Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM

Bi, Fangming; Fu, Xuanyi; Chen, Wei; Fang, Weidong; Miao, Xuzhi; Assefa, Biruk

doi:10.32604/cmc.2020.06258

Cited by 18 publications

(2 citation statements)

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“…Then, we designed an algorithm to select the appropriate link from which to extract the two correlation coefficient features. By setting the short step sliding time window mechanism, we divided the data packet into several data segments to support vector machine (SVM) model off-line training and performance testing [ 46 , 47 , 48 , 49 , 50 ]. Finally, the system was set for real-time detection with the appropriate model parameters.…”

Section: System Designmentioning

confidence: 99%

Develop an Adaptive Real-Time Indoor Intrusion Detection System Based on Empirical Analysis of OFDM Subcarriers

Zhuang

Shen

et al. 2021

Sensors

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Device-free passive intrusion detection is a promising technology to determine whether moving subjects are present without deploying any specific sensors or devices in the area of interest. With the rapid development of wireless technology, multi-input multi-output (MIMO) and orthogonal frequency-division multiplexing (OFDM) which were originally exploited to improve the stability and bandwidth of Wi-Fi communication, can now support extensive applications such as indoor intrusion detection, patient monitoring, and healthcare monitoring for the elderly. At present, most research works use channel state information (CSI) in the IEEE 802.11n standard to analyze signals and select features. However, there are very limited studies on intrusion detection in real home environments that consider scenarios that include different motion speeds, different numbers of intruders, varying locations of devices, and whether people are present sleeping at home. In this paper, we propose an adaptive real-time indoor intrusion detection system using subcarrier correlation-based features based on the characteristics of narrow frequency spacing of adjacent subcarriers. We propose a link-pair selection algorithm for choosing an optimal link pair as a baseline for subsequent CSI processing. We prototype our system on commercial Wi-Fi devices and compare the overall performance with those of state-of-the-art approaches. The experimental results demonstrate that our system achieves impressive performance regardless of intruder’s motion speeds, number of intruders, non-line-of-sight conditions, and sleeping occupant conditions.

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Section: System Designmentioning

confidence: 99%

Develop an Adaptive Real-Time Indoor Intrusion Detection System Based on Empirical Analysis of OFDM Subcarriers

Zhuang

Shen

et al. 2021

Sensors

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“…Traditional features extraction methods rely on eigenvalue function to screen eigenvalues as features [3][4][5]. Traditional classification models include Decision Tree [6][7], Bayesian classifier [8][9], Support Vector Machine [10][11], etc. However, the traditional numerical representation method has two major problems: semantic gap and dimension explosion; the traditional feature extraction method has poor ability to identify typical features; and the traditional classification models relies heavily on specific tasks, and the text association relationship processing is rough [12].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Mapping Method of Intelligent Recorder Configuration Datasets Based on Chinese Semantic Deep Learning

Ren

Gen

et al. 2020

IEEE Access

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Mapping the Intelligent Electronic Devices (IEDs) output interface address description datasets to the intelligent recorder is the groundwork for the recorder to accurately collect IEDs' operation information. These datasets, which are also intelligent recorder configuration datasets, are included in the Substation Configuration Description (SCD) file of an intelligent substation. The mainstream mapping method is manually mapping these datasets based on output interface Chinese description texts. When the number of IEDs is extremely large, the manual operation often takes a huge amount of time, together with the higher labor cost. And since the Chinese description texts have a certain degree of irregularity, it also poses a problem for the automatic mapping of the datasets. Aiming at this problem, this paper proposes an automatic mapping method of IEDs configuration datasets based on a deep learning framework-Dynamic Convolutional Neural Network (DCNN). Firstly, it uses the word representation model Word2vec to vectorize words in Chinese description texts as well as their semantics relationships. Then word vectors will be imported in the DCNN, which, based on its multilayer abstract learning characteristics of typical sample features, can perform semantic law mining and automatic mapping. The configuration datasets of intelligent recorder will be automatically mapped based on the Chinese descriptions mapping result. The Practical example shows that the Chinese description texts classification method based on the Dynamic Convolutional Neural Network model has strong semantic analysis ability and high classification accuracy, which effectively improves the accuracy of automatic mapping of intelligent recorder configuration data.

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Research on Life Prediction Model of Field Seismic Equipment Based on IFA-RVM Algorithm

Yin

Chen

et al. 2021

Advances in Artificial Intelligence and Security

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Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM

Cited by 18 publications

References 0 publications

Develop an Adaptive Real-Time Indoor Intrusion Detection System Based on Empirical Analysis of OFDM Subcarriers

Develop an Adaptive Real-Time Indoor Intrusion Detection System Based on Empirical Analysis of OFDM Subcarriers

An Automatic Mapping Method of Intelligent Recorder Configuration Datasets Based on Chinese Semantic Deep Learning

Research on Life Prediction Model of Field Seismic Equipment Based on IFA-RVM Algorithm

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