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Defeating Ignorance – Ius ad Bellum Heuristics for Modern Professional Soldiers

100%

Zając M.

Diametros

2019

tom 16

nr 62

78-94

Just War Theory debates discussing the principle of the Moral Equality of Combatants (MEC) involve the notion of Invincible Ignorance; the claim that warfighters are morally excused for participating in an unjust war because of their epistemic limitations. Conditions of military deployment may indeed lead to genuinely insurmountable epistemic limitations. In other cases, these may be overcome. This paper provides a preliminary sketch of heuristics designed to allow a combatant to judge whether or not his war is just. It delineates the sets of relevant facts uncontroversially accessible and inaccessible to contemporary professional soldiers. Relevant facts outside these two sets should by default be treated as inaccessible until proven otherwise. Even such a rudimentary heuristic created in this way demonstrates that practical recommendations of MEC-renouncing Just War Theory are not too challenging to follow and still significantly impact a compliant combatant’s behavior.

Convolutional neural networks in the SSI analysis for mine-induced vibrations

63%

Zając M. , Kuźniar K.

Computer Assisted Methods in Engineering and Science

2024

tom Vol. 31, no. 1

3--28

Deep neural networks (DNNs) have recently become one of the most often used softcomputational tools for numerical analysis. The huge success of DNNs in the field of imageprocessing is associated with the use of convolutional neural networks (CNNs). CNNs,thanks to their characteristic structure, allow for the effective extraction of multi-layerfeatures. In this paper, the application of CNNs to one of the important soil-structureinteraction (SSI) problems, i.e., the analysis of vibrations transmission from the free-field next to a building to the building foundation, is presented in the case of mine-induced vibrations. To achieve this, the dataset from in-situ experimental measurements,containing 1D ground acceleration records, was converted into 2D spectrogram imagesusing either Fourier transform or continuous wavelet transform. Next, these images wereused as input for a pre-trained CNN. The output is a ratio of maximal vibration valuesrecorded simultaneously on the building foundation and on the ground. Therefore, the lastlayer of the CNN had to be changed from a classification to a regression one. The obtainedresults indicate the suitability of CNN for the analyzed problem.