Search Results (181)

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has demonstrated significant potential for soil moisture content (SMC) monitoring due to its high spatiotemporal resolution. However, GNSS-IR inversion experiments are notably influenced by vegetation and meteorological factors. To address these challenges, this study proposes a multi-factor SMC inversion method. Six GNSS stations from the Plate Boundary Observatory (PBO) were selected as study sites. A low-order polynomial was applied to separate the reflected signals, extracting parameters such as phase, frequency, amplitude, and effective reflector height. Auxiliary variables, including the Normalized Microwave Reflection Index (NMRI), cumulative rainfall, and daily average evaporation, were used to further improve inversion accuracy. A multi-factor SMC inversion dataset was constructed, and three machine learning models were selected to develop the SMC prediction model: Support Vector Regression (SVR), suitable for small and medium-sized regression tasks; Convolutional Neural Networks (CNN), with robust feature extraction capabilities; and NRBO-XGBoost, which supports automatic optimization. The multi-factor SMC inversion method achieved remarkable results. For instance, at the P038 station, the model attained an R² of 0.98, with an RMSE of 0.0074 and an MAE of 0.0038. Experimental results indicate that the multi-factor inversion model significantly outperformed the traditional univariate model, whose R² (RMSE, MAE) was only 0.88 (0.0179, 0.0136). Further analysis revealed that NRBO-XGBoost surpassed the other models, with its average R² outperforming SVR by 0.11 and CNN by 0.03. Additionally, the analysis of different surface types showed that the method achieved higher accuracy in grassland and open shrubland areas, with all models reaching R² values above 0.9. Therefore, the accuracy of the multi-factor SMC inversion model was validated, supporting the practical application of GNSS-IR technology in SMC inversion. Full article

Since permanent magnets and rotors are core components of electric vehicle drive motors, accurate diagnosis of demagnetization and eccentricity faults is crucial for ensuring the safe operation of electric vehicles. Currently, intelligent diagnostic methods based on three-phase current signals have been widely adopted due to their advantages of easy acquisition, low cost, and non-invasiveness. However, in practical applications, the fault characteristics in current signals are relatively weak, leading to diagnostic performance that falls short of expected standards. To address this issue and improve diagnostic accuracy, this paper proposes a novel diagnostic method. First, branch current is utilized as the data source for diagnosis to enhance the fault characteristics of the diagnostic signal. Next, a dual-modal feature extraction module is constructed, employing Variational Mode Decomposition (VMD) and Fast Fourier Transform (FFT) to concatenate the input branch current along the feature dimension in both the time and frequency domains, achieving nonlinear coupling of time–frequency features. Finally, to further improve diagnostic accuracy, a cascaded convolutional neural network based on dilated convolutional layers and multi-scale convolutional layers is designed as the diagnostic model. Experimental results show that the method proposed in this paper achieves a diagnostic accuracy of 98.6%, with a misjudgment rate of only about 2% and no overlapping feature results. Compared with existing methods, the method proposed in this paper can extract higher-quality fault features, has better diagnostic accuracy, a lower misjudgment rate, and more excellent feature separation ability, demonstrating great potential in intelligent fault diagnosis and maintenance of electric vehicles. Full article

Nonlinear noise power (NLNP) estimation, optical signal-to-noise ratio (OSNR) monitoring, and modulation format identification (MFI) are crucial for optical performance monitoring (OPM) in future dynamic WDM optical networks. This paper proposes an OPM scheme to simultaneously implement these three tasks in both single-channel and WDM systems by combining amplitude-differential phase histograms (ADPH) with the MAML-CNN-ATT algorithm that integrates model-agnostic meta-learning (MAML), the convolutional neural network (CNN), and the attention mechanism (ATT). The meta-learning algorithms can learn optimal initial model parameters across multiple related tasks, enabling them to quickly adapt to new tasks through fine-tuning with a small amount of data. This results in superior self-adaptability and generalizability, making them more suitable for WDM scenarios than the transfer learning (TL) algorithms. The CNN-ATT algorithm can effectively extract comprehensive features, capturing both local and global dependencies, thus improving the quality of the feature representation. The ADPH sequence data combine the amplitude information and the differential phase information that indicate the signal’s overall characteristics. The results demonstrate that the MAML-CNN-ATT algorithm achieves errors of less than 1 dB in both NLNP estimation and OSNR monitoring tasks while achieving 100% accuracy in the MFI task. It exhibits excellent OPM performance not only in the single channel but also in the WDM transmission, with only a few steps of fine-tuning. The MAML-CNN-ATT algorithm provides a solution with high performance and rapid self-adaptation for the multi-task OPM in dynamic optical networks. Full article

Plant phenotyping is crucial for advancing precision agriculture and modern breeding, with 3D point cloud segmentation of plant organs being essential for phenotypic parameter extraction. Nevertheless, although existing approaches maintain segmentation precision, they struggle to efficiently process complex geometric configurations and large-scale point cloud datasets, significantly increasing computational costs. Furthermore, their heavy reliance on high-quality annotated data restricts their use in high-throughput settings. To address these limitations, we propose a novel multi-stage region-growing algorithm based on an octree structure for efficient stem-leaf segmentation in maize point cloud data. The method first extracts key geometric features through octree voxelization, significantly improving segmentation efficiency. In the region-growing phase, a preliminary structural segmentation strategy using fitted cylinder parameters is applied. A refinement strategy is then applied to improve segmentation accuracy in complex regions. Finally, stem segmentation consistency is enhanced through central axis fitting and distance-based filtering. In this study, we utilize the Pheno4D dataset, which comprises three-dimensional point cloud data of maize plants at different growth stages, collected from greenhouse environments. Experimental results show that the proposed algorithm achieves an average precision of 98.15% and an IoU of 84.81% on the Pheno4D dataset, demonstrating strong robustness across various growth stages. Segmentation time per instance is reduced to 4.8 s, offering over a fourfold improvement compared to PointNet while maintaining high accuracy and efficiency. Additionally, validation experiments on tomato point cloud data confirm the proposed method’s strong generalization capability. In this paper, we present an algorithm that addresses the shortcomings of traditional methods in complex agricultural environments. Specifically, our approach improves efficiency and accuracy while reducing dependency on high-quality annotated data. This solution not only delivers high precision and faster computational performance but also lays a strong technical foundation for high-throughput crop management and precision breeding. Full article

The performance of the services provided by Global Navigation Satellite Systems (GNSSs) can be seriously degraded by the presence of wireless interferences, and Machine Learning (ML) has been applied to address this problem using the digital artifacts generated by the GNSS receiver. While such an application is not novel in the literature, the analysis of the impact of the bit-depth at which the GNSS signal is recorded has not received significant attention. The type and power level of the wireless interference are also important factors to investigate in this context. This paper addresses this gap by performing an extensive analysis of the impact of these factors on a data set of GNSS signals subject to three different types of wireless interferences with ML and DL algorithms. The analysis is a combination of a pre-processing phase where the Carrier-to-Noise Ratio (CNR) values of different satellites are evaluated, the extraction of relevant features for ML, and the application of a Convolutional Neural Network (CNN) with a multi-head attention layer. The results show that the proposed approach is able to detect the presence of interference with great accuracy (e.g., 99%) but the type of interference and bit-depth can decrease the performance. Full article

The injection of CO₂ into coal reservoirs occurs in its supercritical state (ScCO₂), which significantly alters the pore structure and chemical composition of coal, thereby influencing the adsorption and diffusion behavior of methane (CH₄). Understanding these changes is crucial for optimizing CH₄ extraction and improving CO₂ sequestration efficiency. This study aims to investigate the effects of ScCO₂ on the pore structure, chemical bonds, and CH₄ diffusion mechanisms in bituminous coal to provide insights into coal reservoir stimulation and CO₂ storage. By utilizing high-pressure CO₂ injection adsorption, low-pressure CO₂ gas adsorption (LP-CO₂-GA), Fourier-transform infrared spectroscopy (FTIR), and reactive force field molecular dynamics (ReaxFF-MD) simulations, this study examines the multi-scale changes in coal at the nano- and molecular levels. The following results were found: Pore Structure Evolution: After ScCO₂ treatment, micropore volume increased by 19.1%, and specific surface area increased by 11.2%, while mesopore volume and specific surface area increased by 14.4% and 5.7%, respectively. Chemical Composition Changes: The content of aromatic structures, oxygen-containing functional groups, and hydroxyl groups decreased, while aliphatic structures increased. Specific molecular changes included an increase in (CH₂)_n, 2H, 1H, and secondary alcohol (-C-OH) and phenol (-C-O) groups, while Car-Car and Car-H bonds decreased. Mechanisms of Pore Volume Changes: The pore structure evolves through three distinct phases: Swelling Phase: Breakage of low-energy bonds generates new micropores. Aromatic structure expansion reduces intramolecular spacing but increases intermolecular spacing, causing a decrease in micropore volume and an increase in mesopore volume. Early Dissolution Phase: Continued bond breakage increases micropore volume, while released aliphatic and aromatic structures partially occupy these pores, converting some mesopores into micropores. Later Dissolution Phase: Minimal chemical bond alterations occur, but weakened π-π interactions and van der Waals forces between aromatic layers result in further mesopore volume expansion. Impact on CH₄ Diffusion: Changes in pore volume directly affect CH₄ migration. In the early stages of ScCO₂ interaction, pore shrinkage reduces the mean square displacement (MSD) and self-diffusion coefficient of CH₄. However, as the reaction progresses, pore expansion enhances CH₄ diffusion, ultimately improving gas extraction efficiency. This study provides a fundamental understanding of how ScCO₂ modifies coal structure and CH₄ transport properties, offering theoretical guidance for enhanced CH₄ recovery and CO₂ sequestration strategies. 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

To mitigate the impact on detection performance caused by insufficient input information in 3D object detection based on single LiDAR data, this study designs three innovative modules based on the PointRCNN framework. Firstly, addressing the issue of the Multi-Layer Perceptron (MLP) in PointNet++ failing to effectively capture local features during the feature extraction phase, we propose the Adaptive Multilayer Perceptron (AMLP). Secondly, to prevent the problem of gradient vanishing due to the increased parameter scale and computational complexity of AMLP, we introduce the Channel Aware Residual module (CA-Res) in the feature extraction layer. Finally, in the head layer of the subsequent processing stage, we propose the Dynamic Attention Head (DA-Head) to enhance the representation of key features in the process of target detection. A series of experiments conducted on the KITTI validation set demonstrate that in complex scenarios, for the small target “Pedestrian”, our model achieves performance improvements of 2.08% and 3.46%, respectively, at the “Medium” and “Difficult” detection difficulty levels. To further validate the generalization capability of the Enhanced DetNet network, we deploy the trained model on the KITTI server and conduct a comprehensive evaluation of detection performance for the “Car”, “Pedestrian”, and “Cyclist” categories. Full article

Unmanned Aerial Vehicles (UAVs) play an extremely important role in real-time object detection for maritime emergency rescue missions. However, marine accidents often occur in low-visibility weather conditions, resulting in poor image quality and a lack of object detection samples, which significantly reduces detection accuracy. To tackle these issues, we propose GD-Det, a low-data object detection model with high accuracy, specifically designed to handle limited sample sizes and low-quality images. The model is primarily composed of three components: (i) A lightweight re-parameterization feature extraction module which integrates RepVGG blocks into multi-concat blocks to enhance the model’s spatial perception and feature diversity during training. Meanwhile, it reduces computational cost in the inference phase through the re-parameterization mechanism. (ii) A cross-scale gather-and-distribute pyramid module, which helps to augment the relationship representation of four-scale features via flexible skip fusion and distribution strategies. (iii) A decoupled prediction module with three branches is to implement classification and regression, enhancing detection accuracy by combining the prediction values from tri-level features. (iv) We also use a domain-adaptive training strategy with knowledge transfer to handle low-data issues. We conducted low-data training and comparison experiments using our constructed dataset AFO-fog. Our model achieved an overall detection accuracy of 84.8%, which is superior to other models. Full article

Magnetotelluric (MT) forward modeling is a key technique in magnetotelluric sounding, and deep learning has been widely applied to MT forward modeling. In three-dimensional (3-D) problems, although existing methods can predict forward modeling results with high accuracy, they often use multiple networks to simulate multiple forward modeling parameters, resulting in low efficiency. We apply multi-task learning (MTL) to 3-D MT forward modeling to achieve simultaneous inference of apparent resistivity and impedance phase, effectively improving overall efficiency. Furthermore, through comparative analysis of feature map differences in various decoder layers of the network, we identify the optimal branching point for multi-task learning decoders. This enhances the feature extraction capabilities of the network and improves the prediction accuracy of forward modeling parameters. Additionally, we introduce an uncertainty-based loss function to dynamically balance the learning weights between tasks, addressing the shortcomings of traditional loss functions. Experiments demonstrate that compared with single-task networks and existing multi-task networks, the proposed network (MT-FeatureNet) achieves the best results in terms of Structural Similarity Index Measure (SSIM), Mean Relative Error (MRE), and Mean Absolute Error (MAE). The proposed multi-task learning model not only improves the efficiency and accuracy of 3-D MT forward modeling but also provides a novel approach to the design of multi-task learning network structures. Full article

Wi-Fi-based human activity recognition (HAR) is a non-intrusive and privacy-preserving method that leverages Channel State Information (CSI) for identifying human activities. However, existing approaches often struggle with robust feature extraction, especially in dynamic and multi-environment scenarios, and fail to effectively integrate amplitude and phase features of CSI. This study proposes a novel model, the Phase–Amplitude Channel State Information Network (PA-CSI), to address these challenges. The model introduces two key innovations: (1) a dual-feature approach combining amplitude and phase features for enhanced robustness, and (2) an attention-enhanced feature fusion mechanism incorporating multi-scale convolutional layers and Gated Residual Networks (GRN) to optimize feature extraction. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on three datasets, including StanWiFi (99.9%), MultiEnv (98.0%), and the MINE lab dataset (99.9%). These findings underscore the potential of the PA-CSI model to advance Wi-Fi-based HAR in real-world applications. Full article

The objective is to address the issue of gas-carrying particles generated by erosion wear problems in the transportation process of gas storage reservoir pipelines. In accordance with the principles of the multiphase flow theory, the particle discrete phase model, high temperature, high pressure, water volume fraction, and other pertinent factors, this paper presents a three-phase gas–liquid–solid erosion mathematical model of a three-way gas storage reservoir. The effects of temperature, pressure, water content volume fraction, gas extraction, particle mass flow rate, and particle size on the tee’s erosion location and erosion rate were investigated based on this model. The findings indicate that, as the pressure and temperature decline, the maximum erosion rate of the tee exhibits a decreasing trend. Gas storage reservoir water production is relatively low, and its maximum erosion rate of the tee exerts a negligible influence. Conversely, the maximum erosion rate of the tee is significantly influenced by the gas extraction rate, exhibiting an exponential relationship with the maximum erosion rate and the rate of gas extraction. It was observed that, when the volume of gas extracted exceeded 70 × 10⁴ m³/d, the maximum erosion rate of the tee exceeded the critical erosion rate of 0.076 mm/a. The maximum erosion rate of the tee caused by the sand mass flow rate remained relatively constant. However, the maximum erosion rate of the tee exhibited a linear correlation with the salt mass flow rate and the maximum erosion rate. The maximum erosion rate of the tee is greater than the critical erosion rate of 0.076 mm/a when the gas extraction volume is greater than 37.3 × 10⁴ m³/d and the salt mass flow rate is greater than approximately 25 kg/d. As the sand and salt particle sizes increase, the maximum erosion rate of the tee initially rises, then declines, and finally stabilizes. The findings of this study offer valuable insights into the mechanisms governing tee erosion under elevated temperatures and pressures within storage reservoirs. Full article

The characteristics of multi-directional rotation and high aspect ratio of targets such as shipwrecks lead to low detection accuracy and difficulty localizing existing detection models for this target type. Through our research, we design three main inconsistencies in rotating target detection compared to traditional target detection, i.e., inconsistency between target and anchor frame, inconsistency between classification features and regression features, and inconsistency between rotating frame quality and label assignment strategy. In this paper, to address the discrepancies in the above three aspects, we propose the Side-scan Sonar Dynamic Rotating Target Detector (S³DR-Det), which is a model with a dynamic rotational convolution (DRC) module designed to effectively gather rotating targets’ high-quality features during the model’s feature extraction phase, a feature decoupling module (FDM) designed to distinguish between the various features needed for regression and classification in the detection phase, and a dynamic label assignment strategy based on spatial matching prior information (S-A) specific to rotating targets in the training phase, which can more reasonably and accurately classify positive and negative samples. The three modules not only solve the problems unique to each stage but are also highly coupled to solve the difficulties of target detection caused by the multi-direction and high aspect ratio of the target in the side-scan sonar image. Our model achieves an average accuracy (AP) of 89.68% on the SSUTD dataset and 90.19% on the DNASI dataset. These results indicate that our model has excellent detection performance. Full article

Gas reservoirs play an increasingly important role in oil and gas consumption and safety in China. To study the problem of erosion and wear caused by gas-carrying particles in the process of gas extraction from gas storage reservoirs, a mathematical model of gas–liquid–solid three-phase erosion of gas storage reservoir columns was established through theories of multiphase flow and particle motion. Based on this model, the effects of the water volume fraction, gas extraction rate, particle mass flow rate, particle size, and bending angle on the erosion location and rate of the pipe columns were investigated. The findings indicate that when the water content volume fraction is low, the water production volume minimally affects the maximum erosion rate of pipe columns. Conversely, the gas extraction rate exerted the most significant influence on the column erosion, showing a power function relationship between the two. When gas extraction volume exceeds 60 × 10⁴ m³/d, the maximum erosion rate surpasses the critical erosion rate of 0.076 mm/a. This coincided with the increased sand mass flow rate, although the maximum erosion rate of the pipe columns remained relatively steady. The salt mass flow rate demonstrated a linear relationship with the erosion rate, with the maximum erosion rate exceeding the critical erosion rate of 0.076 mm/a. The maximum erosion rate of the pipe columns increased, stabilized with larger sand and salt particle sizes, and exhibited an increasing trend with the bending angle. For gas extraction volumes exceeding 46.4 × 10⁴ m³/d and salt mass flow rates exceeding 22 kg/d, the maximum erosion rate of pipe columns exceeds the critical erosion rate of 0.076 mm/a. The conclusions of this study are of some importance for the clarification of the influencing law of pipe column erosion under high temperature and high pressure in gas storage reservoirs and for the formulation of measures for the prevention and control of pipe column erosion in gas storage reservoirs. Full article

Pine wilt disease (PWD) is a highly destructive worldwide forest quarantine disease that has the potential to destroy entire pine forests in a relatively brief period, resulting in significant economic losses and environmental damage. Manual monitoring, biochemical detection and satellite remote sensing are frequently inadequate for the timely detection and control of pine wilt disease. This paper presents a fusion model, which integrates the Mamba model and the attention mechanism, for deployment on unmanned aerial vehicles (UAVs) to detect infected pine trees. The experimental dataset presented in this paper comprises images of pine trees captured by UAVs in mixed forests. The images were gathered primarily during the spring of 2023, spanning the months of February to May. The images were subjected to a preprocessing phase, during which they were transformed into the research dataset. The fusion model comprised three principal components. The initial component is the Mamba backbone network with State Space Model (SSM) at its core, which is capable of extracting pine wilt features with a high degree of efficacy. The second component is the attention network, which enables our fusion model to center on PWD features with greater efficacy. The optimal configuration was determined through an evaluation of various attention mechanism modules, including four attention modules. The third component, Path Aggregation Feature Pyramid Network (PAFPN), facilitates the fusion and refinement of data at varying scales, thereby enhancing the model’s capacity to detect multi-scale objects. Furthermore, the convolutional layers within the model have been replaced with depth separable convolutional layers (DSconv), which has the additional benefit of reducing the number of model parameters and improving the model’s detection speed. The final fusion model was validated on a test set, achieving an accuracy of 90.0%, a recall of 81.8%, a map of 86.5%, a parameter counts of 5.9 Mega, and a detection speed of 40.16 FPS. In comparison to Yolov8, the accuracy is enhanced by 7.1%, the recall by 5.4%, and the map by 3.1%. These outcomes demonstrate that our fusion model is appropriate for implementation on edge devices, such as UAVs, and is capable of effective detection of PWD. Full article