A Priori Modeling of NO Formation with Principal Component Analysis and the Convolutional Neural Network in the Context of Large Eddy Simulation

2021 ◽  
Author(s):  
Jiahao Ren ◽  
Haiou Wang ◽  
Kun Luo ◽  
Jianren Fan
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Large Eddy Simulation ◽  
Convolutional Neural Network ◽  
A Priori ◽  
Principal Component ◽  
Component Analysis ◽  
Eddy Simulation ◽  
No Formation ◽  
Large Eddy

scholarly journals PROSES EKSTRAKSI DAN KLASIFIKASI CITRA EMOSI MENGGUNAKAN METODE PCA DAN CNN

JOUTICA ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 484
Author(s):  
Resty Wulanningrum ◽  
Anggi Nur Fadzila ◽  
Danar Putra Pamungkas
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Recognition

Manusia secara alami menggunakan ekspresi wajah untuk berkomunikasi dan menunjukan emosi mereka dalam berinteraksi sosial. Ekspresi wajah termasuk kedalam komunikasi non-verbal yang dapat menyampaikan keadaan emosi seseorang kepada orang yang telah mengamatinya. Penelitian ini menggunakan metode Principal Component Analysis (PCA) untuk proses ekstraksi ciri pada citra ekspresi dan metode Convolutional Neural Network (CNN) sebagai prosesi klasifikasi emosi, dengan menggunakan data Facial Expression Recognition-2013 (FER-2013) dilakukan proses training dan testing untuk menghasilkan nilai akurasi dan pengenalan emosi wajah. Hasil pengujian akhir mendapatkan nilai akurasi pada metode PCA sebesar 59,375% dan nilai akurasi pada pengujian metode CNN sebesar 59,386%.

scholarly journals REAL-TIME CLASSIFICATION OF FACIAL EXPRESSIONS USING A PRINCIPAL COMPONENT ANALYSIS AND CONVOLUTIONAL NEURAL NETWORK

SINERGI ◽  
2019 ◽  
Vol 23 (3) ◽  
pp. 239
Author(s):  
Dwi Lydia Zuharah Astuti ◽  
Samsuryadi Samsuryadi ◽  
Dian Palupi Rini
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Convolutional Neural Network ◽  
Customer Service ◽  
Facial Expressions ◽  
Principal Component ◽  
Component Analysis ◽  
Point Of View ◽  
In The Wild

Classification of facial expressions has become an essential part of computer systems and human-computer fast interaction. It is employed in various applications such as digital entertainment, customer service, driver monitoring, and emotional robots. Moreover, it has been studied through several aspects related to the face itself when facial expressions change based on the point of view or perspective. Facial curves such as eyebrows, nose, lips, and mouth will automatically change. Most of the proposed methods have limited frontal Face Expressions Recognition (FER), and their performance decrease when handling non-frontal and multi-view FER cases.  This study combined both methods in the classification of facial expressions, namely the Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) methods. The results of this study proved to be more accurate than that of previous studies. The combination of PCA and CNN methods in the Static Facial Expressions in The Wild (SFEW) 2.0 dataset obtained an accuracy amounting to 70.4%; the CNN method alone only obtained an accuracy amounting to 60.9%.

scholarly journals Principal Component Analysis and Prediction of Students’ Physical Health Standard Test Results Based on Recurrent Convolution Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kai Hou
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Physical Fitness ◽  
Physical Health ◽  
Convolutional Neural Network ◽  
Deep Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Standing Long Jump ◽  
Long Jump

The recurrent convolutional neural network is an advanced neural network that integrates deep structure and convolution calculation. The feedforward neural network with convolution operation and deep structure is an important method of deep learning. In this paper, the convolutional neural network and the recurrent neural network are combined to establish a recurrent convolutional neural network model composed of anomalies, LSTM (Long Short-Term Memory), and CNN. This study combines the principal component analysis method to predict and analyze the test results of students’ physical fitness standards. The innovation lies in the introduction of the function of the recurrent convolutional network and the use of principal component analysis to conduct qualitative research on seven evaluation indicators that reflect the three aspects of students’ physical health. The results of the study clearly show that there is a strong correlation between some indicators, such as standing long jump and sitting bends which may have a strong correlation. The first principal component eigenvalue has the highest contribution rate, which mainly reflects the five indicators of standing long jump, sitting forward bend, pull-up, 50 m sprint, and 1000 m long-distance running. This shows that the physical fitness indicators have a great impact on the physical health of students, which also reflects the current status of students’ physical fitness problems. The results of principal component analysis are scientific and reasonable.

scholarly journals A Novel P300 Classification Algorithm Based on a Principal Component Analysis-Convolutional Neural Network

2020 ◽  
Vol 10 (4) ◽  
pp. 1546 ◽  
Author(s):  
Feng Li ◽  
Xiaoyu Li ◽  
Fei Wang ◽  
Dengyong Zhang ◽  
Yi Xia ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Convolutional Neural Network ◽  
Brain Computer Interface ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Algorithm ◽  
Computer Interface ◽  
Daily Lives ◽  
Accuracy Rates

Aiming at enhancing the classification accuracy of P300 Electroencephalogram signals in a non-invasive brain–computer interface system, a novel P300 electroencephalogram signals classification algorithm is proposed which is based on improved convolutional neural network. In the data preprocessing part, the proposed P300 classification algorithm used the Principal Component Analysis algorithm to not only remove the noise and artifacts in the data, but also increase the data processing speed. Furthermore, the proposed P300 classification algorithm employed the parallel convolution method to improve the traditional convolutional neural network framework, which can increase the network depth and improve the network’s ability to classify P300 electroencephalogram signals. The proposed algorithm was evaluated by two datasets (the dataset from the competition and the dataset from the laboratory). The results show that, in the dataset I, the proposed P300 classification algorithm could obtain accuracy rates higher than 95%, and achieve one of the best performances in four classification algorithms, while, in the dataset II, the proposed P300 classification algorithm can get accuracy rates higher than 90%, and is superior to the other three algorithms in all ten subjects. These demonstrated the effectiveness of the proposed algorithm. The proposed classification algorithm can be applied in the actual brain–computer interface systems to help people with disability in the daily lives.

scholarly journals Convolutional Neural Network-Based Fish Posture Classification

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin Li ◽  
Anzi Ding ◽  
Shaojie Mei ◽  
Wenjin Wu ◽  
Wenguang Hou
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Binary Classification ◽  
Principal Component ◽  
Classification Problem ◽  
Histogram Equalization ◽  
Component Analysis ◽  
Actual Situation

Fish killing machines can effectively relieve the workers from the backbreaking labour. Generally, it is necessary to ensure the fish to be in unified posture before being input into the automatic fish killing machine. As such, how to detect the actual posture of fish in real time is a new and meaningful issue. Considering that in the actual situation, we only need to determine the four postures which are related to the head, tail, back, and belly of the fish, and we transfer this task into a four-kind classification problem. As such, the convolutional neural network (CNN) is introduced here to do classification and then to detect the fish’s posture. Before training the network, all sample images are preprocessed to make the fish be horizontal on the image according to the principal component analysis. Meanwhile, the histogram equalization is used to make the grey distribution of different images be close. After that, two kinds of strategies are taken to do classification. The first is a paired binary classification CNN and the second is a four-category CNN. In addition, three kinds of CNN are adopted. By comparison, the four-kind classification can obtain better results with error less than 1/1000.

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