Figure 1 - available via license: Creative Commons Attribution 3.0 Unported
Content may be subject to copyright.
Contexts in source publication
Context 1
... box, we enlarge the width of the box appropriately, and then send the content of the enlarged box to the smoking recognition model to identify whether someone smokes. The specific frame is shown in Fig. ...
Context 2
... the test data, the test process has two forms as shown in Fig. 1. First, the smoking model and the object recognition model are all trained, and then we need no too complicated operations. Instead, we can simply configure the configuration file to complete the two formats of Fig. 1. The configuration of the two processes are as ...
Context 3
... the test data, the test process has two forms as shown in Fig. 1. First, the smoking model and the object recognition model are all trained, and then we need no too complicated operations. Instead, we can simply configure the configuration file to complete the two formats of Fig. 1. The configuration of the two processes are as ...
Similar publications
The analysis on economic data based on time series takes an important position in the field of analysis on time-series data and is also an important task of the field of big data and artificial intelligence. Traditional time-series analysis method is of relatively weak competence in dealing with multi-series analysis. In this research, based on the...
A swarm of robots can collectively select an option among the available alternatives offered by the environment through a process known as collective decision-making. This process is characterised by the fact that once the group makes a decision, it can not be attributed to any of its group members. In swarm robotics, the individual mechanisms for...
Graph convolutional networks (GCNs) are a family of neural network models that perform inference on graph data by interleaving vertex-wise operations and message-passing exchanges across nodes. Concerning the latter, two key questions arise: (i) how to design a differentiable exchange protocol (e.g., a 1-hop Laplacian smoothing in the original GCN)...
This paper focuses on the different bifurcations of fixed points of a delayed discrete neural network model analytically and numerically. The conditions and critical values of different bifurcations including the pitchfork, flip, Neimark–Sacker, and flip–Neimark–Sacker are analyzed. By using the critical coefficients, the structure for each bifurca...
