Feature Points Research Articles

Calculation of Characteristic Point Parameters for Restoring Model of Corroded Short-Pier RC Shear Walls

Based on the quasi-static tests of 12 corroded RC (reinforced concrete) shear walls, it was found that reinforcement corrosion has a great influence on the skeleton curve of RC shear walls. With an increase in the degree of corrosion, the bearing capacity of specimens decreases, and the deformation capacity worsens. Increasing the diameter of longitudinal reinforcements can significantly improve the bearing capacity of corroded RC shear walls, while the deformation capacity of corroded specimens can be improved by increasing the lateral distributed reinforcement or the transverse reinforcement in the embedded column. In order to accurately evaluate the seismic performance of corroded RC shear walls, we considered descent segments of four broken-line models to estimate the skeleton curve. After considering the influence of corrosion on the parameters of the characteristic point for the skeleton curve, the calculation formulas of the characteristic point parameters of the skeleton curve for the corroded RC shear wall were determined based on the test data fitting. It was proven that the formula for the characteristic point parameters for the skeleton curve of corroded RC shear walls has good applicability. This study lays a theoretical foundation for the seismic performance evaluation of an RC shear wall structure in a salt fog environment. It provides a theoretical basis for further improving the life-cycle seismic capacity evaluation system for RC structures.

3D deformation measurement of rotating blades based on concentric ring calibration and GPU-SIFT feature point searching

Image grayscale matching and camera calibration are two key technologies for measuring the three-dimensional deformation of rotating blades. To address the issues of low accuracy in extracting corresponding points caused by unclear grayscale boundaries of traditional chessboard calibration patterns and the high computational complexity of image matching algorithms in high-speed and high-resolution scenarios, this paper proposes a method for measuring the three-dimensional deformation of rotating blades based on concentric circle calibration and GPU-SIFT feature point search. Firstly, a concentric circle calibration method based on arc segment combination for extracting circle center is proposed. By fitting the circular calibration pattern with extracted marker points, errors introduced by unclear grayscale boundaries of chessboard patterns are avoided, thereby improving the accuracy of calibration parameters. Secondly, an image matching algorithm based on GPU-SIFT feature point search is presented. By optimizing the storage location of scale space process data, the performance of the SIFT algorithm is maximized, leading to improved image matching speed. Lastly, the calculation of displacement and strain fields is achieved using the P3P theory and local least squares fitting method, respectively. The experimental results show that the proposed system and method for 3D deformation measurement achieve a strain measurement accuracy of 80 με. Moreover, they successfully measure the surface deformation of turbofan blades under the conditions of 3000 rpm and 6000 rpm. Through this method, it can improve the deformation measurement capability of large engine manufacturers for high-speed rotating blades and provide reliable technical support.

Studying an improved recursive rough method for three-dimensional visual examination of steel plate butt welding surface quality

An improved recursive rough algorithm is proposed to extract feature points in order to address the slow speed and poor accuracy of feature point extraction in 3D visual inspection, as well as the difficulty in meeting the requirements of efficient and quantitative analysis of welded surface quality detection. The preprocessed point cloud is sliced by section. The feature points are obtained according to the improved recursive rough algorithm. The defect evaluation is carried out to obtain the inspection conclusion of the weld appearance defect. Finally, according to the formulated weld defect evaluation process and standards, the typical weld template is selected for weld width, weld misedge and weld straightness test. Weld detection accuracy can reach 3 decimal places, the speed is 4 times the current manual detection speed. The detection results show that the system has the characteristics of high accuracy and fast speed which can replace manual detection. It also has a good application prospect.

UMPL- VINS: Generalized SLAM for Multi-scene Metaverse Applications

The use of line features to improve the localization accuracy of point-based visual inertial SLAM (VINS) is of increasing interest because of the additional constraints they can provide on the scene structure. It is found that although the introduction of line features improves the accuracy of relative position estimation in some scenes due to the additional constraints, the two constraints only achieve an equalization for the estimated relative position. Therefore, in some environments two constraints can reduce the accuracy of the single-feature constraint algorithm and also make the real-time performance of the system more challenging. To address such issues, in this paper, we design a generalized SLAM system with point-line features that can be applied to multiple metaverse scenarios. We first enhance the image frames used for feature extraction by eliminating motion blur frames through the fuzzy metric method and mutation modeling of velocity and rotation. Then we improve the traditional line detection model by short line fusion, uniform distribution of line features, and refinement of edge features to obtain high-quality line features for building SLAM. Finally, based on the application of point features and line features for different scenes, a point and line feature separation-union model is proposed. In addition, we design a transformation model for line-point features to enhance the processing of point-line features. Experimental results on EuRoc, TUM_VI, KITTI, PennCOSYVIO and our own recorded dataset prove that the method proposed in this paper realizes a generalized SLAM with point and line features applied to multi-scenes with good advantages.

Predictors of complication after groin dissection: a single-centre experience.

Inguinal lymphadenectomy (ILND) has historically been associated with substantial morbidity. The objective of this study was to obtain contemporary ILND morbidity rates and to identify potentially preventable risk factors. We carried out a retrospective review of medical records for all superficial, deep, and combination groin dissections performed at a single, high-volume academic centre between January 2007 and December 2020. We collected data points for patient, disease, and surgery characteristics, and cancer outcomes. The outcome of interest was any complication within 30 days of surgery. Complications included wound infection, wound necrosis or disruption, seroma, drainage procedure, hematoma, and lymphedema. We performed multivariate logistic regression using SAS version 9.4. We identified 139 patients having undergone 89 superficial, 12 deep, and 38 combined dissection types, respectively. Melanoma accounted for 84.9% of cases. Of these patients, 56.1% had an adverse postoperative event within 30 days. Increasing age (odds ratio [OR] 1.04, 95% confidence interval [CI] 1.01-1.07, p < 0.01) and number of positive lymph nodes harvested (OR 1.22, 95% CI 1.00-1.50, p = 0.05) were associated with more complications. Patients with deep dissection showed a lower likelihood of complications than those with superficial dissection (OR 0.15, 95% CI 0.03-0.84, p < 0.05). Complication rates after ILND remain high. We identified a number of risk factors, providing opportunities for better selection and prevention.

A Multitask Latent Feature Augmentation Method for Few-Shot Learning.

Few-shot learning (FSL) aims to learn novel concepts quickly from a few novel labeled samples with the transferable knowledge learned from base dataset. The existing FSL methods usually treat each sample as a single feature point in embedding space and classify through one single comparison task. However, the few-shot single feature points on the novel meta-testing episode are still vulnerable to noise easily although with the good transferable knowledge, because the novel categories are never seen on base dataset. Besides, the existing FSL models are trained by only one single comparison task and ignore that different semantic feature maps have different weights on different comparison objects and tasks, which cannot take full advantage of the valuable information from different multiple comparison tasks and objects to make the latent features (LFs) more robust based on only few-shot samples. In this article, we propose a novel multitask LF augmentation (MTLFA) framework to learn the meta-knowledge of generalizing key intraclass and distinguishable interclass sample features from only few-shot samples through an LF augmentation (LFA) module and a multitask (MT) framework. Our MTLFA treats the support features as sampling from the class-specific LF distribution, enhancing the diversity of support features and reducing the impact of noise based on few-shot support samples. Furthermore, an MT framework is introduced to obtain more valuable comparison-task-related and episode-related comparison information from multiple different comparison tasks in which different semantic feature maps have different weights, adjusting the prior LFs and generating the more robust and effective episode-related classifier. Besides, we analyze the feasibility and effectiveness of MTLFA from theoretical views based on the Hoeffding's inequality and the Chernoff's bounding method. Extensive experiments conducted on three benchmark datasets demonstrate that the MTLFA achieves the state-of-the-art performance in FSL. The experimental results verify our theoretical analysis and the effectiveness and robustness of MTLFA framework in FSL.

Research on speculation method for compressor map of PG9351FA gas turbine

A compressor map is usually represented by a limited number of feature points to speculate the entire operating range. Also, accurate compressor map models can be obtained quickly by using the appropriate methods. In this paper, 9351FA gas turbine is used as the research object, and a set of targeted compressor map speculation scheme is proposed. At 15 data points, high-precision compressor maps are obtained based on BP neural network, and this method is suitable for a large number of data points. At 6 data points, compressor maps are obtained based on the parameter estimation method, and this method is suitable for a small number of data points. The mean square deviation of the compressor map obtained by the neural network is about 0.002, while the minimum mean square deviation of the results of the parameter estimation method is 0.026 and the maximum mean square deviation is 0.088. Since the corrected speed line of 106.4 is almost vertical, the maximum error mean squared deviation and the maximum standard deviation occur on this line. Both methods are suitable for different sample sizes, and the speculated compressor maps are more reliable. The combination of the two methods can provide a set of reference methods for compressor map speculation.

Simultaneous geometric calibration and orbit-attitude determination of Hayabusa2’s deployable camera (DCAM3)

Hayabusa2’s deployable camera (DCAM3) was deployed from the spacecraft near the asteroid Ryugu to successfully monitor the artificial cratering experiment. Scientific analyses of the observed impact ejecta require accurate determination of the camera’s position and orientation. However, in contrast to conventional spacecraft operations, DCAM3 lacked the capability to acquire orbit tracking data and attitude sensor data due to its limited onboard resources. Even though the optical images obtained by DCAM3 itself are the only source of geometric information, they were subject to significant distortion caused by the rolling shutter effect and lens distortion. This research is, therefore, designed to simultaneously estimate the image distortion and the orbital and attitude motions of DCAM3, relying solely on its image data. The relative geometry between the camera and the asteroid is reconstructed from the geographic information of the observed feature points by incorporating distortion effects. The proposed method involves segmenting the geometry reconstruction, with more than ten thousand feature measurements, into smaller-scale least-squares problems. The image-based estimation yields consistent distortion, orbit, and attitude solutions with pixel-scale accuracy. The analysis results indicate that DCAM3 experienced significant nutation of its optical axis during ballistic flight in a semi-elliptical orbit. In addition, these motions were sensitive enough to simultaneously determine system parameters, such as the gravitational parameter of Ryugu and the inertia moment of DCAM3. This paper illustrates the potential of deployable camera systems as a promising option for enhancing the scientific and engineering aspects of asteroid exploration.

A new covariance intersection based integrated SLAM framework for 3D outdoor agricultural applications

AbstractThis letter introduces a novel integrated framework for simultaneous localization and mapping (SLAM) tailored for general agricultural applications. The framework combines a cutting‐edge SLAM method, LIO‐SAM, with covariance intersection for sensor fusion. Agricultural robots often operate in unstructured environments with sparse feature points and encounter repeated similar information, such as trees. Therefore, a fusion framework based on 3D SLAM augmented with additional information, such as feature‐independent GPS data, becomes essential. This study proposes an integrated SLAM framework by introducing a convergence strategy based on covariance analysis, incorporating a state‐of‐the‐art 3D SLAM technique. The convergence methods, namely “dynamic weight assignment” and “winner takes all”, are presented alternatively, tailored to seamlessly integrate with the proposed framework. Evaluations using a public dataset and an experiment demonstrate the effectiveness of the approach through numerical analysis and visual representation. The results illustrate that this method surpasses conventional approaches in accurately estimating the robot's position. In the future, this research will focus on automating crop cultivation and harvesting by integrating the proposed system with robot arm control.

Oscillatory instability of magneto-convection during the melting of non-Newtonian ferromagnetic nano-enhanced phase change materials at low Rayleigh number

This work complements mechanism of the oscillatory instability of magneto-convection in the process of melting heat transfer controlled by magnetic field at low Rayleigh number (Ra), aiming to provide a theoretical basis for improving the stability of energy storage system and melting manufacturing process. A latent heat storage cavity unit exposed in uniform magnetic field is designed to reveal the magnetic-controlled heat transfer in solid–liquid phase change process. The volume average method is used to describe porous media composite nano-enhanced phase change materials in latent heat unit. Special attentions are paid to the key parameters of Magnetic number (Mn) and Ra, and coupling flow heat transfer mechanism of magnetic force and natural convection effect is numerically studied. The evolution stages of different flow patterns are divided according to the Kelvin force, vorticity, velocity, flow field and isotherm distributions of characteristic points. Main results show that the magnetic field-modulated phase change process at low Ra can be divided into three stages, of which the second stage is characterized by periodic oscillations. Increasing Mn at fixed Ra accelerates the second stage of oscillatory convection heat transfer coming earlier and promotes melting. When Mn increase to 5 × 106, the full melting time is reduced to the greatest extent, resulting in 11.6 % reduction in heat storage but 17.4 % increase in heat storage efficiency. Three mechanisms of magnetic forced convection coupled with natural convection are found during the increase of Ra: synergistic (Ra < 2 × 103), antagonistic (2 × 103≤ Ra < 1 × 104), and natural convection-dominated (Ra = 1 × 104). Finally, the mode distribution map of Ra-Mn regulation is obtained, in which the unstable transformation mechanism of steady-state, periodic oscillation and chaotic oscillation heat transfer in the melting process under the control of magnetic field is described.

Recommended operating conditions and performance evaluation of commonly used hydrofluoroolefin (HFO) and hydrochlorofluoroolefin (HCFO) refrigerants in organic Rankine cycle

The application of the first three generations of organic refrigerants, namely, CFCs, HCFCs, and HFCs, in an organic Rankine cycle has been widely investigated. As environmentally friendly successors to the above three generations of refrigerants, hydrofluoroolefins (HFOs) and hydrochlorofluoroolefins (HCFOs) have also attracted increasing research interest for their application in the organic Rankine cycle (ORC). Based on molecular complexity, characteristic points on saturation liquid and vapor curve and area of characteristic regions in temperature-entropy (T-s) diagram, eight common hydrofluoroolefins (HFOs) and hydrochlorofluoroolefins (HCFOs) are classified, analyzed, and evaluated. In addition, the type of heat source and specific operating conditions in which these eight hydrofluoroolefins (HFOs) and hydrochlorofluoroolefins (HCFOs) are most effective are recommended. The results show that molecular complexity of hydrofluoroolefins (HFOs) and hydrochlorofluoroolefins (HCFOs) mainly depends on the length of the C chain, and then on the number and location of Cl and F atoms. HFO-1243zf has the best comprehensive evaluation indicator, and it is noteworthy as a recommended working fluid for subcritical ORC systems. The result obtained in this paper may provide a reference for the design and actual operation of Organic Rankine Cycle system using HFO and HCFO as working fluids.

Spot scanning imaging calibration method based on deviation model for wafer inspection

The spot scanning defect detection system has good detection performance for wafer defects. However, due to the existence of system assembly errors, the wafer defect inspection system could not scan following the planned trace, which resulted in distortion of the reconstructed image and had a greater impact on defect feature extraction and accurate defect location. The system deviation calibration and adjustment can further facilitate the accuracy of the system position and image reconstruction. To address this issue, the causes and shapes of imaging distortion were analyzed and simulated, and a simple and effective calibration method based on a scanning deviation model for the spot scanning defect detection system was proposed. First, a scanning deviation model was established following the characteristics of the spot scanning system, and different degrees of reconstructed image distortion were simulated and analyzed as the system deviation changes. Then, an 8-inch wafer with a grid was used as the calibrator and scanned using the developed spot scanning system. Serveral grid corners in the reconstructed image are selected and used as the calibration feature points. The constraint function is constructed using the equidistant features between adjacent grid points, and then the quasi-Newton method is used to optimize the parameters of the function to obtain the best deviation estimation of the spot scanning system. To prove the validity of the estimated deviation parameters, another patterned wafer was scanned and the distortion image was used for reconstruction. The experiment demonstrated that an accurate and distortion-free wafer image can be reconstructed using the calibrated deviation parameters, and the root mean square error of the measured distance between two points of interest was only 0.61 pixels, which proved the effectiveness of the proposed calibration method.

A Mars Local Terrain Matching Method Based on 3D Point Clouds

To address the matching challenge between the High Resolution Imaging Science Experiment (HiRISE) Digital Elevation Model (DEM) and the Mars Orbiter Laser Altimeter (MOLA) DEM, we propose a terrain matching framework based on the combination of point cloud coarse alignment and fine alignment methods. Firstly, we achieved global coarse localization of the HiRISE DEM through nearest neighbor matching of key Intrinsic Shape Signatures (ISS) points in the Fast Point Feature Histograms (FPFH) feature space. We introduced a graph matching strategy to mitigate gross errors in feature matching, employing a numerical method of non-cooperative game theory to solve the extremal optimization problem under Karush–Kuhn–Tucker (KKT) conditions. Secondly, to handle the substantial resolution disparities between the MOLA DEM and HiRISE DEM, we devised a smoothing weighting method tailored to enhance the Voxelized Generalized Iterative Closest Point (VGICP) approach for fine terrain registration. This involves leveraging the Euclidean distance between distributions to effectively weight loss and covariance, thereby reducing the results’ sensitivity to voxel radius selection. Our experiments show that the proposed algorithm improves the accuracy of terrain registration on the proposed Curiosity landing area’s, Mawrth Vallis, data by nearly 20%, with faster convergence and better algorithm robustness.

A novel model-based welding trajectory planning method for identical structural workpieces

Welding robots have been widely used with the development of manufacturing industry. At present, welding trajectory planning and programming of welding robots are performed separately for similar (structural) workpieces with different dimensions or deformations. Besides, the welding torch pose planning using conventional robot programming methods is time-consuming for workpieces with complex welding trajectories. To realize automatic planning of the welding torch pose for similar workpieces and improve the programming efficiency of welding robots, we propose a novel model-based welding trajectory planning method for welding robots. We create a basic model (point set) containing information on welding seam trajectory planning for similar workpieces. The welding seam trajectory extraction of similar workpieces is achieved by the non-rigid point set registration of the feature point cloud of similar workpieces with the basic model. Then, the welding torch pose planning is implemented by the geometric projection method of the local point cloud of the welding seam trajectory. Finally, the results of offline experiments and online experiments demonstrate the effectiveness of the proposed method.

Analysing the use of OpenAerialMap images for OpenStreetMap edits

ABSTRACT OpenAerialMap (OAM) is a crowdsourcing platform for uploading, hosting, sharing, displaying, searching, and downloading openly licensed Earth image data from around the world. OAM was launched with the primary goal to facilitate rapid disaster response mapping after natural events, such as floods or hurricanes. Images contributed to OAM can be used as a background layer in OpenStreetMap (OSM) editors to edit map features, such as buildings or facilities, which may have been affected by such events. This study analyzes how the provision of OAM images is associated with changes in underlying editing patterns of OSM features by comparing the number of edited OSM objects between 2 weeks before and 2 weeks after an image has been shared through OAM. The comparison also involves other aspects of OSM editing patterns, including geometry types and OSM primary feature types of edited objects, type of feature editing operations, edits per user, and contribution distribution across continents. Results show that the number of point features added to OSM more than quadrupled within the first 2 weeks after OAM image upload compared to that of 2 weeks before, and that the number of ways added almost doubled. This suggests that the OSM community utilizes provided OAM images for OSM map updates in the respective geographic areas. The study provides a showcase which demonstrates how information from one crowdsourcing platform (OAM) can be used to enhance the data quality of another (OSM) with regards to completeness and timeliness.

Semi-automatic recognition of rock mass discontinuity based on 3D point clouds

Information on rock mass discontinuities is crucial for rock mass stability analysis. Due to the low efficiency, incompleteness, and potential risk of the traditional compass methods in measuring discontinuities, three-dimensional light detection, ranging, and other remote sensing methods have become essential. In this study, voxel filtering was used to subsample a point cloud so that its feature points were retained while reducing the computational load. An improved regional growing (RG) algorithm was then used to extract rock mass discontinuities. A software Geocloud v1.0 was developed based on the proposed method to semi-automatically recognize discontinuities. Additionally, two groups of sensitivity experiments were performed to analyze the influence of different numbers of nearest neighbors and maximum RG angles on the extraction of discontinuities. Results showed that most of the discontinuities could be accurately recognized with different thresholds. Furthermore, the accuracy of the proposed method was verified by real geometries, on a real highway slope, and in a natural quarry. Finally, the efficiency of the proposed method was proven using comparative computational experiments.

Biomimetic enhanced polarization orientation method for underwater scenes

Underwater polarization information can provide reliable autonomous heading feedback for underwater scenes in the presence of satellite denial and multi-source interference. A biologically inspired highly robust polarization orientation strategy is proposed to address the degradation caused by weak polarization and noisy patterns in underwater environments with poor illumination, turbidity, and cloud recession. This approach initially develops a biomimetic algorithm that enhances weak polarization information, based on the increased perception mechanism of the visual neural pathway of syrphid fly under weak light conditions. After overcoming restricting polarization transmission, it is further optimized with a non-local sparse coding denoising part that takes into account the similarity in the detailed structure of the polarized image's effective zenith region. Relying on the stable ∞ symmetric distribution of the angle of polarization, the ±π/2 feature point distribution is traversed and assigned, and polarization information is quantitatively assigned, compensated, and regulated along the solar meridian region, excluding invalid polarized pixels. The experiments demonstrate that the methodology effectively reconstructs the symmetry of the partially damaged angle of polarization distribution, eliminates external interference, autonomously solves the fitted heading correction problem, and improves the environmental adaptability of the bionic polarization compass. The data from underwater experiments indicate the orientation accuracy: the error in weakly polarized patterns is within 2°, and the accuracy in noisy patterns can be increased by over 50%.

Camera calibration with active standard Gaussian stripes for 3D measurement

The extraction accuracy of the mark points is directly related to the calibration accuracy of the camera. However, traditional ceramic calibration boards with checkerboard or circle dot markers are not suitable for defocused conditions and lack adaptability to changes in the measuring field. Therefore, liquid crystal displays (LCDs) that display phase-shifting patterns are widely used as phase active targets for cameral calibration. While phase-shifting patterns accurately extract feature points in defocused conditions, they are susceptible to LCD screen nonlinear errors. To address these shortcomings, we propose a practical method that involves displaying standard Gaussian stripes on LCD. Specifically, this method displays Gaussian stripes in two directions and extracts their centerline subpixel coordinates. The intersection points of the two orthogonal centerlines are then considered as the feature points. Simulation and experimental results demonstrate that the standard Gaussian stripes still accurately extract feature points when affected by LCD nonlinearity or image defocus.

Efficient Target Classification Based on Vehicle Volume Estimation in High-Resolution Radar Systems

In this paper, we propose a method for efficient target classification based on the spatial features of the point cloud generated by using a high-resolution radar sensor. The frequency-modulated continuous wave radar sensor can estimate the distance and velocity of a target. In addition, the azimuth and elevation angle of the target can be estimated by using a multiple-input and multiple-output antenna system. Using the estimated distance, velocity, and angle, the 3D point cloud of target can be generated. From the generated point cloud, we extract the point cloud for each individual target using the density-based spatial clustering of application with noise method and a camera mounted on the radar sensor. Then, we define the convex hull boundaries that enclose these point clouds in both 3D and 2D spaces obtained by orthogonally projecting onto the xy, yz, and zx planes. Using the vertices of convex hull, we calculate the volume of the targets and the areas in 2D spaces. Several feature points, including the calculated spatial information, are numerized and configured into feature vectors. We design an uncomplicated deep neural network classifier based on minimal input information to achieve fast and efficient classification performance. As a result, the proposed method achieved an average accuracy of 97.1%, and the time required for training was reduced compared to the method using only point cloud data and the convolutional neural network-based method.

PARDet: Dynamic point set alignment for rotated object detection

Due to the inherent mismatch between rotated objects and horizontal features, feature point misalignment has been a challenge in the task of Rotated Object Detection (ROD). Specifically, considering the pattern of convolution, foreground features are often mixed with background noise, which can confuse the model and affect the model from feature point alignment during the training phase. To mitigate this issue, previous methods concentrate on fixed positions derived from predicted boxes by additionally introducing a refinement stage. However, merely learning fixed position priors during training can result in suboptimal alignment and inefficiency during inference. This paper introduces a dynamic point alignment detector to concurrently address issues associated with feature misalignment and inference inefficiency. The method is made up of two components: the fine-grained points generator (FPG) captures key information, and the point alignment module (PAM) derives precise feature representations. Both modules empower the detector with the capability to dynamically perceive rotated objects, extracting more comprehensive and reasoned feature contents from the feature maps. In general, our method enables the model to independently identify and prioritize the valuable features during the training process. Subsequently, during the inference stage, the results can be directly predicted without additional alignment operations. The experimental results demonstrate that our method can achieve competitive and superior results with average precision (AP) values of 79.33%, 95.73%, and 63.37% on the DOTA, HRSC2016, and DIOR-R datasets, respectively. Codes will be publicly available at https://github.com/Xuyihaoby/PARDet.