Minimum Distance Research Articles

Validation of morphological ear classification devised by principal component analysis using three-dimensional images for human identification.

This study attempts to classify ear morphology for human identification in forensic investigations by distinguishing between the upper auricle and lobule areas. A three-dimensional homologous model of the ear was created using 414 ear images of males aged 17-93 years reconstructed from computed tomography scans of forensic autopsy cases. Morphological changes were visualized using principal component analysis and areas of significant individual differences within the entire ear were identified. The classification criterion images for the upper auricle (ten images) and lobule (12 images) were developed by combining multiple principal component values: components 1-5 for the upper auricle and 1-6 for the lobule. Three-dimensional ear images of the upper auricle and lobule areas from 414 subjects were categorized using a measurement method based on the minimum distance between 5,507 corresponding points. The results indicate the applicability of the criterion images for the morphological classification of ears in this study.

The Optimal Placement of the Head in the Noun Phrase. The Case of Demonstrative, Numeral, Adjective and Noun

ABSTRACT The word order of a sentence is shaped by multiple principles. The principle of syntactic dependency distance minimization is in conflict with the principle of surprisal minimization (or predictability maximization) in single head syntactic dependency structures: while the former predicts that the head should be placed at the centre of the linear arrangement, the latter predicts that the head should be placed at one of the ends (either first or last). A critical question is when surprisal minimization (or predictability maximization) should surpass syntactic dependency distance minimization. In the context of single head structures, it has been predicted that this is more likely to happen when two conditions are met, i.e. (a) fewer words are involved and (b) words are shorter. Here, we test the prediction on the noun phrase when it is composed of demonstrative, numeral, adjective, and noun. We find that, across preferred orders in languages, the noun tends to be placed at one of the ends, confirming the theoretical prediction. We also show evidence of anti-locality effects: syntactic dependency distances in preferred orders are longer than expected by chance.

Radiometric calibration of UAV multispectral images under changing illumination conditions with a downwelling light sensor

AbstractUnmanned aerial vehicle (UAV)‐acquired multispectral images commonly suffer from radiometric inaccuracies due to changing illumination produced by intermittent cloud cover. This study addressed cloud shadow effects by integrating direct irradiance measurements from a downwelling light sensor into reflectance calculations. Two reflectance calibration methods were proposed as follows: (1) use of a single calibration reference panel for all images (Method 1) and (2) employment of a minimum distance classifier to assign color‐graded in‐field reflectance calibration targets to individual images based on their proximity in irradiance levels (Method 2). Images were acquired from 30 and 75 m flight altitudes with fixed‐exposure and auto‐exposure settings. Conventional photogrammetric calibration of UAV multispectral images inadequately addressed cloud shadows, resulting in orthomosaics with poor to moderate spatial uniformity (R2 = 0.70–0.92) and high mean absolute percentage error (MAPE = 13.52%–49.18%). The proposed calibration methods improved radiometric accuracy, yielding average R2 = 0.99 and 0.98 at 30 and 75 m images, respectively. Method 1 showed superior performance on the red‐edge and near infrared bands, and Method 2 worked best in the blue, green, and red bands. Post‐processing empirical line calibration of orthomosaics from Methods 1 and 2 reduced MAPE in reflectance estimation by at least 50% compared to conventional calibration. The clear sky reference histograms had lower Jensen–Shannon distance, higher Pearson's correlation, and improved intersection ratios with the histograms from the proposed methods, implying high similarity. Vegetation indices calculated with the proposed methods closely matched those from the reference orthomosaic, exhibiting significantly lower MAPE than conventionally calculated vegetation indices.

Optimal Illumination Distance Metrics for Person Re-identification in Complex Lighting Conditions

Person Re-identification is extensively applied in public security and surveillance. However, environmental factors like time and location often lead to varying lighting conditions in captured pedestrian images, significantly impacting identification accuracy. Current approaches mitigate this issue through lighting transformation techniques, aiming to normalize images to a standard lighting condition for consistent person re-identification results. Yet, these methods overlook the fact that different content may hold distinct identification values under diverse lighting conditions. To address this, we conducted an analysis on the identification distance between images of the same or different pedestrians under predefined lighting conditions. From this analysis, we introduce the concept of optimal lighting: a condition where the distance between image pairs is minimized compared to other lighting scenarios. We propose utilizing this optimal lighting distance in the image retrieval process for final ranking. Our study, validated on synthetic datasets Market-IA and Duke-IA, demonstrates that optimal lighting is independent of image texture information. Each image pair exhibits a unique optimal lighting, yet consistently shows a minimum distance value.

Standard Errors for Calibrated Parameters

Abstract Calibration, the practice of choosing the parameters of a structural model to match certain empirical moments, can be viewed as minimum distance estimation. Existing standard error formulas for such estimators require a consistent estimate of the correlation structure of the empirical moments, which is often unavailable in practice. Instead, the variances of the individual empirical moments are usually readily estimable. Using only these variances, we derive conservative standard errors and confidence intervals for the structural parameters that are valid even under the worst-case correlation structure. In the over-identified case, we show that the moment weighting scheme that minimizes the worst-case estimator variance amounts to a moment selection problem with a simple solution. Finally, we develop tests of over-identifying or parameter restrictions. We apply our methods empirically to a model of menu cost pricing for multi-product firms and to a heterogeneous agent New Keynesian model.

SMALL OBJECT DETECTION APPROACH BASED ON ENHANCED SINGLE-SHOT DETECTOR FOR DETECTION AND RECOGNITION OF INDONESIAN TRAFFIC SIGNS

The detection and recognition of traffic signs are crucial components of advanced driving assistance systems (ADAS) that enhance road safety. Current traffic sign detection and recognition model technology is proficient in identifying and interpreting traffic signs. However, for accurate detection and recognition, the traffic sign in the image must be of a certain minimum pixel size or distance from the driver's sight line for proper detection. The ADAS system should be capable of detecting and recognizing road traffic signs from a considerable distance as they come into the driver's line of vision. The higher the vehicle speed, the greater the distance required for the sign to be detected and recognized, allowing the driver sufficient time to react according to the sign's meaning. Addressing these challenges, this research proposes an enhanced version of the single shot detector (SSD) algorithm, commonly used in object detection, to improve the algorithm's ability to detect small objects. The proposed method involves adding an auxiliary layer module to the original SSD architecture to increase the feature map resolution and expand the conventional layer's receptive space. With the Enhanced SSD algorithm, the detection capability of the SSD can be significantly enhanced in terms of accuracy. The limitations of this study are related to the influence of occlusion and clutter, which might affect the performance of object detection, especially for small objects, which are more susceptible to being influenced by various factors. The research results demonstrate that Enhanced SSD improves object detection accuracy compared to the original SSD, with a mean average precision (mAP) of 97.87 compared to 95.35 for detecting 21 traffic signs in Indonesia.

A Comparative Analysis Machine Learning Techniques for LULC Classification Using Landsat-8 Satellite Imagery

This study compares various categorization methods to assign land use and land cover (LULC) classes. Using Geographic Information Systems (GIS) and Remote Sensing (RS) to leverage the dynamic and complex area of LULC, this study examines the potential of different machine learning classification methods. Precise differentiation and classification of various land cover categories, such as green vegetation, urban areas, water bodies, dark green vegetation, and bare terrain, are made possible by the great spatial and spectral resolution of Landsat imagery. For efficient land management and planning, the integration of Landsat data with GIS and RS approaches provides insightful information about the distribution and temporal changes in LULC. This study uses four classifiers to explore the principles of supervised machine learning techniques and identify their drawbacks and advantages. Testing results show that the Support Vector Machine with four kernel linear-99.17%, radial basis (RBF)-99.11%, Sigmoid-99.11% and Polynomial-99.11% is a reliable option for LULC classification, outperforming than other classifiers in terms of accuracy, including the Minimum Distance Classifier (MD-93.47%), Maximum Likelihood Classifier (MLC- 98.98%), and Mahalanobis Distance Classifier (MH-97.83%). Among the tested classifiers, SVM with four kernels notably shows the highest accuracy. With their essential insights for well-informed decision-making towards sustainable development and resource utilization, our findings add to a thorough understanding of LULC dynamics. For accurate mapping and long-term monitoring of deviations in land cover (LC), the study emphasizes the value of using front-line classification systems in remote sensing applications.

Temporomandibular joint space variation and masticatory muscle activation during clenching with full versus partial covering occlusal splints

ObjectivesOcclusal splints are the main therapeutic choice in the treatment of temporomandibular disorders (TMD). However, their precise working mechanism is unclear. This study aimed to compare the biomechanical effect of three commercially available splint designs (full covering splint, anterior bite splint and posterior bite splint) during biting in a sample of healthy subjects.Materials and methodsMagnetic resonance imaging (MRI) was combined with jaw tracking to measure the minimal intraarticular distance (MID) of 20 human temporomandibular joints (TMJ) whilst simultaneously recording the electromyogram (EMG) of the masticatory muscles. The changes caused by clenching with a bite force of 100 N without splint (baseline) and on each splint were calculated. Repeated measures ANOVA was performed on the means of the MID variations and EMG amplitudes.ResultsClenching on the anterior bite splint resulted in two times less activation of the anterior temporalis muscle than baseline (p = 0.003), full covering (p = 0.011) and posterior bite splint (p = 0.011). MID was reduced by clenching in all conditions, but the reduction was almost three times larger with the anterior bite splint compared to no splint (p = 0.011). The full covering splint and the posterior bite splint did not differ significantly in EMG activation of both masseter and temporalis muscles and MID variation.ConclusionsThis study showed that splint designs have a different impact on the MID and EMG activation while clenching. The anterior bite splint had a greater impact on the reduction of the muscle activation, whereas clenching on the anterior bite splint led to bigger reduction of MID and thus had the greatest influence on alteration in the condylar position.Clinical relevanceThe design of the splint can affect MID and muscle activation and is a variable to consider in the treatment of patients with TMD according to their symptoms.

Multi-line laser 3D reconstruction method based on spatial quadric surface and geometric estimation

The traditional multi-line laser 3D reconstruction is based on binocular epipolar constraints and the equation of the laser optical plane. Firstly, matching points are identified based on the epipolar constraints. Then, the correct matching points are selected based on the optical plane equation of the multi-line laser. Finally, 3D reconstruction is performed using the selected matching points. In actual production processes involving multi-line lasers, noticeable distortion occurs due to the projection of Diffractive Optical Elements (DOE) at a large angle. This results in curved laser light planes and curved reflections on the measured objects. Under such circumstances, efficiently screening matching points using the traditional method based on the laser plane equation becomes challenging. Additionally, inherent noise in multi-line laser systems introduces errors in extracted laser center coordinates, making it impossible to directly obtain high-precision 3D data. To address these issues, this paper proposes a method that utilizes spatial quadric surfaces and geometric estimation for completing multi-line laser 3D reconstructions. By analyzing the distortion principle of DOE and the positional relationship after optical diffraction, the multi-line laser manifests as a quadric surface on the optical output plane. Consequently, by calibrating the equations of the quadric surface and applying binocular polar constraints, suitable matching points for the multi-line laser can be chosen. Once an accurate matching point is identified, a minimum geometric distance estimation can be established based on the distance constraint between the point and its corresponding epipolar line. This distance represents the separation between the left and right camera’s laser center points and their respective epipolar lines. Utilizing this estimate of the minimum geometric distance, a refined calculation can be performed to better satisfy epipolar constraints and obtain new matching points. Ultimately, utilizing these new matching points enables the completion of 3D reconstruction for multi-line lasers. In comparison with methods relying on spatial plane and epipolar constraints, our algorithm exhibits a superior degree of matchability and accuracy.According to multiple groups of test data, the average error before optimization is 0.3126 mm, and can be increased to 0.0336 mm after optimization.

Rapid diagnosis of the geospatial distribution of intertidal macroalgae using large-scale UAVs

Macroalgae have been used as indicators of the health of coastal ecosystems, they function as sinks of CO2 and are essential contributors to primary production. With the increase in anthropogenic activities, it is crucial to assess the impact of such activities on these ecosystems. As traditional surveying techniques, although accurate, are time-consuming and their area coverage is limited, novel techniques are required to monitor the coverage and diversity of intertidal macroalgae. We propose a methodology using the free-source Semi-Automatic Classification Plugin from QGIS to use UAV and multispectral cameras for the spatiotemporal monitoring of intertidal macroalgae. We also compared the performance of six classifiers: Minimum Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapping (SAM), Multi-Layer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM), for three types of macroalgae classification: general, taxonomical groups and species. As proof of concept, an intertidal rocky shore in a marine protected area (NW Spain) was studied for four months. RF and SVM achieved similar results, with both being recommended for the general (OASVM = 97.4±1.7 and OARF = 98.3±1.7) and taxonomical groups (OASVM = 91.6±1.9 and OARF = 89.2±4.5). SVM and ML were found to be more suitable for species classification (OASVM = 77.4±11.4 and OAML = 74.2±9.7). SAM and MLP provided the least performant species classifiers because of the overlap in the macroalgae spectral signatures. The plugin showed limitations when tuning the input parameters of the MLP classifier and did not let to add a validation dataset. Additionally, we present an open-access GIS web application, Alganat 2000 GIS web, to facilitate the monitoring and management of coastal areas. We conclude that the proposed methodology using the SVM or ML classifiers is an effective tool for assessing intertidal macroalgal assemblages. Its easy and rapid implementation is beneficial for researchers who are not very familiar with coding and machine learning frameworks and reduces the time and cost of fieldwork. As future work, we propose the combination of the multispectral bands with topographic and spectral indices and to research the application of deep learning models to the classification of intertidal macroalgae.

Automatic Image Analysis Method as a Tool to Evaluate the Anisotropy of Autoclaved Aerated Concrete for Moisture and Heat Transport

In this article, the results of studies testing the anisotropy of autoclaved aerated concrete in terms of water and heat transport are presented. Using image analysis techniques, a study was conducted on four different samples of concrete produced in the same process. To ensure the comparability of results, the pictures were taken from a fixed distance with the same lens settings trimmed to a set size. Cross-sectional profiles of the material were examined and were arranged in two directions: perpendicular and parallel to the growth direction occurring in the autoclave. For each block, approximately 4750 objects were obtained, with an average of 2700 objects along the wall and 2050 across it. As a result of the comparative analysis, metrics concerning pores, significantly distinguishing the profile direction, were identified. These included the pore area (area), the maximum and minimum distance between points on the perimeter (Feret, MinFeret), lengths of the major and minor axes of the fitted ellipse (major, minor), and the ratio of the area of selection to its convex hull (solidity). As a reference, standard investigations were conducted for moisture transport using the time domain reflectometry setup and for thermal conductivity values using the steady-state heat flow plate apparatus.

Proposed methods of image recognition depend on the PCA

This paper suggest two method of recognition, these methods depend on the extraction of the feature of the principle component analysis when applied on the wavelet domain(multi-wavelet). First method, an idea of increasing the space of recognition, through calculating the eigenstructure of the diagonal sub-image details at five depths of wavelet transform is introduced. The effective eigen range selected here represent the base for image recognition. In second method, an idea of obtaining invariant wavelet space at all projections is presented. A new recursive from that represents invariant space of representing any image resolutions obtained from wavelet transform is adopted. In this way, all the major problems that effect the image and change its characteristic are solved through calculating invariant eigen range of the recursive resolution forms of all sub-images coefficient. These approaches employed here as multi-wavelet transform identifier with minimum Mahalanobis distance. All method recognition proposed in this paper are applied on different images. Different tables of image recognition resulted in accurate and fast.

Parameterized Inapproximability of the Minimum Distance Problem over All Fields and the Shortest Vector Problem in All \({\ell_{{p}}}\) Norms

Parameterized Inapproximability of the Minimum Distance Problem over All Fields and the Shortest Vector Problem in All \({\ell_{{p}}}\) Norms

Improved YOLOv8n for Lightweight Ship Detection

Automatic ship detection is a crucial task within the domain of maritime transportation management. With the progressive success of convolutional neural networks (CNNs), a number of advanced CNN models have been presented in order to detect ships. Although these detection models have achieved marked performance, several undesired results may occur under complex maritime conditions, such as missed detections, false positives, and low detection accuracy. Moreover, the existing detection models endure large number of parameters and heavy computation cost. To deal with these problems, we suggest a lightweight ship model of detection called DSSM–LightNet based upon the improved YOLOv8n. First, we introduce a lightweight Dual Convolutional (DualConv) into the model to lower both the number of parameters and the computational complexity. The principle is that DualConv combines two types of convolution kernels, 3x3 and 1x1, and utilizes group convolution techniques to effectively reduce computational costs while processing the same input feature map channels. Second, we propose a Slim-neck structure in the neck network, which introduces GSConv and VoVGSCSP modules to construct an efficient feature-fusion layer. This fusion strategy helps the model better capture the features of targets of different sizes. Meanwhile, a spatially enhanced attention module (SEAM) is leveraged to integrate with a Feature Pyramid Network (FPN) and the Slim-neck to achieve simple yet effective feature extraction, minimizing information loss during feature fusion. CIoU may not accurately reflect the relative positional relationship between bounding boxes in some complex scenarios. In contrast, MPDIoU can provide more accurate positional information in bounding-box regression by directly minimizing point distance and considering comprehensive loss. Therefore, we utilize the minimum point distance IoU (MPDIoU) rather than the Complete Intersection over Union (CIoU) Loss to further enhance the detection precision of the suggested model. Comprehensive tests carried out on the publicly accessible SeaShips dataset have demonstrated that our model greatly exceeds other algorithms in relation to their detection accuracy and efficiency, while reserving its lightweight nature.

Adaptive robust integrated guidance and control for thrust-vector-controlled aircraft by solving LQR online

In order to enhance the matching relationship between guidance subsystem and control subsystem of the thrust-vector-controlled aircraft, a kind of sufficient modeling and adaptive robust design facing to integrated guidance and control (IGC) is proposed. With respect to the researched aircraft, a linear state-dependent mathematical model facing to IGC design is first established. Based on the as-built model, a new kind of adaptive robust IGC law is proposed and it is composed by an adaptive optimal IGC law and a robustness-improved IGC law. In order to guarantee the global stability of time-varying control system, the adaptive optimal IGC law is designed by solving Riccati matrix equation on line and using matrix Sign function method. Furthermore, in order to enhance the robust ability against the unmatched system uncertainties, the robustness-improved IGC law is designed by using dynamic surface control approach and extended state observing strategy. Simulation results present that, the proposed IGC scheme presents more performance advantages compared with traditional IGC schemes, including the improvement of guidance precision and attitude stabilization. Furthermore, in the conditions of 256 simulation combinations, the minimum relative distances between aircraft position and target position are distributed from 0.103m to 5.333m, and the average value is 2.208m, which means the proposed IGC scheme possesses strong robustness against different and time-varying model uncertainties.

Understanding the adsorption performance of hetero-nanocages (C12-B6N6, C12-Al6N6, and B6N6-Al6N6) towards hydroxyurea anticancer drug: a comprehensive study using DFT.

Cancer is a paramount health challenge to global health, which forms tumors that can invade nearby tissues and spread to neighboring cells. Recently, nanotechnology has been used to control the growth of cancer, in which anticancer drugs are delivered to cancerous cells via nanoparticles without damaging healthy tissues. In this study, DFT investigations were carried out to examine the adsorption behavior of C24, B12N12, and Al12N12 nanocages as well as their heterostructures C12-B6N6, C12-Al6N6, and B6N6-Al6N6 towards the hydroxyurea (HU) anticancer drug. In this regard, adsorption energy, interaction distance between the drug and nanocages, charge transfer, energy gap, dipole moment, quantum molecular descriptors, work function, and COSMO surface analysis were analyzed to understand their adsorption performance. Findings demonstrate that the adsorption energies of two hetero-nanocages on their hexagonal (SH) and tetragonal (ST) sites are favorable for the drug delivery process. The computed adsorption energy of B6N6-Al6N6 of the ST/AlN site is 183.59 kJ mol-1, which is higher than that of the C12-Al6N6 nanocage, including minimum adsorption distances. Negative adsorption energy with low adsorption distances implies an attractive interaction between the drug and nanocages. During the interaction, a significant amount of charge is transferred between the drug and nanocages. Furthermore, for both complexes, larger dipole moments were observed in water media compared to gas media. From DOS spectra, prominent peaks were found in the Fermi level after adsorption of HU on the nanocages, implying the reduction of the energy gap. Noticeable overlaps between the PDOS spectra of the nanocages and HU's close contact atom demonstrate the formation of chemical bonds between two specific atoms. Therefore, it can be concluded that among the nanocages, C12-Al6N6 and B6N6-Al6N6 may be suitable carriers for HU drug.

Coupled Modeling of Sea Surface Launch Flow and Multi-Body Motion

To simulate the launching process of missile complex flow, movement, and constraint states, a multifield coupling model is put forward based on a computational fluid dynamics (CFD) method. In this coupled model, a CFD method is used to solve the three-dimensional compressible transient flow, and the six-degree motion of the launching platform is considered, and the virtual contact method is used to deal with the constraint states of the guideway and the slider. The active force and moment are applied to the launching platform to simulate its rolling, pitching, and heaving motions under the 5-level waves. Collision detection is carried out through the minimum clearance distance between the slider and the guideway, and the contact force is handled by a modified Herz collision model. In the problem of launching a missile from the water surface, the change characteristics of the flow field, the load response characteristics, and the relative motion laws of the missile and the launching platform during the catapulting process are investigated. The results show that the motion laws of the projectile and the launch tube in the constrained direction are the same, and the established coupling model is able to simulate the launch separation process of the missile in the constrained state. In addition, the effect of wind load on the missile ejection process is analyzed using the coupled model.

Study on the ecosystem service flow based on the relationship of between supply and demand in Yangtze River Economic Belt

Ecosystems supply goods and services to humans and are the basis for sustainable development of human society. The study of the supply of ecosystem services and the demand and consumption of ecosystem services by human society, and the analysis of the supply and demand characteristics and flow relationships of ecosystem service flows are of great significance for the management of regional ecosystems and the development of ecological compensation. Taking the Yangtze River Economic Belt as an example, this paper calculates the supply and demand indices of ecosystem services in 2015 and 2020, and determines the ecosystem spatial flow paths and flow volumes from the ecosystem supply area to the demand area based on various methods and models such as the minimum cumulative resistance (MCR) model and distance decay model. The results indicate that 1). In 2015 and 2020, the supply and demand of ecosystem services in the Yangtze River Economic Zone show an increasing trend numerically, and there is spatial heterogeneity in the spatial distribution. In terms of ecosystem service supply per unit area, the midstream region is higher than the upstream and downstream regions. In terms of the demand for ecosystem services per unit area, the downstream is higher than the midstream and upstream. 2). From the supply-demand balance of ecosystem services in the Yangtze River Economic Zone, the midstream region is mainly the area of surplus supply of ecosystem services, and the downstream region is mainly the area of deficit supply. From 2015 to 2020, the number of areas with balanced supply and demand of ecosystem services in the Yangtze River Economic Belt decreases and the number of areas with unbalanced supply and demand increases, which is related to the changes in the level of economic development and land use patterns. 3). The flow of ecosystem services in the Yangtze River Economic Belt shows an increasing trend, from 726.59 billion yuan in 2015 to 1,450.54 billion yuan in 2020, with Jiangxi Province being the main ecosystem service supply area and Zhejiang Province being the main ecosystem service demand area in the Yangtze River Economic Belt.

IDSV-GCN: Integrating Dual Syntactic Views Graph Convolutional Network for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards given aspects in a sentence. Recent research has highlighted that graph neural networks based on syntactic dependency trees effectively convey rich syntactic information and are suitable for ABSA tasks. Most existing work typically adopt either dependency type or minimum distance between nodes in the original syntactic tree to construct different views. However, existing work overlooks the high-dimensional local feature information in dependency relations, resulting in inadequate exploration of this view. Meanwhile, most work ignores that the minimum distance view and the dependency view can be complementary and integrated in syntactic analysis. Unfortunately, directly adding the two views introduces noise issues, leading to unsatisfactory results after their integration. To address these issues, this paper introduces an integrating dual syntactic views graph convolutional network (IDSV-GCN) model that seamlessly integrates dependency type view and minimum distance view, enabling comprehensive utilization of syntactic structure and information. To better address the computation of weights in the dependency type view, we innovatively propose a fusion attention mechanism. This approach effectively captures both global and local feature information. To more effectively integrate the two views and reduce the impact of noise, we employ a syntactic mask layer to filter out noise in the dependency type view and highlighting important dependency feature information. Then, we construct a minimum distance weight matrix based on the matrix obtained from the dependency type view. Finally, we merge the two matrices and feed them into the GCN to facilitate the model’s learning of syntactic information. Experimental results on three widely-used benchmark datasets demonstrate that our proposed IDSV-GCN model outperforms state-of-the-art methods.

Automated monitoring of insulation by ultraviolet imaging employing deep learning

The corona effect on the surface of electrical system equipment and components generally indicates undesirable phenomena that can lead to physical degradation of materials or even equipment failure. One of the most promising techniques for monitoring corona discharges is the use of specialized cameras for the detection of ultraviolet radiation. This paper introduces an innovative algorithm for classifying the criticality of insulation based on attributes extracted from videos recorded using an ultraviolet detection camera. The attributes extracted from each facula origin include maximum persistence, area, and the minimum distance between the facula origin and the insulation. To obtain this distance, a technique combining a deep convolutional neural network model with an adaptive segmentation thresholding method is proposed. To validate the proposed methodology, inspections were conducted at a 500 kV substation. A total of 96 videos were recorded, within which 99 facula origins were identified. The object detection model applied demonstrated an accuracy of 85.5 % in detecting insulation in images, based on a validation set comprising 1,985 images and 8,730 instances. The results of the classification showed that 72.7 % of the facula origins recorded originated from regions far from the insulation (mainly cables and corona rings). These results demonstrate that the distance between the insulation and the facula origin is an essential attribute for video analysis, providing context for recorded discharges and allowing differentiation between cases where ultraviolet radiation originates from insulation and those where discharge location is less critical.