Paper Citation Record · LEDGER

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks

As of 18 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2501.02740.

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2501.02740 v1

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measured 37 of 37 reference resolution

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Outbound references

Observation 495828ae-9554-410f-be3f-58e51ac373b9 · outbound

This paper cites Disturbance robust abnormality diagnosis of fused magnesium furnaces using deep neural networks,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Disturbance robust abnormality diagnosis of fused magnesium furnaces using deep neural networks,

Reference 1

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Observation 78dcd6ad-43c6-48ac-9b6f-44bc423732a4 · outbound

This paper cites Demand forecasting of the fused magnesia smelting process with system identification and deep learning,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Demand forecasting of the fused magnesia smelting process with system identification and deep learning,

Reference 2

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Observation e74a98e8-c21e-43e6-b498-8ffbe241749c · outbound

This paper cites Intelligent demand forecasting of smelting process using data-driven and mechanism model,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Intelligent demand forecasting of smelting process using data-driven and mechanism model,

Reference 3

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Observation d4a2f0cc-b24b-4f14-b838-2e82f5dbf1e4 · outbound

This paper cites Data-driven abnormal condition identification and self-healing control system for fused magnesium furnace,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Data-driven abnormal condition identification and self-healing control system for fused magnesium furnace,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:05.520525Z

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This paper cites Signal-compensation-based adaptive PID control for fused magnesia smelting processes,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Signal-compensation-based adaptive PID control for fused magnesia smelting processes,

Reference 5

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Observation f4a05a18-f67b-48db-b5b8-b2bb393e5418 · outbound

This paper cites Abnormal condition identification and safe control scheme for the electro-fused magnesia smelting process,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Abnormal condition identification and safe control scheme for the electro-fused magnesia smelting process,

Reference 6

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Observation 86640eac-ed5a-42ef-bc01-d9e074ed203f · outbound

This paper cites Semi-supervised condition monitoring and visual- ization of fused magnesium furnace,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Semi-supervised condition monitoring and visual- ization of fused magnesium furnace,

Reference 7

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Observation a020055d-2ec7-4d64-9d7d-b5605898a586 · outbound

This paper cites Abnormal condition diagnosis through deep learning of image sequences for fused magnesium furnaces,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Abnormal condition diagnosis through deep learning of image sequences for fused magnesium furnaces,

Reference 8

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Observation a8fdbdfc-c7a1-4590-ba74-ad002f076ee0 · outbound

This paper cites Operating performance assessment based on multi-source heterogeneous information with deep learning for smelting process of electro-fused magnesium furnace,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Operating performance assessment based on multi-source heterogeneous information with deep learning for smelting process of electro-fused magnesium furnace,

Reference 9

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Observation 661cd5a3-c859-4c83-9d06-516d8242dbf3 · outbound

This paper cites What does a model really look at?: Extracting model-oriented concepts for explaining deep neural networks,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks What does a model really look at?: Extracting model-oriented concepts for explaining deep neural networks,

Reference 10

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This paper cites Evaluating the visualization of what a deep neural network has learned,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Evaluating the visualization of what a deep neural network has learned,

Reference 11

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This paper cites Understanding and accelerating neural architecture search with training-free and theory-grounded metrics,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Understanding and accelerating neural architecture search with training-free and theory-grounded metrics,

Reference 12

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Observation 7e0e7379-efdf-48f9-a170-e61327028497 · outbound

This paper cites A multi-level interpretable sleep stage scoring system by infusing experts’knowledge into a deep network architecture,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks A multi-level interpretable sleep stage scoring system by infusing experts’knowledge into a deep network architecture,

Reference 13

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Observation 0e0d4892-6fa7-493b-ac1a-ddfb640779dc · outbound

This paper cites A generic graph-based neural architecture encoding scheme with multifaceted information,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks A generic graph-based neural architecture encoding scheme with multifaceted information,

Reference 14

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Observation c684236a-47bb-40cd-9e7e-55268ecd7d43 · outbound

This paper cites BlockQNN: Efficient block-wise neural network architecture genera- tion,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks BlockQNN: Efficient block-wise neural network architecture genera- tion,

Reference 15

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Observation cece8066-46ec-40c2-9eea-22d98dfff302 · outbound

This paper cites Functional-link net computing: Theory, system architecture, and functionalities,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Functional-link net computing: Theory, system architecture, and functionalities,

Reference 16

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This paper cites 2-D stochastic configuration networks for image data analytics,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks 2-D stochastic configuration networks for image data analytics,

Reference 17

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Observation 80d19b61-5e8c-46a9-8e47-79ed262cee82 · outbound

This paper cites Deep stochastic configuration networks with uni- versal approximation property,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Deep stochastic configuration networks with uni- versal approximation property,

Reference 18

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Observation a60edd05-a564-48fb-8da0-93eb6a7efdaa · outbound

This paper cites Online self-learning stochastic configura- tion networks for nonstationary data stream analysis,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Online self-learning stochastic configura- tion networks for nonstationary data stream analysis,

Reference 19

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Observation bcd723a5-7d1e-4db3-89b4-c5fa3e133965 · outbound

This paper cites Predicting particle size of copper ore grinding with stochastic configuration networks,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Predicting particle size of copper ore grinding with stochastic configuration networks,

Reference 20

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Observation 313528e8-7e51-4f60-8b47-e9f385b7a7e8 · outbound

This paper cites Fuzzy stochastic configuration networks for nonlinear system modeling,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Fuzzy stochastic configuration networks for nonlinear system modeling,

Reference 21

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This paper cites Ensemble stochastic configuration networks for estimating prediction intervals: A simultaneous robust training algorithm and its application,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Ensemble stochastic configuration networks for estimating prediction intervals: A simultaneous robust training algorithm and its application,

Reference 22

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This paper cites Hierarchical-bayesian-based sparse stochastic configuration networks for construction of prediction inter- vals,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Hierarchical-bayesian-based sparse stochastic configuration networks for construction of prediction inter- vals,

Reference 23

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This paper cites Deep learning,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Deep learning,

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This paper cites Research on working condition recognition method of fmf based on deep convolutional stochastic configuration network,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Research on working condition recognition method of fmf based on deep convolutional stochastic configuration network,

Reference 25

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This paper cites 1xN pattern for pruning convolutional neural networks,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks 1xN pattern for pruning convolutional neural networks,

Reference 26

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Observation 0a969b92-2e7d-443b-99d9-18b0544bec90 · outbound

This paper cites Discrimination-aware network pruning for deep model compression,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Discrimination-aware network pruning for deep model compression,

Reference 27

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Observation b5d03769-e8c6-4daa-ae1d-89cd69dc2a9c · outbound

This paper cites Multi-objective evolutionary architectural pruning of deep convolutional neural networks with weights inheritance,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Multi-objective evolutionary architectural pruning of deep convolutional neural networks with weights inheritance,

Reference 28

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This paper cites Stochastic configuration networks: Fundamentals and algorithms,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Stochastic configuration networks: Fundamentals and algorithms,

Reference 29

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Observation d52c0513-e3c9-4597-b0d5-d090b4bac3f4 · outbound

This paper cites Stochastic configuration networks for self-blast state recognition of glass insulators with adaptive depth and multi-scale representation,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Stochastic configuration networks for self-blast state recognition of glass insulators with adaptive depth and multi-scale representation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:05.102347Z

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This paper cites On the calculation of convolutions with gaussian kernels,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks On the calculation of convolutions with gaussian kernels,

Reference 31

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This paper cites SlimConv: Reducing channel redundancy in convolutional neural networks by features recombining,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks SlimConv: Reducing channel redundancy in convolutional neural networks by features recombining,

Reference 32

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This paper cites Human-level control through deep reinforcement learning,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Human-level control through deep reinforcement learning,

Reference 33

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This paper cites 2D convolutional stochastic configuration net- works,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks 2D convolutional stochastic configuration net- works,

Reference 34

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This paper cites A survey of convolutional neural networks: Analysis, applications, and prospects,.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks A survey of convolutional neural networks: Analysis, applications, and prospects,

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This paper cites Stochastic Configuration Machines for Industrial Artificial Intelligence.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks Stochastic Configuration Machines for Industrial Artificial Intelligence

Reference 36

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This paper cites in Control Science and Engineering as a Master’s-Ph.D.

Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks in Control Science and Engineering as a Master’s-Ph.D

Reference 2018

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