Strong Interference Research Articles

Enhanced detection performance based on a differential ME sensor with strong suppression of vibration interference

Magnetoelectric (ME) sensors have enormous potential for detecting weak magnetic fields because of their high sensitivity, low power consumption, compact size and, low cost. However, inevitable vibration interference limits their application in practical environments, especially in the case of mobile platform mounting. Here, we propose a differential ME sensor, consisting of PZT macro-fiber composites (MFCs) and Metglas laminates. The differential ME sensor has two output terminals with weak mutual mechanical coupling and works in longitudinal vibration mode. MFC cores are polarized in parallel mode to guarantee their consistency of electric characteristics and reversed bias field is provided by attached magnets. Experimental results show that the differential-mode response amplitudes have a gain of −17.6 dB for low-frequency vibration at 2 Hz and ∼6.2 dB for an applied magnetic field at 3 Hz, in comparison with the single-ended mode. In addition, our proposed ME sensor also has a low inherent equivalent magnetic noise of 18.3 pT/√Hz at 1 Hz. Finally, a target detection experiment in the presence of heavy lab noise and strong vibration interference is conducted and the improved detection performance of the proposed differential ME sensor is proved.

Research on frequency shift signal detection algorithm for jointless track circuits based on improved relaxation algorithm

The accuracy of ZPW-2000 frequency shift signal demodulation is related to the safety and efficiency of high-speed trains. This paper proposed a kind of ZPW-2000 frequency-shift signal detection algorithm based on improved Relaxation algorithm (ZFSD-IR) to address the issue of low accuracy in detecting ZPW-2000 frequency shift signal under strong noise interference, as the traditional fast Fourier transform (FFT) spectrum analysis methods are susceptible to fence effects and spectrum leakage effects. Firstly, the principle of ZPW-2000 jointless track circuit was analyzed, and then the ideal and simplified models of ZPW-2000 frequency shift signal were established, respectively. Finally, the simulation experiments were conducted on ZPW-2000 frequency shift signal detection under different sampling durations and signal-to-noise ratios (SNR). The effectiveness of the proposed algorithm was verified through comparative analysis with the traditional algorithms. The research results indicated that the ZFSD-IR algorithm has better accuracy in carrier-frequency and low-frequency estimation than traditional algorithms, and can achieve accurate detection of ZPW-2000 frequency shift signal under the lower SNR conditions. It has high detection accuracy and good anti-interference ability, ensuring the safe operation of high-speed trains.

Insights into the Thermal Expansion of Amorphous Polymers.

We investigated the thermal expansion of amorphous polystyrene (PS) and poly(methyl methacrylate) (PMMA) homopolymers using the temperature dependence of spatial electron-density correlations. The strong and broad X-ray interference maxima arising from inter- and intramolecular correlation distances were distinct, maintaining their shape during the heating of the samples to 250 °C. Three maxima characteristic of electron density correlations between the backbones, substituents along the chain, and repeat-units were observed. A remarkable temperature dependence was identified in the largest correlation distances arising from the intermolecular interactions. Based on the temperature dependence of the correlation distances, the coefficients of molecular expansion were very close to the coefficients of thermal expansion. This study provides a simple yet accurate way to correlate macroscopic volume expansions with the molecular expansion obtained from wide-angle X-ray scattering (WAXS) data.

Adaptive multimodal control of trans-media vehicle based on deep reinforcement learning

To solve the problem that the control system is prone to instability due to the sudden change of physical characteristics, strong interference, and nonlinear in the process of multimodal movement of trans-media vehicle, an adaptive control method combining the Deep Deterministic Policy Gradient (DDPG) and traditional Proportional-Integral-Derivative (PID) controller is proposed in this paper. In this approach, the upper-level DDPG controller continuously monitors the vehicle's state and environmental conditions, dynamically adjusting the PID parameters in real-time. The lower-level PID controller then utilizes these updated parameters to modulate the output thrust of the vehicle's motors, thereby achieving excellent control over the vehicle's entire movement. Firstly, according to the hydrodynamic analysis, the kinematics and dynamics mathematical model of the self-designed trans-media vehicle is constructed. This model includes the multi-stage motion modal process of aerial flight, underwater navigation, and cross-media motion, which is suitable for the simulation and verification of the control method. Then, an adaptive controller called RL-PID combining DDPG and PID is built, so that PID can adjust parameters in real-time according to the changes in the external environment. Finally, after theoretical stability proof, a comparison study is performed across three approaches, namely the novel RL-PID, Fuzzy PID, and PID. The experimental results illustrate the superiority of the proposed approach over the competing ones and the generalization of the proposed approach under different interference.

Background-Free Imaging of Food Freshness Using Curcumin-Functionalized Upconversion Reversible Hydrogel Patch.

Functionalized upconversion nanomaterials can overcome the drawbacks faced of strong background interference, photodamage, and spectral overlap by conventional optical labeling. Here, curcumin-functionalized upconversion hydrogel patch is designed with background-free and reversible for food freshness monitoring by ultra-sensitive response to biogenic amines. By loading the probes onto hydrogel patch, utilizing the good ductility to solve the problem of non-smooth surface coverage, thus accurately capturing biogenic amines. The presence of biogenic amines leads to the conversion of the diketone group on the probe to enolate ions, which triggers fluorescence resonance energy transfer (FRET) and ultimately causes the upconverted fluorescence to gradually change from green to red. The probe exhibits good detection capability for biogenic amines with a low limit of detection (LOD) of 2.73 µm. Interestingly, the patch can be restored to its initial state after water rinsing, realizing reversible detection of biogenic amines. Additionally, combining the color recognition system of smartphone can convert the imaging signal into a data signal to achieve quantitative analysis and show a reliable assessment comparable to the results of high performance liquid chromatography (HPLC). This study demonstrates the practical applicability in real-time monitoring of freshness, suggests great potential in developing optical nano-sensing strategy to ensure food safety.

3D skeleton aware driver behavior recognition framework for autonomous driving system

The recognition of the driver’s behaviors inside an autonomous vehicle can effectively address emergency handling in autonomous driving and is crucial for ensuring the driver’s safety. Driver behavior recognition is a challenging task due to factors such as variations, diversities, complexities, and strong interferences in behaviors. In this paper, to realize the application in the autonomous driving scenes, a novel 3D skeleton aware behavior recognition framework is proposed to recognize various driver behaviors in autonomous driving systems. First, a 3D human pose estimation network (Pose-GTFNet) with temporal Transformer and spatial graph convolutional network (GCN) is designed to infer 3D human poses from 2D pose sequences. Second, based on the obtained 3D human pose sequences, a behavior recognition network (Beh-MSFNet) with multi-skeleton feature fusion is designed to recognize driver behaviors. In the experiments, the Pose-GTFNet and Beh-MSFNet can get the best performance compared with most state-of-the-art (SOTA) methods on the Human3.6M human pose dataset, JHMDB and SHREC action recognition dataset, respectively. In addition, the proposed driver behavior recognition framework can achieve SOTA performance on the Drive&Act and Driver-Skeleton driver behavior datasets.

Multifunctional property of N,N-bis (carboxymethyl) glutamic acid modified biomass material: adsorption and degradation removals of cationic dyes in wastewater

As a common type of pollutants in industrial wastewater, cationic dyes have attracted great attentions. Using biodegradable N,N-di (carboxymethyl) glutamic acid (GLDA) as ligand and corn stalk (CS) as matrix, a novel and green biomass modified material GLDA-CS was successfully prepared. The multifunctional property of GLDA-CS for removing methylene blue (MB), malachite green (MG) and alkaline red 46 (R-46) from wastewater was evaluated. The dyes were removed by the electrostatic adsorption based on the cationic adsorption properties of GLDA-CS. The removal rates of MB, MG and R-46 can quickly reach 90.4%, 96.8% and 94.8% in short time. especially for MG and R-46 even only 20 min. The adsorption capacities of the dyes still remain more than 86.5% of the initial values after 5 cycles. In a heterogeneous system, the dyes were removed by Fenton-like degradation based on the metal chelating property of GLDA-CS. 100% degradation rates of the dyes can be achieved in 35 min under the acidic region. Even if at pH 7, degradation rates are 44.1%, 47.1% and 56.6% higher than those under the conventional homogeneous system, and the degradation rate remained at 83.7% after 5 cycles. Regardless of the adsorption or degradation, GLDA-CS shows strong anti-anion interference ability. The potential mechanisms of adsorption and degradation for the dyes by GLDA-CS were deduced by quantization calculation. It is concluded that the adsorption removal of the dyes by GLDA-CS follows MG > R-46 > MB, and mainly depends on the electrostatic interaction between -COOH in GLDA-CS and -N- in the dye molecules. Based on the degradation mechanism of Fenton-like reaction, the possible active sites of the dyes attacked by free radicals and their possible degradation intermediates were predicted by the calculations of Fukui function.

Performance Analysis of Reconnaissance Coverage for HUAV Swarms under Communication Interference Based on Different Architectures

In environments with unknown communication interference, the mission efficiency of heterogeneous unmanned aerial vehicle (HUAV) swarms is often impacted by communication disruptions due to regions of strong interference encountered when executing reconnaissance and coverage missions. Existing research has rarely focused on communication interference or on the differences in HUAV characteristics under various control architectures; thus, in this paper we explore the performance differences between HUAV swarms based on centralized, distributed, and centralized-distributed architectures when executing reconnaissance and coverage missions in environments with unknown communication interference. First, a communication model in an unknown interference environment is constructed to reflect the real-time communication status of the swarm. Second, in response to the limitations of the traditional artificial potential field (APF) algorithm in this environment, a coverage-oriented artificial potential field (COAPF) algorithm is proposed. Finally, based on the COAPF algorithm, a multi-dimensional comparison of the mission completion efficiency of HUAV swarms with three different architectures is conducted. Our simulation results indicate that the distributed architecture is suitable for large-scale environments with strong interference, while the centralized–distributed architecture performs better in small-scale environments with weak interference. Conversely, the centralized architecture exhibits poor performance in all interference scenarios due to its lack of decision-making capabilities.

Transformer assisted by DSC and BiLSTM for bearing fault pattern recognition under strong noise interference

ABSTRACT Deep learning has greatly promoted intelligent fault diagnosis for bearings and has led to the development of many intelligent fault recognition methods based on deep neural networks. However, bearing fault signals in industrial applications are susceptible to noise interference, and intelligent recognition methods for bearing faults under strong noise interference are lacking. An intelligent fault pattern recognition model termed IFPR-MDL, which is based on a Transformer assisted by depthwise separable convolutional (DSC) and bidirectional long short-term memory (BiLSTM) networks, is designed to identify bearing faults under strong noise conditions. In this model, multiscale features are first extracted by a multiscale feature extraction module designed on the basis of DSC; then, contextual information is further extracted by a BiLSTM, and information from different scales is integrated by Transformer encoders. The DSC network facilitates model parameter reduction, the BiLSTM network has expertise in mining temporal features, and the Transformer network excels at capturing long-range feature correlations. This model fully utilises the advantages of these models and complements them to improve the anti-interference ability of bearing fault pattern recognition. Tests on several public datasets show that the proposed model has higher recognition accuracy, stronger generalisation ability, and noise resistance in strong noise situations.

Effects of Reynolds number and surface modification on wake-induced vibrations of two staggered circular cylinders

This study explores the role of Reynolds numbers (Re) in wake-induced vibrations (WIVs) of two cylinders, specifically wake galloping and two-degree-of-freedom coupled flutter on the downstream cylinder. The center-to-center distances between the cylinders were 3.0D −5.0D in along-flow direction and 0.0D −2.0D in cross-flow direction (D is the diameter of the cylinders). The Re ranged from 1.4 × 104–6.7 × 104, within the subcritical Re regime of a smooth cylinder. The supercritical Re regime was realized by winding 12 spiral protuberances along the cylinders to lower the critical Re. Descriptions of wake interference of two smooth cylinders at Re = 850–6.5 × 105 were summarized from the literature. Wake interference strongly depended on Re, even within the subcritical Re regime. WIVs mostly occurred in the upper subcritical Re regime with strong wake interference. In the supercritical Re regime, the wakes of the cylinders were narrowed and vortex shedding was suppressed. Consequently, the wake interference and WIVs were weakened or completely disappeared. The cylinders fitted with 12 spiral protuberances reportedly reached the supercritical Re regime at Re = 3.1 × 104. Resultantly, they were stable against WIVs above this Re even at close spacing, and suitable for stay cable applications.

A highly specific two-photon fluorescent probe for real-time monitoring of acetylcholinesterase in neurogenic disorders in vivo

Acetylcholinesterase (AChE) hydrolyses choline into thiocholine, which is essential for cholinergic neurons to revert to their resting state following activation. Abnormal changes in AChE activity can directly affect nervous system function. Thus, the specific detection of AChE activity is urgently needed for elucidating the function of the nervous system and diagnosing AChE-related diseases. Current methods for detecting AChE activity have several limitations, including strong background interference and poor tissue penetration. Thus, we designed and synthesized a two-photon (TP) excited fluorescent probe, WZ-AChE, for the specific detection of AChE. Briefly, a carbamate bond was chosen to specifically recognize AChE, which can also be cleaved by AChE. The product, WZ, released strong deep red fluorescence signal under TP excitation at 800 nm. Our results showed that WZ-AChE can detect AChE activity in PC12 cells with both superior sensitivity and selectivity. In addition, we successfully applied WZ-AChE to a C. elegans Parkinson's disease (PD) model and a mouse model of depression. The findings revealed that AChE activity was greater in both disease models than in the control group. To summarize, a novel tool was created to investigate the mechanisms underlying PD and depression.

Fault diagnosis of rolling bearing under complex working conditions based on time-frequency joint feature extraction-deep learning

The same independent distribution is not obeyed for the data collected under complex working conditions such as time-varying speed or loading, and the fault characteristic information is insufficient, resulting in low accuracy by using traditional methods. To solve the above problem, a fault diagnosis method based on time-frequency joint feature extraction combined with deep learning is proposed. Firstly, the original vibration signal is processed by variational mode decomposition (VMD) to obtain several intrinsic mode functions (IMFs), then the sensitive components are selected by calculating the steepness values of each IMF. Subsequently, the characteristic features of the selected sensitive component in time-domain, frequency-domain and time-frequency domain are calculated to form the time-frequency joint feature. The sparse attention mechanism (SAM) is combined with the advantages of recurrent neural network (RNN) and convolutional neural network (CNN) to form a hybrid deep learning model (SAM-RNN-DCNN). Finally, the time-frequency joint features are combined with the hybrid model for fault diagnosis. Experimental verifications are carried out by using data sets under variable rotational speed, variable load and strong noise interference, and the analysis results show that the proposed method has high diagnostic accuracy, good diagnostic performance and robustness under complex working conditions.

Wideband beam domain sparse Bayesian learning passive focusing localisation algorithm

AbstractTo address the challenges of large‐aperture sonar systems passive localisation, this paper proposes the application of sparse Bayesian learning (SBL) for passive target localisation in the wideband beam domain. The proposed algorithm aims to overcome the issues of massive computational requirements for two‐dimensional SBL scanning and increased localisation errors due to interference energy leakage. The wideband beam domain SBL focusing localisation algorithm is developed by constructing an azimuth‐range two‐dimensional transformation matrix to preprocess array data, which effectively reduces the computational load of SBL processing while suppressing strong interference energy leakage in passive sonar operating environments, thus improving the range resolution and parameter estimation accuracy of focusing localisation. Simulation and sea trial data analyses demonstrate the feasibility of the proposed algorithm, with results indicating its superior performance compared to existing algorithms.

Optical Extinction-Based 3D Nano-Imaging of WS2 on Gold.

Broadband nanoextinction images recorded in tip-enhanced optical spectroscopy geometry track the 3D topography of a single layer of WS2 on Au substrate. The described nano-optical method is complementary to conventional atomic force microscopy and offers additional information about the buried material-metal interface that is not accessible using conventional topographic imaging. Beyond 3D optical imaging, we observe large variations in the junction plasmon resonance on the nanoscale. The latter is important to understand and account for in tip-enhanced Raman and photoluminescence studies that target low-dimensional materials specifically. Our observations and (coherent) optical scattering-based method are also relevant to emerging efforts aimed at exploring strong coupling and Fano interferences in hybrid plasmonic-low dimensional quantum material systems.

Silver amplified immunosensor via effective fluorogenic Ag+-imidazole aggregation for detection of AFB1

BackgroundCereals are susceptible to aflatoxin contamination during storage and transportation, which is highly carcinogenic and teratogenic, and seriously threaten human health. The accurate and rapid detection of total aflatoxin (including aflatoxin B1, B2, G1, and G2) is of great importance for food safety. Conventional fluorescence immunoassays have the advantage of being sensitive and fast; however, these methods can be affected by strong background and matrix interference. Therefore, the development of ultrasensitive, cost-effective, and interference rejection sensors for detecting aflatoxins in moldy grains is vital for food safety and human health. ResultsIn this paper, a broad-spectrum aflatoxin monoclonal antibody was prepared by using hybridoma cell fusion technology. An aggregation-induced emission (AIE) based immunosensor via silver amplification coupled with a fluorogenic Ag+ probe was established for AFB1 analysis. Silver nanoparticles are decomposed into numerous Ag+ by H2O2, and then Ag+ further specifically binds with imidazole-modified AIE molecules, improving the sensitivity and anti-interference ability of the method. The IC50 and IC15 of AIE-based immunosensor for AFB1 were 0.019 and 0.0014 μg/L, respectively, 2.3-fold and 5.8-fold higher than those of icELISA. The AIE-based immunosensor was also used to analyze AFB1 from actual cereal samples, with spiked recoveries ranging from 72.91 to 115.92 %. In addition, the method was used to detect total aflatoxins in moldy grains. SignificanceBased on the advantages of broad-spectrum aflatoxin monoclonal antibody, high-efficiency metal signal amplification, and functional AIE molecule, a sensitive, accurate, cost-effective, and time-saving method was developed for the analysis of total aflatoxins in cereals. Moreover, the proposed signal amplification strategy shows great potential for analyzing other trace-level small molecular pollutants.

Performance Evaluation of Real-Time Kinematic Global Navigation Satellite System with Survey-Grade Receivers and Short Observation Times in Forested Areas

The Real-Time Kinematic (RTK) method is currently the most widely used method for positioning using Global Navigation Satellite Systems (GNSSs) due to its accuracy, efficiency and ease of use. In forestry, position is a critical factor for numerous applications, with GNSS currently being the preferred solution for obtaining such data. However, the decreased performance of GNSS observations in challenging environments, such as under the forest canopy, must be considered. This paper analyzes the performance of a survey-grade GNSS receiver under coniferous/deciduous tree cover. Unlike most previous research concerning this topic, the focus here is on employing a methodology that is as close as possible to real working conditions in the field of forestry. To achieve this, short observation times of 30 s were used, with corrections received directly in the field from a Continuously Operating Reference Station (CORS) of the national RTK network in Romania. In total, 84 test points were determined, randomly distributed under the canopy, with reference data collected by topographical surveys using total station equipment. In terms of the overall horizontal accuracy, an RMSE of 2.03 m and MAE of 1.63 m are found. Meanwhile, the overall vertical accuracy is lower, as expected, with an RMSE of 4.85 m and MAE of 4.01 m. The variation in GNSS performance under the different forest compositions was found to be statistically significant, while GNSS-specific factors such as DOP values only influenced the precision and not the accuracy of observations. We established that this methodology offers sufficient accuracy, which is application-dependent, even if the majority of GNSS solutions were code-based, rather than carrier-phase-based, due to strong interference from the vegetation.

De-noising magnetotelluric data based on machine learning

The magnetotelluric (MT) sounding is a common geophysical exploration technique, but it is highly polluted by various types of cultural noise. In the realm of MT data processing, traditional techniques often rely on the quality of the measured MT data. Conventional MT time domain denoising methods tend to eliminate valuable signals, potentially leading to unreliable resistivity estimates. To address this concern, we propose employing machine learning to effectively suppress strong noise interference in MT data, thereby preventing the loss of valuable signals. We augment this approach with mathematical morphological filtering (MMF) to capture low-frequency signals, preserving their integrity. We constructed a signal sample library based on a substantial volume of signal samples. Through consistent training, we establish a support vector machine (SVM) classification model that distinguishes high-quality signal fragments from noisy signals. Subsequently, we use adaptive K-singular value decomposition (K-SVD) dictionary learning to extract noise profiles and suppress noisy signals. To validate the feasibility of our method, we apply machine learning to measured data from two distinct observation areas. The measured data were analyzed and processed, and the results were compared with the robust results. This method can effectively eliminate large-scale strong interference in time domain sequences and preserve more low-frequency slow change information and high-quality signals in the reconstructed signals. The apparent resistivity phase curve of synthetic data is smoother and more continuous, and the data quality in the low-frequency range is significantly improved. The results can more accurately and reliably reflect underground electrical structure information.

Beam-space spatial spectrum reconstruction under unknown stationary near-field interference: Algorithm design and experimental verification.

In acoustic array signal processing, spatial spectrum estimation and the corresponding direction-of-arrival estimation are sometimes affected by stationary near-field interferences, presenting a considerable challenge for the target detection. To address the challenge, this paper proposes a beam-space spatial spectrum reconstruction algorithm. The proposed algorithm overcomes the limitations of common spatial spectrum estimation algorithms designed for near-field interference scenarios, which require knowledge of the near-field interference array manifold. The robustness and efficacy of the proposed algorithm under strong stationary near-field interference are confirmed through the analysis of simulated and real-life experimental data.

Retinol semisolid preparations in cosmetics: transcutaneous permeation mechanism and behaviour

Retinol is widely used to treat skin ageing because of its effect on cell differentiation, proliferation and apoptosis. However, its potential benefits appear to be limited by its skin permeability. Herein, we investigated the transcutaneous behavior of retinol in semisolid cosmetics, in both in vitro and in vivo experiments. In vitro experiments used the modified Franz diffusion cell combined with Raman spectroscopy. In in vivo experiments, the content of retinol in rat skin and plasma was detected with HPLC. Retinol in semisolid cosmetics was mainly concentrated in the stratum corneum in the skin of the three animal models tested, and in any case did not cross the skin barrier after a 24 h dermatologic topical treatment in Franz diffusion cells tests. Similar results were obtained in live mice and rats, where retinol did not cross the skin barrier and did not enter the blood circulation. Raman spectroscopy was used to test the penetration depth of retinol in skin, which reached 16 μm out of 34 μm in pig skin, whereas the skin of mouse and rat showed too strong bakground interference. To explore epidermal transport mechanism and intradermal residence, skin transcriptomics was performed in rats, which identified 126 genes upregulated related to retinol transport and metabolism, relevant to the search terms “retinoid metabolic process” and “transporter activity”. The identity of these upregulated genes suggests that the mechanism of retinol action is linked to epidermis, skin, tissue and epithelium development.

A Fault Diagnosis Method for Pumped Storage Unit Stator Based on Improved STFT-SVDD Hybrid Algorithm

Stator faults are one of the common issues in pumped storage generators, significantly impacting their performance and safety. To ensure the safe and stable operation of pumped storage generators, a stator fault diagnosis method based on an improved short-time Fourier transform (STFT)-support vector data description (SVDD) hybrid algorithm is proposed. This method establishes a fault model for inter-turn short circuits in the stator windings of pumped storage generators and analyzes the electrical and magnetic states associated with such faults. Based on the three-phase current signals observed during an inter-turn short circuit fault in the stator windings, the three-phase currents are first converted into two-phase currents using the principle of equal magnetic potential. Then, the STFT is applied to transform the time-domain signals of the stator’s two-phase currents into frequency-domain signals, and the resulting fault current spectrum is input into the improved SVDD network for processing. This ultimately outputs the diagnosis result for inter-turn short circuit faults in the stator windings of the pumped storage generator. Experimental results demonstrate that this method can effectively distinguish between normal and faulty states in pumped storage generators, enabling the diagnosis of inter-turn short circuit faults in stator windings with low cross-entropy loss. Through analysis, under small data sample conditions, the accuracy of the proposed method in this paper can be improved by up to 7.2%. In the presence of strong noise interference, the fault diagnosis accuracy of the proposed method remains above 90%, and compared to conventional methods, the fault diagnosis accuracy can be improved by up to 6.9%. This demonstrates that the proposed method possesses excellent noise robustness and small sample learning ability, making it effective in complex, dynamic, and noisy environments.