement, i.e. ‘the smallest unit about which it makes sense to make the judgement true or false’ (Anderson 1980, p. 102). Secondly, the links between words are labelled to define the relationship between propositions. These relations might be in terms of subject and object (in grammatical terms), with a corresponding relation term. On the basis of such descriptions, it is possible to claim that one can produce dictionarylike definitions of concepts through the application of basic propositions and operators (Ogden 1987).
A Simplified Prediction Model of Structural Seismic Vulnerability Considering a Multivariate Fuzzy Membership Algorithm
Published in Journal of Earthquake Engineering, 2023
According to fuzzy complementary judgment sequence theory (Harirchian 2020), it is assumed that is the fuzzy symmetric complementary matrix and , . Given the assumption that is the synthetic fuzzy matrix (l = 1, 2, , s) under the influence of multivariate factors, , , and ＞ 0，. The fuzzy matrix of s influencing factors is synthesized, and is obtained.
Smart water management framework for irrigation in agriculture
Published in Environmental Technology, 2022
Akashdeep Bhardwaj, Manoj Kumar, Mohammed Alshehri, Ismail Keshta, Ahed Abugabah, Sunil Kumar Sharma
Industrial Internet of Things (IIoT) is a physical information service built on top of existing industrial control networks. The IIoT is a popular target for adversaries engaging in advanced persistent threats since it is one of the most vital infrastructure systems (APTs). [16] investigated a deep-learning-based proactive APT detection technique in IIoT to overcome this issue. The suggested approach used a well-known deep learning model, bidirectional encoder representations from Transformers (BERT), to detect APT attack sequences, taking into account the features of extended attack sequences and long-term continuous APT attacks. To assure the model's long-term sequence judgment efficacy, the APT attack sequence is also optimised. The results demonstrate that the proposed deep learning approach is not only feasible and effective for APT detection but also that the BERT model has greater accuracy and a lower probability of false alarm than existing time series models for detecting APT attack sequences.
Multiple collaborative filtering recommendation algorithms for electronic commerce information
Published in International Journal of Computers and Applications, 2021
Based on the extracted trend value characteristics of each sliding window, the relative change trend value of the sliding window is defined, the second feature is , Based on Equation (11) gives the second judgment model: Assume that there are dependent variable for the MTS sequence with variables. The relative change trend value of the jth target window for these variables is . If Equation (12) is satisfied, the MTS sequence is considered abnormal in the jth window µ represents the detection threshold.