Real-time rock mass condition prediction with TBM tunneling big data using a novel rock–machine mutual feedback perception method

The geological condition is essential for mining design and disaster control in underground coal mines. The present research focuses on the real-time assessment method on rock mass condition during drilling boreholes. In situ comprehensive experiments were carried out using three methods, which are measurement while drilling (MWD) system, vibration measurement while drilling (VMWD) system, and borehole camera detecting system. In the MWD system, the operating parameters of the drilling machine were recorded, and a dimensionless index Id based on the collected parameters was adopted to assess the geological condition along the borehole. The results show that the state of rock mass can be well classified using the MWD system for both the cross-layer and in-seam boreholes. In the VMWD system, the vibration of the drilling bit was monitored, and the signal was analyzed in both time domain and frequency domain. The results indicate that the rock mass condition can be quantitatively evaluated using the mean square value of the signal and qualitatively estimated using the energy of the spectrum. In the borehole camera system, the photos of the rock mass along the borehole could be well captured, and the identified rock mass condition was used to verify the results of the MWD and VMWD systems. Comprehensive compassion between the results from the three systems shows that all the methods can give valuable information for the geological condition, and the outcomes of the different methods are generally comparable. For practical purposes, the advantages of the involved three detecting systems are discussed.

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Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning

Journal of Rock Mechanics and Geotechnical Engineering ◽

10.1016/j.jrmge.2021.05.004 ◽

2021 ◽

Author(s):

Shaokang Hou ◽

Yaoru Liu ◽

Qiang Yang

Keyword(s):

Big Data ◽

Rock Mass ◽

Real Time ◽

Ensemble Learning ◽

Rock Mass Classification ◽

Time Prediction ◽

Mass Classification

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Real-Time Internet of Things (IOT) Application Big Data Stream Graph Optimization Framework

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i8.163167 ◽

2019 ◽

Vol 7 (8) ◽

pp. 163-167

Author(s):

Sharmila G.

Keyword(s):

Big Data ◽

Internet Of Things ◽

Real Time ◽

Data Stream ◽

Optimization Framework ◽

Graph Optimization

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Cognitive Automation, Big Data-driven Manufacturing, and Sustainable Industrial Value Creation in Internet of Things-based Real-Time Production Logistics

Economics Management and Financial Markets ◽

10.22381/emfm15420204 ◽

2020 ◽

Vol 15 (4) ◽

pp. 39

Keyword(s):

Big Data ◽

Internet Of Things ◽

Real Time ◽

Value Creation ◽

Data Driven ◽

Production Logistics ◽

Time Production ◽

Cognitive Automation

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Internet of Things-enabled Smart Devices in Medical Practice: Healthcare Big Data, Wearable Biometric Sensors, and Real-Time Patient Monitoring

American Journal of Medical Research ◽

10.22381/ajmr7120204 ◽

2020 ◽

Vol 7 (1) ◽

pp. 27

Keyword(s):

Big Data ◽

Internet Of Things ◽

Real Time ◽

Medical Practice ◽

Patient Monitoring ◽

Smart Devices

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A Frequency Pattern Mining Model Based on Deep Neural Network for Real-Time Classification of Heart Conditions

Healthcare ◽

10.3390/healthcare8030234 ◽

2020 ◽

Vol 8 (3) ◽

pp. 234 ◽

Cited By ~ 3

Author(s):

Hyun Yoo ◽

Soyoung Han ◽

Kyungyong Chung

Keyword(s):

Neural Network ◽

Big Data ◽

Fourier Transform ◽

Fast Fourier Transform ◽

Real Time ◽

Normal Control ◽

Input Data ◽

Deep Neural Network ◽

Pattern Mining ◽

F Measure

Recently, a massive amount of big data of bioinformation is collected by sensor-based IoT devices. The collected data are also classified into different types of health big data in various techniques. A personalized analysis technique is a basis for judging the risk factors of personal cardiovascular disorders in real-time. The objective of this paper is to provide the model for the personalized heart condition classification in combination with the fast and effective preprocessing technique and deep neural network in order to process the real-time accumulated biosensor input data. The model can be useful to learn input data and develop an approximation function, and it can help users recognize risk situations. For the analysis of the pulse frequency, a fast Fourier transform is applied in preprocessing work. With the use of the frequency-by-frequency ratio data of the extracted power spectrum, data reduction is performed. To analyze the meanings of preprocessed data, a neural network algorithm is applied. In particular, a deep neural network is used to analyze and evaluate linear data. A deep neural network can make multiple layers and can establish an operation model of nodes with the use of gradient descent. The completed model was trained by classifying the ECG signals collected in advance into normal, control, and noise groups. Thereafter, the ECG signal input in real time through the trained deep neural network system was classified into normal, control, and noise. To evaluate the performance of the proposed model, this study utilized a ratio of data operation cost reduction and F-measure. As a result, with the use of fast Fourier transform and cumulative frequency percentage, the size of ECG reduced to 1:32. According to the analysis on the F-measure of the deep neural network, the model had 83.83% accuracy. Given the results, the modified deep neural network technique can reduce the size of big data in terms of computing work, and it is an effective system to reduce operation time.

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A Real Time Processing system for big data in astronomy: Applications to HERA

Astronomy and Computing ◽

10.1016/j.ascom.2021.100489 ◽

2021 ◽

pp. 100489

Author(s):

Paul La Plante ◽

P.K.G. Williams ◽

M. Kolopanis ◽

J.S. Dillon ◽

A.P. Beardsley ◽

...

Keyword(s):

Big Data ◽

Real Time ◽

Processing System ◽

Real Time Processing ◽

Time Processing

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Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

Molecules ◽

10.3390/molecules26010020 ◽

2020 ◽

Vol 26 (1) ◽

pp. 20

Author(s):

Reynaldo Villarreal-González ◽

Antonio J. Acosta-Hoyos ◽

Jaime A. Garzon-Ochoa ◽

Nataly J. Galán-Freyle ◽

Paola Amar-Sepúlveda ◽

...

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Real Time ◽

Binary Classification ◽

Simulated Data ◽

Classification Model ◽

Rt Pcr ◽

Simon Bolivar ◽

Polymerase Chain ◽

Available Information

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

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