Strong Interference Research Articles

Fault location in high-voltage direct-current grids based on panoramic voltage features and vision transformers

This paper presents a novel fault location method designed for high-voltage direct-current (HVDC) grids equipped with quick-action protections and circuit breakers. The method relies on a very short data window of single-ended voltage signals, which allows the acquisition of necessary measurements before the ultra-fast fault-isolation stage. Through fault analysis of the simulated four-terminal HVDC grid, it is observed that the fault distance induces a unique transient response in the voltage signal. However, due to the complex mathematical representation of loop components and the multiple fault factors involved, a data-driven strategy is adopted for fault distance estimation. Based on this strategy, four signal processing techniques, including Gramian angular field, recurrence plot, Markov transition field, and continuous wavelet transform, are used to extract panoramic voltage features. Additionally, four advanced lightweight variants of vision transformers (ViT), including MobileViT, EfficientFormer, Efficient-Model, and EdgeViT, are considered for constructing the regression model. The optimal combination of panoramic voltage features and regression models for configuring the locator is determined by statistical evaluation. The results of semi-physical experiments based on real-time digital simulators demonstrate that the proposed method, first, can accurately estimate the fault distance, second, possesses robustness against high transition resistance, low sampling frequency, and strong noise interference, and third, is practically feasible.

Highest resolution high signal-to-noise ratio sonic slowness and application to Brothers Volcano, Kermadec Arc, New Zealand

High resolution and accurate acquisition of underground sonic properties is essential for energy detection and earth exploration. An effective method is developed to accurately calculate sonic slowness at the highest resolution (receiver spacing of 0.5 ft) with a high signal-to-noise ratio. The method uses supervirtual interferometry to reconstruct a large number of waveforms for slowness extraction using redundant information from overlapping interreceiver spacing. Synthetic calculations are used to verify the validity of the method, especially in the limited case of strong noise interference and the presence of damaged channels in the array receivers, to accurately calculate the formation of sonic slowness. The theory is applied to process and interpret low data-quality field measurements acquired at Brothers Volcano from the International Ocean Discovery Program. The processing analysis indicates that in the case of very weak formation signals, the residual tool waves from the wireline sonic logging can still interfere with the high-resolution processing, whereas this method can effectively suppress this interference and accurately obtain the formation sonic velocity.

Tris buffer-accelerated ligand exchange rate for instant fluorescence detection of trivalent chromium ion

Functional nucleic acids (FNAs) have attracted a lot of attention for the rapid detection of metal ions. Cr3+ is one of the major heavy metal ions in natural waters. Due to the slow ligand exchange rate of Cr3+, the FNA-based Cr3+ sensors require long assay times, limiting the on-site applications. In this study, we report that the good's buffers containing amino and polyhydroxy groups greatly increase the ligand exchange rate of Cr3+. Using EDTA as a model coordinate ligand, the Tris buffer (100 mM, pH 7.0) showed the best acceleration effect among the eight buffers. It improved the rate constant ∼20-fold, shorten the half-time 19-fold, and lowered the activation energy ∼70% at 40 °C. The Tris buffer was then applied for sensor based on the Cr3+-binding induced fluorescence quenching of fluorescein (FAM)-labeled and single-stranded DNA (ssDNA), which shortened the assay time from 1 h to 1 min. The Tris buffer also ∼100% enhanced the fluorescence intensity of FAM, achieving the 11.4-fold lower limit of detection (LOD = 6.97 nM, S/N = 3). By the combination use of the Tris buffer and ascorbic acid, the strong interference from Cu2+, Pb2+, and Fe3+ suffered in many previous reported Cr3+ sensors was avoided. The practical application of the sensor for the detection of Cr3+ spiked in the real water samples were demonstrated with high recovery percentages. The Tris buffer could be applied for other metal ions with slow ligand exchange rate (such as V2+, Co3+ and Fe2+) to solve diverse issues such as long assay time and low synthesis yield of metal complexes, without the need of heating treatment.

Nearshore Ship Detection in PolSAR Images by Integrating Superpixel-Level GP-PNF and Refined Polarimetric Decomposition

Nearshore ship detection has significant applications in both the military and civilian domains. Compared to synthetic aperture radar (SAR), polarimetric synthetic aperture radar (PolSAR) provides richer information for analyzing the scattering mechanisms of ships and enables better detection of ship targets. However, ships in nearshore areas tend to be highly concentrated, and ship detection is often affected by adjacent strong scattering, resulting in false alarms or missed detections. While the GP-PNF detector performs well in PolSAR ship detection, it cannot obtain satisfactory results in these scenarios, and it also struggles in the presence of azimuthal ambiguity or strong clutter interference. To address these challenges, we propose a nearshore ship detection method named ECD-PNF by integrating superpixel-level GP-PNF and refined polarimetric decomposition. Firstly, polarimetric superpixel segmentation and sea–land segmentation are performed to reduce the influence of land on ship detection. To estimate the sea clutter more accurately, an automatic censoring (AC) mechanism combined with superpixels is used to select the sea clutter superpixels. By utilizing refined eight-component polarimetric decomposition to improve the scattering vector, the physical interpretability of the detector is enhanced. Additionally, the expression of polarimetric coherence is improved to enhance the target clutter ratio (TCR). Finally, this paper combines the third eigenvalue of eigenvalue–eigenvector decomposition to reduce the impact of azimuthal ambiguity. Three spaceborne PolSAR datasets from Radarsat-2 and GF-3 are adopted in the experiments for comparison. The proposed ECD-PNF method achieves the highest figure of merit (FoM) value of 0.980, 1.000, and 1.000 for three datasets, validating the effectiveness of the proposed method.

Improved Cycle-Consistency Generative Adversarial Network-Based Clutter Suppression Methods for Ground-Penetrating Radar Pipeline Data

Ground-penetrating radar (GPR) is a widely used technology for pipeline detection due to its fast detection speed and high resolution. However, the presence of complex underground media often results in strong ground clutter interference in the collected B-scan echoes, significantly impacting detection performance. To address this issue, this paper proposes an improved clutter suppression network based on a cycle-consistency generative adversarial network (CycleGAN). By employing the concept of style transfer, the network aims to convert clutter images into clutter-free images. This paper introduces multiple residual blocks into the generator and discriminator, respectively, to improve the feature expression ability of the deep learning model. Additionally, the discriminator incorporates the squeeze and excitation (SE) module, a channel attention mechanism, to further enhance the model’s ability to extract features from clutter-free images. To evaluate the effectiveness of the proposed network in clutter suppression, both simulation and measurement data are utilized to compare and analyze its performance against traditional clutter suppression methods and deep learning-based methods, respectively. From the result of the measured data, it can be found that the improvement factor (Im) of the proposed method has reached 40.68 dB, which is a significant improvement compared to the previous network.

Damage detection of road domain waveform guardrail structure based on machine learning multi-module fusion.

The current highway waveform guardrail recognition technology has encountered problems with low segmentation accuracy and strong noise interference. Therefore, an improved U-net semantic segmentation model is proposed to improve the efficiency of road maintenance detection. The model training is guided by mixed expansion convolution and mixed loss function, while the presence of guardrail shedding is investigated by using partial mean values of gray values in ROI region based on segmentation results, while the first-order detail coefficients of wavelet transform are applied to detect guardrail defects and deformation. It has been determined that the Miou and Dice of the improved model are improved by 8.63% and 17.67%, respectively, over the traditional model, and that the method of detecting defects in the data is more accurate than 85%. As a result of efficient detection of highway waveform guardrail, the detection process is shortened and the effectiveness of the detection is improved later on during road maintenance.

LoRa Communication Quality Optimization on Agriculture Based on the PHY Anti-Frame Loss Mechanism

Agricultural environments are usually characterized by height differences and tree shading, which pose challenges for communication in smart agriculture. This study focuses on optimizing the packet loss rate and power consumption of LoRa’s practical communication quality. The research includes the investigation of the PHY anti-frame loss mechanism, encompassing PHY frame loss detection and the response mechanism between gateways and nodes. By implementing a closed loop for transmission and reception, the study enhances the communication network’s resistance to interference and security. Theoretical performance calculations for the SX1278 radio frequency chip were conducted under different parameters to determine the optimal energy efficiency, reducing unnecessary energy waste. An experimental assessment of the packet loss rate was conducted to validate the practical efficacy of the research findings. The results show that the LoRa communication with the anti-frame loss mechanism and the optimal energy ratio parameter exhibits an adequate performance. In the presence of strong and weak interferences, the reception rates are maximally improved by 37.8% and 53.4%, with effective distances of 250 m and 600 m, corresponding to enhancements of 100 m and 400 m, respectively. This research effectively reduces LoRa energy consumption, mitigates packet loss, and extends communication distances, providing insights for wireless transmission in agricultural contexts.

Transient and Dynamic Simulation of the Fluid Flow through Five-Way Electric Coolant Control Valve of a 100 kW Fuel Cell Vehicle by CFD with Moving Grid Technique

In order to maintain the performance of a fuel cell vehicle, it is essential to maintain a constant temperature of the stack. Therefore, it is very important to distribute the optimal coolant flow rate to each major component under very diverse and rapidly changing dynamic operating conditions. The part responsible for this is a five-way electric coolant valve. Therefore, this study aims to investigate transient dynamic flow characteristics of the fluid flow through a five-way electric coolant valve (PCCV: Penta-Control Coolant Valve). To achieve this goal, this paper attempts a three-dimensional dynamic simulation of the fluid flow through the valve using a commercial CFD solver with moving mesh technique to consider flow inertia and dynamic flow in the opening and closing stages of the ball valve rotating motion. The dynamic flow characteristics and the thermal mixing inside the PCCV ball valve during the opening and closing stages are analyzed. It was found that the discrepancies between dynamic and steady-state simulations are remarkable when fluxes with different levels of enthalpy and momentum flow into the PCCV, leading to strong flow interference and flow inertia, while the discrepancies are relatively small at low rotation speed and weak flow interference. Subsequently, the effect of the dynamic flow characteristics of the valve on the dynamic thermal mixing characteristics at two different ball valve rotation speeds and rotation directions are investigated. It was found that the dynamic flow and thermal mixing characteristics inside the PCCV are greatly affected by the rotation speed, rotation direction, and degree of flow interference between fluxes. It also helps design better coolant control strategies and improves the FCEV thermal management system.

Frequency-selective perovskite photodetector for anti-interference optical communications

Free-space coupling, essential for various communication applications, often faces significant signal loss and interference from ambient light. Traditional methods rely on integrating complex optical and electronic systems, leading to bulkier and costlier communication equipment. Here, we show an asymmetric 2D–3D–2D perovskite structure device to achieve a frequency-selective photoresponse in a single device. By combining two electromotive forces of equal magnitude in the opposite directions, the device output is attenuated to zero under constant light illumination. Because these reverse photodiodes have different response speeds, the device only responds near a certain frequency, which can be tuned by manipulating the 2D perovskite components. The target device achieves an ultrafast response of 19.7/18.3 ns in the frequency-selective photoresponse range 0.8–9.7 MHz. This anti-interference photodetector can accurately transmit character and video data under strong light interference with a source intensity of up to 454 mW cm−2.

Intelligent interference cancellation and ambient backscatter signal extraction for wireless-powered UAV IoT network

Unmanned aerial vehicles (UAVs) offer a new approach to wireless communication, leveraging their high mobility, flexibility, and visual communication capabilities. Ambient backscatter communication enables Internet of Things devices to transmit data by reflecting and modulating ambient radio waves, eliminating the need for additional wireless channels, and reducing energy consumption and cost for sensors. However, passive ambient backscatter communication has limitations such as limited range and poor communication quality. By utilizing UAVs as communication nodes, these limitations can be overcome, expanding the communication range and improving the quality of communication. Although some research has been conducted on combining UAVs and ambient backscatter, several challenges remain. One key challenge is the strong direct link interference in ambient backscatter under UAV conditions, which significantly affects communication quality. In this paper, we propose an intelligent backward and forward straight link interference cancellation algorithm based on NOMA decoding technique to enhance ambient backscatter communication quality under UAV conditions and extract more ambient energy for UAV energy supply. The paper includes theoretical derivation, algorithm description, and simulation analysis to validate the error bit rate of labeled information bits. The results demonstrate that the forward algorithm reduces the error bit rate by approximately 20% under low signal-to-noise ratio (SNR) conditions, while the backward algorithm reduces the error bit rate under high SNR conditions. The combination of the forward and backward algorithms reduces the error bit rate under both high and low SNR conditions. The proposed method contributes to improving the quality of ambient backscatter communication in UAV settings.

A bearing RUL prediction approach of vibration fault signal denoise modeling with Gate-CNN and Conv-transformer encoder

The operating conditions of rolling bearings are complex and variable, and their vibration monitoring signals are filled with strong noise interference, resulting in a low accuracy in remaining useful life (RUL) prediction. For this issue, this paper proposes a denoising method with vibration fault signals modeling, and a novel RUL prediction method with Gate-convolutional neural networks (CNN) and Conv-Transformer encoder. Firstly, the theoretical fault signal is obtained through the vibration fault signal model, and the quality of the extracted features is improved by the wavelet threshold denoising algorithm in the process of feature extraction and selection. Moreover, the CNN is combined with the gating mechanism to construct a feature extractor with the feature evaluation function, and the convolution layers are introduced into the transformer to expand the encoder’s ability to explore local information in temporal data. By using fixed-time step temporal features as the input to the prediction module and minimizing the Huber function as the optimization objective, the relationship between temporal features and RUL is obtained. The comparison with the existing state-of-the-art RUL methods illustrates that the combination of gate control and convolutional structure proposed in this paper can not only reduce the prediction error of the model but also improve its generalization ability and robustness.

A Cryogenic Wideband (2.5–14 GHz) Receiver System for the Arecibo Observatory 12 m Telescope

AbstractIn this paper we present details of the construction of a wideband, cryogenic receiver and its successful commissioning on the Arecibo Observatory 12 m telescope. The cryogenic receiver works in the 2.5–14 GHz frequency range. We upgraded the current narrow band, room temperature receivers of the telescope with the new wideband receiver. The current receiver is built around a Quadruple‐Ridged Flared Horn (QRFH) developed by Akgiray et al. (2013, https://doi.org/10.1109/tap.2012.2229953). To mitigate strong radio frequency interference (RFI) below 2.7 GHz, we installed a highpass filter before the first stage low noise amplifier (LNA). The QRFH, highpass filter, noise coupler and LNA are located inside a cryostat and are cooled to 15 K. The measured receiver temperature is 25 K (median value) over 2.5–14 GHz. The system temperature measured at zenith is about 40 K near 3.1 and 8.6 GHz and the zenith antenna gains are 0.025 and 0.018 K/Jy at the two frequencies respectively. We recommend the following improvements to the telescope system: (a) Upgrade the highpass filter to achieve better RFI rejection near 2.5 GHz; (b) Improve aperture efficiency at 8.6 GHz; (c) Upgrade the intermediate frequency system to increase the upper frequency of operation from 12 to 14 GHz.

Use of the dummy approach for the synthesis of ion imprinted polymers with Ni(II) or Zn(II) as template ion for the solid-phase extraction of Cu(II).

There is a strong interest in monitoring copper in environmental waters, but its direct analysis suffers from strong matrix interferences. This is why, a sample pretreatment based on solid-phase extraction (SPE) is often used but conventional sorbents usually lack specificity. It is overcome with ion-imprinted polymers (IIPs). This work evaluates for the first time the use of the dummy approach for the synthesis of Cu(II)-targeting IIPs. Two analog ions Ni(II) and Zn(II) were tested as templates and the resulting IIPs were packed in SPE cartridges. The SPE procedure was designed by optimizing a washing step following the sample percolation, to eliminate the interfering ions retained on the IIP by non-specific interactions. To optimize the washing step, solutions at different pH or containing tris(hydroxymethyl)aminomethane as a complexing agent at different concentrations were tested and combined. Zn-IIP appeared more promising than Ni-IIP, showing excellent specificity and a high selectivity. Its retention capacity was determined to be 100µg/g, and different isotherm models were evaluated to fit with the adsorption data. Finally, applications to mineral and sea waters were successfully completed and led to high and repeatable extraction recoveries for Cu(II) (88±1% and 83±3%, respectively).

Combining Polarization-Division Multiplexing and Ferromagnetic Nonreciprocity to Achieve In-Band Ultra-High Isolation for Full-Duplex Wireless Systems

The in-band full-duplex (IBFD) wireless system is a promising candidate for 6G and beyond, as it can double data throughput and enormously lower transmission latency by supporting simultaneous in-band transmission and reception of signals. Enabling IBFD systems requires a substantial mitigation of a transmitter (Tx)’s strong self-interference (SI) signal into the receiver (Rx) channel. However, current state-of-the-art approaches to tackle this challenge are inefficient in terms of performance, cost, and complexity, hindering the commercialization of IBFD techniques. In this work, we devise and demonstrate an innovative approach to realize IBFD systems that exhibit superior performance with a low-cost and less-complex architecture in an all-passive module. Our scheme is based on meticulously combining polarization-division multiplexing (PDM) with ferromagnetic nonreciprocity to achieve ultra-high isolation between Tx and Rx channels. Such an unprecedented conception has become feasible thanks to a concurrent dual-mode circulator—a new component introduced for the first time—as a key feature of our module, and a dual-mode waveguide that transforms two orthogonally polarized waves into two orthogonal waveguide modes. In addition, we propose a unique passive tunable secondary SI cancellation (SIC) mechanism, which is embedded within the proposed module and boosts the isolation over a relatively broad bandwidth. We report, solely in the analog domain, experimental isolation levels of 50, 70, and 80 dB over 340, 101, and 33 MHz bandwidth at the center frequency of interest, respectively, with excellent tuning capability. Furthermore, the module is tested in two real IBFD scenarios to assess its performance in connection with Tx-to-Rx leakage and modulation error in the presence of a Tx’s strong interference signal.

DOA Estimation of Broadband Sources Using Dimension-Reduced Matrix Filter with Deep Nulling in a Strong Interference Environment

To enhance the performance of the adaptive matrix filter with nulling, a dimension-reduced matrix filter with deep nulling (DR-MFDN) is proposed in this paper and used for direction of arrival (DOA) estimation of broadband sources in a strong interference environment. Consider the change of noise after filtering, the gaussian matrix is used for prewhitening to prevent the noise from converting into colored noise without affecting the response characteristics of the beam-space. Finally, the multiple measurement vector orthogonal matching pursuit (MOMP) algorithm is used for DOA estimation of weak targets after filtering, and the simulation results show a better DOA estimation performance compared to the OMP and MUSIC algorithms with short snapshots.

Performance and mechanism of biochar@FeMg-LDH for efficient activation of persulfate for degradation of 2, 4-dichlorophenol in groundwater.

Groundwater environments are complex, and traditional advanced oxidation technologies mainly based on free radicals have limitations such as poor selectivity and low interference resistance, making it difficult to efficiently degrade target pollutants in groundwater. Therefore, we developed a sludge-based biochar-supported FeMg-layered double hydroxide catalyst (BC@FeMg-LDH) for the catalytic degradation of 2, 4-dichlorophenol (2, 4-DCP) using persulfate (PDS) as an oxidant. The removal efficiency of the catalyst exceeded 95%, showing high oxidation activity in a wide pH range while being almost unaffected by reducing substances and ions in the environment. Meanwhile, under neutral conditions, the leaching of metal ions from BC@FeMg-LDH was minimal, thereby eliminating the risk of secondary pollution. According to quenching experiments and electron paramagnetic resonance spectroscopy, the main active species during BC@FeMg-LDH/PDS degradation of 2, 4-DCP is 1O2, indicating a non-radical reaction mechanism dominated by 1O2. Characterization techniques, including X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy, revealed that the carbonyl (C = O) and metal hydroxyl (M-OH) groups on the material surface were the main reactive sites mediating 1O2 generation. The 1O2 generation mechanism during the reaction involved ketone-like activation of carbonyl groups on the biochar surface and complexation of hydroxyl groups on the material surface with PDS, resulting in the formation of O2·- and further generation of 1O2. 1O2 exhibited high selectivity toward electron-rich organic compounds such as 2, 4-DCP and demonstrated strong interference resistance in complex groundwater environments. Therefore, BC@FeMg-LDH holds promising applications for the remediation of organic-contaminated groundwater.

A novel robust intelligent fault diagnosis method for rolling bearings based on SPAVMD and WOA-LSSVM under noisy conditions

Aiming at the problem that the fault information of rolling bearings under harsh operation environment is easily submerged by strong noise interference, which causes the traditional method to be difficult to identify bearing faults effectively, this paper proposes a novel robust intelligent fault diagnosis method for rolling bearings based on sparsity-assisted parameter adjustable variational mode decomposition (VMD) and whale optimization algorithm-based optimized least-squares support vector machine (WOA-LSSVM). Firstly, a sparsity measurement named the improved Gini index is introduced as the fitness function of grid search algorithm to adaptively adjust and search for the optimal decomposed mode number K and penalty factor α of VMD. Additionally, VMD containing the optimal parameters is adopted to decompose the original bearing vibration signal into several intrinsic mode function (IMF), and the effective signal reconstruction is performed by screening the sensitive IMF components according to the effective weighted kurtosis Gini index criterion. Subsequently, the refine composite multi-scale dispersion entropy of the reconstructed signal is further calculated to establish a multi-dimensional feature vector set. Finally, the constructed feature vector set is fed into the WOA-LSSVM to achieve automatic fault identification of rolling bearings. The effectiveness of the proposed method is verified by two experimental examples. Experimental results show that the proposed method has higher fault recognition accuracy and better robustness against noise than other homologous methods in noisy conditions. This study provides a new perspective for the developing of robust diagnosis methods.

Ship SINS/CNS Integrated Navigation Aided by LSTM Attitude Forecast

Under the strong interference of sky background noise, the reliability of celestial navigation system (CNS) measurement will drop sharply, which leads to performance deterioration for ships’ strapdown inertial navigation system (SINS)/CNS integrated navigation. To solve this problem, a long short-term memory (LSTM) model is trained to forecast a ship’s attitude to detect the attitude provided by the CNS, and the LSTM forecasted attitude can also be used as a backup in case of CNS failure. First, the SINS/CNS integrated model is derived based on an attitude solution of the CNS, which provides more favorable feature data for LSTM learning. Then, the key techniques of LSTM modeling such as dataset construction, LSTM coding method, hyperparameter optimization and training strategy are described in detail. Finally, an experiment is conducted to evaluate the actual performance of the investigated methods. The results show that the LSTM model can accurately forecast a ship’s attitude: the horizon reference error is less than 0.5′ and the yaw error is less than 0.6′, which can provide reliable reference attitude for the SINS when the CNS is invalid.

Initial development of skill with a reversed bicycle and a case series of experienced riders

Riding a bicycle is considered a durable skill that cannot be forgotten. Here, novice participants practiced riding a reversed bicycle, in which a reversing gear inverted the handlebar’s rotation. Although learning to ride the reversed bicycle was possible, it was slow, highly variable, implicit, and followed an S-shape pattern. In the initial learning phase, failed attempts to ride the normal bicycle indicated strong interference between the two bicycle skills. While additional practice decreased this interference effect, a subset of learners could not ride either bicycle after eight sessions of practice. Experienced riders who performed extensive practice could switch bicycles without failed attempts and exhibited similar performance (i.e., similar handlebar oscillations) on both bicycles. However, their performance on the normal bicycle was worse than that of the novice bicycle riders at baseline. In conclusion, “unlearning” of the normal bicycle skill precedes the initial learning of the reversed bicycle skill, and a signature of such unlearning is still present following extensive practice.

Dynamic Positioning Control of Large Ships in Rough Sea Based on an Improved Closed-Loop Gain Shaping Algorithm

In order to solve the problem of the dynamic positioning control of large ships in rough sea and to meet the need for fixed-point operations, this paper proposes a dynamic positioning controller that can effectively achieve large ships’ fixed-point control during Level 9 sea states (wind force Beaufort No. 10). To achieve a better control effect, a large ship’s forward motion is decoupled to establish a mathematical model of the headwind stationary state. Meanwhile, the closed-loop gain shaping algorithm is combined with the exact feedback linearization algorithm to design the speed controller and the course-keeping controller. This effectively solves the problem of strong external interferences impacting the control system in rough seas and guarantees the comprehensive index of robustness performance. In this paper, three large ships—the “Mariner”, “Taian kou”, and “Galaxy”—are selected as the research objects for simulation research and the final fixing error is less than 10 m. It is proven that the method is safe, feasible, practical, and effective, and provides technical support for the design and development of intelligent marine equipment for use in rough seas.