Feature optimization method for the localization technology on loose particles inside sealed electronic equipment

Sun, Zhigang; Jiang, Aiping; Gao, Mengmeng; Min, Zhang; Wang, Guotao

doi:10.1016/j.eswa.2022.117569

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…In order to eliminate the influence of the unit and scale differences between the features, we chose the min–max standardization method to process the preliminary data set [ 34 ]. The calculation formula is:

…”

Section: Methodsmentioning

confidence: 99%

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

Tao

Wang

Sun

et al. 2022

Sensors

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In view of the limited number of extracted sound features, the lack of in-depth analysis of applicable sound features, and the lack of in-depth study of the selection basis and optimization process of classification models in the existing broiler sound classification or recognition research, the author proposes a recognition method for broiler sound signals based on multi-domain sound features and classification models. The implementation process is divided into the training stage and the testing stage. In the training stage, the experimental area is built, and multiple segments of broiler sound signals are collected and filtered. Through sub-frame processing and endpoint detection, the combinations of start frames and end frames of multiple sound types in broiler sound signals are obtained. A total of sixty sound features from four aspects of time domain, frequency domain, Mel-Frequency Cepstral Coefficients (MFCC), and sparse representation are extracted from each frame signal to form multiple feature vectors. These feature vectors are labeled manually to build the data set. The min-max standardization method is used to process the data set, and the random forest is used to calculate the importance of sound features. Then, thirty sound features that contribute more to the classification effect of the classification model are retained. On this basis, the classification models based on seven classification algorithms are trained, the best-performing classification model based on k-Nearest Neighbor (kNN) is obtained, and its inherent parameters are optimized. Then, the optimal classification model is obtained. The test results show that the average classification accuracy achieved by the decision-tree-based classifier (abbreviated as DT classifier) on the data set before and after min–max standardization processing is improved by 0.6%, the average classification accuracy achieved by the DT classifier on the data set before and after feature selection is improved by 3.1%, the average classification accuracy achieved by the kNN-based classification model before and after parameter optimization is improved by 1.2%, and the highest classification accuracy is 94.16%. In the testing stage, for a segment of the broiler sound signal collected in the broiler captivity area, the combinations of the start frames and end frames of multiple sound types in the broiler sound signal are obtained through signal filtering, sub-frame processing, endpoint detection, and other steps. Thirty sound features are extracted from each frame signal to form the data set to be predicted. The optimal classification model is used to predict the labels of each piece of data in the data set to be predicted. By performing majority voting processing on the predicted labels of the data combination corresponding to each sound type, the common labels are obtained; that is, the predicted types are obtained. On this basis, the definition of recognition accuracy for broiler sound signals is proposed. The test results show that the classification accuracy achieved by the optimal classification model on the data set to be predicted is 93.57%, and the recognition accuracy achieved on the multiple segments of the broiler sound signals is 99.12%.

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…”

Section: Methodsmentioning

confidence: 99%

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

Tao

Wang

Sun

et al. 2022

Sensors

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“…In their research, the processing and optimisation of loose particle location feature and material recognition datasets were investigated, and localisation processing methods were proposed for acoustic data based on machine learning. In a similar vein, Sun et al [19] proposed a multiindex combined sorting method to screen and optimise the material characteristic parameters of the loose particles. Liang et al [20] classified the PIND signals of the sealed relay using the parameter-optimised decision tree method and achieved better detection efficiency than the traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components

Li,

Zhai,

Wang

et al. 2024

Meas. Sci. Technol.

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Aerospace-sealed electronic components exhibit favourable anti-interference capability and high reliability and are widely utilised in satellites, rockets, and missiles. Loose particle detection is crucial to ensure high reliability. However, the classification problem of loose particle detection signals based on the Particle Impact Noise Detection (PIND) method has been a challenge for the high reliability of aerospace-sealed electronic components. To address this issue, this paper systematically proposes a synchronised classification method based on PIND and validates its feasibility. The proposed approach combines a self-developed empirical model (referred to as Algorithm 1) with a classical machine learning model (referred to as Algorithm 2). The key findings are as follows: this method has a recognition rate for loose particle signals of 91.86%. Compared to a single algorithmic mode, the overall speed improves by 300%. The maximal recognition accuracy rates for component signals, mixed signals, and excessive signals are 90.03%, 81.04%, and 95.17%, respectively. The test results demonstrate that the method effectively balances the accuracy and speed of loose particle detection and leverages the complementary advantages of the two algorithms, thereby addressing the multi-classification issue of loose particle detection signals.

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“…There are many researches on electronic seal [13][14][15][16][17][18]. A clip-on type RFID electronic seal [13] has been developed to solve the security requirements during the on-site sealing process.…”

Section: Introductionmentioning

confidence: 99%

Blockchain based Multi-signature Smart Contract Electronic Seal Orienting Mobile IoT Terminals

Liu,

Feng

2023

Preprint

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The surging interest in digitalization has revitalized research for digital business model. Mobile Internet of Things (IoT) terminals will reach billions and become an important infrastructure for a digital and intelligent society. In the digital business model of IoT terminals, the electronic seal of commercial contracts are especially investigated to provide same level security as guaranteed by the traditional seal. However, the existing electronic seal is only applicable to the centralized network applications, which severely rely on the certificate authority. In this paper, we propose a blockchain based multi-signature smart contract electronic seal. The key of our design is to deploy a smart contract of business parties on Ethereum for secure electronic seal orienting mobile IoT terminals. Unlike traditional seal, this electronic seal is verified and managed collectively by all mining nodes. Combining it with the optimization in terms of a multi-signature algorithm encoded in smart contract, our design achieves complete digital contract function in all business participants of terminals. As an example, we show an implementation of Goerli testnet of the Ethereum network with the smart contract deployment. The security analysis and evaluation demonstrate that our design is secure of its multi-signature and can be used in practice.

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Feature optimization method for the localization technology on loose particles inside sealed electronic equipment

Cited by 8 publications

References 29 publications

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

Recognition Method for Broiler Sound Signals Based on Multi-Domain Sound Features and Classification Model

Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components

Blockchain based Multi-signature Smart Contract Electronic Seal Orienting Mobile IoT Terminals

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