Computer Vision Technology Based on Sensor Data and Hybrid Deep Learning for Security Detection of Blast Furnace Bearing

Yang, Aimin; Zhi, Jian-Ming; Wang, Jiahao; Xue, Tao

doi:10.1109/jsen.2021.3077468

Cited by 15 publications

(4 citation statements)

References 25 publications

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“…First of all, the data should be processed for outliers. Outliers refer to one or several values in the data which are quite different from other values [ 23 ]. The occurrence of outliers may be caused by the instability of the XRD data detection and other instruments and may also be caused by human operating errors.…”

Section: Sfcam Reaction Process Data Analysismentioning

confidence: 99%

Research on the Formation Mechanism of MgO and Al2O3 on Composite Calcium Ferrite Based on DA-RBF Neural Network

Zhang

2022

Computational Intelligence and Neuroscience

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Calcium complex ferrate is an ideal binder phase in the sintered ore phase, and a detailed study of the whole process of calcium complex ferrate generation is of great significance to improve the quality of sintered ore. In this paper, we first investigated calcium ferrate containing aluminum (CFA), which is an important precursor compound for the generation of complex calcium ferrate (SFCA), followed by a series of composite calcium ferrate generation process phase XRD detections and data preprocessing of data. Data correlation and data fitting analysis were combined with composite calcium ferrite phase diagram energy spectrum analysis to obtain the effect of MgO and Al2O3 on the formation of composite calcium ferrite. Then a modified RBF neural network model using the resource allocation network algorithm (RAN) was used to predict the generation trend of complex calcium ferrate. The parameters of the neural network are optimized with the Dragonfly algorithm, compared with the traditional RBF neural network. The prediction accuracy of the improved algorithm was found to be higher, with a prediction result of 97.6%. Finally, the predicted results were based on comparative metallurgical experimental results and data analysis. The validity and accuracy of the findings in this paper were verified.

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Section: Sfcam Reaction Process Data Analysismentioning

confidence: 99%

Research on the Formation Mechanism of MgO and Al2O3 on Composite Calcium Ferrite Based on DA-RBF Neural Network

Zhang

2022

Computational Intelligence and Neuroscience

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“…A deep learning model Mask R-CNN [21] trained through images captured from real blasting sites in Nui Phao open-pit mine is developed to evaluate the blasting results, expanding the possibility of the automated measurement of blast fragmentation. Literature [22] combined a hybrid deep learning-based computer vision method with a VMD algorithm for security detection of blasting furnace bearings, which achieves remarkable calculation speed and accuracy of bearing fault diagnosis. Literature [23] constructed a multi-hidden-layer neural network model and an LSTM neural network model based on PyTorch and Keras framework to predict the failure mode of the RC (reinforced concrete) columns under blast loading.…”

Section: Data Securitymentioning

confidence: 99%

A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting Sites

Zhang,

Chen,

Guo

et al. 2024

CMES

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The data analysis of blasting sites has always been the research goal of relevant researchers. The rise of mobile blasting robots has aroused many researchers' interest in machine learning methods for target detection in the field of blasting. Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience, which has aroused people's interest in how to use it in the field of machine learning. In this paper, we design a distributed machine learning training application based on the AWS Lambda platform. Based on data parallelism, the data aggregation and training synchronization in Function as a Service (FaaS) are effectively realized. It also encrypts the data set, effectively reducing the risk of data leakage. We rent a cloud server and a Lambda, and then we conduct experiments to evaluate our applications. Our results indicate the effectiveness, rapidity, and economy of distributed training on FaaS.

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“…Owing to the advancements in computational resources and algorithmic techniques, machine learning (ML) models have been widely employed, thus resulting in novel approaches for natural language processing, [32,33] computer vision, [34,35] and soft sensing [36,37] applications. Data-driven soft sensing systems can effectively integrate multiple sensor signals to provide other output features that are difficult to measure or when conventional sensing systems are unavailable (e.g., environmental sensing, [37,38] product quality, [39,40] equipment failures, [34,41] and medical diagnostics [35,42] ). In addition, ML models can be used to derive a nonlinear relationship between physically coupled input and output features.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Machine Learning Techniques in Hot‐Film Based Airflow Rate Sensors for Improving Flow Measurement

Shin,

Baek,

Choi

et al. 2024

Advanced Intelligent Systems

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This article presents a novel airflow rate sensing method based on the principle of a hot‐film flow sensing device with data‐driven machine learning (ML) models. In addition, to combine the two signals of the sensor (the resistance changes of the heaters) and predict the output (airflow rate), three different ML multivariate regression models, i.e., multiple linear regression (MLR), k‐nearest neighbor (KNN), and deep neural network (DNN) models, are trained and compared using 8400 experimentally obtained data. Using sensor fusion techniques, the average mean absolute error (MAE) and mean squared error (MSE) of the KNN model are determined to be 0.01522 and 0.00132, respectively, in the range of 0–5.07 standard liters per minute. Compared with the average results obtained using only a single input, those obtained using a dual input indicate a significant decrease in the MAE and MSE by 85.69% and 96.68%, respectively. Furthermore, a transient analysis of the ML‐based flow sensor is conducted to investigate the response time and transient characteristics of the MLR, KNN, and DNN models. The results of this study contribute to the advancement of airflow management systems for various industrial applications, such as building ventilation, gas leakage detection, and energy systems.

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Computer Vision Technology Based on Sensor Data and Hybrid Deep Learning for Security Detection of Blast Furnace Bearing

Cited by 15 publications

References 25 publications

Research on the Formation Mechanism of MgO and Al2O3 on Composite Calcium Ferrite Based on DA-RBF Neural Network

Research on the Formation Mechanism of MgO and Al2O3 on Composite Calcium Ferrite Based on DA-RBF Neural Network

A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting Sites

Utilization of Machine Learning Techniques in Hot‐Film Based Airflow Rate Sensors for Improving Flow Measurement

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