Electromagnetic Radiation Signals Research Articles

A study on the time–frequency variation law of electromagnetic radiation signals during concrete cracking

A study on the time–frequency variation law of electromagnetic radiation signals during concrete cracking

Signal recognition and prediction system based on random forest model

To accurately predict the occurrence of rock bursts during deep coal mining and ensure the safe and efficient operation of coal mines, a signal recognition and prediction system based on the Random Forest model is proposed. This system utilizes electromagnetic radiation (EMR) and acoustic emission (AE) signal data, employing feature extraction and point biserial correlation analysis to screen out the most relevant feature parameters. A Random Forest binary classification model is constructed to identify interference signals. Subsequently, by introducing new features such as moving average slope and exponentially weighted moving average (EWMA), a time series analysis of precursor feature signals is conducted. A real-time warning model based on the Random Forest algorithm is developed, dynamically calculating the probability of precursor feature signal occurrence by integrating historical data and real-time data changes. This approach improves the accuracy and recall rate of signal recognition and prediction, providing reliable data support for mine safety management.

Identification and prediction method for acoustic emission and electromagnetic radiation signals of rock burst based on deep learning

With the deep development of underground rock engineering, the threat of rock burst disasters is increasing. At present, the identification and prediction of rock burst mostly rely on the experience of field staff to determine the critical value and development trend, and there is a lack of efficient and intelligent methods for the utilization of massive data. Therefore, this paper constructs a rock burst signal recognition and prediction model based on deep learning methods to solve the above problems. In this paper, the acoustic emission (AE) and electromagnetic radiation (EMR) data of the site are first marked and input into the long-short-term memory-fully connected neural network model to realize the identification of rock burst danger signals. Then, the graph data of the AE and EMR sensor monitoring networks are constructed and input into the spatiotemporal graph convolutional network signal prediction model to predict future monitoring data. Finally, this paper uses the same dataset to compare and analyze several other commonly used deep learning models. The results show that the model constructed in this paper has the best performance in the identification and prediction of AE and EMR signals with rockburst risk. This study can provide theoretical reference for intelligent monitoring and early warning of rock burst in underground rock engineering.

A TEMPEST Attack Implementation based on Hidden Markov model in Smart Grid

Abstract A Smart Grid contains a large number of terminals to gather voltage, current, and power data. If these terminals are attacked by hackers to steal data and disrupt the operation of the grid, the grid will encounter a huge challenge. And side channel attack is an attack way that uses information signals (such as electromagnetic radiation, and computer hardware running sound) inadvertently released by the computer to decipher. The computer keyboard generates electromagnetic radiation signals during operation, and hackers may acquire your account and password through the keyboard’s tapping sound, resulting in the leakage of important data. This article analyzes the electromagnetic radiation leakage vulnerabilities of keyboards and designs a way to implement a TEMPEST (Transient Electromagnetic Pulse Emanation Surveillance Technology) Attack based on the Hidden Markov model, which can provide a foundation for avoiding potential risk.

A Study on the Time-Frequency Characteristics of Electromagnetic Radiation Signals during Concrete Cracking

At the moment of rupture, brittle materials such as concrete, coal, and rocks experience a sudden stress change that generates oscillating positive and negative charges on the crack surfaces, resulting in the emission of electromagnetic radiation (EMR) signals. While extensive investigations have focused on the EMR signals produced during the fracturing of coal and rocks, limited attention has been given to the signals arising from concrete cracking. Previous studies have predominantly concentrated on amplitude or frequency domain analysis, lacking a comprehensive exploration of the time-frequency domain characteristics of electromagnetic signals. In this study, various magnetic field sensors capable of collecting frequencies ranging from 50Hz to 50kHz are developed. A laboratory testing platform for concrete cracking is established with the utilization of data acquisition equipment and a rock point load tester. The EMR signals are subjected to time-frequency domain analysis using Short-Time Fourier Transform (STFT) methods. The study explores the relationship between the time-frequency domain characteristics of EMR signals and concrete dimensions, as well as the instantaneous stress drop at the moment of fracture. Experimental findings reveal that the dominant frequency of EMR signals generated during concrete cracking is in the low-frequency range. As the instantaneous stress drop at the moment of fracture increases, the frequency of the EMR signals shifts towards higher frequencies and the signals around 10kHz has a longer duration. When the frequency of EMR signals is lower than 20kHz, larger concrete dimensions result in greater stress drops at the moment of fracture, leading to higher intensity EMR signals. Utilizing the EMR characteristics of concrete cracking holds promise for monitoring the safety and stability of concrete structures.

Influence of an Engineered Notch on the Electromagnetic Radiation Performance of NiTi Shape Memory Alloy.

This work explores the influence of a pre-engineered notch on the electromagnetic radiation (EMR) parameters in NiTi shape memory alloy (SMA) during tensile tests. The test data showed that the EMR signal fluctuated between oscillatory and exponential, signifying that the specimen's viscosity damping coefficient changes during strain hardening. The EMR parameters, maximum EMR amplitude, and average EMR energy release rate remained constant initially but rose sharply with the plastic zone radius with progressive loading. It was postulated that new Frank-Read sources permit dislocation multiplication and increase the number of edge dislocations participating in EMR emissions, leading to a rise in the value of EMR parameters. The study of the correlation between EMR emission parameters and the plastic zone radius before the crack tip is a vital crack growth monitoring tool. An analysis of the interrelationship of the EMR energy release rate at fracture with the elastic strain energy release rate would help develop an innovative approach to assess fracture toughness, a critical parameter for the design and safety of metals. The microstructural analysis of tensile fractures and the interrelation between deformation behaviours concerning the EMR parameters offers a novel and real-time approach to improve the extant understanding of the behaviour of metallic materials.

Analysis method of explosive electromagnetic radiation energy

AbstractThe energy of electromagnetic radiation from explosions is coupled to electronic equipment circuits, disrupting the initiation sequence or causing failure in an increasing number of cases, which seriously affects the stability of weapon systems. There is a significant difference between the characteristics of the explosive electromagnetic radiation signals and modulated electromagnetic signals. The electric field intensity and signal power cannot directly represent the magnitude of the explosive electromagnetic radiation energy, and traditional electromagnetic signal analysis methods are unsuitable for explosion electromagnetic signal analysis. To solve this problem, the mechanism of explosive electromagnetic radiation was first analyzed. Through verification experiments of the explosion electromagnetic intensity and temperature, it was concluded that there is a strong correlation between the explosion plasma temperature and electromagnetic intensity. The temperature of the explosive plasma is derived based on the measured surface temperature of the explosive fireball, a functional relationship is established between the energy of the explosive plasma and the temperature of the plasma, and plasma energy is introduced as a parameter for electric field intensity correction. The interference signal analysis method based on eye diagram is used to calibrate the electromagnetic radiation damage ability, achieving quantitative analysis of the interference degree of electromagnetic radiation energy on the signal, and providing a new approach for the analysis of explosive electromagnetic radiation energy.

Insights into the Dead Sea Transform Activity through the study of fracture-induced electromagnetic radiation (FEMR) signals before the Syrian-Turkey earthquake (Mw-6.3) on 20.2.2023

Observations of fracture-induced electromagnetic radiation (FEMR) were conducted along the Dead Sea Transform (DST) from Sodom to Jericho, coinciding with a magnitude (Mw) 6.3 aftershock earthquake (EQ) in the Turkey-Syrian region on February 20, 2023. The FEMR parameters (“hits,” Benioff strain release, frequency, rise-time, energy) and associated crack dimensions were analyzed, focusing on trends leading up to the EQ. This study investigated the Benioff Strain plot and other parameters in three consecutive earthquake nucleation stages leading to the catastrophe. The first stage showed increased FEMR hits and frequency, decreased rise time (T′), and crack dimensions. In the second stage, FEMR hits and crack width decreased while other parameters continued to rise, accumulating the second-highest energy, likely due to high-stress drop. The third stage exhibited steadily increasing FEMR hits and energy and a notable increase in crack dimensions, suggesting an imminent macro failure event. The cyclic trend in FEMR hits indicates alternating periods of high activity and silence, potentially linked to stress changes during crack propagation. Taken shortly before the earthquake, these measurements offer valuable insights into how FEMR parameters vary before seismic events, bridging the gap between lab-scale studies of rock collapses under stress and large-scale failure phenomena.

The experimental and theoretical study on the spatial‐temporal distribution of electromagnetic radiation from JO‐8 explosions.

AbstractThe detonating fuse in the multistage warhead will be subjected to strong electromagnetic interference, derived from electromagnetic radiation generated by explosion of the shaped charge warhead, which may cause premature detonation or misfire. In order to explore the possible electromagnetic environment surrounded the detonating fuse, the spatial‐temporal distribution of electromagnetic radiation after the explosion of JO‐8 explosive was investigated in this paper. The electromagnetic radiation signal was collected and its frequency coverage was analyzed in the far‐field area by the field blast test. Moreover, based on electromagnetic theory, a theoretical model of electromagnetic radiation generated by the explosion of JO‐8 explosive was established, and the spatial‐temporal distribution of the electric field intensity was illustrated in detail for several typical positions after the explosion. The better agreement between experimental and theoretical results indicates that the proposed theoretical model and computational method are reasonable. On this basis, the distributions of electric field intensity for different positions and various explosive weights were predicted respectively by using distance and explosive weight as variables. This study is expected to provide a reference for the research on the electromagnetic radiation for explosive explosion and anti‐explosive electromagnetic interference.

Direction finding of sources of electromagnetic radiation signals by digital antenna arrays with invariant phase center

Direction finding of sources of electromagnetic radiation signals by digital antenna arrays with invariant phase center

Improving Mining Sustainability and Safety by Monitoring Precursors of Catastrophic Failures in Loaded Granite: An Experimental Study of Acoustic Emission and Electromagnetic Radiation

Effective monitoring and early warning methods are crucial for enhancing safety and sustainability in deep coal resource extraction, particularly in mitigating rock burst disasters triggered by abrupt rock failure under high–ground–stress conditions. This paper presents the results of experimental investigations that involved conventional uniaxial direct and graded mechanical tests on granite that concurrently collected acoustic emission (AE) and electromagnetic radiation (EMR) signals. This study focused on the temporal evolution patterns of characteristic parameters in AE and EMR signals during granite deformation and fracture processes. To deconstruct and understand these temporal evolution characteristics, multifractal and critical slowing–down theories are introduced. The research findings reveal significant correlations between the evolution of AE and EMR characteristic parameters and the stages of rock deformation and fracture. Notably, dynamic changes in multifractal parameters (Δα and Δf) quantitatively reflected the deformation and fracture processes, with abrupt increases in Δα and sudden decreases in Δf closely associated with large–scale rock fractures. The temporal continuity of critical slowing–down parameters (variance and autocorrelation coefficient) demonstrated increased sensitivity as rock destruction approaches, with the variance emerging as a crucial indicator for large–scale fractures. This study observed a sudden increase in the variance of AE and EMR signals when the stress level reached 80–90% of the peak stress. Joint monitoring through diverse methods and multiple indicators enhanced the effectiveness of rock burst disaster warnings, contributing to the safety and sustainability of coal resource extraction. Further refinement and exploration of these indicators offer promising avenues for advancing rock failure monitoring and early warning capabilities in coal mines.

Synthetic Aperture Interferometric Passive Radiometer Imaging to Locate Electromagnetic Leakage From Spacecraft Surface

The localization of electromagnetic radiation leakage through cabin gaps is a critical and challenging aspect of electromagnetic compatibility (EMC) design for spacecraft with complex electromagnetic environments. This paper proposes a localization method based on synthetic aperture interferometric passive radiometry imaging. Electromagnetic radiation signals are measured at a certain distance from the spacecraft surface to form visibility samples. A Fourier transform pair between the visibility sample and the corrected brightness temperature for electromagnetic radiation leakage is established. The spacecraft surface electromagnetic leakage location image is obtained through the inverse Fourier transform. A sparse sampling method based on ant colony optimization was proposed to improve testing efficiency. The impacts of various factors, including positional parameters, positioning accuracy of the test antenna, scanning parameters, and measurement receiver amplitude/phase errors on the imaging results are analyzed. Experiments were conducted on a 1 m × 1 m × 1 m cabin with 51 holes on one surface, and the algorithm proposed in this paper was validated to effectively image and locate electromagnetic leakage points at different frequencies. The effectiveness of sparse sampling was also verified, with a localization accuracy of 90.2% and a testing time savings of 81.9%.

Giant Stark effect assisted radio frequency energy harvesting using atomically thin earth-abundant iron sulphide (FeS2)

2D FeS2 has the potential to convert ambient radiofrequency electromagnetic radiation signals into usable energy, which can be utilized to power portable and wearable electronic devices.

A Study of the Characteristics of Micro-Seismic (ME) and Electromagnetic Radiation (EMR) Signals under the Static Load Conditions of Rocks

Geological hazards, such as the frequent occurrence of rock bursts in deep mining, emphasize the critical necessity for the early warning and prediction of dynamic fractures in coal and rock masses, as well as the destabilization of the surrounding rock. This study delves into the mechanisms of electromagnetic radiation (EMR) signals and their synchronous coupling with micro-seismic (ME) signals. EMR and ME signals from rock specimens were systematically collected during the uniaxial compression fracture process using a dedicated monitoring and acquisition system. Employing the wavelet analysis method, the original data underwent reconstruction and denoising, while the EMR and ME spectra, derived through fast Fourier transform, were subjected to detailed scrutiny. The comprehensive analysis unveiled that EMR signals arising from rock fractures exhibited precise timing synchronization with ME signals. Moreover, the dominant frequencies of both signals are closely aligned within the low-frequency band, indicating a remarkable degree of similarity and homology. These findings establish an experimental basis for the development of monitoring and early warning systems geared toward assessing damage to coal and rock masses using EMR and ME signals.

Blind source separation of electromagnetic signals based on deep focusing U-Net

The unintentional electromagnetic radiation of digital electronic devices during operation can cause information leakage and threaten the information security of the system. In order to explore the leakage level of important information, it is necessary to separate the electromagnetic leakage signal from the complex environmental electromagnetic wave, so the blind source separation technology is studied.Traditional blind source separation methods are mainly unsupervised learning methods, and their separation results are not as expected. In recent years, deep learning technology based on supervised learning has achieved good results in speech separation and other fields, indicating that it is a feasible method.In order to solve the problem of separating source signals from mixed electromagnetic radiation signals and reducing noise interference in electromagnetic safety detection. this paper proposes a Deep Focusing U-Net neural network, which makes the model pay more attention to the features at deeper layer. The network is applied to the blind separation of LCD electromagnetic leakage signals, and the good separation performance proves the effectiveness of this method.

Discrete characteristics of instantaneous frequency of EMR induced by coal and rock fracture

To reveal the discrete characteristics of the instantaneous frequency of the electromagnetic radiation (EMR) waveform induced by coal and rock fracture, the uniaxial compression experiments for coal and rock samples were carried out, and the EMR signals with full waveform were acquainted and stored. The empirical wavelet transform is used to filter and de-noise the EMR waveform, and then the short-time Fourier transform is used to analyze the time-frequency characteristics of the waveform. The discrete characteristics of the instantaneous frequency with a larger amplitude and the relationship between the centroid frequencies and peak-to-peak values (Vpps) of the EMR waveforms are statistically analyzed. The results show that the centroid frequency of 0–100 kHz is negatively correlated with the Vpp, and the relationship between them shows a logarithm function relation. The instantaneous frequency of the EMR waveform of coal and rock fracture has significant discrete characteristics. In detail, for the rock sample, the instantaneous frequencies with relatively large amplitude are mainly 4.5 kHz, 19.5 kHz, 22.0 kHz, and 27.5 kHz; for the coal sample, the instantaneous frequencies are mainly 1.0 kHz, 4.5 kHz, 9.0 kHz, and 74.0 kHz. This discrete characteristic is determined by the natural properties and fracture characteristics of the sample. Compared with the homogeneous rock samples, the internal cracks of the coal samples are well developed and show strong anisotropy, resulting in the discrete characteristics of the instantaneous frequency being relatively weaker. The findings have certain guiding significance for optimizing the design of the EMR monitoring frequency band and improving the pertinence and accuracy of the monitoring and early warning for coal and rock dynamic disasters.

Spontaneous Symmetry Breaking in Systems Obeying the Dynamics of On–Off Intermittency and Presenting Bimodal Amplitude Distributions

In this work, first, it is confirmed that a recently introduced symbolic time-series-analysis method based on the prime-numbers-based algorithm (PNA), referred to as the “PNA-based symbolic time-series analysis method” (PNA-STSM), can accurately determine the exponent of the distribution of waiting times in the symbolic dynamics of two symbols produced by the 3D Ising model in its critical state. After this numerical verification of the reliability of PNA-STSM, three examples of how PNA-STSM can be applied to the category of systems that obey the dynamics of the on–off intermittency are presented. Usually, such time series, with on–off intermittency, present bimodal amplitude distributions (i.e., with two lobes). As has recently been found, the phenomenon of on–off intermittency is associated with the spontaneous symmetry breaking (SSB) of the second-order phase transition. Thus, the revelation that a system is close to SSB supports a deeper understanding of its dynamics in terms of criticality, which is quite useful in applications such as the analysis of pre-earthquake fracture-induced electromagnetic emission (also known as fracture-induced electromagnetic radiation) (FEME/FEMR) signals. Beyond the case of on–off intermittency, PNA-STSM can provide credible results for the dynamics of any two-symbol symbolic dynamics, even in cases in which there is an imbalance in the probability of the appearance of the two respective symbols since the two symbols are not considered separately but, instead, simultaneously, considering the information from both branches of the symbolic dynamics.

Comprehensive early warning method of microseismic, acoustic emission, and electromagnetic radiation signals of rock burst based on deep learning

Microseismic, acoustic emission, and electromagnetic radiation monitoring methods are often used to monitor rock burst disasters in coal mines. In the process of coal mining, the time series characteristics and amplitude characteristics of microseismic, acoustic emission, and electromagnetic radiation data are mainly used to identify rockburst risk, but the results of risk identification through the three monitoring methods are quite different. Consequently, the accurate and comprehensive early warning of rock burst risk is still an urgent problem to be solved. The development of deep learning provides a new means for intelligent early warning of rock burst risk. In this paper, a comprehensive early warning method of microseismic, acoustic emission, and electromagnetic radiation (MS-AE-EMR) signals of rock bursts was proposed based on a deep learning algorithm. This method uses long short-term memory recurrent neural networks (LSTM-RNNs) to intelligently identify the MS-AE-EMR precursor signal of rock burst risk, predicts the MS-AE-EMR signal by a convolution neural network (CNN), analyses the MS-AE-EMR precursor signal of rock burst risk through the data analysis method and obtains the risk coefficient of rock burst. Moreover, by using the MS-AE-EMR original signal and risk coefficient, it trains the multi-input CNN and inputs the predicted signal into the trained multi-input CNN to obtain the predicted risk coefficient of rock burst. Analysing the risk coefficient completes the comprehensive early warning of the MS-AE-EMR signal of rock burst. After field verification, the RNN-based comprehensive early warning method of the MS-AE-EMR signal can respond positively to rock burst risk and capture the information in advance. Therefore, this method is of great significance for accurate monitoring and early warning of rock burst in coal mines.

In-situ gas content evaluation of producing coalbed methane reservoirs based on electromagnetic radiation anomalies

In-situ gas content evaluation in coalbed methane (CBM) reservoirs is one of the most critical issues in the fields of gas disaster forecasting, stable production and resource control. Traditionally, the methods for the CBM assessment of coal reservoirs have relied on laboratory evaluation, well testing, logging and seismics, which are very expensive and inefficient in terms of accurate regional investigation. This paper proposes a non-contact in-situ evaluation method based on electromagnetic radiation (EMR) anomaly. Electrical-mechanical coupling models have been theoretically investigated based on the Super-Low Frequency (SLF) electromagnetic radiation (EMR) generating process. Model simulation and field survey results have further demonstrated that in-situ EMR anomalies’ collection with a level of 0.01–1000 nT on the surface is feasible. Furthermore, using the BD-6 SLF detector with built-in processing algorithms such as background field removal and the ensemble empirical mode decomposition (EEMD), the natural-source EMR extraction method is proposed in support of statistical models established to estimate the in-situ reservoir pressure. Using nonlinear cubic polynomials, an ideal model of reservoir pressure and the normalized EMR signals is built, satisfying theoretical predictions (the maximum R-square value is above 0.83). The “Reservoir Pressure (RP)-EMR"-based approach for evaluating gas content in relation to producing reservoirs is then further suggested. In the study area, the proposed methodology is effectively used with a good ability for the local CBM resource monitoring of 6.4 × 1010m3. From August 2011 to October 2011, the estimated gas production is 6.3 million m3 (the actual gas production is 5.9 million m3). Therefore, this promising method is expected to be one of the most effective ways for industrials to realize the CBM evaluation in producing reservoirs.

Study on Synchronous Response Law of Acoustic and Electrical Signals of Outburst Coal Rock under Load and Fracture

A multisignal nanosecond synchronous acquisition system to measure acoustic emission (AE) and electromagnetic radiation (EMR) generated during the process of loading and failure of coal and rock samples is established. The correlation between the energy of the AE and EMR signals and the loading stress of outburst coal-rock samples was studied, and the characteristics of the AE and EMR signals during the process of loading and fracturing the outburst coal and rock samples were analyzed. The results show that (1) before the failure of the outburst coal and rock samples, the fluctuation of the AE and EMR signals is the largest, with the same rising and falling trend, and the intensity is not strictly positively correlated, with the phenomenon of low EMR when the AE intensity is high; (2) the EMR and AE deviation degree and frequency exhibit a good response to coal and rock fracturing. The correlation between EMR and stress drop is stronger than that of AE, and the AE signal is richer than the EMR signal. The results show that it is feasible to develop combined AE and EMR early warning technology to improve the early forecasting accuracy of coal and gas outbursts.