scholarly journals Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6685
Author(s):  
Pu Yanan ◽  
Yan Jilong ◽  
Zhang Heng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Sensors ◽  
Inertial Sensors ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Table Tennis ◽  
User Privacy ◽  
Tennis Players ◽  
Wearable Inertial Sensors

Compared with optical sensors, wearable inertial sensors have many advantages such as low cost, small size, more comprehensive application range, no space restrictions and occlusion, better protection of user privacy, and more suitable for sports applications. This article aims to solve irregular actions that table tennis enthusiasts do not know in actual situations. We use wearable inertial sensors to obtain human table tennis action data of professional table tennis players and non-professional table tennis players, and extract the features from them. Finally, we propose a new method based on multi-dimensional feature fusion convolutional neural network and fine-grained evaluation of human table tennis actions. Realize ping-pong action recognition and evaluation, and then achieve the purpose of auxiliary training. The experimental results prove that our proposed multi-dimensional feature fusion convolutional neural network has an average recognition rate that is 0.17 and 0.16 higher than that of CNN and Inception-CNN on the nine-axis non-professional test set, which proves that we can better distinguish different human table tennis actions and have a more robust generalization performance. Therefore, on this basis, we have better realized the enthusiast of table tennis the purpose of the action for auxiliary training.

Flower growth status recognition method based on feature fusion convolutional neural network

2021 ◽  
pp. 1-12
Author(s):  
Haiming Liu ◽  
Shixuan Guan ◽  
Weizhong Lu ◽  
Haiou Li ◽  
Hongjie Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Input Data ◽  
Network Performance ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Growth Environment ◽  
Growth Status ◽  
Current Growth ◽  
Flower Growth

The growth state of flowers is affected by many factors such as temperature, humidity, and light. Therefore, the maintenance of flowers often requires more professional knowledge. Ordinary people are often at a loss when face with various flower representations and do not know where the problem is. In response to the above problems, this article proposes the use of deep learning to identify the growth status of flowers to assist people in successfully raising flowers. In this article, we propose that the mainstream convolutional neural network has the limitation of only inputting images. In terms of network input, data of the current growth environment of flowers will also be input to supplement the input data of the network. In view of the lack of information interaction in the network, in terms of network structure, the shallow and deep characteristics of the network are integrated to make the network performance more advantageous. Experiments show that this method can effectively improve the recognition rate of flower growth status, so as to correctly distinguish the current growth status of flowers.

scholarly journals Hand-drawn sketch recognition with a double-channel convolutional neural network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Input Image ◽  
Contour Extraction ◽  
Sketch Recognition ◽  
Softmax Classifier ◽  
Dual Channel ◽  
Extraction Algorithm

AbstractIn hand-drawn sketch recognition, the traditional deep learning method has the problems of insufficient feature extraction and low recognition rate. To solve this problem, a new algorithm based on a dual-channel convolutional neural network is proposed. Firstly, the sketch is preprocessed to get a smooth sketch. The contour of the sketch is obtained by the contour extraction algorithm. Then, the sketch and contour are used as the input image of CNN. Finally, feature fusion is carried out in the full connection layer, and the classification results are obtained by using a softmax classifier. Experimental results show that this method can effectively improve the recognition rate of a hand-drawn sketch.

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.

scholarly journals Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

2020 ◽  
Vol 17 (4) ◽  
pp. 572-578
Author(s):  
Mohammad Parseh ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

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