Local Method Research Articles

A threatened heritage: sustainable alternatives for Mediterranean small-scale fisheries

This study underscores the crucial role of small-scale fisheries (SSF) in promoting sustainable fishing practices within the Mediterranean context, emphasizing the necessity to align fisheries commerce with local seafood consumption. SSF in the Mediterranean, particularly in the Balearic Islands, are vital for local communities, contributing significantly to fish catches, while being deeply intertwined with cultural heritage and traditional values. However, these fisheries face several threats, including resource depletion, management strategies favouring fleet reduction, and increase of seafood imports. Our research investigates the relationships between the local fishing sector and resident population, exploring perspectives on sustainability. We examine SSF activities and perceptions through face-to-face questionnaires in nine fishing ports and assess seafood consumption habits through phone questionnaires. The study reveals an aging fishing sector rooted in family traditions that face administrative hurdles in implementing strategies such as fish tourism and direct sales, but seeks to enhance local product consumption. Despite the traditional relevance of fish in the Mediterranean diet, our findings also show low consumption of fresh and local seafood due to consumer’s limited knowledge on local species and related cooking methods and perceived high costs. However, the cultural importance of these products remains undiminished. To reinforce the cultural heritage of SSF and improve the future perspectives of the sector, we advocated for a multi-pronged approach, including raising consumers’ awareness about local species, controlling local prices, and promoting certified products through a reduction in the administrative burden and by promoting the active involvement of SSF.

Video process detection for space electrostatic suspension material experiment in China’s Space Station

The purpose of video process detection is to identify key processes of interest and associated semantic categories, which is an important computer vision task for localizing key processes in space science experiments. To detect experimental processes in space electrostatic suspension material experiment on China’s Space Station, we propose a Slow-and-Fast Dual-Stream Network. It extracts multi-scale temporal semantic information by designing a Slow-and-Fast Stream structure and effectively detects the experimental processes with various temporal scales. In addition, the Two Path Temporal Pyramid Network (TP2N) enhances the bi-direction flow among multi-scale features on the temporal dimension to alleviate boundary sensitivity. The validation experiments on our established dataset demonstrate the effectiveness of our proposed method with 62.43% mAP, outperforming the related state-of-the-art temporal action localization methods with nearly 7% mAP gain. This paper is the first application of deep learning in space electrostatic suspension material experiments on China’s Space Station and further provides important technical support for the identification of key processes in space science experiments.

RCEAU-Net: Cascade Multi-Scale Convolution and Attention-Mechanism-Based Network for Laser Beam Target Image Segmentation with Complex Background in Coal Mine

Accurate and reliable pose estimation of boom-type roadheaders is the key to the forming quality of the tunneling face in coal mines, which is of great importance to improve tunneling efficiency and ensure the safety of coal mine production. The multi-laser-beam target-based visual localization method is an effective way to realize accurate and reliable pose estimation of a roadheader body. However, the complex background interference in coal mines brings great challenges to the stable and accurate segmentation and extraction of laser beam features, which has become the main problem faced by the long-distance visual positioning method of underground equipment. In this paper, a semantic segmentation network for underground laser beams in coal mines, RCEAU-Net, is proposed based on U-Net. The network introduces residual connections in the convolution of the encoder and decoder parts, which effectively fuses the underlying feature information and improves the gradient circulation performance of the network. At the same time, by introducing cascade multi-scale convolution in the skipping connection section, which compensates for the lack of contextual semantic information in U-Net and improves the segmentation effect of the network model on tiny laser beams at long distance. Finally, the introduction of an efficient multi-scale attention module with cross-spatial learning in the encoder enhances the feature extraction capability of the network. Furthermore, the laser beam target dataset (LBTD) is constructed based on laser beam target images collected from several coal mines, and the proposed RCEAU-Net model is then tested and verified. The experimental results show that, compared with the original U-Net, RCEAU-Net can ensure the real-time performance of laser beam segmentation while increasing the Accuracy by 0.19%, Precision by 2.53%, Recall by 22.01%, and Intersection and Union Ratio by 8.48%, which can meet the requirements of multi-laser-beam feature segmentation and extraction under complex backgrounds in coal mines, so as to further ensure the accuracy and stability of long-distance visual positioning for boom-type roadheaders and ensure the safe production in the working face.

A Deep Learning Localization Method for Acoustic Source via Improved Input Features and Network Structure

Shallow water passive source localization is an essential problem in underwater detection and localization. Traditional matched-field processing (MFP) methods are sensitive to environment mismatches. Many neural network localization methods still have room for improvement in accuracy if they are further adjusted to underwater acoustic characteristics. To address these problems, we propose a deep learning localization method via improved input features and network structure, which can effectively estimate the depth and the closest point of approach (CPA) range of the acoustic source. Firstly, we put forward a feature preprocessing scheme to enhance the localization accuracy and robustness. Secondly, we design a deep learning network structure to improve the localization accuracy further. Finally, we propose a method of visualizing the network to optimize the estimated localization results. Simulations show that the accuracy of the proposed method is better than other compared features and network structures, and the robustness is significantly better than that of the MFP methods. Experimental results further prove the effectiveness of the proposed method.

Application of a Centroid Frequency-Based Back Propagation Neural Network Fault Location Method for a Distribution Network Considering Renewable Energy Assessment

The distribution network is a crucial component of the power system as it directly connects to users and serves the purpose of distributing power and balancing the load. With the integration of new energy sources through distributed generation (DG), the distribution network has undergone a transformation from a single power radial network into a complex multi-source network. Consequently, traditional fault location methods have proven inadequate in this new network structure. Therefore, the focus of this paper is to investigate fault location techniques specifically tailored for DG integration into distribution networks. This paper analyzes how fault conditions impact the characteristics of single-phase grounding faults and extracts spectral feature quantities to describe differences in zero-sequence currents under various fault distances. This paper also proposes a fault location method based on centroid frequency and a BPNN (back propagation neural network). The method uses centroid frequency to describe the features of zero-sequence currents; to simulate the mapping relationship between fault conditions and spectral features, BPNN is employed. The mapping relationship differs for different lines and distribution networks. When a line faults, the spectral features are calculated, along with the transition resistance and fault closing angle. The corresponding mapping relationship is then called upon to complete distance measurements. This location method can be applied to various types of distribution lines and fault conditions with high accuracy. Even with insufficient training samples, sample expansion can ensure accuracy in fault distance measurement. We built a distribution network consisting of four feeders with different types and lengths of each line on Simulink and verified the effectiveness of the proposed method by setting different fault conditions. The results suggest that the method has a clear advantage over other frequency domain-based approaches, especially for hybrid lines and feeder lines with branches in distribution networks. Additionally, the method achieves a measurement accuracy within a range of 100 m.

Methods of detection and localization of the sources of noise and vibration on car gearboxes: a review

One of the primary sources of noise and vibration in automobiles is gearboxes. Shafts, gears, and bearings are the main causes of noise and vibration in vehicle gearboxes. Various studies have reported that vibrations’ root cause is bearing excitation. Besides bearing fatal defects or extreme structure resonance amplification, gear mesh is the primary source of high-frequency vibration and noise, even in newly built units. Gear damage detection is frequently crucial in automotive gearboxes and vehicle safety. Furthermore, vibrations caused by shaft imbalances, shaft misalignments, and other factors can cause noise and vibrations in the drivetrain's transfer path. In addition, the vibration of an automobile gearbox is closely related to poor design, construction quality, and production accuracy. This paper reviewed previous research and methods on car gearboxes for conventional vehicles. It was obvious that frequency analysis and order analysis were commonly used in noise and vibration analysis on car gearboxes. Envelope analysis is usually used to analyze bearing faults. Finally, rolling-element bearing diagnostic techniques were also reviewed.

Leakage detection applied to the Da-Hedong Reservoir dam (China) based on the flow-field fitting method

Detecting leakage in concrete gravity dams presents a formidable challenge. The Flow-Field Fitting (FFF) method is used to identify leaks in China’s Da-Hedong reservoir dam. We propose vertical- and horizontal-gradient approaches based on the transmitted signal electrical field. A plastic frame is developed to allow the probe to measure horizontal gradients. We determine the exact location of leaks at the reservoir’s bottom and upstream face, respectively, assuming that current leakages are located in hydraulic leakage zones. Numerical experiments and water pressure tests reveal that the conductive silt layer beneath the reservoir can lower the response of the potential values at the leakage inlet, and the effect of the silt layer should be considered and not ignored when interpreting measured data. In addition, with regards to the principle of superposition, the contribution associated with metal pipes in the dam body can be roughly determined by subtracting it from the original potential field data, thereby aiding in locating leaks around the metal pipes. Additionally, the FFF measurements from the reservoir’s bottom also confirm the position of existing faults in the substratum at the reservoir site. Lastly, the results of the FFF method are tested against supporting evidence including ground penetrating radar and water pressure tests. In conclusion, these independent data confirm the effectiveness of the FFF method for leakage localization in the concrete gravity dam.

Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence

This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare. By providing granular, case-specific insights, local XAI methods like LORE enhance physicians’ and patients’ understanding of machine learning models and their outcome. Our paper reviews significant contributions to local XAI in healthcare, highlighting its potential to improve clinical decision making, ensure fairness, and comply with regulatory standards.

Evaluation of the Bond Quality of Metal-Clad Plates Using Laser Ultrasonic Local Resonance

The effective detection of delamination defects, especially sub-millimeter delamination defects, in metal-clad plates is of great significance in improving product quality. In this work, the laser ultrasonic (LU) local resonance method is used to locate and characterize the sub-millimeter defects in stainless/carbon steel-clad plates. The influence of the delamination radius on the amplitude and resonant frequency of the laser ultrasound was investigated using 2D axisymmetric finite element (FE) simulations. The simulation results show that both the amplitude and the first resonance frequency (FRF) are effective features for detecting large-scale delamination defects, but the FRF is a better feature for detecting tiny delamination defects. A 304/Q235/304-clad plate specimen was made through a hot rolling bonding process, which contained a large number of self-forming delamination defects. The laser ultrasonic signals of different composite states collected in the experiment have good consistency with the simulated waveforms. The experimental results show that the laser ultrasonic local resonance method is a high-resolution imaging method, which can locate and characterize the sub-millimeter delamination defects in stainless/carbon steel-clad plates.

Collaborative Localization Method Based on Hybrid Network for Aerial Swarm

In light of the satellite rejection environment and how aircraft can obtain high-precision positioning, this paper proposes a collaborative correction algorithm for aircraft based on the rank-defect network. Aiming at the problem of insufficient anchor points, which result in insufficient observations and the divergence of aircraft inertial navigation errors, this algorithm can effectively improve the navigation performance of cluster aircraft. On the basis of the observation information provided by the anchor aircraft, the observation information between aircraft is fully utilized to improve the observability of the aircraft cluster positioning method. At the same time, the pseudo-observation equation of heterogeneous aircraft cluster positioning is introduced, and the divergence of inertial navigation positioning errors caused by insufficient observations is suppressed by the pseudo-observation solution. On the basis of introducing the pseudo-observation equation, the inertial navigation error is solved and corrected by the Newton iterative method and the divergence of the inertial navigation position error is restrained. Compared with an aircraft cluster positioning method that does not use the inertial navigation error co-correction based on the pseudo-observation solution, this paper can achieve better overall cluster positioning accuracy when the available observations are insufficient, which is suitable for practical applications.

A Decentralized Voltage Regulation Scheme Using Improved Volt‐Var Function of PV Smart Inverter

With the growing distributed PV installation rate in distribution systems, voltage regulation difficulties such as local voltage violations and fluctuations have become common. To solve the voltage regulation problems, the local voltage regulation method using volt‐var (VV) function is effective for its high regulation speed, high accuracy, and flexibility. However, there are still hurdles on real application, such as parameter setting difficulties, insufficient voltage fluctuation mitigation effect, and unfairness of reactive power generation between PV customers. To further improve the VV function, this paper proposes a PID closed‐loop based VV function and mode‐switching function of PV smart inverter (SI). To validate the proposed methods, numerical simulations were conducted using a 6‐feeder distribution system model based on the JST‐CREST 126 distribution feeder model. According to the simulation result, the proposed method can better mitigate the local voltage violation compared to basic VV function based on the VV curve, also improve the fairness between PV customers, while the total reactive power generation and tap operations increases for the aim of more adequate voltage regulation. © 2024 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Local Water Inrush Risk Assessment Method Based on Moving Window and Its Application in the Liangshuijing Mining Area

Most of the existing coal mine water inrush risk assessment methods are global assessment methods, which have the following problems: they ignore the difference in importance of the evaluation indicators at different locations in the study area and assign the same weight value; the area of the danger zone in the evaluation results is thus too large. The evaluation results improve the prediction accuracy by reducing the safe zone area. To address the aforementioned issues, this study employs a local analysis method based on a moving circular window, taking into account the spatial heterogeneity of criterion indicators in the decision-making process. By traversing each position of the raster with a circular moving window, the method performs local standardization and calculates local weights of indicators within the local window range. Based on the obtained maps of locally standardized evaluation criteria and local weights, a local water inrush risk assessment model is established using Geographic Information Systems (ArcGIS), considering the differences in the importance of evaluation indicators within the study area. Taking the Liangshuijing mining area as an example, both global and local evaluation models were employed to assess its water inrush risk. The evaluation results obtained from these two models were compared and validated against geological survey data and historical water inrush points. The comparative analysis between the two methods reveals that the local evaluation model demonstrates higher accuracy. It offers a more precise delineation of the distribution of water inrush risk zones, which better corresponds to the actual conditions within the mine. The localized water inrush risk assessment method proposed in this paper breaks away from the traditional approach of uniformly weighting evaluation indicators across the entire area, offering a novel method for assessing water inrush risk.

Testing and Analysis of Selected Navigation Parameters of the GNSS/INS System for USV Path Localization during Inland Hydrographic Surveys

One of the main methods of the path localization of moving objects is positioning using Global Navigation Satellite Systems (GNSSs) in cooperation with Inertial Navigation Systems (INSs). Its basic task is to provide high availability, in particular in areas with limited access to satellite signals such as forests, tunnels or urban areas. The aim of the article is to carry out the testing and analysis of selected navigation parameters (3D position coordinates (Northing, Easting, and height) and Euler angles (pitch and roll)) of the GNSS/INS system for Unmanned Surface Vehicle (USV) path localization during inland hydrographic surveys. The research used the Ellipse-D GNSS/INS system working in the Real Time Kinematic (RTK) mode in order to determine the position of the “HydroDron” Autonomous Surface Vehicle (ASV). Measurements were conducted on four representative routes with a parallel and spiral arrangement of sounding profiles on Lake Kłodno (Poland). Based on the obtained research results, position accuracy measures of the “HydroDron” USV were determined using the Ellipse-D GNSS/INS system. Additionally, it was determined whether USV path localization using a GNSS/INS system working in the RTK mode meets the positioning requirements for inland hydrographic surveys. Research has shown that the Ellipse-D system operating in the RTK mode can be successfully used to position vessels when carrying out inland hydrographic surveys in all International Hydrographic Organization (IHO) Orders (Exclusive, Special, 1a/1b and 2) even when it does not work 100% correctly, e.g., loss of RTK corrections for an extended period of time. In an area with limited coverage of the mobile network operator (30–40% of the time the receiver operated in the differential mode), the positioning accuracy of the “HydroDron” USV using the Ellipse-D GNSS/INS system working in the RTK mode was from 0.877 m to 0.941 m for the R95(2D) measure, depending on the route travelled. Moreover, research has shown that if the Ellipse-D system performed GNSS/INS measurements using the RTK method, the pitch and roll error values amounted to approx. 0.06°, which is almost identical to that recommended by the device manufacturer. However, when working in the differential mode, the pitch and roll error values increased from 0.06° to just over 0.2°.

Towards achieving geo‐indistinguishability for 3D GPS location: A 3D Laplace mechanism approach

SummaryAs the scope of human exploration continues to expand from land to space and the oceans, location‐based data analysis and services are facing unprecedented opportunities and challenges. The wide application of various services based on spatial location in the fields of medical, transportation, financial, social and so forth not only provides great convenience but also exposes the disadvantages of location privacy leakage. Most present location privacy protection solutions are only relevant to 2D locations. However, with the widespread use of positioning‐enabled aircraft, sensing devices and acquisition equipment, there is an urgent need to achieve privacy protection for 3D spatial location data. Therefore, this article proposes a 3D geo‐indistinguishability perturbation method for 3D GPS Locations. The suggested approach calculates GPS coordinate perturbation accurately using the 3D Laplace mechanism, allowing for point‐to‐point perturbation while protecting the privacy of 3D spatial GPS coordinates. Experimental examination of real spatial location datasets demonstrates that the proposed 3D spatial location perturbation method outperforms other existing methods in terms of location service quality loss, data availability, and operational efficiency with the same privacy budget.

Research on transient temperature rise characteristics of composite cylindrical roller bearings

Composite roller bearings have received extensive attention from bearing design engineers because of their good contact and running performance. According to the contact mechanical characteristics of composite cylindrical roller, a bearing heat generation power calculation model is established. Then, using the local heat source analysis method, and considering the heat conduction mode, the transient temperature solution model of bearings is established. The influence of filling ratio, radial force, angular velocity and roller numbers on the heat generation power and steady-state temperature of the bearing components is calculated and analyzed. The calculated results are in good agreement with the experimental results, which verifies the correctness of the model. The model can accurately predict the temperature distribution of bearings, providing new ideas and methods for temperature rise analysis, and laying the foundation for temperature analysis of bearings.

Learning Reliable Dense Pseudo-Labels for Point-Level Weakly-Supervised Action Localization

Point-level weakly-supervised temporal action localization aims to accurately recognize and localize action segments in untrimmed videos, using only point-level annotations during training. Current methods primarily focus on mining sparse pseudo-labels and generating dense pseudo-labels. However, due to the sparsity of point-level labels and the impact of scene information on action representations, the reliability of dense pseudo-label methods still remains an issue. In this paper, we propose a point-level weakly-supervised temporal action localization method based on local representation enhancement and global temporal optimization. This method comprises two modules that enhance the representation capacity of action features and improve the reliability of class activation sequence classification, thereby enhancing the reliability of dense pseudo-labels and strengthening the model’s capability for completeness learning. Specifically, we first generate representative features of actions using pseudo-label feature and calculate weights based on the feature similarity between representative features of actions and segments features to adjust class activation sequence. Additionally, we maintain the fixed-length queues for annotated segments and design a action contrastive learning framework between videos. The experimental results demonstrate that our modules indeed enhance the model’s capability for comprehensive learning, particularly achieving state-of-the-art results at high IoU thresholds.

How Accurate Is Frameless Fiducial—Free Deep Brain Stimulation?

BACKGROUND AND OBJECTIVES: Frameless deep brain stimulation (DBS) offers advantages in terms of patient comfort and reduced operative time. However, the need for bony fiducial markers for localization remains a drawback due to the time-consuming and uncomfortable procedure. An alternative localization method involves the direct tracking of an intraoperative 3-dimensional scanner. This study aims to assess the accuracy of the NexFrame frameless DBS system in conjunction with the O-Arm (Medtronic Inc.), both with and without fiducial markers. METHODS: The locations of 100 DBS leads were determined, with 50 cases using fiducial-free localization and 50 involving fiducial markers. The coordinates were compared with the expected intraoperative targets. Absolute errors in the X, Y, and Z coordinates (ΔX, ΔY, and ΔZ) were calculated, along with the vector error (Euclidean) (vector error ). RESULTS: The vector error averaged 1.61 ± 0.49 mm (right) and 1.52 ± 0.60 mm (left) for the group without fiducial bone markers and 1.66 ± 0.69 (right) and 1.44 ± 0.65 mm (left) for the other cohort (P = .76 right; P = .67 left). Absolute errors in the X, Y, and Z coordinates for the fiducial-free group were 0.88 ± 0.55, 0.79 ± 0.45, and 0.79 ± 0.57 mm (right) and 0.72 ± 0.37, 0.78 ± 0.56, and 0.77 ± 0.71 mm (left). For the group with fiducial markers, these errors were 0.87 ± 0.72, 0.92 ± 0.39, and 0.86 ± 0.50 mm (right) and 0.75 ± 0.33, 0.80 ± 0.51, and 0.73 ± 0.64 mm (left) with no statistically significant difference. CONCLUSION: Our analysis of the accuracy of NexFrame DBS, both with and without fiducial markers, using an intraoperative navigable cone-beam computed tomography, demonstrates that both techniques provide sufficient and equivalent 3-dimensional accuracy.

Transmission tower bolt-loosening time–frequency analysis and localization method considering time-varying characteristics

To address the issues of high concealment and difficult positioning of loose bolts in transmission towers, this paper proposes a new method for locating loose bolts in transmission towers. In this method, we divide the vibration response of the transmission tower into low-frequency signals of 2–25 Hz and high-frequency signals of 25–75 Hz. For the low-frequency signals, the single signal component is obtained by adaptive Chirp mode decomposition and uses the general demodulation transformation to deeply denoise the non-modal information. Since frequency characteristics themselves do not contain time information, considering the importance of time information for positioning, we propose a low-frequency time-varying frequency feature that preserves time characteristics based on synchronous wavelet transform and peak search. For the high-frequency signals, we use singular value decomposition to remove signal outliers caused by pulse excitation and eliminate forced vibrations through wavelet packet transform. Without altering its inherent characteristics, this method enables high-frequency time-domain signals to better represent the nonlinear characteristics of transmission towers. Furthermore, based on the powerful capabilities of Timesnet and Transformer in dealing with time series data, we propose a fault diagnosis model, which ultimately achieves the positioning of loose bolts in transmission towers. The bolt node model proves that this approach can better represent the loose bolt characteristics, and the transmission tower model verifies the effectiveness of this approach in locating loose bolts in transmission towers. Finally, bolt-loosening tests were conducted on a 110 kV transmission tower, and the accuracy of the positioning results reached 92.8%, demonstrating the effectiveness and efficiency of this method in practical positioning applications.

Tetris-inspired detector with neural network for radiation mapping

Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.

Numerical Studies on the Classical Long Wave System Describing Shallow Water Waves With Dispersion

In this study, a reliable and efficient local discontinuous Galerkin scheme for numerically solving the classical long wave system is proposed. This scheme approximates the temporal derivatives using the explicit strong-stability-preserving higher-order Runge-Kutta method, while the space derivatives are approximated using the local discontinuous Galerkin method, yielding an ordinary differential equation system. Numerical examples for various test problems are presented to offer a comprehensive understanding of the accuracy and reliability of the proposed method. The results obtained, which confirm the sub-optimal order of accuracy, are displayed in multiple tables. Additionally, several two-dimensional and three-dimensional graphical depictions of the problem have been provided to illustrate the behavior of the solution.