Local Method Research Articles

A Spaceborne Passive Localization Algorithm Based on MSD-HOUGH for Multiple Signal Sources

Recently, the passive synthetic aperture (PSA) technique has been used in passive localization to improve the position accuracy of single source by estimating the Doppler parameter of the received signal. However, in the presence of multiple sources, time-frequency aliasing will lead to serious cross-term interference during Doppler signal extraction, resulting in low localization performance. To solve this problem, a spaceborne passive synthetic aperture localization algorithm based on the multiple-stay detector HOUGH transform (MSD-HOUGH) is proposed in this paper. Firstly, an improved convolutional neural network based on the adaptive histogram equalization method (AHE-CNN) is proposed to achieve source number estimation. Then, the PSA Doppler equations are established in the HOUGH domain, which can suppress the cross-term interference of the multiple emitters. Meanwhile, a multiple-stay detector (MSD) is designed to reduce the pseudo-peaks in HOUGH domain. The estimated source number determines when the MSD will be terminated. Finally, a PSA cost function is established based on the estimated Doppler parameter to achieve signal source localization. Experimental results show that compared with other localization methods, the proposed algorithm has a significant improvement for multiple signal source localization.

A Method for Fault Localization in Distribution Networks with High Proportions of Distributed Generation Based on Graph Convolutional Networks

To address the issues arising from the integration of a high proportion of distributed generation (DG) into the distribution network, which has led to the transition from traditional single-source to multi-source distribution systems, resulting in increased complexity of the distribution network topology and difficulties in fault localization, this paper proposes a fault localization method based on graph convolutional networks (GCNs) for distribution networks with a high proportion of distributed generation. By abstracting busbars and lines into graph structure nodes and edges, GCN captures spatial coupling relationships between nodes, using key electrical quantities such as node voltage magnitude, current magnitude, power, and phase angle as input features to construct a fault localization model. A multi-type fault dataset is generated using the Matpower toolbox, and model training is evaluated using K-fold cross-validation. The training process is optimized through early stopping mechanisms and learning rate scheduling. Simulations are conducted based on the IEEE 33-node distribution network benchmark, with photovoltaic generation, wind generation, and energy storage systems connected at specific nodes, validating the model’s fault localization capability under various fault types (single-phase ground fault, phase-to-phase short circuit, and line open circuit). Experimental results demonstrate that the proposed model can effectively locate fault nodes in complex distribution networks with high DG integration, achieving an accuracy of 98.5% and an AUC value of 0.9997. It still shows strong robustness in noisy environments and is significantly higher than convolutional neural networks and other methods in terms of model localization accuracy, training time, F1 score, AUC value, and single fault detection inference time, which has good potential for practical application.

The Ground-Penetrating Radar Image Matching Method Based on Central Dense Structure Context Features

Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively.

An ANN-Based Method for On-Load Tap Changer Control in LV Networks with a Large Share of Photovoltaics—Comparative Analysis

The paper proposes a new local method of controlling the on-load tap changer (OLTC) of a transformer to mitigate negative voltage phenomena in low-voltage (LV) networks with a high penetration of photovoltaic (PV) installations. The essence of the method is the use of the load compensation (LC) function with settings determined via artificial neural network (ANN) algorithms. The proposed method was compared with other selected local methods recommended in European regulations, in particular with those currently required by Polish distribution system operators (DSOs). Comparative studies were performed using the model of the 116-bus IEEE test network, taking into account the unbalance in the network and the voltage variation on the medium voltage (MV) side.

Quadrotor Trajectories Optimisation using Metaheuristic Algorithm in Python

Quadrotors, known for their agility and ability to perform intricate manoeuvres, have become increasingly popular in the last decade. Precisely tracking complex flight paths has been a major research focus. While trajectory optimization offers a continuous search space and allows for utilizing problem-specific knowledge, its reliance on local optimization methods necessitates a global planner for generating feasible trajectories from arbitrary starting points to goals. Designing such trajectories for quadrotors, with their five degrees of freedom and intricate dynamic constraints (e.g., limitations on state variables), presents a significant challenge. Existing global planners struggle to solve these trajectory generation problems for complex dynamical systems in a practical timeframe. This paper explores the use of metaheuristic optimization for planning quadrotor flight trajectories, specifically focusing on the FSA and FSASCA algorithms. These methods have demonstrated promising results in specific scenarios, but their performance depends heavily on the application and its constraints. The paper emphasizes a trade-off between solution quality and execution speed: seeking faster execution may require sacrificing solution quality, while prioritizing optimality may lead to longer execution times.

Unexpected Anti-tumor Effect of Selective Internal Radiation Therapy and Radiofrequency Ablation Followed by Immune Checkpoint Inhibitors for Hepatocellular Carcinoma

Introduction: Hepatocellular carcinoma (HCC) has become an increasingly serious health problem worldwide. Combining immune checkpoint inhibitors (ICIs) for HCC with local treatments can produce synergistic effects. Although the abscopal effect was noted in 1953, most studies on this phenomenon have been conducted in the context of external beam radiotherapy (EBRT). The incidence of the abscopal effect induced by selective internal radiation therapy (SIRT) alone is lower. This research reports a case of a patient with multifocal HCC who received SIRT plus radiofrequency ablation (RFA) followed by ICI therapy. During the subsequent course of ICI therapy, an anti-tumor effect was detected in the left hepatic lobe, far from the site targeted by the local treatments. Case Presentation: The abscopal effect has recently attracted widespread attention because ICIs can enhance this phenomenon. However, little is known about the relationship between inducing the abscopal effect and local treatment against HCC. A 76-year-old male patient presented with multifocal HCC. During hepatic angiography, MRI showed multiple HCCs in both lobes of the liver. Selective internal radiation therapy was performed on a tumor with a diameter of approximately 80 millimeters in the right lobe. A month later, RFA was conducted on another tumor in the right lobe, approximately 30 mm in diameter, and ICI therapy was administered on the second day post-RFA. Hepatic arterial phase imaging of contrast-enhanced magnetic resonance performed 10 months after SIRT showed the disappearance of the tumor in the left liver lobe. Conclusions: This case highlights that the combination of multiple local treatment methods with ICI therapy can enhance the anti-tumor immune response and benefit patients with multifocal

Benchmarking of monocular camera UAV-based localization and mapping methods in vineyards

UAVs equipped with various sensors offer a promising approach for enhancing orchard management efficiency. Up-close sensing enables precise crop localization and mapping, providing valuable a priori information for informed decision-making. Current research on localization and mapping methods can be broadly classified into SfM, traditional feature-based SLAM, and deep learning-integrated SLAM. While previous studies have evaluated these methods on public datasets, real-world agricultural environments, particularly vineyards, present unique challenges due to their complexity, dynamism, and unstructured nature.To bridge this gap, we conducted a comprehensive study in vineyards, collecting data under diverse conditions (flight modes, illumination conditions, and shooting angles) using a UAV equipped with high-resolution camera. To assess the performance of different methods, we proposed five evaluation metrics: efficiency, point cloud completeness, localization accuracy, parameter sensitivity, and plant-level spatial accuracy. We compared two SLAM approaches against SfM as a benchmark.Our findings reveal that deep learning-based SLAM outperforms SfM and feature-based SLAM in terms of position accuracy and point cloud resolution. Deep learning-based SLAM reduced average position error by 87% and increased point cloud resolution by 571%. However, feature-based SLAM demonstrated superior efficiency, making it a more suitable choice for real-time applications. These results offer valuable insights for selecting appropriate methods, considering illumination conditions, and optimizing parameters to balance accuracy and computational efficiency in orchard management activities.

A local discontinuous Galerkin methods with local Lax-Friedrichs flux and modified central flux for one dimensional nonlinear convection-diffusion equation

In this paper, we study the local discontinuous Galerkin (LDG) method for one-dimensional nonlinear convection-diffusion equation. In the LDG scheme, local Lax-Friedrichs numerical flux is adopted for the convection term, and a modified central flux is proposed and applied to the nonlinear diffusion coefficient. The modified central flux overcomes the shortcomings of the traditional flux, and it is beneficial in proving the L2 stability of the LDG scheme. By virtue of the Gauss-Radau projections and the local linearization technique, the optimal error estimates are also obtained. Numerical experiments are presented to confirm the validity of the theoretical results.

A Reliable Robot Localization Method Using LiDAR and GNSS Fusion Based on a Two-Step Particle Adjustment Strategy

A Reliable Robot Localization Method Using LiDAR and GNSS Fusion Based on a Two-Step Particle Adjustment Strategy

Location Method of Buried Polyethylene Gas Pipeline Based on Acoustic Signal Ellipse Method.

This study proposes a buried PE gas pipeline positioning method based on the elliptical method of an acoustic signal analysis. The cross-correlation time delay positioning technology is combined with the elliptical equation, forming an effective mechanism for pipeline depth positioning. First, a dual-tree complex wavelet transform is employed to denoise the collected signals, enhancing the quality and accuracy of the data. Subsequently, the cross-correlation function is utilized to extract the delay times between the signals. The obtained delay times are then substituted into the elliptical equation to calculate the depth of the buried PE pipeline. Based on this theoretical framework, a simulation model is established in COMSOL, and positioning simulation analyses are conducted under three different conditions: pipeline depth, relative sensor positions, and distances between sensors and excitation points. The simulation results indicate that a clear correlation exists between the signal delay time and the pipeline position, with simulation errors controlled within 5%, thus validating the theoretical feasibility of the method. To further assess the effectiveness of this approach, an experimental testing system is constructed. The experimental study was carried out under four different conditions: pipeline burial depth, relative sensor positions, distances between sensors and excitation points, and excitation frequencies. The experimental results demonstrate that these factors significantly affect the pipeline depth positioning. The comparison results show that the method has a high accuracy in depth positioning, with experimental errors controlled within 10%. This study proves that accurate positioning of pipeline depth could be achieved by substituting signal delay times into the elliptical equation, thereby validating the method's feasibility in practical applications. The proposed method effectively addressed the shortcomings of existing pipeline depth positioning technologies, providing important theoretical support and a practical reference for future pipeline positioning research.

A reinforcement learning-enhanced multi-objective iterated greedy algorithm for weeding-robot operation scheduling problems

With technological advancements, robots have been widely used in various fields and play a vital role in the production execution system of a smart farm. However, the operation scheduling problem of robots within production execution systems has not received much attention so far. To enable efficient management, this paper develops a multi-objective mathematical model concerning both the efficiency and economic indicators. We propose a population-based iterated greedy algorithm enhanced with Q-learning (Q_DPIG) for a multi-weeding-robots operation scheduling problem. An index-based heuristic (IBH) is designed to generate a diverse set of initial solutions, while an adaptive destruction phase, guided by the Q-learning framework, ensures effective neighborhood search and solution optimization. Additionally, a local search method focusing on the high-load and the critical robots is employed to further optimize the two objectives. Finally, Q_DPIG is demonstrated to be effective and significantly outperform the state-of-the-art algorithms through comprehensive test datasets and a real case study from a farmland management center.

Fault detection method for electric vehicle battery pack based on improved kurtosis and isolation forest

ABSTRACT Early detection of abnormal battery characteristics is crucial for ensuring personnel safety and minimizing property damage. Conventional fault diagnosis methods often struggle to detect minor faults in their early stages. To address this challenge, this paper proposes a fault diagnosis method for lithium-ion batteries in electric vehicles that utilizes real-world operational data. Initially, a wavelet threshold denoising algorithm is used to effectively remove voltage data noise while retaining fault characteristics. Subsequently, an early fault warning method based on improved kurtosis index is proposed, which is capable of capturing the weak abnormal features of the battery and issuing warnings. Furthermore, a faulty battery cell localization method based on two-dimension feature extraction and Isolation Forest algorithm is proposed to capture minor fault features and accurately locate faulty cells. Finally, experiments conducted on four operational electric vehicles demonstrated that the early fault warning method could promptly detect faults and issue alarms, while the faulty battery cell positioning method accurately identified the faulty cell, achieving 100 % Accuracy and 0 % False Positive Rate. This confirms the effectiveness of the proposed method. Additionally, Bootstrap was used to evaluate the performance metrics, further verifying the robustness of the method.

Online Learning and Decision Making Under Generalized Linear Model with High-Dimensional Data

We propose a minimax concave penalized multiarmed bandit algorithm under the generalized linear model (G-MCP-Bandit) for decision-makers facing high-dimensional data in an online learning and decision-making environment. We demonstrate that in the data-rich regime, the G-MCP-Bandit algorithm attains the optimal cumulative regret in the sample size dimension and a tight bound in the covariate dimension and the significant covariate dimension. In the data-poor regime, the G-MCP-Bandit algorithm maintains a tight regret upper bound. In addition, we develop a local linear approximation method, the two-step weighted Lasso procedure, to identify the minimax concave penalty (MCP) estimator for the G-MCP-Bandit algorithm when samples are not independent and identically distributed. Under this procedure, the MCP estimator can match the oracle estimator with high probability and converge to the true parameters at the optimal convergence rate. Finally, through experiments based on both synthetic and real data sets, we show that the G-MCP-Bandit algorithm outperforms other benchmarking algorithms in terms of cumulative regret and that the benefits of the G-MCP-Bandit algorithm increase in the data’s sparsity level and the size of the decision set. This paper was accepted by J. George Shanthikumar, big data analytics. Funding: This work was supported by the National Natural Science Foundation of China (NSFC) [Grants W2441021, 72371172, 72342023, and 71929101] and the National Science Foundation (NSF) [Grant DMS 1820702]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01557 .

Two-Dimensional Radiographic Diagnosis of Maxillary Canine Impactions.

Background: The primary aim of this study was to evaluate, using conventional radiographic imaging, the height, angulation, and mesiodistal position of impacted maxillary canines to determine if these variables are significantly associated with the palatal or buccal positioning of the tooth. Methods: A retrospective analysis was conducted on all patients diagnosed and treated for at least one impacted maxillary canine over a 4-year period in the Principality of Asturias. The final sample included 159 impacted canines. The variables analyzed were age, gender, associated pathology, location, angulation, height, mesiodistal position, buccopalatal position, and treatment method. Follow-up periods ranged from 12 to 50 months. Results: The mean age of the patients was 21 years (range 10-41 years), with most cases occurring in patients aged 14 to 30 years. Females accounted for 66.1% of the sample, with a female-to-male ratio of 1.95:1. The type of treatment (extraction/traction, surgical-orthodontic approach) was significantly associated with patient age. Additionally, the proximity of the impacted canine crown to the palatal or buccal cortices was significantly related to the treatment choice. However, no significant relationship was observed between the palatal/buccal position of the canine crown and its height or angulation. Conclusions: In the present work, the upper canines included by the palatal did not have a greater height or angulation than the canines included by the vestibule.

Comparative study of the therapeutic effects of radiofrequency ablation of ganglionated plexi guided by high-frequency stimulation and anatomical localization methods in the treatment of vagal syncope in young people.

To investigate the differences in safety and efficacy between high-frequency stimulation (HFS) and anatomically guided endocardial catheter ablation (AA) of the ganglionated plexi (GPs) for treating vasovagal syncope (VVS) in individuals engaged in high-intensity physical training. Forty-five patients (age 22.5 ± 4.4 years) undergoing high-intensity physical training were included from January 2020 to January 2023 at our hospital. Patients underwent GP ablation for recurrent syncope. Comprehensive evaluations, including head MRI, cardiac ultrasound, electrocardiogram (ECG), ambulatory ECG (Holter), ambulatory blood pressure monitoring, plate motion tests, and head-up tilt tests (HUT), were conducted to exclude other systemic disorders causing syncope. HFS and AA-guided GP ablation were performed on 10 and 35 patients, respectively, all of whom tested positive for HUT. Differences between the two groups were compared regarding ablation sites, ablation time, safety, and effectiveness. The ablation time was significantly shorter in the AA group compared to the HFS group (P < 0.001). The number of GPs selected for ablation using the AA method was reduced (P < 0.001). All patients in the HFS group experienced palpitations and discomfort, whereas only 31.43% of patients in the AA group reported these symptoms (P = 0.001). Fentanyl analgesia was administered in both groups, and no significant complications arose from the ablation. The longest follow-up duration was 52 months, while the shortest was 15 months. One case of pre-syncope occurred in the HFS group 8 months post-ablation, and one case of pre-syncope and two cases of syncope occurred in the AA group at 1 and 3 months post-ablation, respectively. There were no statistically significant differences in heart rate variability (HRV) and cardiac deceleration capacity (DC) between the two groups after ablation (P > 0.05). Two cases in the AA group still exhibited type II second-degree atrioventricular block during sleep. Both groups of patients were able to complete high-intensity physical training and showed significant symptom improvement post-ablation. Young individuals with VVS engaged in high-intensity physical training can benefit from GP ablation using both HFS and AA methods. The AA method requires relatively simple equipment, shorter procedure time, and results in less discomfort during the ablation.

A new target color adaptive graying and segmentation method for gear contact spot detection

Abstract Gear contact spot plays a crucial role in evaluating gear mesh quality. Traditionally, tooth surfaces of gear pairs have been manually brushed with red lead powder and visually inspected after a certain operating period. To enhance detection accuracy and efficiency, computer vision has emerged as an appealing approach for gear contact spot detection. However, determining the graying weights and segmentation threshold poses challenges, particularly in non-ideal illumination environments. To address this problem, this paper proposes a new target color adaptive graying and segmentation (TC-AGS) method for the gear contact spot detection. With the proposed method, the illumination scene is firstly distinguished by the blue color component weight due to its high sensitivity to illumination intensity. Then, an improved adaptively graying algorithm for target color is employed to determine the graying weights for RGB, ensuring the maximum proportion of the desired red color. Finally, either a global threshold method or a local threshold method is selected for image segmentation and contact spot detection according to the illumination intensity. To validate the effectiveness of the proposed method, comprehensive simulation and experimental tests were conducted. The results demonstrate that the proposed method achieves better gear contact spot detection performance than traditional methods. It exhibits higher extraction accuracy under different illumination environments and even in the presence of blurred image sources.

Optimized agricultural management reduces global cropland nitrogen losses to air and water.

Nitrogen (N) losses from croplands substantially contribute to global N pollution. Assessing the reduction in N losses through improved N management practices is complex due to varying site conditions, such as land use, climate, soil properties and local farming methods. In this Article, we conducted a meta-analysis to evaluate the effects of improved practices on N loss reduction, analysing data from 1,065 studies with 6,753 pairs of observations comparing standard and optimized practices. Without considering site-specific conditions, optimized management practices can reduce N2O emissions by 3-39%, NH3 emissions by 15-68%, N run-off by 21-37% and N leaching by 19-52%. After considering local conditions and current practices, average reductions on a global scale were 31% for N2O, 23% for NH3, 18% for N run-off and 17% for N leaching. The effectiveness of N loss reduction was mainly influenced by optimized management practices and, to a lesser extent, site conditions. The results of this study underscore the importance of implementing optimized, site-specific management to effectively reduce N losses from global croplands.

A momentum-conserving wake superposition method for wind-farm flows under pressure gradient

Pressure gradient over topography will significantly affect wind-farm flow. However, knowledge gaps still exist on how to superpose wind-turbine wakes in the wind-farm flow analytical model to account for this effect, leading to systematic errors in evaluating wind-farm wake effects. To this end, we derive an implicit momentum-conserving wake superposition method under pressure gradient (PG-IMCM) based on the total momentum deficit equation, which is linearised by the convection velocity introduced by Zong &amp; Porté-Agel (J. Fluid Mech., vol. 889, 2020, A8). The PG-IMCM method consists of the linear-weighted sum of individual velocity deficits, the sum of the individual pressure correction terms and the total pressure correction term. Based on a sensitivity analysis, we demonstrate that the last two terms nearly cancel out and, thus, can be neglected, resulting in a simplified form, which has the same form as its counterpart under zero pressure gradient but with the single-wake quantities redefined based on the wake model under pressure gradient. This motivates us to further examine the performance of the combination of five empirical superposition methods and the stand-alone wake model under pressure gradient. Validation results based on large-eddy simulation show that PG-IMCM has an overall satisfactory performance in both the magnitude and shape of the velocity-deficit profiles, provided that the stand-alone turbine wake can be modelled accurately, which is virtually identical with its simplified form. Further comparison with empirical superposition methods shows that local linear and wind product superposition methods based on the updated base flow also have comparable performance, with only discernable differences with the PG-IMCM method in the near-wake region of downstream turbines. Therefore, they are two attractive methods for engineering applications.

Geometric feature extraction and its profile accuracy evaluation for pockets on curved aircraft skin from scanned point clouds

Abstract Dimension inspection is crucial for aircraft skin manufacturing and assembly. To meet weight loss and other functional requirements, large numbers of shallow pockets are distributed on the surface of thin-walled aircraft skin. It is difficult to evaluate the profile accuracy of the pockets, as the aircraft skin is composed of curved surfaces and its actual geometry is inconsistent with the nominal model. This study proposes a method to evaluate the profile and position accuracy for the pockets on aircraft skin utilizing point clouds obtained by 3D scanners. Firstly, the feature contour lines are identified and extracted from the scanned aircraft skin surface point clouds by adopting the local curvature-aware method and minimum bounding rectangle algorithm. Then, an algorithm is proposed to mitigate the contour identification deviations of the segmented area by employing the shortest geodesic distance and transition zone section projection, so as to enhance the measurement accuracy. Finally, the viability of the proposed method for evaluating pockets on aircraft skin is verified by the simulation, and the reliability and robustness of the proposed method are verified by the simulation and experiment. Experimental results show that the measurement accuracy of the pocket can be improved from 0.5 mm to 0.0505 mm, compared with traditional specialized conformation fixture methods.

STGRL: SNN based two-stage geomagnetic road localization method

Abstract Geomagnetic navigation is a widely used positioning method capable of correcting the cumulative errors of odometers and inertial navigation systems, thereby ensuring long-distance positioning for vehicles in GPS-denied environments. However, common geomagnetic road navigation algorithms are susceptible to measurement noise, which hinder improvements in positioning efficiency and accuracy. To address this issue, this paper proposes a Siamese Neural Network (SNN) based two-stage geomagnetic road localization method. First, attitude angle information is combined with geomagnetic scalar and vector value to establish geomagnetic reference database to increase the feature dimensions of geomagnetic matching. Then, we use the Random Forest algorithm to perform a coarse matching of the data sequence to determine the current road, balancing the increased computational load resulting from the addition of feature dimensions. Finally, to further reduce the impact of random noise, this paper employs the SNN algorithm based on Transformer Encoder for fine matching of the data sequence. Experiments show that compared to existing methods, the average absolute positioning error of our algorithm has been reduced from 32.36 m to 4.07 m, and the increase in computational load is kept within an acceptable range.