Search Results (142)

The localization of multiple interference sources in Global Navigation Satellite Systems (GNSS) can be achieved using carrier-to-noise ratio (C/N₀) information provided by GNSS receivers, such as those embedded in smartphones. However, in increasingly prevalent complex scenarios—such as the coexistence of multiple directional interferences, increased diversity and density of GNSS interference, and the presence of multiple low-power interference sources—conventional localization methods often fail to provide reliable results, thereby limiting their applicability in real-world environments. This paper presents a multi-interference sources localization method using object detection in GNSS C/N₀ distribution maps. The proposed method first exploits the similarity between C/N₀ data reported by GNSS receivers and image grayscale values to construct C/N₀ distribution maps, thereby transforming the problem of multi-source GNSS interference localization into an object detection and localization task based on image processing techniques. Subsequently, an Oriented Squeeze-and-Excitation-based Faster Region-based Convolutional Neural Network (OSF-RCNN) framework is proposed to process the C/N₀ distribution maps. Building upon the Faster R-CNN framework, the proposed method integrates an Oriented RPN (Region Proposal Network) to regress the orientation angles of directional antennas, effectively addressing their rotational characteristics. Additionally, the Squeeze-and-Excitation (SE) mechanism and the Feature Pyramid Network (FPN) are integrated at key stages of the network to improve sensitivity to small targets, thereby enhancing detection and localization performance for low-power interference sources. The simulation results verify the effectiveness of the proposed method in accurately localizing multiple interference sources under the increasingly prevalent complex scenarios described above. Full article

The inter-shaft bearing is an important component of aero-engine rotor systems. It works between a high-pressure rotor and a low-pressure rotor. Effective fault diagnosis of it is significant for an aero-engine. The casing vibration signals can promptly and intuitively reflect changes in the operational health status of an aero-engine’s support system. However, affected by a complex vibration transmission path and vibration of the dual-rotor, the intrinsic vibration information of the inter-shaft bearing is faced with strong noise and a dual-frequency excitation problem. This excitation is caused by the wide span of vibration source frequency distribution that results from the quite different rotational speeds of the high-pressure rotor and low-pressure rotor. Consequently, most existing fault diagnosis methods cannot effectively extract inter-shaft bearing characteristic frequency information from the casing signal. To solve this problem, this paper proposed the denoised improved envelope spectrum (DIES) method. First, an improved envelope spectrum generated by a spectrum subtraction method is proposed. This method is applied to solve the multi-source interference with wide-band distribution problem under dual-frequency excitation. Then, an improved adaptive-thresholding approach is subsequently applied to the resultant subtracted spectrum, so as to eliminate the influence of random noise in the spectrum. An experiment on a public run-to-failure bearing dataset validates that the proposed method can effectively extract an incipient bearing fault characteristic frequency (FCF) from strong background noise. Furthermore, the experiment on the inter-shaft bearing of an aero-engine test platform validates the effectiveness and superiority of the proposed DIES method. The experimental results demonstrate that this proposed method can clearly extract fault-related information from dual-frequency excitation interference. Even amid strong background noise, it precisely reveals the inter-shaft bearing’s fault-related spectral components. Full article

Monolayer (ML) molybdenum disulfide (MoS₂) is a typical valleytronic material which has important applications in, for example, polarization optics and information technology. In this study, we examine the effect of continuous wave (CW) laser pumping on the basic optoelectronic properties of ML MoS₂ placed on a sapphire substrate, where the pump photon energy is larger than the bandgap of ML MoS₂. The pump laser source is provided by a compact semiconductor laser with a 445 nm wavelength. Through the measurement of THz time-domain spectroscopy, we obtain the complex optical conductivity for ML MoS₂, which are found to be fitted exceptionally well with the Drude–Smith formula. Therefore, we expect that the reduction in conductivity in ML MoS₂ is mainly due to the effect of electronic backscattering or localization in the presence of the substrate. Meanwhile, one can optically determine the key electronic parameters of ML MoS₂, such as the electron density n_e, the intra-band electronic relaxation time τ, and the photon-induced electronic localization factor c. The dependence of these parameters upon CW laser pump intensity is examined here at room temperature. We find that 445 nm CW laser pumping results in the larger n_e, shorter τ, and stronger c in ML MoS₂ indicating that laser excitation has a significant impact on the optoelectronic properties of ML MoS₂. The origin of the effects obtained is analyzed on the basis of solid-state optics. This study provides a unique and tractable technique for investigating photo-excited carriers in ML MoS₂. Full article

In real-world scenarios, the rotational speed of bearings is variable. Due to changes in operating conditions, the feature distribution of bearing vibration data becomes inconsistent, which leads to the inability to directly apply the training model built under one operating condition (source domain) to another condition (target domain). Furthermore, the lack of sufficient labeled data in the target domain further complicates fault diagnosis under varying operating conditions. To address this issue, this paper proposes a spatiotemporal feature fusion domain-adaptive network (STFDAN) framework for bearing fault diagnosis under varying operating conditions. The framework constructs a feature extraction and domain adaptation network based on a parallel architecture, designed to capture the complex dynamic characteristics of vibration signals. First, the Fast Fourier Transform (FFT) and Variational Mode Decomposition (VMD) are used to extract the spectral and modal features of the signals, generating a joint representation with multi-level information. Then, a parallel processing mechanism of the Convolutional Neural Network (SECNN) based on the Squeeze-and-Excitation module and the Bidirectional Long Short-Term Memory network (BiLSTM) is employed to dynamically adjust weights, capturing high-dimensional spatiotemporal features. The cross-attention mechanism enables the interaction and fusion of spatial and temporal features, significantly enhancing the complementarity and coupling of the feature representations. Finally, a Multi-Kernel Maximum Mean Discrepancy (MKMMD) is introduced to align the feature distributions between the source and target domains, enabling efficient fault diagnosis under varying bearing conditions. The proposed STFDAN framework is evaluated using bearing datasets from Case Western Reserve University (CWRU), Jiangnan University (JNU), and Southeast University (SEU). Experimental results demonstrate that STFDAN achieves high diagnostic accuracy across different load conditions and effectively solves the bearing fault diagnosis problem under varying operating conditions. Full article

To address the challenges in existing multi-sensor data fusion methods for rail surface defect diagnosis, particularly their limitations in fully exploiting potential synergistic information among multimodal data and effectively bridging the semantic gap between heterogeneous multi-source data, this paper proposes a diagnostic approach based on a Progressive Joint Representation Graph Attention Fusion Network (PJR-GAFN). The methodology comprises five principal phases: Firstly, shared and specific autoencoders are used to extract joint representations of multimodal features through shared and modality-specific representations. Secondly, a squeeze-and-excitation module is implemented to amplify defect-related features while suppressing non-essential characteristics. Thirdly, a progressive fusion module is introduced to comprehensively utilize cross-modal synergistic information during feature extraction. Fourthly, a source domain classifier and domain discriminator are employed to capture modality-invariant features across different modalities. Finally, the spatial attention aggregation properties of graph attention networks are leveraged to fuse multimodal features, thereby fully exploiting contextual semantic information. Experimental results on real-world rail surface defect datasets from domestic railway lines demonstrate that the proposed method achieves 95% diagnostic accuracy, confirming its effectiveness in rail surface defect detection. Full article

Seismic exploration is widely used in shallow engineering applications, yet extracting reflected wave information remains challenging due to contamination from Rayleigh waves. To overcome this, we propose a common shot point time-domain differential method that leverages the distinct velocity contrast between slow Rayleigh waves and faster P-wave reflections. These waves exhibit lower velocity and minimal dispersion in the radiation direction under the same seismic source excitation. This study establishes two closely spaced track records termed “far main and near slave” along the direction of the measurement line to counteract this interference. This method employs the difference in travel time between Rayleigh waves and subsurface interface reflection waves for time-domain differential analysis. The interference is minimized while preserving the reflected wave signal by conducting slight amplitude compensation on the far-field Rayleigh wave signal and subtracting the master and slave records. The application of time-domain differential detection technology in shallow engineering seismic exploration and marble plate thickness detection experiments demonstrated that this method effectively eliminates the influence of Rayleigh surface waves and enhances the resolution of reflection signals from anomalous bodies. Additionally, this study examines the impact of boundaries on time-domain differential technology. Without relying on long array shot records, this approach provides a promising result for Rayleigh wave suppression and offers broad potential in elastic wave exploration. Full article

The distribution network line has the risk of an unsuccessful three-phase blind reclosing in permanent fault. Based on the response of the inverter of the distributed generation (DG) to the short-term low-frequency voltage disturbance to the line to be detected, this paper proposes a non-fault identification method for the distribution network before three-phase reclosing, based on model parameter identification. During the disturbance period, when there is no fault after the arc is extinguished, the detection line is three-phase symmetrical, and each phase-to-ground loop is its own loop resistance and inductance linear network, which is independent of the fault location, transition resistance and other factors. Furthermore, the R–L network without fault is used as the identification reference model, and the least squares algorithm is used to identify the resistance and inductance parameters of each phase loop of the detection line by using the voltage and current response information of the line side during the excitation period so as to identify the fault state. The non-fault criterion before three-phase reclosing, characterized by the difference between the calculated value of resistance and inductance and the corresponding actual value, is designed. Finally, PSCAD is used to build a distribution network with DG for verification, and simulations under different fault locations and transition resistances are carried out. The results show that when the line is in a non-fault state, the parameter identification results of the three phase-to-ground circuits are highly consistent with the true value; that is, the non-fault state is determined. When the fault continues, there is a large deviation between the parameter identification results of at least one phase-to-ground loop and the corresponding real value, which does not meet the condition of the non-fault criterion. The method in this paper is more sensitive than the detection method using response voltage. Moreover, it is not necessary to add additional disturbance sources, which is expected to improve the economy and feasibility of three-phase adaptive reclosing applications for distribution lines with a large number of DGs. Full article

The complex and variable operating conditions of sucker-rod pumping wells pose a significant challenge for the timely and accurate identification of oil well operating conditions. Effective deep learning based on measured multi-source data obtained from the sucker-rod pumping well production site offers a promising solution to the challenge. However, existing deep learning-based operating condition recognition methods are constrained by several factors: the limitations of traditional operating condition recognition methods based on single-source and multi-source data, the need for large amounts of labeled data for training, and the high robustness requirement for recognizing complex and variable data. Therefore, we propose a semi-supervised class-incremental sucker-rod pumping well operating condition recognition method based on measured multi-source data distillation. Firstly, we select measured ground dynamometer cards and measured electrical power cards as information sources, and construct the graph neural network teacher models for data sources, and dynamically fuse the prediction probability of each teacher model through the Squeeze-and-Excitation attention mechanism. Then, we introduce a multi-source data distillation loss. It uses Kullback-Leibler (KL) divergence to measure the difference between the output logic of the teacher and student models. This helps reduce the forgetting of old operating condition category knowledge during class-incremental learning. Finally, we employ a multi-source semi-supervised graph classification method based on enhanced label propagation, which improves the label propagation method through a logistic regression classifier. This method can deeply explore the potential relationship between labeled and unlabeled samples, so as to further enhance the classification performance. Extensive experimental results show that the proposed method achieves superior recognition performance and enhanced engineering practicality in real-world class-incremental oil extraction production scenarios with complex and variable operating conditions. Full article

In this paper, we propose Sound Source Localization (SSL) using deep learning for Human–Robot Interaction (HRI) under intelligent robot environments. The proposed SSL method consists of three steps. The first step preprocesses the sound source to minimize noise and reverberation in the robotic environment. Excitation source information (ESI), which contains only the original components of the sound source, is extracted from a sound source in a microphone array mounted on a robot to minimize background influence. Here, the linear prediction residual is used as the ESI. Subsequently, the cross-correlation signal between each adjacent microphone pair is calculated by using the ESI signal of each sound source. To minimize the influence of noise, a Generalized Cross-Correlation with the phase transform (GCC-PHAT) algorithm is used. In the second step, we design a single-channel, multi-input convolutional neural network that can independently learn the calculated cross-correlation signal between each adjacent microphone pair and the location of the sound source using the time difference of arrival. The third step classifies the location of the sound source after training with the proposed network. Previous studies have primarily used various features as inputs and stacked them into multiple channels, which made the algorithm complex. Furthermore, multi-channel inputs may not be sufficient to clearly train the interrelationship between each sound source. To address this issue, the cross-correlation signal between each sound source alone is used as the network input. The proposed method was verified on the Electronics and Telecommunications Research Institute-Sound Source Localization (ETRI-SSL) database acquired from the robotic environment. The experimental results revealed that the proposed method showed an 8.75% higher performance in comparison to the previous works. Full article

This paper presents an autonomous perfectly secure low-bit-rate voice communication system (APS-VCS) based on the mixed-excitation linear prediction voice coder (MELPe), Vernam cipher, and sequential key distillation (SKD) protocol by public discussion. An authenticated public channel can be selected in a wide range, from internet connections to specially leased radio channels. We found the source of common randomness between the locally synthesized speech signal at the transmitter and the reconstructed speech signal at the receiver side. To avoid information leakage about open input speech, the SKD protocol is not executed on the actual transmitted speech signal but on artificially synthesized speech obtained by random selection of the linear spectral pairs (LSP) parameters of the speech production model. Experimental verification of the proposed system was performed on the Vlatacom Personal Crypto Platform for Voice encryption (vPCP-V). Empirical measurements show that with an adequate selection of system parameters for voice transmission of 1.2 kb/s, a secret key rate (KR) of up to 8.8 kb/s can be achieved, with a negligible leakage rate (LR) and bit error rate (BER) of order ${10}^{-3}$ for various communications channels, including GSM 3G and GSM VoLTE networks. At the same time, by ensuring perfect secrecy within symmetric encryption systems, it further highlights the importance of the symmetry principle in the field of information-theoretic security. To our knowledge, this is the first autonomous, perfectly secret system for low-bit-rate voice communication that does not require explicit prior generation and distribution of secret keys. Full article

With the widespread application of fuel cell technology in the fields of transportation and energy, Battery Management Systems (BMSs) have become one of the key technologies for ensuring system stability and extending battery lifespan. As an auxiliary power source in fuel cell systems, the prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for enhancing the reliability and efficiency of fuel cell ships. However, due to the complex degradation mechanisms of lithium batteries and the actual noisy operating conditions, particularly capacity regeneration noise, accurate RUL prediction remains a challenge. To address this issue, this paper proposes a lithium battery RUL prediction method based on an Adaptive Modal Enhancement Network (RIME-VMD-SEInformer). By incorporating an improved Variational Mode Decomposition (VMD) technique, the RIME algorithm is used to optimize decomposition parameters for the adaptive extraction of key modes from the signal. The Squeeze-and-Excitation Networks (SEAttention) module is employed to enhance the accuracy of feature extraction, and the sparse attention mechanism of Informer is utilized to efficiently model long-term dependencies in time series. This results in a comprehensive prediction framework that spans signal decomposition, feature enhancement, and time-series modeling. The method is validated on several public datasets, and the results demonstrate that each component of the RIME-VMD-SEInformer framework is both necessary and justifiable, leading to improved performance. The model outperforms the state-of-the-art models, with a MAPE of only 0.00837 on the B0005 dataset, representing a 59.96% reduction compared to other algorithms, showcasing outstanding prediction performance. Full article

The generalization of Physics-Informed Neural Networks (PINNs) used to solve the inhomogeneous Helmholtz equation in a simplified three-dimensional room is investigated. PINNs are appealing since they can efficiently integrate a partial differential equation and experimental data by minimizing a loss function. However, a previous study experienced limitations in acoustics regarding the source term. A challenging but realistic excitation case is a confined (e.g., single-point) excitation area, yielding a smooth spatial wave field periodically with the wavelength. Compared to studies using smooth (unrealistic) sound excitation, the network’s generalization capabilities regarding a realistic sound excitation are addressed. Different methods like hyperparameter optimization, adaptive refinement, Fourier feature engineering, and locally adaptive activation functions with slope recovery are tested to tailor the PINN’s accuracy to an experimentally validated finite element analysis reference solution computed with openCFS. The hyperparameter study and optimization are conducted regarding the network depth and width, the learning rate, the used activation functions, and the deep learning backends (PyTorch 2.5.1, TensorFlow 2.18.0 1, TensorFlow 2.18.0 2, JAX 0.4.39). A modified (feature-engineered) PINN architecture was designed using input feature engineering to include the dispersion relation of the wave in the neural network. For smoothly (unrealistic) distributed sources, it was shown that the standard PINNs and the feature-engineered PINN converge to the analytic solution, with a relative error of 0.28% and $2\times {10}^{-4}$%, respectively. The locally adaptive activation functions with the slope lead to a relative error of 0.086% with a source sharpness of $s=1$ m. Similar relative errors were obtained for the case $s=0.2$ m using adaptive refinement. The feature-engineered PINN significantly outperformed the results of previous studies regarding accuracy. Furthermore, the trainable parameters were reduced to a fraction by Bayesian hyperparameter optimization (around 5%), and likewise, the training time (around 3%) was reduced compared to the standard PINN formulation. By narrowing this excitation towards a single point, the convergence rate and minimum errors obtained of all presented network architectures increased. The feature-engineered architecture yielded a one order of magnitude lower accuracy of 0.20% compared to 0.019% of the standard PINN formulation with a source sharpness of $s=1$ m. It outperformed the finite element analysis and the standard PINN in terms time needed to obtain the solution, needing 15 min and 30 s on an AMD Ryzen 7 Pro 8840HS CPU (AMD, Santa Clara, CA, USA) for the FEM, compared to about 20 min (standard PINN) and just under a minute of the feature-engineered PINN, both trained on a Tesla T4 GPU (NVIDIA, Santa Clara, CA, USA). Full article

Background/Objectives: Alkyl nitrites are a class of inhalant, commonly known as ‘poppers’. Although having medical uses, some other effects include a ‘rush’, ‘high’, ‘euphoria’, or feeling of excitement. This has led to their recreational use, in different scenarios, since the mid-1960s. Adverse effects include tachycardia, migraine headaches, fainting and dizziness, and ventricular fibrillation. Death can occur from the inhalation or ingestion of nitrites. As part of its updated advice to the United Kingdom (UK) Government, the Advisory Council on the Misuse of Drugs considered popper-related mortality, seeking an accurate estimate of deaths. Methods: Data from a range of sources, including specialist mortality databases, were collated and analysed in terms of the key characteristics of decedents and fatal incidents, including the use mode. The chemical names of the nitrites were used in searches. Results: At least forty-two deaths occurred during 1987–2018; two were female. The mean age at death was 44 (range of 20–75) years. Most were White. Most fatalities occurred in England. The specific nitrites mentioned (N = thirty-two) were isobutyl (fourteen); amyl (seven); isopropyl (six); alkyl (three); and butyl (two). The mode of use was only known in 23/42 cases. The product was definitely swallowed in five cases, and very likely in a further one. Four additional cases were identified from the literature and media searches. Conclusions: The lack of a current systematic identification of relevant deaths and shortcomings in historical specialist mortality databases have severely limited what could be established with certainty about these cases. The same criticisms also apply to inhalant mortality data more generally. Nevertheless, the information presented here allows for some conclusions to be drawn and inform UK policy development. Full article

Background: Neurodegenerative diseases (NGD) encompass a range of progressive neurological conditions, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), characterised by the gradual deterioration of neuronal structure and function. This degeneration manifests as cognitive decline, movement impairment, and dementia. Our focus in this investigation is on PD, a neurodegenerative disorder characterized by the loss of dopamine-producing neurons in the brain, leading to motor disturbances. Early detection of PD is paramount for enhancing quality of life through timely intervention and tailored treatment. However, the subtle nature of initial symptoms, like slow movements, tremors, muscle rigidity, and psychological changes, often reduce daily task performance and complicate early diagnosis. Method: To assist medical professionals in timely diagnosis of PD, we introduce a cutting-edge Multimodal Diagnosis framework (PMMD). Based on deep learning techniques, the PMMD framework integrates imaging, handwriting, drawing, and clinical data to accurately detect PD. Notably, it incorporates cross-modal attention, a methodology previously unexplored within the area, which facilitates the modelling of interactions between different data modalities. Results: The proposed method exhibited an accuracy of 96% on the independent tests set. Comparative analysis against state-of-the-art models, along with an in-depth exploration of attention mechanisms, highlights the efficacy of PMMD in PD classification. Conclusions: The obtained results highlight exciting new prospects for the use of handwriting as a biomarker, along with other information, for optimal model performance. PMMD’s success in integrating diverse data sources through cross-modal attention underscores its potential as a robust diagnostic decision support tool for accurately diagnosing PD. Full article

The geographical distribution and scientific evaluation of wind energy potential are crucial for regional energy planning. Wind energy is a renewable energy that can mitigate climate change. Several open-access World Bank databases and the ESRI (Environmental Systems Research Institute) Global were used to gather and process data through wind energy siting optimization in Fujian Province. This paper uses the fuzzy quantifiers of the multi-criteria decision-making (MCDM) approach in arc geographic information system (ArcGIS Pro) and the analytical hierarchy process (AHP) to handle the associated wind data uncertainties to obtain wind energy technology siting optimization for nine cities in Fujian Province. The converted database options and characteristics used the weighted overlay tool (WOT) to reflect the importance of wind farm project objectives. The sensitivity analysis tested the robustness and resilience of the integrated MCDM design for feasibility or viability. The results revealed that 21.743% of the area of Longyan City is suitable for siting wind energy. Other cities’ suitable areas comprise 14.117%, 12.800%, 5.250%, 4.621%, 4.020%, 4.020%, 3.430%, and 2.300%, respectively (Sanming, Ningde, Quanzhou, Putian, Zhangzhou, Nanping, Xiamen, and Fuzhou cities). Furthermore, a considerable amount of wind power is needed to supply the current primary energy deficit (60.0–84.0%) and satisfy the carbon emission reduction target. Wind farm installation in Fujian province is an opportunity to provide inexhaustible energy, generally affected by generation volume and operational span. Wind power is highly acceptable to local Chinese. Reasonably high understanding and excitement for wind farm investments exist among local authorities. Future research should consider wind data of the identified onshore optimization sites and design wind farms for the respective output power for pessimistic, average, and optimistic scenarios for possible wind farm development. Similarly, the long shoreline of about 1680.0 miles (or 2700.0 km) is a considerable source of offshore wind power prospecting, future research, and energy exploitation and harvesting opportunities. Full article