THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK BASED TRAFFIC CLASSIFICATION FOR WIRELESS COMMUNICATIONS

2018 ◽  
Author(s):  
Jing Ran ◽  
Yexin Chen ◽  
Shulan Li
Keyword(s):  
Neural Network ◽  
Wireless Communications ◽  
Convolutional Neural Network ◽  
Three Dimensional ◽  
Traffic Classification

Finding Features of Positioning Error for Large Industrial Robots Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Daiki Kato ◽  
Kenya Yoshitugu ◽  
Naoki Maeda ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Production Systems ◽  
Industrial Robot ◽  
Three Dimensional ◽  
Industrial Robots ◽  
Numerical Control ◽  
Robot Motion ◽  
Positioning Error ◽  
End Effector

Abstract Most industrial robots are taught using the teaching playback method; therefore, they are unsuitable for use in variable production systems. Although offline teaching methods have been developed, they have not been practiced because of the low accuracy of the position and posture of the end-effector. Therefore, many studies have attempted to calibrate the position and posture but have not reached a practical level, as such methods consider the joint angle when the robot is stationary rather than the features during robot motion. Currently, it is easy to obtain servo information under numerical control operations owing to the Internet of Things technologies. In this study, we propose a method for obtaining servo information during robot motion and converting it into images to find features using a convolutional neural network (CNN). Herein, a large industrial robot was used. The three-dimensional coordinates of the end-effector were obtained using a laser tracker. The positioning error of the robot was accurately learned by the CNN. We extracted the features of the points where the positioning error was extremely large. By extracting the features of the X-axis positioning error using the CNN, the joint 1 current is a feature. This indicates that the vibration current in joint 1 is a factor in the X-axis positioning error.

scholarly journals RICNN: A ResNet&Inception convolutional neural network for intrusion detection of abnormal traffic

2021 ◽  
pp. 55-55
Author(s):  
Benhui Xia ◽  
Dezhi Han ◽  
Ximing Yin ◽  
Gao Na
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Detection Rate ◽  
Recognition Rate ◽  
Identification Accuracy ◽  
Detection Accuracy ◽  
Traffic Classification ◽  
High Detection Rate ◽  
Network Intrusion

To secure cloud computing and outsourced data while meeting the requirements of automation, many intrusion detection schemes based on deep learn ing are proposed. Though the detection rate of many network intrusion detection solutions can be quite high nowadays, their identification accuracy on imbalanced abnormal network traffic still remains low. Therefore, this paper proposes a ResNet &Inception-based convolutional neural network (RICNN) model to abnormal traffic classification. RICNN can learn more traffic features through the Inception unit, and the degradation problem of the network is eliminated through the direct map ping unit of ResNet, thus the improvement of the model?s generalization ability can be achievable. In addition, to simplify the network, an improved version of RICNN, which makes it possible to reduce the number of parameters that need to be learnt without degrading identification accuracy, is also proposed in this paper. The experimental results on the dataset CICIDS2017 show that RICNN not only achieves an overall accuracy of 99.386% but also has a high detection rate across different categories, especially for small samples. The comparison experiments show that the recognition rate of RICNN outperforms a variety of CNN models and RNN models, and the best detection accuracy can be achieved.

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