Electromagnetic Interference Signals Research Articles

Electromagnetic force from electric vehicles: Potential electromagnetic interference source for subcutaneous implantable defibrillator.

Electromagnetic interference (EMI) encompasses electromagnetic field signals that can be detected by a device's circuitry, potentially resulting in adverse effects such as inaccurate sensing, pacing, device mode switching, and defibrillation. EMI may impact the functioning of Cardiac Implantable Electronic Devices (CIEDs) and lead to inappropriate therapy. An experimental measuring device, a loop antenna mimicking the implantable cardioverted defibrillator (ICD) antenna, was developed, and validated at the US Food and Drug Administration (FDA) and sent to Wright State University for testing. Two sets of measurements were conducted while the vehicle was connected to a 220-Volt outlet with charging at ON and OFF. Each measurement set involved three readings at various locations, with the antenna oriented in three different positions to account for diverse patient postures. The experiment utilized a Tesla Model 3 electric vehicle (EV), assessing scenarios both inside and outside the car, including the driver's seat, driver's seat floor, passenger's seat, rear seat, rear seat floor, cup holder, charging port (car), and near the charging station. The detected voltage (max 400 to 504 millivolts) around the cup holder inside the car differed from all other measurement scenarios. The investigation highlights the identification of EMI signals originating from an EV) that could potentially interrupt the functionality of a Subcutaneous Implantable Cardioverter-Defibrillator (S-ICD). These signals fell within the R-wave Spectrum of 30-300Hz. Further in-vivo studies are essential to determine accurately the level of interference between S-ICDs and EMI from Electric Vehicles.

Evaluation method for electromagnetic interference complexity of high voltage switch based on feature extraction and GCC‐GRU network

AbstractWhen some new‐type sensors are subjected electromagnetic interference (EMI), the traditional standard can no longer serve as the guidance for their EMI protection. Therefore, it is necessary to evaluate the complexity of the new sensing equipment's suffering from electromagnetic interference. A hybrid method of feature extraction and complexity evaluation of Electromagnetic Interference signals based on generalised correntropy criterion (GCC) is proposed. First, a variational mode decomposition algorithm for adaptive extraction of decomposed signals is constructed using GCC, and Generalised S‐transform is used for processing and reorganisation. Then, according to the time‐frequency space model, a new quantitative evaluation criterion interference factor is proposed, which overcomes the shortcomings of the traditional qualitative classification and the inability to solve the cross classification of indicators. Then, the Gated Recurrent Unit is introduced, and the GCC‐GRU evaluation model with GCC as the loss function is derived. The authors conduct EMI tests and measured signal evaluation with Medium Voltage and High Voltage switches, and the results show that the method proposed is correct and effective.

Robust EMI elimination for RF shielding-free MRI through deep learning direct MR signal prediction.

To develop a new electromagnetic interference (EMI) elimination strategy for RF shielding-free MRI via active EMI sensing and deep learning direct MR signal prediction (Deep-DSP). Deep-DSP is proposed to directly predict EMI-free MR signals. During scanning, MRI receive coil and EMI sensing coils simultaneously sample data within two windows (i.e., for MR data and EMI characterization data acquisition, respectively). Afterward, a residual U-Net model is trained using synthetic MRI receive coil data and EMI sensing coil data acquired during EMI signal characterization window, to predict EMI-free MR signals from signals acquired by MRI receive and EMI sensing coils. The trained model is then used to directly predict EMI-free MR signals from data acquired by MRI receive and sensing coils during the MR signal-acquisition window. This strategy was evaluated on an ultralow-field 0.055T brain MRI scanner without any RF shielding and a 1.5T whole-body scanner with incomplete RF shielding. Deep-DSP accurately predicted EMI-free MR signals in presence of strong EMI. It outperformed recently developed EDITER and convolutional neural network methods, yielding better EMI elimination and enabling use of few EMI sensing coils. Furthermore, it could work well without dedicated EMI characterization data. Deep-DSP presents an effective EMI elimination strategy that outperforms existing methods, advancing toward truly portable and patient-friendly MRI. It exploits electromagnetic coupling between MRI receive and EMI sensing coils as well as typical MR signal characteristics. Despite its deep learning nature, Deep-DSP framework is computationally simple and efficient.

The technological development of infrared sensors in substations and the key determining factors affecting the accuracy of measurement data

An overview of the developmental history, characteristics, and applications of infrared sensors are firstly provided in the paper. Subsequently, it delves into the sensing mechanisms of commonly employed infrared sensors in substations, namely the thermistor sensors, pyroelectric sensors, and photoconductive sensors. Thermistor infrared sensors gauge infrared radiation by tracking changes in material resistance with temperature. Pyroelectric sensors detect thermal changes in objects based on the thermal effects induced by infrared radiation. Photoconductive-type sensors leverage the photoconductive effect of semiconductors to convert optical signals into electrical signals, adapting to various light conditions. In the process of measurements, variables like temperature, distance between the target object and the infrared sensor, and electromagnetic interference impact accuracy. In extreme temperatures, the cooling precision of infrared thermal imaging systems may decline, affecting measurement accuracy. The distance between the target and sensor also influences results due to energy attenuation in the infrared radiation path through the atmosphere. Faraday’s law of electromagnetic induction warns of potential electromagnetic interference signals in substations, causing device breakdowns, output image halo, or increased internal noise. Finally, the paper envisions the future development of infrared sensors.

Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering

Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-Electromagnetic Satellite (CSES) electric field detector. First, the waveform data are transformed into time–frequency spectrograms with a duration of 8 s using the short-time Fourier transform. Then, the spectrograms are subjected to grayscale transformation, vertical line feature extraction, and binarization preprocessing. In the third step, the preprocessed data are dimensionally reduced and fed into an unsupervised K-means++ clustering model to achieve automatic recognition and labeling of VLPTs. By recognizing and studying VLPT, not only can interference be recognized, but the temporal and spatial locations of these interferences can also be determined. This lays the foundation for identifying VLPT sources and gaining deeper insights into the generation, propagation, and characteristics of electromagnetic radiation.

Suppression of voltage gain fluctuation in LLC Resonant Converter Based on chaotic spread spectrum

In order to realize the voltage stable output of the LLC resonant converter under different loads, a voltage gain fluctuation suppression technology of the LLC resonant converter based on a chaotic spread spectrum is proposed. The gain fluctuation electromagnetic interference signal is eliminated by the Chua’s circuit; The rated voltage value is discretized, and the target network and the evaluation network are used to form a deep Q network, with the multi-threshold judgment method and the variable step method respectively completed the gain fluctuation suppression target under different working conditions. The experimental results show that the proposed technology can achieve stable voltage output in both dynamic and steady-state conditions, with good gain fluctuation suppression effect and strong timeliness. It provides data reference for the rational application of the LLC resonant converter.

Identification method for microseismic, acoustic emission and electromagnetic radiation interference signals of rock burst based on deep neural networks

Microseismic, acoustic emission, and electromagnetic radiation (MS, AE, and EMR) are promising methods for the monitoring and early warning of rock bursts. In the underground mining process, personnel activities and electromechanical equipment produce MS, AE, and EMR interference signals that affect the accuracy of MS, AE, and EMR monitoring. This study is aimed at overcoming the difficulties in identifying MS, AE, and EMR interference signals by means of on-site monitoring and deep learning methods. First, MS waveform signals and AE and EMR time series signals are collected by the SOS monitoring system and GDD12 monitor. According to the field records, the MS effective waveform, MS interference waveform, AE and EMR interference signals were labeled, and training sets and verification sets were made. An identification model for MS waveforms based on the ResNet-50 convolutional neural network and an identification model for AE and EMR interference signals based on the recurrent neural network were constructed using the original MS, AE, and EMR data. In addition, the identification accuracy and generalization ability of the models were analyzed. The results show that the proposed method can respond positively to MS, AE, and EMR interferences and accurately eliminate MS, AE, and EMR interference signals. It can significantly improve the reliability of MS, AE, and EMR monitoring data and effectively monitor rock burst disasters.

Tailored distribution of 1D nanoparticles in co-continuous EMA/TPO flexible polymeric blends used as emerging materials for suppressing electromagnetic radiation

Electromagnetic interference (EMI) is unwanted noise or interference that often affects electronic equipment and human health. An effective EMI shielding material is needed to protect electronic equipment and human health to block EMI signals. In this work, we have developed electrically conductive polymer composites via the preferential localization of MWCNTs in a co-continuous ethylene-co-methyl acrylate (EMA)/thermoplastic polyolefin (TPO) (80/20 w/w) binary blend system using a solution mixing technique. The electrical percolation threshold was achieved by incorporating 3 to 5 wt% of MWCNT in the blend matrix. FESEM and TEM images indicated a co-continuous morphology in which MWCNT was thermodynamically and kinetically driven into the EMA phase of the polymer blend. The DC and AC conductivity of the fabricated composite was significantly increased upon loading the MWCNT in the polymer composite. A total EMI shielding effectiveness of −35 dB was achieved by adding 15 wt% MWCNT into the matrix in the frequency range of 14.5–20.0 GHz, along with good thermal conductivity, dielectric properties, and mechanical properties.

Investigation into Electromagnetic Compatibility Conducted Susceptibility of a Laser Ranging System

Laser ranging systems are a widely used form of electronic system and are generally influenced by complex electromagnetic environments. We used a laser ranging system comprising the VL53L0X laser ranging sensor module as the research object and designed a sensitive experimental platform based on the laser ranging system. In this work, we have shown that electromagnetic interference signals can affect the performance of laser ranging systems. The electromagnetic interference signal was found to enter through the chip selection port of the laser sensor chip to affect the work of the laser sensor chip. Finally, we obtained the susceptibility threshold characteristics of the laser ranging system under the influence of different types of interference signal. This study deepens the understanding of the electromagnetic susceptibility mechanism of laser ranging systems and helps in the design of their electromagnetic compatibility.

EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN

In this work, a multi-task convolutional neural network with multi-input (MIMT-CNN) is proposed for electromagnetic interference (EMI) signals recognition and electromagnetic environment risk evaluation of the data link of unmanned aerial vehicle (UAV). The visualized performance parameters, short-time Fourier transform (STFT) spectrograms, and constellation diagrams are obtained by experiment on the electromagnetic susceptibility of UAV’s datalink. In particular, the constellation diagram is further enhanced by calculating the density distribution of sampling points to obtain the normalized density constellation. Taking the above different categories of images as the input of the expected model, the multi-element and high correlation EMI features are extracted and fused in the MIMT-CNN. Besides, the structure of series-parallel connection is adopted in the trained model and the Bayesian optimization is also used to select hyperparameters. In this case, the perception model with higher reliability can be obtained. On this basis, the performance and complexity of the obtained model with different input channels are compared. The results show that with the input of constellation diagram, especially the normalized density constellation, can significantly improve the accuracy of the model. Besides the normalized density constellation, the model with visualized performance parameters and STFT spectrogram as inputs has a much better performance.

Electrically conductive fibers fabrication and characterization via in-situ polymerization of aniline for the protection against EMI and thermal imaging signals

Polyester fibers (PEF) were successfully coated with Polyaniline (PANI) via in situ polymerization to make electrically conductive fibers (ECF). X-ray diffraction (XRD) analysis confirms the synthesis and scanning electron microscopy (SEM) confirms the coating of PANI on the surface of PEF. Both coated and uncoated fibers were tested for their DC conductivity, and the results showed a clear difference between the two. From a highly insulating state, it transitions to about 0.1 S/cm. Both the tensile strength and modulus seemed to rise, going from 0.5 MPa to 2.5 MPa and from 0.04 MPa to 0.4 MPa, respectively. Blocking electromagnetic (EM) waves primarily requires a material with good electrical conductivity. The fabric proved effective in blocking about 99.9% of the ultraviolet (UV) and near-infrared rays. Fabrics that effectively block electromagnetic waves are able to conceal the high-temperature human body from a thermal imaging camera, which detects heat signatures by observing the infrared (IR) radiation generated by objects. We also measured the dielectric characteristics using impedance analysis and found that the values for the dielectric constant, dielectric loss, and AC conductivity dramatically increased from the frequency range of 100 Hz to 5 MHz. Further, electromagnetic interference (EMI) and shielding efficiency were determined with the aid of the dielectric constant and dielectric loss (SE). The overall SE in the same 100 Hz to 5 MHz range was similarly more than 80 dB. Improved EMI shielding was feasible due to the non-woven fabric's strong electrical conductivity, homogenous PANI coating on the surface, and great compactness. These findings are enough to predict that these fibers will provide good EMI shielding and thermal imaging when used in any application.

Radiated Magnetic Field Research of Primary–Secondary Fusion MV Switch Subjected to Ultraserious Transient Current

With the extensive application of primary-secondary fusion Medium Voltage(MV) switch, the fault rate of acquisition circuit board of medium voltage switch increases obviously. The maintenance staff highly suspect that the electromagnetic compatibility of the MV switch has been damaged due to the short-range radiation interference. However, this kind of new switch is not equipped with electromagnetic field sensor to record the signal in real time. To solve this problem, firstly, electromagnetic interference signals under two kinds of ultra-serious current conditions (lightning current and inrush current) were found and obtained by originality test in this paper. Then, the finite integral time domain (FITD) method was introduced into the primary- secondary fusion switch, and the radiated magnetic fields of two kinds of transient currents are obtained. Finally, the radiated magnetic field near the secondary equipment were calculated and analyzed, and the research results have reference value for the resist of radiated magnetic field and the formulation of EMC standards of MV switch. The obtained radiated magnetic field can cause the sensitive equipment to malfunction.

Degradation monitoring of SiC MOSFET gate-oxide based on differential mode conducted interference spectrum

The gate-oxide degradation of power devices has an important impact on the normal operation of electrical equipment. Therefore, the research on the condition monitoring of gate oxide is of great significance to improve the operation reliability. In this article, a new monitoring method for the gate oxide in silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) is proposed. Firstly, the degradation mechanism of gate oxide and its influence on the change rate of drain current (di/dt) during device switching are analyzed. Secondly, taking the Buck converter composed of SiC MOSFET as the research object, combined with the EMI equivalent model and conducted interference coupling path of the converter, the relationship between differential mode electromagnetic interference (DM EMI) signal and gate-oxide degradation is established. Finally, the amplitude of DM interference signal is used as the characteristic parameter to monitor the condition of SiC MOSFET gate oxide, and the effectiveness of the monitoring method is verified by experiments. The monitoring method proposed in this paper is applicable to different switching frequencies and load currents, and can monitor the healthy state of gate oxide in SiC MOSFET under laboratory environment and actual working conditions, which has strong practicability.

Active EMI Suppression System for a 50 mT Unshielded Portable MRI Scanner.

Passive shielding is usually applied to block electro-magnetic interference (EMI) for portable very-low field MRI scanners, but it goes against the mobility of the scanners. Here, the reference channel-based active EMI suppression (AES) system was proposed to discard them. Different from the existing studies, this work started with analyzing the interference transmission paths and discovered that the human body coupling was the main path. Then, the "ring-" shaped EMI receiving coil designed here together with the electrocardiograph (ECG) electrode patch were used to suppress the human-body-coupled interferences. For the first time, the periphery data of the k-spaces of the RF coil and the EMI detectors were utilized to calculate the transfer factors in multiple frequency bands by using the least square method. The reference EMI signals were transferred to the interferences in the MR signal by multiplying the transfer factors in each frequency band, and the denoised MR signal could be obtained by subtracting the transferred reference EMI signals. The prototype of the AES system was applied to a 50 mT unshielded portable MRI scanner. The in-vivo experiments indicated that the interference suppression rate of the AES system equipped with the "ring-" shaped EMI receiving coil could reach 96.8%. Meanwhile, the SNR of the images after interference suppression by the AES system equipped with both types of detectors was 97.2% of that of the images scanned inside the shielded room. The proposed AES system ensures that the portable MRI scanner works normally in unshielded environment. Our study provides a solution to make portable MRI scanners truly movable.

Research on the Effectiveness of Deep Convolutional Neural Network for Electromagnetic Interference Identification Based on I/Q Data

With the development of wireless communication technology, the electromagnetic interference (EMI) of artificial radio to weather radar increases significantly, which has a serious impact on the quality of radar data. Most of the research on detecting and suppressing electromagnetic interference was based on the primary product of the radar. This paper researches the effectiveness of deep convolutional neural networks (DCNN) to identify and suppress electromagnetic interference based on the I/Q data output from the front end of a radar receiver. Firstly, this paper selected UNet, ResNet with UNet structure, and DeepLab V3+ for the semantic segmentation of electromagnetic interference and other signals. After semantic segmentation, this paper used the linear interpolation method to suppress EMI. Finally, this paper selected the prediction precision of the model and compared the quality of primary products before and after EMI suppression to evaluate the effectiveness of DCNN. The results showed that all three models could effectively identify the electromagnetic interference and the quality of the data were improved after suppression. It suggests that the use of DCNN on the I/Q data output from the front end of a radar receiver can play a certain effect on the identification of electromagnetic interference.

Prediction on EMS of UAV's Data Link Based on SSA-Optimized Dual-Channel CNN

In this article, a novel method is presented for electromagnetic interference (EMI) perception and electromagnetic susceptibility (EMS) assessment of an unmanned aerial vehicle (UAV) against interference. A large number of in-phase and quadrature data of the UAV's data link and their corresponding critical EMI signal were collected by the equivalent injection test method. After visualization and preprocessing of the in-phase and quadrature data, the obtained time–frequency spectrum and the data-link state parameter histogram were joined to further preprocess as the input of the model. A dual-channel convolutional neural network optimized by a sparrow search algorithm (SSA-DCNN) was proposed to predict the EMS of the data link of the UAV. The proposed method can not only avoid manual extraction of data features but also improve the intelligence level of EMI perception and safety evaluation on UAVs. The performance of the dual-channel convolutional neural network is compared with that of the SSA-DCNN method under different optimization algorithms. The results show that the SSA-DCNN model can significantly enhance the convergence speed and prediction accuracy of the training process compared with the dual-channel convolutional neural network. Besides, among the optimization algorithms, the stochastic gradient descent with momentum applied to the SSA-DCNN model shows a better performance.

Electromagnetic transient characteristics and overvoltage suppression strategy of renewable energy-oriented power system connected by gas-insulated transmission lines

New energy systems, such as wind power and photovoltaic and distributed power supply, in the power generation and grid-connected transmission system basically will use stronger insulation design, higher operational safety, and smaller footprint GIL equipment, while the new energy transmission system of the smart grid development trend requires accompanying the electronic transformer equipment to promote a large area. However, the current GIL equipment used in new energy transmission systems has caused great electromagnetic interference to the electronic equipment due to the compact layout of the equipment and the transient signals generated by normal operation, which has seriously restricted the development of smart grids and became a major obstacle to the grid connection of new energy systems. In order to study the characteristics and developmental law of transient electromagnetic interference signal of GIL equipment in new energy transmission and transformation system, this study proposes a dynamic arc model based on an optimized arc extinguishing criterion, verifies the correctness of the improved dynamic arc model through theoretical analysis, simulates calculation combined with the test measurement data, and compares and analyzes the simulation results with test measurement data to study the transient electromagnetic interference source signal during arc burning duration. The simulation results and test data are compared and analyzed to study the pulse steepness, size of the wave head, and overall waveform development law of the transient electromagnetic interference source signal during the arc burning process. It is concluded that the dynamic arc model based on the optimized arc extinguishing criterion can be better used to study the transient electromagnetic interference signal in the GIL equipment. Meanwhile, the transient electromagnetic interference signal has the characteristics of large amplitude and high frequency, the maximum overvoltage can reach 1.9 p.u., the UHF can reach 30 MHz, and the wave head rise time is about 12 ns. This study provides theoretical support for subsequent research on suppressing transient electromagnetic interference source signals, improving the anti-electromagnetic interference performance of electronic devices, and is dedicated to solving the obstruction problem in the process of grid connection of new energy transmission and substation systems.

Electromagnetic Interference Effects of Continuous Waves on Memristors: A Simulation Study.

As two-terminal passive fundamental circuit elements with memory characteristics, memristors are promising devices for applications such as neuromorphic systems, in-memory computing, and tunable RF/microwave circuits. The increasingly complex electromagnetic interference (EMI) environment threatens the reliability of memristor systems. However, various EMI signals’ effects on memristors are still unclear. This paper selects continuous waves (CWs) as EMI signals. It provides a deeper insight into the interference effect of CWs on the memristor driven by a sinusoidal excitation voltage, as well as a method for investigating the EMI effect of memristors. The optimal memristor model is obtained by the exhaustive traversing of the possible model parameters, and the interference effect of CWs on memristors is quantified based on this model and the proposed evaluation metrics. Simulation results indicate that CW interference may affect the switching time, dynamic range, nonlinearity, symmetry, time to the boundary, and variation of memristance. The specific interference effect depends on the operating mode of the memristor, the amplitude, and the frequency of the CW. This research provides a foundation for evaluating EMI effects and designing electromagnetic protection for memristive neuromorphic systems.

Identification of Microcontroller Unit Instruction Execution Using Electromagnetic Leakage and Neural Network Classification

In this article, a novel method is proposed for determining the running state of a system through the classification of electromagnetic interference (EMI) leakage using neural network (NN) models. A modified IEC 61967 measurement platform is used to analyze the EMI signals of a microcontroller unit during its operation. A total of 17 NN models are developed and tested to determine the optimal model. The optimal NN model has ungrouped-Top3 and ungrouped-Top5 accuracies of 77.13% and 91.94%, respectively. The ungrouped-Top1 accuracy is improved by 7.53%. They are the highest improvements ever achieved to the best of the author's knowledge.

Dynamic Noise Reduction with Deep Residual Shrinkage Networks for Online Fault Classification.

Fault signals in high-voltage (HV) power plant assets are captured using the electromagnetic interference (EMI) technique. The extracted EMI signals are taken under different conditions, introducing varying noise levels to the signals. The aim of this work is to address the varying noise levels found in captured EMI fault signals, using a deep-residual-shrinkage-network (DRSN) that implements shrinkage methods with learned thresholds to carry out de-noising for classification, along with a time-frequency signal decomposition method for feature engineering of raw time-series signals. The approach will be to train and validate several alternative DRSN architectures with previously expertly labeled EMI fault signals, with architectures then being tested on previously unseen data, the signals used will firstly be de-noised and a controlled amount of noise will be added to the signals at various levels. DRSN architectures are assessed based on their testing accuracy in the varying controlled noise levels. Results show DRSN architectures using the newly proposed residual-shrinkage-building-unit-2 (RSBU-2) to outperform the residual-shrinkage-building-unit-1 (RSBU-1) architectures in low signal-to-noise ratios. The findings show that implementing thresholding methods in noise environments provides attractive results and their methods prove to work well with real-world EMI fault signals, proving them to be sufficient for real-world EMI fault classification and condition monitoring.