Infrared Target Extraction Based on Immune Extension Neural Network

Dong, Enzeng; Hao, Jian; Du, Shengzhi; Dong, Chun

doi:10.1109/access.2020.2978659

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…The convolution layer and pooling layer enable the CNN to identify image details, whereas other neural networks extract data for operations. The ENN is a classification recognition method formed by combining extension theory with a neural network [36][37][38][39]. When the neural network is used for classification and recognition, the extension theory is integrated into the neural network to efficiently use the extension correlation grade computing method and shorten the neural network training time.…”

Section: Convolutional Extension Neural Network (Cenn)mentioning

confidence: 99%

A novel fault diagnosis method for PV arrays using convolutional extension neural network with symmetrized dot pattern analysis

Lu,

Wu,

Sian

2023

IET Science Measure & Tech

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PV fault diagnosis remains difficult due to the non‐linear characteristic of PV output, which makes PV output to be likely disturbed by the ambient environment. This study proposes a novel convolutional extension neural network (CENN) algorithm, which is a jointed architecture based on convolutional neural network (CNN) and extension neural network (ENN), takes advantage of CNN and ENN. The CENN is combined with the symmetrized dot pattern (SDP) analysis method to diagnose the common eight PV array faults. The SDP is used to transform the measured PV signals into the point coordinate feature image; then, the CENN is trained to identify the different PV faults. Experimental results show an obvious improvement in short detection times and high accuracy compared with traditional CNN and the histogram of oriented gradient (HOG) extraction method with support vector machine (SVM), K‐nearest neighbours (KNN), and back propagation neural network (BPNN) classifiers, with 95.3%, 94%, 93.5%, and 93.3% accuracy, respectively. Using the proposed CENN, the accuracy can be raised to 97.3%. Additionally, the signals measured by various sensors are collected using programmable logic controller (PLC). The human–machine interface (HMI) and the proposed algorithm are developed using LabVIEW for graphical design. Finally, the information is transmitted to a tablet PC for performing real‐time remote monitoring.

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Section: Convolutional Extension Neural Network (Cenn)mentioning

confidence: 99%

A novel fault diagnosis method for PV arrays using convolutional extension neural network with symmetrized dot pattern analysis

Lu,

Wu,

Sian

2023

IET Science Measure & Tech

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An Improved Smart Helmet for Safe Travel of Deaf People Based on Embedded System

Sun,

Luo,

Wang

et al. 2024

2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI)

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Creating Smart House via IoT and Intelligent Computation

Sung¹,

Hsiao²

2023

Intelligent Automation &Amp; Soft Computing

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This study mainly uses the concept of the Internet of Things (IoT) to establish a smart house with an indoor, comfortable, environmental, and real-time monitoring system. In the smart house, this investigation employed the temperature-and humidity-sensing module and the lightness module to monitor any condition for an intelligent-living house. The data of temperature, humidity, and lightness are transmitted wirelessly to the human-machine interface. The correlation of the weight of the extension theory is used to analyze the ideal and comfortable environment so that people in the indoor environment can feel better thermal comfort and lightness. In this study, improved particle swarm optimization (IPSO) is employed-an effective evolutionary method used to search the function extreme. It is simple and has a fast convergence. The convergence accuracy of this algorithm is not high at the beginning, and it can easily fall into the local extreme points. The effect of the inertia weight in mix extension theory and PSO becomes IPSO-Extension Neural Network (ENN), which was analyzed and found reliable. Motivated by the idea of power function, a new non-linear strategy for decreasing inertia weight (DIW) was proposed based on the existing linear DIW. Then, a novel hierarchical multi-sensor data fusion algorithm adopting this strategy was presented, and the weight factor of the data fusion was estimated. The distinctive feature of this algorithm is its capability of fusing data in a near-optimal manner when there is no available information about the reliability of the information sources, the degree of redundancy/complementarities of the information sources, and the structure of the hierarchy. It obtained effective information from the fusion data, successfully removed the noise disturbance, and achieved favorable results.

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Infrared Target Extraction Based on Immune Extension Neural Network

Cited by 3 publications

References 35 publications

A novel fault diagnosis method for PV arrays using convolutional extension neural network with symmetrized dot pattern analysis

A novel fault diagnosis method for PV arrays using convolutional extension neural network with symmetrized dot pattern analysis

An Improved Smart Helmet for Safe Travel of Deaf People Based on Embedded System

Creating Smart House via IoT and Intelligent Computation

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