scholarly journals Computationally Efficient Wildfire Detection Method Using a Deep Convolutional Network Pruned via Fourier Analysis

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2891 ◽  
Author(s):  
Hongyi Pan ◽  
Diaa Badawi ◽  
Ahmet Enis Cetin
Keyword(s):  
Neural Network ◽  
Detection Method ◽  
Fire Detection ◽  
Computationally Efficient ◽  
Convolutional Network ◽  
Video Clips ◽  
Wild Fire ◽  
The Neural Network ◽  
Analysis Strategy ◽  
Regular Network

In this paper, we propose a deep convolutional neural network for camera based wildfire detection. We train the neural network via transfer learning and use window based analysis strategy to increase the fire detection rate. To achieve computational efficiency, we calculate frequency response of the kernels in convolutional and dense layers and eliminate those filters with low energy impulse response. Moreover, to reduce the storage for edge devices, we compare the convolutional kernels in Fourier domain and discard similar filters using the cosine similarity measure in the frequency domain. We test the performance of the neural network with a variety of wildfire video clips and the pruned system performs as good as the regular network in daytime wild fire detection, and it also works well on some night wild fire video clips.

An Efficient Neural Network Model for Path Planning of Car-like Robots in Dynamic Environment

2000 ◽  
Vol 4 (3) ◽  
pp. 220-229 ◽  
Author(s):  
Simon X. Yang ◽  
◽  
Max Meng ◽  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Real Time ◽  
Dynamic Environment ◽  
Lyapunov Stability Theory ◽  
Network Size ◽  
Computationally Efficient ◽  
Neural Network Approach ◽  
The Neural Network ◽  
The Stability

In this paper, an effcient neural network approach to real-time path planning with obstacle avoidance of holonomic car-like robots in a dynamic environment is proposed. The dynamics of each neuron in this biologically inspired, topologically organized neural network is characterized by a shunting equation or an additive equation. The state space of the neural network is the configuration space of the robot. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The real-time collision-free path is planned through the dynamic neural activity landscape of the neural network without explicitly searching over neither the free workspace nor the collision paths, without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of the robot movement. Therefore it is computationally efficient. The stability of the neural network is proven by both qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency are demonstrated through simulation studies.

scholarly journals DESIGN AND ANALYSIS OF AN INTELLIGENT FIRE DETECTION SYSTEM FOR AIRCRAFT

2020 ◽  
Vol 5 (2) ◽  
pp. 260-273
Author(s):  
Amit Yadav ◽  
Abhijeet Agawal ◽  
Pramod Kumar ◽  
Tejaswi Sachwani
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Fire Detection ◽  
Sensor Data ◽  
Correct Prediction ◽  
The Neural Network ◽  
Fire Condition ◽  
Fire Alarm System ◽  
Actual Fire ◽  
Fire Detection System

Fire detection system and fire warning are design features of an aircraft. Fire detection system protects the aircraft and passengers both in case of actual fire during flight. But spurious fire warning during flight creates a panic situation in flight crews and passengers. The conventional fire alarm system of an aircraft is triggered by false signal. ANN based fire detection system provides real observation of deployed zones. An intelligent fire detection system is developed based on artificial neural network using three detection information such as heat (temperature), smoke density and CO gas. This Information helps in determining the probability of three representative of Fire condition which is Fire, smoke and no fire. The simulated MATLAB results Show that the errors in identification are very less. The neural network based fire detection system integrates different types of sensor data and improves the ability of system to correct prediction of fires. It gives early alarm when any kind of fire broke out and helps to decrease in spurious warning.

scholarly journals Series Arc Fault Detection Method Based on Category Recognition and Artificial Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1367
Author(s):  
Xiangyu Han ◽  
Dingkang Li ◽  
Lizong Huang ◽  
Hanqing Huang ◽  
Jin Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Detection Method ◽  
Recognition Rate ◽  
Local Optimum ◽  
Line Current ◽  
The Neural Network ◽  
Artificial Neural ◽  
Simple Neural Network

The influence of a series arc on line current is different with different loads, which makes it difficult to accurately extract arc fault characteristics suitable for all loads according to line current signal. To improve the accuracy of arc fault detection, a series arc fault detection method based on category recognition and an artificial neural network is proposed on the basis of analyzing the current characteristics of arc faults under different loads. According to the waveform of current and voltage, the load is divided into three types: Resistive category (Re), resistive-inductive category (RI), and rectifying circuit with a capacitive filter category (RCCF). Based on the wavelet transform, the characteristics of line current in the time domain and frequency domain when the series arc occurs under different types of loads are analyzed, and then the time and frequency indicators are taken as the inputs of the artificial neural network to establish three-layer neural networks corresponding to three types of loads to realize the detection of the series arc fault of lines under different categories of loads. To avoid the neural network falling into a local optimum, the initial weight and threshold of the neural network are optimized by a genetic algorithm, which further improves the accuracy of the neural network in arc identification. The experimental results show that the proposed arc detection method has the advantages of high recognition rate and a simple neural network model.

Fire Detection Method Based on Localization Confidence and Region-Based Fully Convolutional Network

2020 ◽  
Vol 57 (20) ◽  
pp. 201021
Author(s):  
张鸿 Zhang Hong ◽  
严云洋 Yan Yunyang ◽  
刘以安 Liu Yian ◽  
高尚兵 Gao Shangbing
Keyword(s):  
Detection Method ◽  
Fire Detection ◽  
Convolutional Network ◽  
Fully Convolutional Network

Research on Fault Detection Method of Converters

2012 ◽  
Vol 229-231 ◽  
pp. 1150-1153
Author(s):  
Wen Zhong Ma ◽  
Ke Cheng Chen ◽  
Yang Shan ◽  
Yan Li Wang
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Fault Location ◽  
Detection Method ◽  
Fault Tolerant ◽  
Experimental Results ◽  
The Neural Network ◽  
Operation Control ◽  
Fault Information

The influence of converter faults to the system is introduced and the fault detection method based on wavelet transform and neural network is proposed in this paper. The fault information can be decomposed by wavelet transform, then the fault eigenvectors can be extracted and put into neural network for training and testing. Finally the neural network outputs specific codes, and thus the fault location and fault components of converters are confirmed, which lays the foundation for the fault-tolerant operation control of converters. Simulation and experimental results show the correctness and effectiveness of the method.

scholarly journals Heterogeneous convolutional neural networks to build movement routes of the objects in spatial environment with accumulated energy potential

2021 ◽  
Vol 16 (93) ◽  
pp. 21-37
Author(s):  
Yuriy N. Lavrenkov ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Processing ◽  
Optimal Path ◽  
Rhythmic Activity ◽  
Main Role ◽  
Energy Potential ◽  
Convolutional Network ◽  
The Neural Network ◽  
Neural Populations

We consider the synthesis of a hybrid neural convolutional network with the modular topology-based architecture, which allows to arrange a parallel convolutional computing system to combine both the energy transfer and data processing, in order to simulate complex functions of natural biological neural populations. The system of interlayer neural commutation, based on the distributed resonance circuits with the layers of electromagnetic metamaterial between the inductive elements, is a base for simulation of the interaction between the astrocyte networks and the neural clusters responsible for information processing. Consequently, the data processing is considered both at the level of signal transmission through neural elements, and as interaction of artificial neurons and astrocytic networks ensuring their functioning. The resulting two-level neural system of data processing implements a set of measures to solve the issue based on the neural network committee. The specific arrangement of the neural network enables us to implement and configure the educational procedure using the properties absent in the neural networks consisting of neural populations only. The training of the convolutional network is based on a preliminary analysis of rhythmic activity, where artificial astrocytes play the main role of interneural switches. The analysis of the signals moving through the neural network enables us to adjust variable components to present information from training bunches in the available memory circuits in the most efficient way. Moreover, in the training process we observe the activity of neurons in various areas to evenly distribute the computational load on neural network modules to achieve maximum performance. The trained and formed convolutional network is used to solve the problem of determining the optimal path for the object moving due to the energy from the environment

scholarly journals Text classification using convolutional neural network

2019 ◽  
pp. 219-226
Author(s):  
L E Sapozhnikova ◽  
O A Gordeeva
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Classification Accuracy ◽  
Convolutional Network ◽  
The Neural Network ◽  
Internet Information ◽  
And Control ◽  
Classification Quality ◽  
Control Samples

In this article, the method of text classification using a convolutional neural network is presented. The problem of text classification is formulated, the architecture and the parameters of a convolutional neural network for solving the problem are described, the steps of the solution and the results of classification are given. The convolutional network which was used was trained to classify the texts of the news messages of Internet information portals. The semantic preprocessing of the text and the translation of words into attribute vectors are generated using the open word2vec model. The analysis of the dependence of the classification quality on the parameters of the neural network is presented. The using of the network allowed obtaining a classification accuracy of about 84%. In the estimation of the accuracy of the classification, the texts were checked to belong to the group of semantically similar classes. This approach allowed analyzing news messages in cases where the text themes and the number of classification classes in the training and control samples do not equal.

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