/npsi/ctrl?lang=en http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/ctrl?lang=fr Access and use of this website and the material on it are subject to the Terms and Conditions set forth at http://nparc.cisti-icist.nrc-cnrc.gc.ca/npsi/jsp/nparc_cp.jsp?lang=en NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. Geotechnical Journal, 27, 3, pp. 320-329, 1990-06 An Energy approach for assessing seismic liquefaction potential Law, K. T.; Cao, Y. L.; He, G. N. An energy method for assessing liquefaction potential of granular soils was developed based on laboratory tests and observational data obtained in past major earthquakes. Cyclic triaxial and cyclic simple shear tests were conducted and the results show that a unique relation exists between the dissipated energy during cyclic load and the excess pore pressure that eventually led to liquefaction failure. This unique relation has been combined with an energy attenuation equation to develop a criterion for defining the liquefaction potential of a site. Parameters for the criterion were evaluated from 136 sites involved in 13 major earthquakes over the world. A comparison was made between the energy method and the commonly used stress method. The energy method was found to be simpler to apply and more reliable. Canadian

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Target threat assessment technology is one of the key technologies of intelligent tactical aid decision-making system. Aiming at the problem that traditional beyond-visual-range air combat threat assessment algorithms are susceptible to complex factors, there are correlations between assessment indicators, and accurate and objective assessment results cannot be obtained. A target threat assessment algorithm based on linear discriminant analysis (LDA) and improved glowworm swarm optimization (IGSO) algorithm to optimize extreme learning machine (ELM) is proposed in this paper. Firstly, the linear discriminant analysis method is used to classify the threat assessment indicators, eliminate the correlation between the assessment indicators, and achieve dimensionality reduction of the assessment indicators. Secondly, a prediction model with multiple parallel extreme learning machines as the core is constructed, and the input weights and thresholds of extreme learning machines are optimized by the improved glowworm swarm optimization algorithm, and the weighted integration is carried out according to the training level of the kernel. Then, the threat assessment index functions of angle, speed, distance, altitude, and air combat capability are constructed, respectively, and the sample data of air combat target threat assessment are obtained by combining the structure entropy weight method. Finally, the air combat data is selected from the air combat maneuvering instrument (ACMI), and the accuracy and real-time performance of the LDA-IGSO-ELM algorithm are verified through simulation. The results show that the algorithm can quickly and accurately assess target threats.

The authors would like to acknowledge the project, which is supported by the Innovation Method Fund of the Ministry of Science and Technology of China (Project2016IM010300). The authors also thank the anonymous reviewers for their valuable comments.