Correction to: Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis

Raja, Hina; Akram, M. Usman; Shaukat, Arslan; Khan, Shoab Ahmed; Alghamdi, Norah Saleh; Khawaja, Sajid Gul; Nazir, Noman

doi:10.1007/s10278-021-00468-9

Cited by 2 publications

(2 citation statements)

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“…An ICCD detector (Andor, DH734i-18F-03) was used to detect the signal. In recent years, deep learning methods have become a focus in quantitative analysis research for LIBS due to their powerful self-learning ability and sharp senses of discovering the intrinsic patterns in high-dimensional data [11,12]. Convolutional neural network (CNN) is a typical deep learning algorithm which extracts useful information and reduces high dimensionality.…”

Section: Experiments 21 Libs Instrumentationmentioning

confidence: 99%

Quantitative Analysis of Steel Alloy Elements Based on LIBS and Deep Learning of Multi-Perspective Features

Gu,

Chen,

Chen

et al. 2023

Electronics

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The Si and Mn contents in steel alloys are important characteristic indexes that influence the plasticity and welding properties of these alloys. In this work, the quantitative analysis methods for trace elements under complex alloy matrices by laser-induced breakdown spectroscopy (LIBS) are studied, which provide a foundation for utilizing LIBS technology in the rapid online detection of steel alloy properties. To improve the quantitative analysis accuracy of LIBS, deep learning algorithm methods are introduced. Given the characteristics of LIBS spectra, we explore multi-perspective feature extraction and backward differential methods to extract the spatio-temporal characteristics of LIBS spectra. The Text Convolutional Neural Network (TextCNN) model, combined with multi-perspective feature extraction, displays good stability and lower average relative errors (6.988% for Si, 6.280% for Mn) in the test set compared to the traditional quantitative analysis method and deep neural network (DNN) model. Finally, the backward differential method is employed to optimize the two-dimensional LIBS spectral input matrix, and the results indicate that the average relative errors of Si and Mn elements in the test set decrease to 5.139% and 3.939%, respectively. The method proposed in this work establishes a theoretical basis and technical support for precise prediction and online quality monitoring.

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Section: Experiments 21 Libs Instrumentationmentioning

confidence: 99%

Quantitative Analysis of Steel Alloy Elements Based on LIBS and Deep Learning of Multi-Perspective Features

Gu,

Chen,

Chen

et al. 2023

Electronics

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show abstract

“…However, due to the power system's extraordinarily enormous size, how to properly analyze the current data swiftly necessitates the direction of matching control algorithms. Existing data processing and analysis techniques include fuzzy clustering [13], K-means clustering [14][15][16], neural networks [17][18][19][20], and others. Literature [15] proposes a fault routing method based on VMD and permutation entropy feature extraction and combined with the K-means clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on the strategy of locating abnormal data in internet of things management platform based on improved modified particle swarm optimization convolutional neural network algorithm

Yuan

et al. 2023

The Journal of Engineering

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The Internet of Things (IOT) management platform is used to manage and transmit data from a variety of terminal devices in the power system. In terms of detecting abnormal data, the existing IOT management platform has a low data processing efficiency and a high error rate. In addition, the optimal selection and determination of the structural parameters of a convolutional neural network (CNN) have a substantial effect on its prediction performance. On this basis, the paper proposes a decision algorithm for locating anomalous data in an IOT integrated management platform using a CNN and a global optimization decision of key structural parameters of a CNN using an improved particle swarm optimization (APSO) algorithm. Initially, an index model is developed to identify whether the data obtained from the IOT management platform is abnormal. Second, the structure of the CNN‐based anomaly detection approach is investigated. Next, an improved particle swarm optimization approach is designed to optimize the structural parameters of the CNN, and an APSO‐CNN with higher performance for anomalous data localization is constructed. Using the Adam optimizer, the accuracy, feasibility, and efficiency of the established method were assessed. The results demonstrate that the developed APSO‐CNN‐based decision method for anomaly data localization offers significant advantages in terms of precision and execution speed, with potentially intriguing application potential.

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Correction to: Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis

Cited by 2 publications

References 0 publications

Quantitative Analysis of Steel Alloy Elements Based on LIBS and Deep Learning of Multi-Perspective Features

Quantitative Analysis of Steel Alloy Elements Based on LIBS and Deep Learning of Multi-Perspective Features

Research on the strategy of locating abnormal data in internet of things management platform based on improved modified particle swarm optimization convolutional neural network algorithm

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