Signal Reduction Research Articles

Investigation on the failure of the space HgCdTe detector with long-term storage tests

The HgCdTe detector is the core device of the infrared imaging system, its reliability directly restricts the performance of the whole imaging system. For some special applications, it maintains in a non-operating storage state for a long time, it is significant to study its storage reliability. Due to the long lifetime of detectors, most studies have been conducted with accelerated life tests, there is a lack of experimental information concerning the failure of these detectors after longtime storage. In this article, the performance degradation of Hg1-xCdxTe detector with the 127 months long-term storage test is studied. The failure mode and failure mechanism of detectors are investigated through the statistics analysis and optical photomicrography. The results indicate that there are three failure modes, such as low-temperature opening circuit, chip circuit opening, and signal reduction accompanied by increased noise. The main type of failure is in the mode of signal reduction accompanied by increased noise, which is a typical failure mode for long-term storage of HgCdTe detectors. In this typical failure mode, corrosion products are observed near the photosensitive surface in the failure detector, which prove to be a metal product of indium. The long-term storage results can provide data support for accelerating storage test and improve the HgCdTe detectors design.

MR Molecular Image Guided Treatment of Pancreatic Cancer with Targeted ECO/miR-200c Nanoparticles in Immunocompetent Mouse Tumor Models.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by desmoplasia due to increased deposition of extracellular matrix (ECM) proteins. This work investigates the efficacy of targeted ECO/miR-200c nanoparticles (ELNP) on ECM remodeling in PDAC and tumor proliferation with MR molecular imaging (MRMI) with MT218 in immunocompetent mouse models. The miR-200c mediated regulation of EMT markers was measured in PDAC cells in vitro. Wild-type mice bearing mutated KRAS-driven KPC subcutaneous or orthotopic tumors were dosed weekly with RGD-ELNP/miR-200c at 1mg-RNA/kg for a total of 4 doses. We utilized MT218-MRMI to non-invasively monitor the alteration of tumor ECM EDN-FN levels by miR-200c and tumor response to the treatment. The changes were also validated by posthumous histopathology. Transfection of PDAC cells with ELNP/miR-200c downregulated the expression of FN1 and EDB-FN and some mesenchymal markers, inhibiting 3D spheroid formation and migration of KPC PDAC cells. RGD-ELNP/miR-200c treatment resulted in significant signal reduction in the MT218 enhanced MRMI images of both subcutaneous and orthotopic KPC tumors compared to those prior to treatment and treated with a non-specific control. MT218-MRMI results were suggestive of EDB-FN downregulation in tumors, which was later confirmed by immunohistochemistry. Tumor growth in subcutaneous tumors was significantly attenuated with RGD-ELNP/miR-200c and was an observed trend in orthotopic tumors. Substantial necrosis and remodeling were observed in both models treated with RGD-ELNP/miR-200c based on H&E staining. These results demonstrate the feasibility of RGD-ELNP/miR-200c in modulating PDAC ECM and restraining tumor growth and the utility of MT218-MRMI for non-invasively monitoring miR-200c efficacy.

Fault Diagnosis Method for Wind Turbine Gearbox Based on Ensemble-Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy.

The diagnosis of faults in wind turbine gearboxes based on signal processing represents a significant area of research within the field of wind power generation. This paper presents an intelligent fault diagnosis method based on ensemble-refined composite multiscale fluctuation-based reverse dispersion entropy (ERCMFRDE) for a wind turbine gearbox vibration signal that is nonstationary and nonlinear and for noise problems. Firstly, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and stationary wavelet transform (SWT) are adopted for signal decomposition, noise reduction, and restructuring of gearbox signals. Secondly, we extend the single coarse-graining processing method of refined composite multiscale fluctuation-based reverse dispersion entropy (RCMFRDE) to the multiorder moment coarse-grained processing method, extracting mixed fault feature sets for denoised signals. Finally, the diagnostic results are obtained based on the least squares support vector machine (LSSVM). The dataset collected during the gearbox fault simulation on the experimental platform is employed as the research object, and the experiments are conducted using the method proposed in this paper. The experimental results demonstrate that the proposed method is an effective and reliable approach for accurately diagnosing gearbox faults, exhibiting high diagnostic accuracy and a robust performance.

Mechanism exploration of synergistic photo-immunotherapy strategy based on a novel exosome-like nanosystem for remodeling the immune microenvironment of HCC

The immunosuppressive tumor microenvironment (TME) has become a major challenge in cancer immunotherapy, with abundant tumor-associated macrophages (TAMs) playing a key role in promoting tumor immune escape by displaying an immunosuppressive (M2) phenotype. Recently, it was reported that M1 macrophage-derived nanovesicles (M1NVs) can reprogram TAMs to an anti-tumor M1 phenotype, thereby significantly alleviating the immunosuppressive TME and enhancing the anti-tumor efficacy of immunotherapy. Herein, we developed M1NVs loaded with mesoporous dopamine (MPDA) and indocyanine green (ICG), which facilitated the recruitment of M2 TAMs through synergistic photothermal and photodynamic therapy. Thereafter, M1NVs can induce M1 repolarization of TAMs, resulting in increased infiltration of cytotoxic T lymphocytes within the tumor to promote tumor regression. This study investigated the effect of phototherapy on the immune environment of liver cancer using single-cell RNA sequencing (scRNA-seq) by comparing HCC tissues before and after MPDA/ICG@M1NVs + NIR treatment. The results showed significant shifts in cell composition and gene expression, with decreases in epithelial cells, B cells, and macrophages and increases in neutrophils and myeloid cells. Additionally, gene analysis indicated a reduction in pro-inflammatory signals and immunosuppressive functions, along with enhanced B-cell function and anti-tumor immunity, downregulation of the Gtsf1 gene in the epithelial cells of the MPDA/ICG @M1NVs + NIR group, and decreased expression of the lars2 gene in immune subpopulations. Eno3 expression is reduced in M1 macrophages, whereas Clec4a3 expression is downregulated in M2 macrophages. Notably, the B cell population decreased, whereas Pou2f2 expression increased. These genes regulate cell growth, death, metabolism, and tumor environment, indicating their key role in HCC progression. This study highlights the potential for understanding cellular and molecular dynamics to improve immunotherapy.Graphical

Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC

Abstract Functional magnetic resonance imaging (fMRI) has emerged as an essential tool for exploring human brain function. Submillimeter fMRI, in particular, has emerged as a tool to study mesoscopic computations. The inherently low signal-to-noise ratio (SNR) at submillimeter resolutions warrants the use of denoising approaches tailored at reducing thermal noise—the dominant contributing noise component in high-resolution fMRI. NOise Reduction with DIstribution Corrected Principal Component Analysis (NORDIC PCA) is one of such approaches, and has been benchmarked against other approaches in several applications. Here, we investigate the effects that two versions of NORDIC denoising have on auditory submillimeter data. While investigating auditory functional responses poses unique challenges, we anticipated NORDIC to have a positive impact on the data on the basis of previous applications. Our results show that NORDIC denoising improves the detection sensitivity and the reliability of estimates in submillimeter auditory fMRI data. These effects can be explained by the reduction of the noise-induced signal variability. However, we did observe a reduction in the average response amplitude (percent signal change) within regions of interest, which may suggest that a portion of the signal of interest, which could not be distinguished from general i.i.d. noise, was also removed. We conclude that, while evaluating the effects of the signal reduction induced by NORDIC may be necessary for each application, using NORDIC in high-resolution auditory fMRI studies may be advantageous because of the large reduction in variability of the estimated responses.

Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis.

Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.

Sociological and Political Origins in IBN Khaldun's State Theory

AbstractEven after centuries, the social and political insights of Islamic scholar Ibn Khaldun remain pertinent. The most important concepts in state theory are social interactions and classifications. Khaldun used sociological deductions to evaluate how society and the state have changed. He linked the transformation of the Badavi social structure to the Hadari society's economic structure through Alm Al‐Umran. In Badavi Alm Al‐Umran, a particular chief's leadership is the cornerstone of governance, and a strong sense of unity and brotherhood prevails. From a social, cultural, and economic perspective, Badavi society is less developed than Hadari society. The reason Asabiyyahh and the lineage Asabiyyahh are two types of Asabiyyahh that serve as the cornerstone of state theory. While the reason Asabiyyahh is based on a country's shared language, history, culture, and physical location, the lineage Asabiyyahh is based on ancestry. Asabiyyahh is crucial to the maintaining and preserving of social ties. According to the organismic perspective of society, which views the social structure as a living organism, a reduction in economic and social upheaval signals the implosion of the condition. The fundamental dynamic that provides political legitimacy is the Asabiyyahh dynamic, which creates the social substructure of the state system. This study aims to assess the scientific concept of umran and the political idea of asabiyyahh, which are the foundations of Khaldun's state theory. Umran is a social phenomenon. This study applies the literature review approach to analyze Khaldun's state theory, paying particular attention to its political and administrative aspects. The political and administrative aspects of Khaldun's state theory are examined in this study using a literature review approach, focusing on how social structure integration or loosening affects the political structure.

Background radioactivity level estimation and passive shield optimization using adjoint Monte Carlo method

The current study proposes a procedure to estimate the activity concentration of natural radionuclides and to optimize passive shielding solutions for HPGe detectors using adjoint Monte Carlo (MC) simulation technique of Geant4 for the first time. The background spectrum is acquired for 1.56 × 106 s using an HPGe detector model (GC3020), set inside a shielding solution, during 2021–2022 to estimate the activity concentration of natural radionuclides inside the shielding. While, a background spectrum for 65,000 s is acquired with shielding removed to estimate the concentration of natural radionuclides in the building materials of the laboratory. The detector design used in the simulations is validated by comparing computed and measured Full Energy Peak Efficiency (FEPE) for point sources 241Am, 152Eu, 137Cs, 133Ba, and 60Co. Adjoint MC simulations are used to compute the activity concentration of natural radionuclides assuming an isotropic distribution. The activity concentration of 40K, 226Ra and 232Th in the building material is found to be 524 ± 140, 83 ± 20 and 65 ± 18 Bqkg−1, respectively. The computed values are found in good agreement with the published data. The natural radioactivity levels of 40K, 226Ra and 232Th measured in lead shielding are 155.7 ± 0.1 mBqkg−1, 24 ± 13 mBqkg−1 and 33 ± 17 mBqkg−1 respectively. The radiological risks arising due to natural radioactivity is assessed by calculating radium equivalent activity (Raeq), indoor radiation hazard index (Hin) and annual effective dose equivalent. All the radiological parameters are found below their permissible limits and building materials may be considered radiologically safe. The optimal lead shield thickness for the detector is determined to be 12 cm, resulting in reduction of background signal by two orders of magnitude compared to an unshielded detector. The adjoint MC simulations in Geant4 are 103-104 times more rapid as compared to normal simulations for shield optimization of HPGe detectors and therefore, are identified as viable computing solution to calculate the activity of the background radiation.

Improved Non-Negative Matrix Factorization-Based Noise Reduction of Leakage Acoustic Signals.

The detection of gas leaks using acoustic signals is often compromised by environmental noise, which significantly impacts the accuracy of subsequent leak identification. Current noise reduction algorithms based on non-negative matrix factorization (NMF) typically utilize the Euclidean distance as their objective function, which can exacerbate noise anomalies. Moreover, these algorithms predominantly rely on simple techniques like Wiener filtering to estimate the amplitude spectrum of pure signals. This approach, however, falls short in accurately estimating the amplitude spectrum of non-stationary signals. Consequently, this paper proposes an improved non-negative matrix factorization (INMF) noise reduction algorithm that enhances the traditional NMF by refining both the objective function and the amplitude spectrum estimation process for reconstructed signals. The improved algorithm replaces the conventional Euclidean distance with the Kullback-Leibler (KL) divergence and incorporates noise and sparse constraint terms into the objective function to mitigate the adverse effects of signal amplification. Unlike traditional methods such as Wiener filtering, the proposed algorithm employs an adaptive Minimum Mean-Square Error-Log Spectral Amplitude (MMSE-LSA) method to estimate the amplitude spectrum of non-stationary signals adaptively across varying signal-to-noise ratios. Comparative experiments demonstrate that the INMF algorithm significantly outperforms existing methods in denoising leakage acoustic signals.

Copper(II)-Tannic Acid@Cu with In Situ Grown Gold Nanoparticles as a Bifunctional Matrix for Facile Construction of Label-Free and Ultrasensitive Electrochemical cTnI Immunosensor.

Sensitive detection of cardiac troponin I (cTnI) is of great significance in the diagnosis of a fatal acute myocardial infarction. A redox-active nanocomposite of copper(II)-tannic acid@Cu (CuTA@Cu) was herein prepared on the surface of a glassy carbon electrode by electrochemical deposition of metallic copper combined with a metal stripping strategy. Then, HAuCl4 was in situ reduced to gold nanoparticles (AuNPs) by strong reductive catechol groups in the TA ligand. The AuNPs/CuTA@Cu composite was further utilized as a bifunctional matrix for the immobilization of the cTnI antibody (anti-cTnI), producing an electrochemical immunosensor. Electrochemical tests show that the immunoreaction between anti-cTnI and target cTnI can cause a significant reduction of the electrochemical signal of CuTA@Cu. It can be attributed to the insulating characteristic of the immunocomplex and its barrier effect to the electrolyte ion diffusion. From the signal changes of CuTA@Cu, cTnI can be analyzed in a wide range from 10 fg mL-1 to 10 ng mL-1, with an ultralow detection limit of 0.65 fg mL-1. The spiked recovery assays show that the immunosensor is reliable for cTnI determination in human serum samples, demonstrating its promising application in the early clinical diagnosis of myocardial infarction.

An extraction method for early fault features of rolling bearings based on resonance sparse signal decomposition and non-convex second-order total variation denoising

A primary challenge in the field of fault diagnosis is extracting weak fault characteristics of bearings under large background noise and non-stationary conditions. Due to significant noise interference in the signals of rolling bearings, resonance sparse signal decomposition (RSSD) cannot efficiently extract the transient impact components in the early failure stage and the total variation denoising (TVD) method distorts signal waveforms. In this study, a combined extraction method for early fault features of rolling bearings based on RSSD and non-convex second-order total variation denoising (NCSOTVD) is proposed. Firstly, a non-convex function is introduced to define the regularisation term in the second-order TVD method, and the regularisation parameter and the convexity parameter in the NCSOTVD method are respectively screened using the noise standard deviation and the permutation entropy value to enhance the impact characteristics of signals and induce signal sparsity. The NCSOTVD model is solved using the optimisation-minimisation algorithm, so as to achieve noise reduction and feature enhancement of the vibration signals. Then, the low-resonance components of RSSD are denoised using the NCSOTVD method to highlight periodic pulse signals and extract the fault features of the rolling bearings. The simulation results and experimental data show that the method largely suppresses the noise interference, highlights the fault characteristics and reduces the problems of waveform distortion and poor sparsity of the TVD method in the denoising process.

PRNet with Convolution Layer for PAPR Reduction of OFDM Signals

PRNet with Convolution Layer for PAPR Reduction of OFDM Signals

Calcium homeostasis restoration in pyramidal neurons through micrometer-scale wireless electrical stimulation in spinal cord injured mice

Spinal Cord Injury (SCI) is a significant neurological disorder that can result in severe motor and cognitive impairments. Neuronal regeneration and functional recovery are critical aspects of SCI treatment, with calcium signaling being a crucial indicator of neuronal excitability. In this study, we utilized a murine model to investigate the effects of targeted wireless electrical stimulation (ES) on neuronal activity following SCI. After establishing a complete SCI model in normal mice, flexible electrodes were implanted, and targeted wireless ES was administered to the injury site. We employed fiber-optic photometric in vivo calcium imaging to monitor calcium signals in pyramidal neurons within the CA3 region of the hippocampus and the M1 region of the primary motor cortex. The experimental results demonstrated a significant reduction in calcium signals in CA3 and M1 pyramidal neurons following SCI (reduced by 76 % and 59 %, in peak respectively). However, the application of targeted wireless ES led to a marked increase in calcium signals in these neurons (increased by 118 % and 69 %, in peak respectively), indicating a recovery of calcium activity. These observations suggest that wireless ES has a positive modulatory effect on the excitability of pyramidal neurons post-SCI. Understanding these mechanisms is crucial for developing therapeutic strategies aimed at enhancing neuronal recovery and functional restoration following spinal cord injuries.

An automated sleep staging tool based on simple statistical features of mice electroencephalography (EEG) and electromyography (EMG) data.

Electroencephalogram (EEG) and electromyogram (EMG) are fundamental tools in sleep research. However, investigations into the statistical properties of rodent EEG/EMG signals in the sleep-wake cycle have been limited. The lack of standard criteria in defining sleep stages forces researchers to rely on human expertise to inspect EEG/EMG. The recent increasing demand for analysing large-scale and long-term data has been overwhelming the capabilities of human experts. In this study, we explored the statistical features of EEG signals in the sleep-wake cycle. We found that the normalized EEG power density profile changes its lower and higher frequency powers to a comparable degree in the opposite direction, pivoting around 20-30 Hz between the NREM sleep and the active brain state. We also found that REM sleep has a normalized EEG power density profile that overlaps with wakefulness and a characteristic reduction in the EMG signal. Based on these observations, we proposed three simple statistical features that could span a 3D space. Each sleep-wake stage formed a separate cluster close to a normal distribution in the 3D space. Notably, the suggested features are a natural extension of the conventional definition, making it useful for experts to intuitively interpret the EEG/EMG signal alterations caused by genetic mutations or experimental treatments. In addition, we developed an unsupervised automatic staging algorithm based on these features. The developed algorithm is a valuable tool for expediting the quantitative evaluation of EEG/EMG signals so that researchers can utilize the recent high-throughput genetic or pharmacological methods for sleep research.

Laser-Induced Interference to Infrared Detector Using Continuous Wave and Short-Pulse Lasers.

The response of a DPbS3200 infrared detector irradiated by a nanosecond pulsed laser and CW laser has been investigated to study laser-induced interference. A laser interference experiment system was constructed to measure the time-varying response signal. A nanosecond pulsed laser and a CW laser of 10 Hz were used, with a 1064 nm wavelength and a millimeter-scale irradiation spot diameter. Firstly, the characteristics of transient interference signals induced by pulsed lasers were analyzed. Then, the characteristics of response signal interference by both CW laser and pulsed laser irradiation were further investigated. The results showed that the pulsed laser only produced transient interference. However, the CW laser led to a significant amplitude reduction of the response signal, which could continuously interfere in the operating time. For transient interferences, the amplitude of the interference signal increased linearly with the laser fluence. The relation between the pulse repetition rate of the incident laser and the operating frequency of the detector determined the numbers of transient interference signals in one response period; for the interference induced by both the CW laser and pulsed laser, CW laser interference played a leading role when CW laser power density increased to 4.1 W/cm2 or more. As the CW laser fluence reached 6.1 W/cm2, the PbS infrared detector was no longer able to detect any signal, which caused temporary blindness. In the end, a probit model was used to determine the interference threshold.

Measurement Error Analysis of Seawater Refractive Index: A Measurement Sensor Based on a Position-Sensitive Detector.

The seawater refractive index is an essential parameter in ocean observation, making its high-precision measurement necessary. This can be effectively achieved using a position-sensitive detector-based measurement system. However, in the actual measurement process, the impact of the jitter signal measurement error on the results cannot be ignored. In this study, we theoretically analysed the causes of long jitter signals during seawater refractive index measurements and quantified the influencing factors. Through this analysis, it can be seen that the angle between the two windows in the seawater refractive index measurement area caused a large error in the results, which could be effectively reduced by controlling the angle to within 2.06°. At the same time, the factors affecting the position-sensitive detector's measurement accuracy were analysed, with changes to the background light, the photosensitive surface's size, and the working environment's temperature leading to its reduction. To address the above factors, we first added a 0.9 nm bandwidth, narrow-band filter in front of the detector's photosensitive surface during system construction to filter out any light other than that from the signal light source. To ensure the seawater refractive index's measuring range, a position-sensitive detector with a photosensitive surface size of 4 mm × 4 mm was selected; whereas, to reduce the working environment's temperature variation, we partitioned the measurement system. To validate the testing error range of the optimised test system, standard seawater samples were measured under the same conditions, showing a reduction in the measurement system's jitter signal from 0.0022 mm to 0.0011 mm, before and after optimisation, respectively, as well as a reduction in the refractive index's deviation. The experimental results show that the refractive index of seawater was effectively reduced by adjusting the measurement system's optical path and structure.

Enhanced Discrete Wavelet Transform–Non-Local Means for Multimode Fiber Optic Vibration Signal

Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are very weak and susceptible to noise interference, leading to inaccurate diagnostic results. In this paper, a combined enhanced discrete wavelet transform (DWT) and non-local mean estimation (NLM) denoising method is proposed to remove noise from heartbeat signals, which adaptively determines the filtering parameters of the DWT-NLM composite method using objective noise reduction quality assessment metrics by denoising different ECG signals from multiple databases with the addition of additive Gaussian white noise (AGW) with different signal-to-noise ratios (SNRs). The noise reduction results are compared with those of NLM, enhanced DWT, and conventional DWT combined with NLM method. The results show that the output SNR of the proposed method is significantly higher than the other methods compared in the range of −5 to 25 dB input SNR. Further, the proposed method is employed for noise reduction of heartbeat signals measured by fiber optic microvibration sensing. It is worth mentioning that the proposed method does not need to obtain the exact noise level, but only the adaptive filtering parameters based on the autocorrelation nature of the denoised signal. This work greatly improves the signal quality of the multimode fiber microvibration sensing system and helps to improve the diagnostic accuracy.

Nanozyme-Inhibited SERS Multichannel Paper-Based Sensor Array for the Quantification and Identification of Biothiols and Cancer Cells Based on Three Ag-Based Nanomaterials.

Biothiols play essential roles in maintaining normal physiological functions, resisting oxidative stress, and protecting cell health. Establishing an effective and reliable sensor array for the accurate quantification and discrimination of diverse biothiols is extremely meaningful. In this work, Ag/Mn3O4, Ag3PO4, and Ag3Cit with excellent oxidase-mimetic activity and surface-enhanced Raman scattering (SERS)-enhanced features have been prepared and loaded onto Whatman filter paper (WFP) to build SERS paper chips as three sensing channels, which can induce 3,3',5,5'-tetramethylbenzidine (TMB) oxidation to SERS-active reporters (TMBox) and concurrently generate prominent SERS signals. Nevertheless, the addition of biothiols can suppress conversion from TMB to TMBox, which can cause the reduction of the SERS signal from TMBox. Interestingly, each SERS sensing channel can generate different TMBox signals' variations due to differences in the oxidative inhibition abilities of diverse biothiols and exclusive properties of each paper chip, which can be plotted as specific fingerprint patterns of each biothiol and further translated into intuitive two-dimensional (2D) clustering profiles through linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA) techniques for precise identification of these six biothiols with the minimum concentration of 1 μM. More importantly, this SERS sensor array is exploited for the precise quantification of intracellular glutathione (GSH), and can differentiate between normal and cancer cells based on different intracellular GSH contents and even identify different types of tumor cells, demonstrating its powerful application prospects in disease diagnosis.

Identification method of circumferential declination based on amplitude reduction of pipeline ultrasonic internal inspection signals

ABSTRACT The echo signal of ultrasonic internal inspection is highly sensitive to the incident deflection angle between the probe and the measured surface of the pipeline, which is the key factor affecting the accuracy of ultrasonic inspection. In practical engineering, the echo signal of ultrasonic inspection is easy to be interfered by the external environment, which makes it difficult to identify the deflection angle. The data of defect echo signal under different circumferential declination were collected by the pipeline ultrasonic internal inspection system, and the characteristics of signal variation in time domain under different circumferential declination were analysed. The results show that the existence of circumferential declination reduced the absolute value of the overall amplitude of ultrasonic echo signal. A signal processing method based on amplitude reduction is proposed to reduce the sampling random error. Comparative analysis shows that the circumferential declination can be identified based on amplitude reduction method, and the accuracy of ultrasonic recognition can be improved. By extracting the characteristic value of waveform, the empirical formula is obtained. Within 2°, the maximum deviation is 0.137°. The research results have certain guiding significance for the identification of circumferential declination in practical engineering.

Overview of welding defect detection utilising metal magnetic memory technology

Welded joints frequently endure the composite stress of steel components. The presence of defects within these welded joints can significantly jeopardise the safety and performance of the welded structure. Magnetic memory testing technology has garnered substantial attention due to its ability to evaluate welding defects. However, the conventional zero-point pole theory, which serves as the foundation for defect assessment in practical detection, may lead to defect location and omission errors. In response to this challenge, scholars have conducted extensive research to accurately pinpoint the location and identify the types of defect within welds. This paper systematically reviews the mechanisms of magnetic memory welding defect detection, the factors that influence it, signal characteristic parameters, noise reduction in magnetic memory signals and the application of machine learning for quantitative assessment. By summarising these research advancements, this paper aims to address the current issues and provide guidance for the precise quantitative evaluation of welding defects in the future using metal magnetic memory technology.