Starch-based aerogel prepared by freeze-drying: establishing a BP neural network prediction model

Sun, Gang; Zeng, Guangsheng; Hu, Can; Jiang, Taijun

doi:10.1007/s13726-022-01105-0

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…where, the Φ(y) represents the activation function of the hidden layer 14 . The wtj denotes the weight parameter between the j-th node in the hidden layer and the tth node in the output layer, j=1,...,q.…”

Section: Methodsmentioning

confidence: 99%

Prediction of glass chemical composition and type identification based on machine learning algorithms

Chen

Zhang

et al. 2023

Preprint

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Glass was a significant symbol of early trade exchanges on the Silk Road. However, the glass artifacts were susceptible to environmental influences when buried underground, leading to weathering and changes in their chemical composition. Analyzing the chemical composition of ancient glass was conducive to evaluate weathering status and propose the measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learnings were used. Results indicated a significant correlation (p < 0.05) was only observed between surface weathering and the glass types (high-potassium and lead-barium). Based on the random forest and logistic regression, the primary chemical components that signifying glass types and weathering status were determined by PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented superior capability for identifying glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates great potential of machine learning for glass chemical component estimation and glass type and weathering status identification providing technical guidance for the appraisal of ancient glass artifacts.

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Section: Methodsmentioning

confidence: 99%

Prediction of glass chemical composition and type identification based on machine learning algorithms

Chen

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…where Φ(y) represents the activation function of the hidden layer [27]. w tj denotes the weight parameter between the j-th node in the hidden layer and the t-th node in the output layer, j = 1, .…”

Section: Neural Network Modelsmentioning

confidence: 99%

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Chen,

Xu,

Zhang

et al. 2024

Applied Sciences

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Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products. Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learning techniques were used. The results indicated that a significant correlation (p < 0.05) could only observed between surface weathering and the glass types (high-potassium and lead–barium). Based on the random forest and logistic regression models, the primary chemical components that signify glass types and weathering status were determined using PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented a superior ability to identify glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.

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“…BPNN is one of the widely used and more successful neural networks [16]. When the signal is propagated forward, the data flows from the input layer (IL) to the hidden layer (HL), and the error is back-propagated by comparing the actual value with the expected value in the output layer [17].…”

Section: A System Design Based On the Bpnn Modelmentioning

confidence: 99%

Design of a Decentralized AI IoT System Based on Back Propagation Neural Network Model

Zhang

2023

IJACSA

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In the Internet of Things (IoT) era, when user needs are continually evolving, the coupling of AI and IoT technologies is unavoidable. Fog devices are introduced into the IoT system and given the function of hidden layer neurons of Back Propagation neural network, and Docker containers are combined to realize the mapping of devices and neurons in order to improve the quality of service of IoT devices. This study proposes the design of a decentralized AI IoT system based on Back Propagation neural network model. The testing data revealed that, at various data transfer intervals, the average transmission rate between the fog device and the sensing device was 8.265Mbps, and that the device's transmission rate could satisfy user demand. When the data transmission interval was 20s, the network data transmission rate was greater than 8.5Mbps and did not vary much when the number of data transmissions rose. The research demonstrates that the decentralized AI IoT system's network performance, which is based on a back propagation neural network model, can match user usage requirements and has good stability.

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Starch-based aerogel prepared by freeze-drying: establishing a BP neural network prediction model

Cited by 5 publications

References 28 publications

Prediction of glass chemical composition and type identification based on machine learning algorithms

Prediction of glass chemical composition and type identification based on machine learning algorithms

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Design of a Decentralized AI IoT System Based on Back Propagation Neural Network Model

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