Abstract: The article deals with the readiness of military
professionals for challenging situations. It discusses higher
requirements on the psychical endurance of military professionals
arising from the specific nature of the military occupation, which is
typical for being very difficult to maintain regularity, which is in
accordance with the hygiene of work alternated by relaxation. The
soldier must be able to serve in the long term and constantly intense
performance that goes beyond human tolerance to stress situations. A
challenging situation is always associated with overcoming
difficulties, obstacles and complicated circumstances or using
unusual methods, ways and means to achieve the desired (expected)
objectives, performing a given task or satisfying an important need.
This paper describes the categories of challenging situations, their
classification and characteristics. Attention is also paid to the
formation of personality in challenging situations, coping with stress
in challenging situations, Phases of solutions of stressful situations,
resistance to challenging life situations and its factors. Finally, the
article is focused on increasing the readiness of military professionals
for challenging situations.
Abstract: In this paper, a novel algorithm based on Ridgelet
Transform and support vector machine is proposed for human action
recognition. The Ridgelet transform is a directional multi-resolution
transform and it is more suitable for describing the human action by
performing its directional information to form spatial features
vectors. The dynamic transition between the spatial features is carried
out using both the Principal Component Analysis and clustering
algorithm K-means. First, the Principal Component Analysis is used
to reduce the dimensionality of the obtained vectors. Then, the kmeans
algorithm is then used to perform the obtained vectors to form
the spatio-temporal pattern, called set-of-labels, according to given
periodicity of human action. Finally, a Support Machine classifier is
used to discriminate between the different human actions. Different
tests are conducted on popular Datasets, such as Weizmann and
KTH. The obtained results show that the proposed method provides
more significant accuracy rate and it drives more robustness in very
challenging situations such as lighting changes, scaling and dynamic
environment