scholarly journals Runoff Prediction Model Based on Improved Convolutional Neural Network

2021 ◽  
Author(s):  
Yichao Xu ◽  
Yi Liu ◽  
Zhiqiang Jiang ◽  
Xin Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activation Function ◽  
Hunan Province ◽  
Ann Model ◽  
One Dimensional ◽  
Runoff Series ◽  
Runoff Prediction ◽  
Hydropower Stations ◽  
And Storage

Abstract Due to the influence of human regulation and storage factors, the runoff series monitored at the hydropower stations often show the characteristics of non-periodicity, which makes runoff prediction simulation difficult. This paper attempts to construct an improved one-dimensional convolutional neural network (CNN) model for runoff prediction simulation. The improved CNN model consists of two convolution layers and a full connection layer and uses LeakyRelu as the activation function. Based on the historical rainfall and runoff data of the ZheXi reservoir in Hunan Province, this paper uses the improved CNN model to simulate runoff prediction and compares the results with the traditional ANN model and the traditional CNN model. The results show that the improved CNN model is superior to the traditional ANN model and the traditional CNN model. It proves that the improved CNN model is suitable for the non-periodic runoff prediction simulation, and it can avoid the data problems such as gradient disappearance that may occur in the traditional neural network model.

scholarly journals Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6350
Author(s):  
Bin Wu ◽  
Shibo Yuan ◽  
Peng Li ◽  
Zehuan Jing ◽  
Shao Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Radar Signal ◽  
Signal Recognition ◽  
Electromagnetic Environment ◽  
One Dimensional ◽  
The Given ◽  
And Storage

As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition. In recent years, convolutional neural network (CNN) has shown its superiority in recognition so that experts have applied it in radar signal recognition. However, in the field of radar emitter signal recognition, the data are usually one-dimensional (1-D), which takes more time and storage space than by using the original two-dimensional CNN model directly. Moreover, the features extracted from convolutional layers are redundant so that the recognition accuracy is low. In order to solve these problems, this paper proposes a novel one-dimensional convolutional neural network with an attention mechanism (CNN-1D-AM) to extract more discriminative features and recognize the radar emitter signals. In this method, features of the given 1-D signal sequences are extracted directly by the 1-D convolutional layers and are weighted in accordance with their importance to recognition by the attention unit. The experiments based on seven different radar emitter signals indicate that the proposed CNN-1D-AM has the advantages of high accuracy and superior performance in radar emitter signal recognition.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

scholarly journals Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 262
Author(s):  
Chih-Yung Huang ◽  
Zaky Dzulfikri
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Template Matching ◽  
Health Condition ◽  
Vibration Measurement ◽  
One Dimensional ◽  
Stamping Process ◽  
Metalworking Industry ◽  
Increasing Demand ◽  
Wear Condition

Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models.

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