Search Results (475)

To address the decline in measurement accuracy of magnetometers due to process errors and environmental interference, as well as the insufficient robustness of traditional calibration algorithms under strong interference conditions, this paper proposes an ellipsoid fitting algorithm based on Dynamic Adaptive Elite Particle Swarm Optimization (DAEPSO). The proposed algorithm integrates three enhancement mechanisms: dynamic stratified elite guidance, adaptive inertia weight adjustment, and inferior particle relearning via Lévy flight, aiming to improve convergence speed, solution accuracy, and noise resistance. First, a magnetometer calibration model is established. Second, the DAEPSO algorithm is employed to fit the ellipsoid parameters. Finally, error calibration is performed based on the optimized ellipsoid parameters. Our simulation experiments demonstrate that compared with the traditional Least Squares Method (LSM) the proposed method reduces the standard deviation of the total magnetic field intensity by 54.73%, effectively improving calibration precision in the presence of outliers. Furthermore, when compared to PSO, TSLPSO, MPSO, and AWPSO, the sum of the absolute distances from the simulation data to the fitted ellipsoidal surface decreases by 53.60%, 41.96%, 53.01%, and 27.40%, respectively. The results from 60 independent experiments show that DAEPSO achieves lower median errors and smaller interquartile ranges than comparative algorithms. In summary, the DAEPSO-based ellipsoid fitting algorithm exhibits high fitting accuracy and strong robustness in environments with intense interference noise, providing reliable theoretical support for practical engineering applications. Full article

The frequency-domain controlled-source electromagnetic method (CSEM) has been widely used in fields such as oil and gas and mineral resource exploration. In areas with a significant IP response, the CSEM signals will be modified by the IP response of the subsurface. Accurately extracting resistivity and polarization information from CSEM signals may significantly improve the exploration interpretations. In this study, we replaced real resistivity with the Cole–Cole complex resistivity model in a forward simulation of the CSEM to obtain electric field responses that included both induced polarization and electromagnetic effects. Based on this, we used the adaptive differential evolution algorithm to perform a 1-d inversion of these data to extract both the resistivity and IP parameters. Inversion of the electric field responses from representative three-layer geoelectric models, as well as from a more realistic seven-layer model, showed that the inversions were able to effectively recover resistivity and polarization information from the modeled responses, validating our methodology. The electric field response of the real geoelectric model, with 20% random noise added, was then used to simulate actual measured CSEM signals, as well as subjected to multiple inversion tests. The results of these tests continued to accurately reflect the resistivity and polarization information of the model, confirming the applicability and reliability of the algorithm. These results have significant implications for the processing and interpretation of CSEM data when induced polarization effects merit consideration and are expected to promote the use of the CSEM in more fields. Full article

Quantum memory is essential for the prolonged storage and retrieval of quantum information. Nevertheless, no current studies have focused on the creation of effective quantum memory for continuous variables while accounting for the decoherence rate. This work presents an effective continuous-variable quantum key distribution method with parameter optimization utilizing the Elitist Elk Herd Random Immigrants Optimizer (2E-HRIO) technique. At the outset of transmission, the quantum device undergoes initialization and authentication via Compressed Hash-based Message Authentication Code with Encoded Post-Quantum Hash (CHMAC-EPQH). The settings are subsequently optimized from the authenticated device via 2E-HRIO, which mitigates the effects of decoherence by adaptively tuning system parameters. Subsequently, quantum bits are produced from the verified device, and pilot insertion is executed within the quantum bits. The pilot-inserted signal is thereafter subjected to pulse shaping using a Gaussian filter. The pulse-shaped signal undergoes modulation. Authenticated post-modulation, the prediction of link failure is conducted through an authenticated channel using Radial Density-Based Spatial Clustering of Applications with Noise. Subsequently, transmission occurs via a non-failure connection. The receiver performs channel equalization on the received signal with Recursive Regularized Least Mean Squares. Subsequently, a dataset for side-channel attack authentication is gathered and preprocessed, followed by feature extraction and classification using Adaptive Depthwise Separable Convolutional Neural Networks (ADS-CNNs), which enhances security against side-channel attacks. The quantum state is evaluated based on the signal received, and raw data are collected. Thereafter, a connection is established between the transmitter and receiver. Both the transmitter and receiver perform the scanning process. Thereafter, the calculation and correction of the error rate are performed based on the sifting results. Ultimately, privacy amplification and key authentication are performed using the repaired key via B-CHMAC-EPQH. The proposed system demonstrated improved resistance to decoherence and side-channel attacks, while achieving a reconciliation efficiency above 90% and increased key generation rate. Full article

We present a corrosion-resistant quadrupole ion trap mass spectrometer (QIT-MS) designed for trace detection of volatiles in sulfuric acid aerosols, with a specific focus on phosphine (PH₃). Here, we detail the gas calibration methodology using permeation tube technology for generating certified ppb-level PH₃/H₂S/CO₂ mixtures, and report results from mass spectra with sufficient resolution to distinguish isotopic envelopes that validate the detection of PH₃ at a concentration of 62 ppb. Fragmentation patterns for PH₃ and H₂S agree with NIST data, and signal-to-noise performance confirms ppb sensitivity over 2.6 h acquisition periods. We further assess spectral interferences from oxygen isotopes and propose a detection scheme based on isolated phosphorus ions (P⁺) to enable specific and interference-resistant identification of PH₃ and other reduced phosphorus species of astrobiological interest in Venus-like environments. This work extends the capabilities of QIT-MS for trace gas analysis in chemically aggressive atmospheric conditions. Full article

Accurate settlement prediction for high-speed railway (HSR) soft foundations remains challenging due to the irregular and dynamic nature of real-world monitoring data, often represented as non-equidistant and non-stationary time series (NENSTS). Existing empirical models lack clear applicability criteria under such conditions, resulting in subjective model selection. This study introduces a Monte Carlo-based evaluation framework that integrates data-driven simulation with geotechnical principles, embedding the concept of symmetry across both modeling and assessment stages. Equivalent permeability coefficients (EPCs) are used to normalize soil consolidation behavior, enabling the generation of a large, statistically robust dataset. Four empirical settlement prediction models—Hyperbolic, Exponential, Asaoka, and Hoshino—are systematically analyzed for sensitivity to temporal features and resistance to stochastic noise. A symmetry-aware comprehensive evaluation index (CEI), constructed via a robust entropy weight method (REWM), balances multiple performance metrics to ensure objective comparison. Results reveal that while settlement behavior evolves asymmetrically with respect to EPCs over time, a symmetrical structure emerges in model suitability across distinct EPC intervals: the Asaoka method performs best under low-permeability conditions (EPC ≤ 0.03 m/d), Hoshino excels in intermediate ranges (0.03 < EPC ≤ 0.7 m/d), and the Exponential model dominates in highly permeable soils (EPC > 0.7 m/d). This framework not only quantifies model robustness under complex data conditions but also formalizes the notion of symmetrical applicability, offering a structured path toward intelligent, adaptive settlement prediction in HSR subgrade engineering. Full article

To address the issues of high model complexity and weak noise resistance in convolutional neural networks for bearing fault diagnosis, this paper proposes a novel lightweight multi-angle feature fusion convolutional neural network (LMAFCNN). First, the original signal was preprocessed using a wide-kernel convolutional layer to achieve data dimensionality reduction and feature channel expansion. Second, a lightweight multi-angle feature fusion module was designed as the core feature extraction unit. The main branch fused multidimensional features through pointwise convolution and large-kernel channel-wise expansion convolution, whereas the auxiliary branch introduced an efficient channel attention (ECA) mechanism to achieve channel-adaptive weighting. Feature enhancement was achieved through the addition of branches. Finally, global average pooling and fully connected layers were used to complete end-to-end fault diagnosis. The experimental results showed that the proposed method achieved an accuracy of 99.5% on the Paderborn University (PU) artificial damage dataset, with a computational complexity of only 14.8 million floating-point operations (MFLOPs) and 55.2 K parameters. Compared with existing mainstream methods, the proposed method significantly reduces model complexity while maintaining high accuracy, demonstrating excellent diagnostic performance and application potential. Full article

Quantum neural networks (QNNs) represent an emerging technology that uses a quantum computer for neural network computations. The QNNs have demonstrated potential advantages over classical neural networks in certain tasks. As a core component of a QNN, the parameterized quantum circuit (PQC) plays a crucial role in determining the QNN’s overall performance. However, quantum circuit architectures designed manually based on experience or using specific hardware structures can suffer from inefficiency due to the introduction of redundant quantum gates, which amplifies the impact of noise on system performance. Recent studies have suggested that the advantages of quantum evolutionary algorithms (QEAs) in terms of precision and convergence speed can provide an effective solution to quantum circuit architecture-related problems. Currently, most QEAs adopt a fixed rotation mode in the evolution process, and a lack of an adaptive updating mode can cause the QEAs to fall into a local optimum and make it difficult for them to converge. To address these problems, this study proposes an adaptive quantum evolution algorithm (AQEA). First, an adaptive mechanism is introduced to the evolution process, and the strategy of combining two dynamic rotation angles is adopted. Second, to prevent the fluctuations of the population’s offspring, the elite retention of the parents is used to ensure the inheritance of good genes. Finally, when the population falls into a local optimum, a quantum catastrophe mechanism is employed to break the current population state. The experimental results show that compared with the QNN structure based on manual design and QEA search, the proposed AQEA can reduce the number of network parameters by up to 20% and increase the accuracy by 7.21%. Moreover, in noisy environments, the AQEA-optimized circuit outperforms traditional circuits in maintaining high fidelity, and its excellent noise resistance provides strong support for the reliability of quantum computing. Full article

Accurately monitoring coupled water–nitrogen stress is critical for wheat (Triticum aestivum L.) productivity under climate change. This study developed a machine learning framework utilizing multimodal leaf electrophysiological signals––intrinsic resistance, impedance, capacitive reactance, inductive reactance, and capacitance––to decouple water and nitrogen stress signatures in wheat. A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. Controlled pot experiments revealed IZ as the paramount biomarker across leaf positions, indicating its sensitivity to ion flux perturbations under abiotic stress. Crucially, algorithm-feature specificity was identified: Ridge Regression excelled in modeling linear responses due to its superior noise suppression, while GB effectively captured nonlinear dynamics. Flag leaves during reproductive stages provided significantly more stable predictions compared to vegetative third leaves, aligning with their physiological primacy as source organs. This framework offers a robust, non-invasive approach for real-time water and nitrogen stress diagnostics in precision agriculture. Full article

This study investigates the acoustic and mechanical performance of three types of 3D-printed polylactic acid (PLA) panels with varying infill densities (5–100%) and structural configurations. Using fused filament fabrication (FFF), panels were designed as follows: Type 1 (core infill only), Type 2 (core infill + 1.6 mm shell), and Type 3 (core infill + multi-layer shells). Acoustic testing via impedance tube revealed that Type 2 panels with a 65% infill density achieved the highest sound absorption coefficient (α = 0.99), while Type 1 panels exhibited superior sound transmission loss (TLn = 53.3 dB at 60% infill). Mechanical testing demonstrated that shell layers improved tensile and bending resistance by 25.7% and 36.9%, respectively, but reduced compressive strength by 23.6%. Microscopic analysis highlighted ductile failure in Type 2 and brittle fracture in Type 3. The optimal panel thickness for acoustic performance was identified as 4 mm, balancing material efficiency and sound absorption. These findings underscore the potential of tailored infill parameters in sustainable noise-control applications. Full article

To address the challenges in identifying NAPL contamination within low-permeability clay sites, this study innovatively integrates high-density electrical resistivity tomography (ERT) with a UNet deep learning model to establish an intelligent contamination detection system. Taking an industrial site in Shanghai as the research object, we collected apparent resistivity data using the WGMD-9 system, obtained resistivity profiles through inversion imaging, and constructed training sets by generating contamination labels via K-means clustering. A semantic segmentation model with skip connections and multi-scale feature fusion was developed based on the UNet architecture to achieve automatic identification of contaminated areas. Experimental results demonstrate that the model achieves a mean Intersection over Union (mIoU) of 86.58%, an accuracy (Acc) of 99.42%, a precision (Pre) of 75.72%, a recall (Rec) of 76.80%, and an F1 score (f1) of 76.23%, effectively overcoming the noise interference in electrical anomaly interpretation through conventional geophysical methods in low-permeability clay, while outperforming DeepLabV3, DeepLabV3+, PSPNet, and LinkNet models. Time-lapse resistivity imaging verifies the feasibility of dynamic monitoring for contaminant migration, while the integration of the VGG-16 encoder and hyperparameter optimization (learning rate of 0.0001 and batch size of 8) significantly enhances model performance. Case visualization reveals high consistency between segmentation results and actual contamination distribution, enabling precise localization of spatial morphology for contamination plumes. This technological breakthrough overcomes the high-cost and low-efficiency limitations of traditional borehole sampling, providing a high-precision, non-destructive intelligent detection solution for contaminated site remediation. Full article

To enhance the resolution of shallow geological structure detection, this study developed a Controlled-Source wide frequency range Magnetotelluric Detector (called CSUMT) with a frequency range spanning from 1 Hz to 1 MHz, and conducted systematic field experiments in Fengxian County, Shaanxi Province. The CSUMT system employs a high-precision 24-bit analog-to-digital converter and is compatible with both inductive magnetic sensors and superconducting quantum interference device (SQUID) magnetic sensors, featuring wide bandwidth and high dynamic range. Comparative experiments with the commercial V8 instrument demonstrated high consistency in electric field, magnetic field, and apparent resistivity measurements, confirming the CSUMT system’s reliability in field applications. In addition, this study compared the performance of inductive and SQUID magnetic sensors in actual surveys, revealing that SQUID sensors exhibit lower noise and more stable data output, making them suitable for signal detection across a broader frequency range. The results validate the practicality of the CSUMT system in complex geological environments and provide experimental support for the appropriate selection of magnetic sensors. Full article

This study focuses on the application of electrical resistivity tomography (ERT) for monitoring the growth process of CO₂ hydrate in subsea carbon sequestration, aiming to provide technical support for the safety assessment of marine carbon storage. By designing single-target, dual-target, and multi-target hydrate samples, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and residual neural networks (ResNets) were constructed and compared with traditional image reconstruction algorithms (e.g., back-projection) to quantitatively analyze ERT imaging accuracy. The experiments used boundary voltage as the input and internal conductivity distribution as the output, employing the relative image error (RIE) and image correlation coefficient (ICC) to evaluate algorithmic performance. The results demonstrate that neural network algorithms—particularly RNNs—exhibit superior performance compared to traditional image reconstruction methods due to their strong noise resistance and nonlinear mapping capabilities. These algorithms significantly improve the edge clarity in target identification, enabling the precise capture of the hydrate distribution during carbon sequestration. This advancement effectively enhances the monitoring capability of CO₂ hydrate reservoir characteristics and provides reliable data support for the safety assessment of hydrate reservoirs. Full article

Accurate and robust detection of fabric defects under noisy conditions is a major challenge in textile quality control systems. To address this issue, we introduce a new model called the Logarithmic Deep Residual Shrinkage Network (Log-DRSN), which integrates a deep attention module. Unlike standard residual shrinkage networks, the proposed Log-DRSN applies logarithmic transformation to improve resistance to noise, particularly in cases with subtle defect features. The model is trained and tested on both clean and artificially noised images to mimic real-world manufacturing conditions. The experimental results reveal that Log-DRSN achieves superior accuracy and robustness compared to the classical DRSN, with performance scores of 0.9917 on noiseless data and 0.9640 on noisy data, whereas the classical DRSN achieves 0.9686 and 0.9548, respectively. Despite its improved performance, the Log-DRSN introduces only a slight increase in computation time. These findings highlight the model’s potential for practical deployment in automated fabric defect inspection. Full article

Composite rotors, attributing to their leveraging characteristics of the light weight, high strength, high rigidity, corrosion resistance, and low noise, can significantly reduce the moment of inertia and enhance equipment operational efficiency. Using carbon fiber/glass fiber hybrid-reinforced resin–matrix composites as the rotor base material, the radial stability of a rotor can be effectively increased by regulating the fiber volume content. Meanwhile, the introduction of glass fiber not only enables the transition between the metal hub and composite rim but also optimizes the cost structure of the composite system, overcoming the economic bottleneck of single carbon fiber-reinforced resin–matrix composite rotors. This paper employs the finite element method to analyze a three-dimensional model of a composite rotor, investigating the performance of its metal hub and hybrid-reinforced resin–matrix composite rim. According to the radial stress distribution of the composite rotor during operation, the mixing ratio of carbon fiber/glass fiber is adjusted. The high-speed rotation condition of the composite rotor at 18,000 revolutions per minute is simulated to verify its safety and reliability. Full article

Noise reduction is essential in analyzing electrochemical impedance spectroscopy (EIS) data for accurate parameter identification of models of electrochemical sources and interfaces. EIS is widely used to study the behavior of electrochemical systems as it provides information about the processes occurring at electrode surfaces. However, measurement noise can severely compromise the accuracy of parameter identification and the interpretation of EIS data. This paper presents methods for parameter identification of Randles (also known as R-RC or 2R-1C) equivalent electrical circuits and noise reduction in EIS data using recursive filtering. EIS data obtained at the estimated characteristic frequency is processed with three equations in the closed form for the parameter estimation of series resistance, charge transfer resistance, and double-layer capacitance. The proposed recursive filter enhances estimation accuracy in the presence of random noise. Filtering is embedded in the estimation procedure, while the optimal value of the recursive filter weighting factor is self-tuned based on the proposed search method. The distinguished feature is that the proposed method can process EIS data and perform estimation with filtering without any input from the user. Synthetic datasets and experimentally obtained impedance data of lithium-ion batteries were successfully processed using PC-based and microcontroller-based systems. Full article