Localization Accuracy Research Articles

An Approach to Calculate Source Locations for Thicker Plates when Acoustic Emission Signals are Dominated by Trailing Waves

In many acoustic emission (AE) applications, signal arrival times from an array of sensors are coupled with an appropriate velocity to calculate the source location of AE events. To date, the AE signals present in these applications are Lamb and/or Rayleigh waves. In a previous publication [1], finite element modeled (FEM) AE signals in thicker plates were dominated by trailing wave (TW) packets, when no Rayleigh wave was present. The typical character of the TW dominated signals was described as a spaced-in-time train of short-in-time wave packets. Further, it was pointed out, that when TW dominate the signals, it is not clear how arrival times for source location calculations might be obtained along with an appropriate propagation velocity. The goal of this work is to develop a source location approach for cases of dominant TWs in the AE signals in thicker plates. The FEM-signal database from the previous publication was used for this study. The database consisted of in-plane dipole sources at five different depths and four different steel plate thicknesses (50, 75, 100 and 125 mm). Bottom-surface out-of-plane displacement signals versus time for propagation distances from 250 to 1500 mm were examined. The FEM signals were frequency filtered to examine a broadband case of 80 to 500 kHz and a narrowband case of 100 to 300 kHz. To study the potential source location accuracy, a total of seven four-sensor arrays were employed. The study results describe a process to select appropriate arrival times and obtain a propagation “velocity.” This information was used to calculate source locations for the sensor arrays. The location errors were shown to be less than the plate thickness.

Lightning Location Method Using Orthogonal Coded Polarization Optical Time-Domain Reflectometer in Optical Fiber Transmission

Lightning strike is one of the main factors affecting power transmission lines and may lead to broken strands and damage of transmission lines, resulting in abnormal power supply. To improve the accuracy of lightning location, this paper presents a polarization optical time-domain reflectometer (POTDR) lightning location method based on orthogonal coding technology. The lightning strike point can be located by detecting the state of polarization (SOP) mutation of Rayleigh backscattering in the POTDR system. The orthogonal coding technology is used to improve the distance and accuracy of POTDR lightning location. Through calculation and simulation analysis, the location accuracy is significantly improved at the same sensing distance compared with the ordinary POTDR lightning location method. The lightning strike location error can be limited to 200 m in 20 km sensing distance which meets the actual engineering needs. The results of this paper are important to improve the operation and maintenance efficiency of transmission lines and reduce maintenance costs in the power industry.

A Self-Developed Mobility Augmented Reality System Versus Conventional X-rays for Spine Positioning in Intraspinal Tumor Surgery: A Case-Control Study.

To evaluate the efficacy of a self-developed mobile augmented reality navigation system (MARNS) in guiding spinal level positioning during intraspinal tumor surgery based on a dual-error theory. This retrospective study enrolled patients diagnosed with intraspinal tumors admitted to Fujian Provincial Hospital between May and November 2023. The participants were divided into conventional x-rays and self-developed MARNS groups according to the localization methods they received. Position time, length of intraoperative incision variation, and location accuracy were systematically compared. A total of 41 patients (19 males) with intraspinal tumors were included, and MARNS was applied to 21 patients. MARNS achieved successful lesion localization in all patients with an error of 0.38±0.12 cm. Compared to x-rays, MARNS significantly reduced positioning time (129.00±13.03 seconds vs. 365.00±60.43 seconds, p<0.001) and length of intraoperative incision variation (0.14 cm vs. 0.67 cm, p=0.009). The self-developed MARNS, based on augmented reality technology for lesion visualization and perpendicular projection, offers a radiation-free complement to conventional x-rays.

Head-to-head comparison of GA-68 PSMA PET/CT and multiparametric MRI findings with postoperative results in preoperative locoregional staging and localization of prostate cancer.

Accurate staging of prostate cancer (PCa) is essential for determining the appropriate treatment and predicting outcomes. This study is comparing the effectiveness of Gallium-68 Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography (Ga-68 PSMA PET/CT) and multiparametric MRI (mpMRI) in preoperative locoregional staging and localizing PCa. A retrospective analysis was conducted on 78 patients who underwent both mpMRI and Ga-68 PSMA PET/CT scans before surgery. The imaging was reviewed by radiologists and nuclear medicine specialists and compared with the final histopathology, which was reviewed by an experienced uropathologist. mpMRI demonstrated higher sensitivity in detecting extraprostatic extension (EPE) and bladder neck invasion (BNI) compared to Ga-68 PSMA PET/CT (83% vs. 44% and 29% vs. 17%, respectively). Conversely, Ga-68 PSMA PET/CT showed higher sensitivity in detecting seminal vesicle invasion (SVI) and lymph node metastasis (LNM) (75% vs. 55% and 50% vs. 30%, respectively). When both methods were combined, sensitivity increased in detecting both EPE and SVI. The index tumor localization in mpMRI and Ga-68 PSMA PET/CT was found to be in complete agreement with histopathological findings at 36.4% and 41.8%, respectively. When both imaging methods were combined, the agreement with histopathology in predicting index tumor localization reached 72.1%. Both mpMRI and Ga-68 PSMA PET/CT provide valuable and complementary information for tumor localization and locoregional staging. While mpMRI showed higher sensitivity in detecting EPE, Ga-68 PSMA PET/CT demonstrated superior performance in detecting LNM and SVI. The combined use of these imaging modalities enhance accuracy of index tumor localizations.

Novel intelligent defects detection of boiler water walls in thermal power plants based on OFM_SSD

The boiler is a critical component of conventional thermal power systems, where surface flaws in boiler water walls can significantly compromise safety and availability, potentially leading to substantial loss of life and property. Traditional detection methods, whether manual or based on machine learning, often prove costly, inefficient and time-consuming, failing to meet the stringent requirements for water wall inspection. Therefore, a novel surface defect detection model integrating an improved single shot multibox detector (SSD) with the optical flow method (OFM) (OFM_SSD) is proposed. The OFM enhances data sample diversity by augmenting the dataset derived from thermal power plants, while the incorporation of deconvolution techniques improves the model receptive field, thereby enhancing its ability to detect and classify small defects. Comprehensive experiments demonstrate that the OFM_SSD outperforms several existing models including the SSD model based on traditional expanded datasets (T_SSD), you only look once (YOLO), ordinary SSD, Regions with the CNN(R_CNN), and Deconvolution-only SSD (DSSD) in terms of accuracy in defect localization and classification. This advancement of the OFM_SSD not only reduces operational costs but also enhances detection capabilities, ultimately contributing to safer and more efficient operations within thermal power plants.

Accuracy of polyether and vinylpolysiloxane impressions when using different types of 3D-printed impression trays - an in vitro study.

To investigate dimensional accuracy of polyether (PE) and vinylpolysiloxane (VPS) impressions taken with manually fabricated and 3D-printed trays. To evaluate impression accuracy, highly precise digital data of a metallic lower jaw model with prepared teeth (regions 34 and 36), an implant (region 47) and three precision balls placed occlusally along the dental arch served as reference. PE (Impregum, 3M Oral Care) and VPS (Aquasil, Dentsply Sirona) impressions (n = 10/group) were taken with trays fabricated using different materials and manufacturing techniques (FDM: filament deposition modeling, material: Arfona Tray, Arfona; printer: Pro2, Raise3D; DLP: digital light processing, material: V-Print Tray, VOCO, printer: Max, Asiga; MPR: manual processing with light-curing plates, material:LC Tray,Müller-Omicron) including an open implant impression. Scans of resulting stone models were compared with the reference situation. Global distance and angular deviations as well as local trueness and precision for abutment teeth and scan abutment were computed. Possible statistical effects were analyzed using ANOVA. Clinically acceptable global accuracy was found (all mean absolute distance changes < 100μm) and local accuracy for single abutments was excellent. All factors (abutment type, impression material, tray material) affected global accuracy (p < 0.05). In particular with PE impressions, MPR trays led to the best accuracies, both in horizontal and vertical direction. Within the limitations of this in vitro study, impression accuracy was high in use of both polyether and vinylpolysiloxane combined with different 3D-printed and customized trays making them recommendable for at least impressions for smaller fixed dental prostheses. Manually fabricated trays were overall still the best choice if utmost precision is required. Based on the results of this study, use of innovative CAD-CAM fabrication of individual impression trays fulfills the perquisites to be a viable option for impression making. In the sense of translational research, performance should be proved in a clinical setting.

A 3-D Near-Field Source Localization Approach Based on the Combination of a Phase Interferometer, the Centroid Algorithm and the Perpendicular Foot Algorithm.

In this study, several 3-dimensional (3-D) parameter estimation and localization algorithms for wireless near-field (NF) sources are proposed employing the uniform circular array (UCA) structure. In the single-base-station case, the algebraic relation is demonstrated between the azimuth angle under the far-field (FF) assumption and the actual NF source firstly. Secondly, two groups of antenna pairs are selected with distances less than half the wavelength, which are called short baselines in the interferometer method. The foregoing short-baseline method is qualified to localize an NF source. In addition, a long-baseline method is also proposed with further research. Two groups of antenna pairs with distances greater than half the wavelength are selected as two long baselines. In the multiple-base-stations case, another two novel algorithms are also proposed. The first one is the centroid algorithm, which is based on the centroid calculation of three estimated source locations. And the second one is the perpendicular foot algorithm, which takes the perpendicular foot within three estimated source locations as the final positioning location. Simulation results illustrate that the proposed algorithms can achieve higher localization accuracy than the conventional 3-D Root MUSIC method. Moreover, the long-baseline method performs better than the short-baseline method. And it is also shown that the proposed perpendicular foot algorithm shows better performance than the proposed centroid algorithm.

Use of indocyanine green-human serum albumin complexes in fluorescence image-guided laparoscopic anatomical liver resection: a case series study (with video)

BackgroundThis study aimed to investigate the feasibility and efficacy of near-infrared fluorescence-guided laparoscopic anatomical hepatectomy (LAH) using a novel indocyanine green (ICG)-human serum albumin complex (HSA) in patients with hepatocellular carcinoma.MethodsClinical data of hepatocellular carcinoma patients who underwent ICG-HSA fluorescence-guided LAH at our center from January 2024 to April 2024 were prospectively collected and analyzed. Ultraviolet absorption spectroscopy was used to test the absorption and stability of ICG-HSA complex solutions under different conditions. After determining the optimal ratio, the complex was administered intravenously during surgery to perform negative staining via Glissonean pedicle isolation. LAH was performed along the fluorescence-demarcated boundaries.ResultsThirty-one patients were included (24 men; mean age, 54.61 ± 13.54 years). The median maximum tumor diameter was 2.80 (interquartile range [IQR], 2.00–4.00) cm. S8 segmentectomy (22.6%) and right posterior segmentectomy (19.4%) were the most common resections performed. Successful fluorescence negative staining was achieved in all patients using ICG and HSA at a 1:6 molar ratio at room temperature. Mean operation time was 297.58 ± 85.53 min, Median intraoperative blood loss was 100.0 mL (IQR, 50.0–200.0). The median surgical margin distance was 0.90 cm (IQR, 0.40–1.50). The postoperative complication rate was 45.2% (35.5% Clavien–Dindo grade I and 9.7% grade II). The median length of hospital stay was 5.0 days (IQR, 4.0–5.0).ConclusionICG-HSA-assisted LAH is safe and feasible. Compared with free ICG, the novel ICG-HSA complex exhibits better optical properties and in vivo stability, which can improve the accuracy of intraoperative liver segment localization and optimize the anatomical dissection plane. It has the potential to become an ideal fluorescent imaging agent for anatomical hepatectomy.Graphical abstractA In vitro binding experiments of indocyanine green (ICG) and human serum albumin (HSA) suggested that a 1:6 molar ratio and a configuration temperature of 37°C were optimal. B During surgery, after extrahepatic Glissonean isolation of the liver pedicle planned for resection, the ICG-HSA complex was administered through a peripheral vein using an infusion pump. C ICG-HSA metabolism comprises three stages: intravascular, liver, and systemic clearance. D The actual intraoperative fluorescence imaging and guidance effects of ICG-HSA in laparoscopic fluorescence negative-staining right hemihepatectomy.

Accuracy of Mandibular Foramen Localization Using Digital Orthopantomogram (OPG) in Middle Eastern Population.

Locating the mandibular foramen (MF) through imaging is clinically important for inferior alveolar nerve (IAN) anesthesia and mandibular ramus osteotomies. Although cone-beam computed tomography (CBCT) is superior in imaging the mandible, an orthopantomogram (OPG) is preferred for its ease of use and availability. Therefore, the present study aimed to evaluate the accuracy of digital OPG in localizing the MF, in a subset of the Middle Eastern population. Radiographic images (OPG and CBCT) of selected patients (adults, dentulous and no mandibular abnormalities) were used to locate the MF through digital measurements (mm) of the anteroposterior distance from the anterior border of the ramus (MF-AP) and the superoinferior position from the mandibular occlusal plane (MF-SI). Measurements were statistically compared between OPG and CBCT for accuracy. Differences in measurements between OPG and CBCT were compared against the anatomic location (right/left), age and biological sex, assuming a p-value < 0.05 as significant. A total of 204 radiographic records (males: 100/females: 104/mean age: 34.65 ± 11.55 years) were evaluated. The measurements for the MF were MF-AP-OPG (right: 13.53 ± 2.44/left: 13.19 ± 2.25), MF-AP-CBCT (right: 13.61 ± 2.39/left: 13.36 ± 2.19), MF-SI-OPG (right: 5.25 ± 1.71/left: 5.41 ± 1.65) and MF-SI-CBCT (right: 5.59 ± 1.66/left: 5.52 ± 1.61). Measurements between OPG and CBCT were not significantly different, except for MF-SI (right) (p = 0.042). While the overall difference between OPG and CBCT (MF-AP/MF-SI) measurements showed a significant association (p < 0.01) with the anatomic location (right/left), a significant association (p < 0.05) with biological sex was observed only for MF-AP. Based on this study's outcomes, digital OPG is an accurate modality to locate the MF based on anteroposterior (MF-AP) and superoinferior (MF-SI) measurements. This would be clinically beneficial for dental and oral surgeons to achieve the optimum IAN block anesthesia based on preoperative panoramic radiographs. Similarly, it would assist maxillofacial surgeons in planning mandibular orthognathic surgeries and ramus osteotomies without complications.

Tag-Array-Based UHF Passive RFID Tag Attitude Identification of Tracking Methods.

Attitude information is as important as position information in describing and localizing objects. Based on this, this paper proposes a method for object attitude sensing utilizing ultra-high frequency passive RFID technology. This method adopts a double tag array strategy, which effectively enhances the spatial freedom and eliminates phase ambiguity by leveraging the phase difference information between the two tags. Additionally, we delve into the issue of the phase shift caused by coupling interference between the two tags. To effectively compensate for this coupling effect, a series of experiments were conducted to thoroughly examine the specific impact of coupling effects between tags, and based on these findings, a coupling model between tags was established. This model was then integrated into the original phase model to correct for the effects of phase shift, significantly improving the sensing accuracy. Furthermore, we considered the influence of the object rotation angle on phase changes to construct an accurate object attitude recognition and tracking model. To reduce random errors during phase measurement, we employed a polynomial regression method to fit the measured tag phase information, further enhancing the precision of the sensing model. Compared to traditional positioning modes, the dual-tag array strategy essentially increases the number of virtual antennas available for positioning, providing the system with more refined directional discrimination capabilities. The experimental results demonstrated that incorporating the effects of inter-tag coupling interference and rotation angle into the phase model significantly improved the recognition accuracy for both object localization and attitude angle determination. Specifically, the average error of object positioning was reduced to 12.3 cm, while the average error of attitude angle recognition was reduced to 8.28°, making the method suitable for various practical application scenarios requiring attitude recognition.

Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks.

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs.

Improved Road Target Detection Algorithm for YOLOv7-Tiny

In complex road scenes, we propose an enhanced road target detection algorithm for YOLOv7-Tiny to address issues such as large model size, misclassification, and low localization accuracy. Our approach involves several key modifi-cations. Firstly, we replace the LeakyReLU activation function in YOLOv7-Tiny with H-swish. This replacement not only reduces the number of parameters in the model but also enhances its feature extraction capabilities. Additionally, we replace the ELAN module in the Neck with the DPCH-ELAN module and introduce the darknetblock module along with the pcconv convolution. These modifications improve the network's ability to comprehend complex patterns and semantics, thereby enabling it to capture features at different levels of input data. Moreover, we introduce the pcconv convolution building block at the output side to handle heterogeneous information in complex road scenes, thereby enhancing the network's performance in detecting road targets and abnormalities. In our experiments using a mul-ti-source dataset, the improved model exhibits a reduction in GFLOPs by 20.90% and a decrease in the number of pa-rameters by 24.49%. Furthermore, the mean average precision scores (map) at thresholds of 0.5 and 0.5~0.9 are im-proved to 77.3% and 51.8%, respectively, compared to the original YOLOv7-Tiny model. These experimental results demonstrate that our enhanced model achieves a reduction in model size while simultaneously enhancing detection accuracy, thereby meeting the requirements for real-time detection. To assess the generalizability of our approach, we conducted comparison experiments on the VOC2012 dataset. The results indicate that the improved algorithm exhibits robust generalization capabilities across different datasets.

Enhancing Indoor Localization Accuracy in Wireless Sensor Networks: A Hybrid Approach Integrating Hierarchical Structure Poly Particle Swarm Optimization and Teaching–Learning-Based Optimization (HSPPSO-TLBO)

Background and Objective: Enhancing localization accuracy while minimizing development costs poses significant challenges in deploying and managing wireless sensor networks (WSNs). This paper presents an advanced algorithm for node localization in indoor environments, integrating sophisticated optimization techniques. The hybrid algorithm, HSPPSO-TLBO, combines Hierarchical Structure Poly Particle Swarm Optimization (HSPPSO) with Teaching– Learning-Based Optimization (TLBO). Methods: The proposed algorithm HSPPSO-TLBO aims to minimize the mean squared range error (MSRE) resulted by calculating internal distances between nodes using Received Signal Strength Indicator (RSSI). TLBO, with its robust global search capabilities, complements HSPPSO’s local search, preventing convergence to inappropriate local optima. HSPPSO-TLBO offers easy implementation and leverages the cost-free feature of RSSI, making it an attractive choice for enhancing localization precision. Results: Simulation results demonstrate the superior performance of HSPPSO-TLBO compared to other algorithms using different meta-heuristic optimization techniques. The outstanding performance of HSPPSO-TLBO is evident across various evaluation metrics, including localization error, localization rate, and simulation runtime. Conclusion: The proposed algorithm utilizing HSPPSO and TLBO is exceptionally effective in improving localization precision in indoor WSNs due to several key characteristics. These include the seamless integration and easy implementation of both HSPPSO and TLBO, along with the costfree advantage of using the RSSI technique. This combination makes the algorithm a highly functional solution for improving localization accuracy. other: N/A

Acupuncture navigation method integrated with augmented reality.

Acupuncture and moxibustion are effective in alleviating symptoms, but the large number of acupoints can make accurate needle placement and training difficult. To address these challenges, this study aims to develop an augmented reality (AR) acupuncture navigation system designed to improve the accuracy and intuitiveness of acupoint localization. The proposed system employs a six-point registration and positioning technique, enabling the AR navigation model to adapt to the specific characteristics of each patient. In testing, discrepancies between virtual and actual acupuncture points ranged from 0.6mm to 3.9mm, which is within the acceptable tolerance range for acupuncture. This AR-based system shows promise in enhancing the precision of acupuncture point localization, potentially leading to improved treatment outcomes.

Multi-Source Information Fusion-Based Localization in Wireless Sensor Networks

In wireless sensor networks, accurate node localization is essential for ensuring the precision of data collection. The DV-Hop algorithm, a popular range-free localization method, estimates distances between nodes by multiplying hop counts with average hop distances obtained through distance vector routing. However, this algorithm often experiences localization errors in randomly distributed network environments due to considerable inaccuracies in average hop distance calculations and the approximation of actual paths by straight-line paths. This paper introduces an enhanced DV-Hop localization algorithm, which constructs a mathematical model to optimize the mean square error of the average hop distance for any anchor node. This optimization corrects the average hop distance across the network, bringing it closer to the actual value, thus reducing errors and enhancing accuracy. Simulation results indicate that with 150 nodes, a 30% beacon node ratio and a 100-m communication range, the localization error of the improved FuncDV-Hop model decreased from 0.3916 to 0.1705, and the Root Mean Square Error (RMSE) decreased from 24.78[Formula: see text]m to 14.39[Formula: see text]m, thereby improving localization accuracy by 56.46%.

Local differential privacy federated learning based on heterogeneous data multi-privacy mechanism

Federated learning enables the development of robust models without accessing users data directly. However, recent studies indicate that federated learning remains vulnerable to privacy leakage. To address this issue, local differential privacy mechanisms have been incorporated into federated learning. Nevertheless, local differential privacy will reduce the availability of data. To explore the balance between privacy budgets and data availability in federated learning, we propose federated learning for clustering hierarchical aggregation with adaptive piecewise mechanisms under multiple privacy-FedAPCA as a way to balance the relationship between privacy preservation and model accuracy. First, we introduce an adaptive piecewise mechanism that dynamically adjusts perturbation intervals based on the data ranges across different layers of the model, ensuring minimized perturbation variance while maintaining the same level of privacy. Second, we propose two dynamic privacy budget allocation methods, which are allocating the privacy budget based on global accuracy and global loss, and allocating the privacy budget based on local accuracy and loss, to ensure that better model accuracy can be achieved under the same privacy budget. Finally, we propose a clustering hierarchical aggregation method in the model aggregation stage, and the model is updated and aggregated after the unbiased estimation of the disturbance in each cluster according to the variance of each layer. FedAPCA improves the balance between privacy preservation and model accuracy. Our experimental results, comparing FedAPCA with the SOTA multi-privacy local differential privacy federated learning frameworks on the MNIST and CIFAR-10 datasets, demonstrate that FedAPCA improves model accuracy by 1%–2%.

VSLAM based on deep learning in low-textured scenes

Abstract Traditional Visual Simultaneous Localization and Mapping (VSLAM) algorithms often suffer from the low positioning accuracy and robustness of the traditional feature extraction methods. These methods typically require environments with clear features, perform poorly or even fail in low-textured scenes. To solve this problem, we propose an RGB-D VSLAM algorithm that integrates a deep learning feature extraction network. First, to solve the problem in which sufficient feature points cannot be extracted, the proposed algorithm uses the R2D2 feature extraction network to extract keypoints and generate descriptors. We also perform lightweight optimization on the R2D2 network, ensuring accuracy while doubling the inference speed. Subsequently, we employ the L2 distance to measure the distance between descriptors, thereby enhancing the accuracy of keypoint matching. Ultimately, we add a dense mapping thread to obtain a dense 3D point map generated by the RGB-D information and poses of the keyframes. The experimental results on the TUM dataset show that the average absolute trajectory error is reduced by 59% and the relative trajectory error is reduced by 56%. The results demonstrate that our algorithm significantly improves localization accuracy and system robustness in low-textured environments.&amp;#xD;

A uniformly convergent analysis for multiple scale parabolic singularly perturbed convection-diffusion coupled systems: Optimal accuracy with less computational time

This study addresses time-dependent multiple-scale reaction-convection-diffusion initial boundary value systems characterized by strong coupling in the reaction matrix and weak coupling in the convection terms for a locally optimal accurate solution. The discrete problem, which typically loses its tridiagonal structure, expands its bandwidth to four in such coupled systems, resulting in a substantial computational load. Our objective is to mitigate this computational burden through a splitting approach that transforms the non-tridiagonal matrix into a tridiagonal form while maintaining the consistency, local optimal accuracy in space, and stability of the numerical scheme. We employ equidistributed non-uniform grids, guided by a carefully chosen monitor function, to approximate the continuous space domain. The discretization strategy targets local optimal linear accuracy across space and time on the domain's interior points. In addition, we have also provided the global convergence analysis of the present splitting approach, mathematically. The mathematical evidence is also obtained from the numerical experiments by comparing the splitting approach (either diagonal or triangular forms) of the reaction matrix to its coupled form. The results strongly confirm the effectiveness of this approach in delivering uniform linear accuracy, based on the present problem discretizations while significantly reducing the computational costs.

Research on the Conversion Coefficient in Coherent Φ-OTDR and Its Intrinsic Impact on Localization Accuracy

Research on the Conversion Coefficient in Coherent Φ-OTDR and Its Intrinsic Impact on Localization Accuracy

Research on satellite structural health monitoring based on ultrashort femtosecond grating array and artificial neural network

The safety of spacecraft and satellite in orbit is very important, and structural health monitoring is needed. At present, the existing technology is limited by load and difficult to realize. In this paper, we propose a feasible method to detect and locate the damage of satellite by combining ultrashort femtosecond grating array inscribed on oxide-doped fiber with multilayer artificial neural network. An oxide-doped fiber with high robustness is designed, and ultrashort grating arrays are fabricated on the fiber by femtosecond laser point-by-point writing technology. The effects of impactor velocity and angle on impact response was investigated by numerical simulations and physical experiments. Subsequently, repeated impact experiments were conducted on the satellite to obtain the training dataset and testing dataset for two-dimensional convolutional neural network. The network with symmetric convention kernels has an 88.12% localization accuracy and a better performance in boundary region, and the network architecture with asymmetric convention kernels has a 90.31% accuracy and a better performance in middle region.