How animals interact and develop social relationships regarding, individual attributes, sociodemographic and ecological pressures is of great interest. New methodologies, in particular Social Network Analysis, allow us to elucidate these types of questions. However, the different methodologies developed to that end and the speed at which they emerge make their use difficult. Moreover, the lack of communication between the different software developed to provide an answer to the same/different research questions is a source of confusion. The R package Animal Network Toolkit (ANT) was developed with the aim of implementing in one package the many different social network analysis techniques currently used in the study of animal social networks. Hence, ANT is a toolkit for animal research allowing among other things to: 1) measure global, dyadic and nodal networks metrics; 2) perform data randomization: pre-network and network (node and link) permutations; 3) perform statistical permutation tests. The package is partially coded in C++ for an optimal coding speed, and it gives researchers a workflow from raw data to the achievement of statistical analyses, allowing for a multilevel approach: from individual position and role within the network, to the identification of interaction patterns, and the analysis of the overall network properties.
Abstract. The grounded electrical source airborne transient
electromagnetic (GREATEM) system is an important method for obtaining
subsurface conductivity distribution as well as outstanding detection
efficiency and easy flight control. However, there are the superposition of
desired signals and various noises for the GREATEM signal. The baseline wander
caused by the receiving coil motion always exists in the process of data
acquisition and affects measurement results. The baseline wander is one of
the main noise sources, which has its own characteristics such as being low frequency, large amplitude, non-periodic, and non-stationary and so on.
Consequently, it is important to correct the GREATEM signal for an inversion explanation. In this paper, we propose improving the method of ensemble empirical mode decomposition (EEMD) by adaptive
filtering (EEMD-AF) based on EEMD to
suppress baseline wander. Firstly, the EEMD-AF method will decompose the
electromagnetic signal into multi-stage intrinsic mode function (IMF)
components. Subsequently, the adaptive filter will process higher-index IMF
components containing the baseline wander. Lastly, the de-noised signal will
be reconstructed. To examine the performance of our introduced method, we
processed the simulated and field signal containing the baseline wander by
different methods. Through the evaluation of the signal-to-noise ratio (SNR)
and mean-square error (MSE), the result indicates that the signal using
the EEMD-AF method can get a higher SNR and lower MSE. Comparing correctional data
using the EEMD-AF and the wavelet-based method in the anomaly curve profile
images of the response signal, it is proved that the EEMD-AF method is practical and effective for the suppression of the baseline wander in
the GREATEM signal.
In view of the fact that it is difficult for existing algorithms to identify the movements of a player in an accurate way, this paper puts forward an artificial intelligence (AI) motion model on the basis of the deep learning neural network instruction set architecture (ISA). Firstly, a mobile neural network (MNN) inference engine was utilized to create a new AI sports project-side intelligent practice model. Under this model, a movement can be segmented into a series of decomposition movements, which are recognized and judged separately for the purpose of measuring the entire movement. In order to test its feasibility, the study compares the MNN inference engine with the traditional reasoning engine in terms of their algorithmic capabilities and compares the results obtained through this algorithm and traditional online motion app. Research shows that, in the MNN of the AI sports project proposed in this paper, the datasets of action recognition exceed the results of other inference engines, characterized by lightweight, high performance, and accessibility. Research also demonstrates that the AI sports project model can adapt to the needs of sports projects with a variety of themes and improve the accuracy of movement recognition details.
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